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1602 KiB

Open AccessProceeding Paper

Benchmarking the Reliability of Sentinel-2 Satellite Data for Estimating Vineyard NDVI and Leaf Area Index Parameters through UAV LiDAR and Multispectral Imagery

by Sergio Vélez, Mar Ariza-Sentís and João Valente

Environ. Sci. Proc. 2024, 29(1), 1; https://doi.org/10.3390/ECRS2023-15859 - 6 Nov 2023

Viewed by 210

Abstract

The use of satellite data in precision agriculture, especially for woody crops, has gained prominence. Such data aid in forecasting yield, predicting crop quality, and irrigation management by providing insights into crop conditions. However, the accuracy of parameters such as the Leaf Area [...] Read more.

The use of satellite data in precision agriculture, especially for woody crops, has gained prominence. Such data aid in forecasting yield, predicting crop quality, and irrigation management by providing insights into crop conditions. However, the accuracy of parameters such as the Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) derived from satellites is questioned. This research compares Sentinel-2 satellite imagery with LiDAR data and multispectral imagery gathered over a vineyard in northern Spain using Unmanned Aerial Vehicles (UAVs) in July, August, and September 2022, focusing on veraison, a key stage in precision viticulture. The findings reveal a moderate correlation between satellite and UAV NDVI values (up to R² = 0.6) but a discrepancy in leaf area estimations from satellite imagery, suggesting its limited use for such applications in hedgerow systems in agriculture. Nonetheless, satellite data’s ability to detect crop spatial variability remains useful for field management. The study emphasizes the potential benefits and drawbacks of diversifying remote sensing techniques for effective agricultural management. Full article

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2827 KiB

Open AccessProceeding Paper

Enhancing Corn Yield Prediction in Iowa: A Concatenate-Based 2D-CNN-BILSTM Model with Integration of Sentinel-1/2 and SoilGRIDs Data

by Mahdiyeh Fathi, Reza Shah-Hosseini and Armin Moghimi

Environ. Sci. Proc. 2024, 29(1), 2; https://doi.org/10.3390/ECRS2023-15852 - 6 Nov 2023

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Abstract

Ensuring food security in precision agriculture demands early prediction of corn yield in the USA at international, regional, and local levels. Accurate corn yield estimation can play a crucial role in averting famine by offering insights into food availability during the growing season. [...] Read more.

Ensuring food security in precision agriculture demands early prediction of corn yield in the USA at international, regional, and local levels. Accurate corn yield estimation can play a crucial role in averting famine by offering insights into food availability during the growing season. To address this, we propose a Concatenate-based 2D-CNN-BILSTM model that integrates Sentinel-1, Sentinel-2, and Soil GRIDS (global gridded soil information) data for corn yield estimation in Iowa State from 2018 to 2021. This approach utilizes Sentinel-2 features, including spectral bands (Blue, Green, Red, Red Edge 1/2/3, NIR, n-NIR, and SWIR 1/2), and vegetation indices (NDVI, LSWI, DVI, RVI, WDRVI, SAVI, VARIGREEN, and GNDVI), alongside Sentinel 1 features (VV, VH, difference VV, and VH, and RVI), and soil data (Silt, Clay, Sand, CEC, and pH) as initial inputs. To extract high-level features from this data each month, a dedicated 2D-CNN was designed. This 2D-CNN concatenates high-level features from the previous month with low-level features of the subsequent month, serving as input features for the model. Additionally, to incorporate single-time soil data features, another 2D-CNN was implemented. Finally, high-level features from soil, Sentinel-1, and Sentinel-2 data were concatenated and fed into a BILSTM layer for accurate corn yield prediction. Comparative analysis against random forest (RF), Concatenate-based 2D-CNN, and 2D-CNN models, using metrics like RMSE, MAE, MAPE, and the Index of Agreement, revealed the superiority of our model. It achieved an Index of Agreement of 84.67% with an RMSE of 0.698 t/ha. The Concatenate-based 2D-CNN model also performed well with an RMSE of 0.799 t/ha and an Index of Agreement of 72.71%. The 2D-CNN model followed closely with an RMSE of 0.834 t/ha and an Index of Agreement of 69.90%. In contrast, the RF model lagged with an RMSE of 1.073 t/ha and an Index of Agreement of 69.60%. Integration of Sentinel 1–2 and Soil-GRIDs data with the Concatenate-based 2D-CNN-BILSTM model significantly improved accuracy. Combining soil data with Sentinel 1–2 features reduced the RMSE by 16 kg and increased the Index of Agreement by 2.59%. This study highlighted the potential of advanced machine learning (ML)/deep learning (DL) models in achieving precise and reliable predictions, which could support sustainable agricultural practices and food-security initiatives. Full article

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1300 KiB

Open AccessProceeding Paper

Normalized Burn Ratio and Land Surface Temperature Pre- and Post-Mediterranean Forest Fires

by Fatima Ezahrae Ezzaher, Nizar Ben Achhab, Naoufal Raissouni, Hafssa Naciri and Asaad Chahboun

Environ. Sci. Proc. 2024, 29(1), 3; https://doi.org/10.3390/ECRS2023-15829 - 6 Nov 2023

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Abstract

Fire is a natural disruption that affects the structure and function of forest systems by changing the vegetation composition, climatic situation, carbon cycle, wildlife habitat, and many other major properties. The measure of the degree of these changes’ degree is known as fire [...] Read more.

Fire is a natural disruption that affects the structure and function of forest systems by changing the vegetation composition, climatic situation, carbon cycle, wildlife habitat, and many other major properties. The measure of the degree of these changes’ degree is known as fire severity, and it can be assessed using remote sensing data (i.e., satellite images, aerial images, etc.) and various biophysical indices (such as Normalized Burn Ratio (NBR), Char Soil Index (CSI), Burn Area Index (BAI), etc.), in addition to the measurement of Land Surface Temperature (LST). This research aims to assess the response of the NBR and LST both pre- and post-forest fires, taking a Mediterranean forest located in the northern part of Morocco, which burned in the summer of 2022, as the study area. We used seven Landsat-8 images spanning three years: three images from 2021 (i.e., pre-fire), one image from the summer of 2022 (i.e., fire period), and three images from 2023 (i.e., post-fire). The results demonstrated a negative correlation between the LST and NBR in the pre-fire period; when the temperature rises, the NBR drops. The same was found for the fire period in summer 2022, in which the LST reached its peak at 50 °C, while the NBR decreased to its lowest point at −0.2, whereas in the recovery period (i.e., 2023), the LST and NBR showed changes in fluctuation patterns; the LST variated normally according to seasons, dropping from 50 °C to 12 °C in winter and reaching 37 °C in summer, while the NBR increased over time, going from −0.2 to −0.04 in winter to 0.03 in summer, which indicates the gradual restoration of vegetation in the study area. This study concludes that in the post-fire period when a forest is recovering, the NBR is unaffected by seasonal changes in temperature and is more reflective of the vegetation it projects more than the vegetation situation in the area, unlike the LST. Thus, relying only on the LST to measure fire severity can give biased results due to changes in seasons. Full article

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712 KiB

Open AccessProceeding Paper

A First Approximation for Acid Sulfate Soil Mapping in Areas with Few Soil Samples

by Virginia Estévez, Stefan Mattbäck and Anton Boman

Environ. Sci. Proc. 2024, 29(1), 4; https://doi.org/10.3390/ECRS2023-15831 - 15 Jan 2024

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Abstract

Acid sulfate soil mapping is the first step to avoid possible environmental damages created by one of the most problematic soils existing in nature. One of the problems in acid-sulfate soil mapping is the lack of soil samples in some regions. This prevents [...] Read more.

Acid sulfate soil mapping is the first step to avoid possible environmental damages created by one of the most problematic soils existing in nature. One of the problems in acid-sulfate soil mapping is the lack of soil samples in some regions. This prevents the creation of occurrence maps. For the first recognition of these regions, a possible solution could be the use of soil samples from other areas with similar characteristics. In this study, we analyze if a machine learning method is able to correctly classify the soil samples in an area where it has not been trained. For this, Random Forest and two different regions located in southern Finland with a similar composition of soils are considered. Full article

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3158 KiB

Open AccessProceeding Paper

An Integrated Modeling Framework to Estimate Time Series of Evapotranspiration on a Regional Scale Using MODIS Data and a Two-Source Energy Balance Model

by Mahsa Bozorgi and Jordi Cristóbal

Environ. Sci. Proc. 2024, 29(1), 5; https://doi.org/10.3390/ECRS2023-15845 - 1 Dec 2023

Viewed by 182

Abstract

Satellite remote sensing has become an important tool for monitoring and evaluating the impacts of drought. In this study, a modeling framework aimed at estimating the time series of evapotranspiration (ET), a key variable for drought monitoring, at a regional scale is presented. [...] Read more.

Satellite remote sensing has become an important tool for monitoring and evaluating the impacts of drought. In this study, a modeling framework aimed at estimating the time series of evapotranspiration (ET), a key variable for drought monitoring, at a regional scale is presented. A two-source energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset. The modeling framework is based on the SEN-ET scheme to calculate the surface energy balance of the soil-canopy-atmosphere continuum and estimate ET at 1 km spatial resolution. The model was applied for the whole Iberian Peninsula, and it was evaluated with a pistachio orchard flux tower data in Lleida (NE Iberian Peninsula). Preliminary daily ET evaluation results for the Terra dataset showed an RMSE, MBE, and R² of around 1.43 W·m⁻², −1.27 W·m⁻², and 0.56, respectively, and for the Aqua dataset were 1.05 W·m⁻², −0.84 W·m⁻² and 0.48, respectively within 100 days in 2022. Ongoing evaluation is being carried out on two forested watersheds as well as mountain meadows and semi-arid vegetation flux towers. Full article

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1734 KiB

Open AccessProceeding Paper

Temporal Variations in Mixing Layer Height in a Rural Environment under Clear Sky Conditions Using a Campbell Ceilometer CS135: Preliminary Results

by Niki Papavasileiou and Stavros Kolios

Environ. Sci. Proc. 2024, 29(1), 6; https://doi.org/10.3390/ECRS2023-15837 - 15 Nov 2023

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Abstract

The scope of this study is to analyze the variations in the mixing layer height (MLH) under different cloud conditions on a daily and monthly basis. For this scope, the data of the first five months from the Campbell ceilometer CS135 were analyzed. [...] Read more.

The scope of this study is to analyze the variations in the mixing layer height (MLH) under different cloud conditions on a daily and monthly basis. For this scope, the data of the first five months from the Campbell ceilometer CS135 were analyzed. The instrument is operating in a rural place on Euboea Island (Greece), and the study presents preliminary results about the atmospheric profile of this area, which is also related to the air transport of the largest airport in Greece (Athen’s airport). Full article

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2345 KiB

Open AccessProceeding Paper

Satellite-Based Analysis of Lake Okeechobee’s Surface Water: Exploring Machine Learning Classification for Change Detection

by Madan Thapa Chhetri and Sandip Rijal

Environ. Sci. Proc. 2024, 29(1), 7; https://doi.org/10.3390/ECRS2023-15835 - 6 Nov 2023

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Abstract

Water is an essential resource for the survival of living beings. Remote-sensing data provides the best possible way to detect water bodies and monitor change over time. With a surplus amount of remote sensing data, machine learning approaches have become an effective and [...] Read more.

Water is an essential resource for the survival of living beings. Remote-sensing data provides the best possible way to detect water bodies and monitor change over time. With a surplus amount of remote sensing data, machine learning approaches have become an effective and efficient way to detect and monitor surface water bodies. This research focused on utilizing remote sensing and machine learning approaches to monitor changes in the surface water of Lake Okeechobee, Florida, USA. This investigation used two sources of remotely sensed data, Landsat 7, and Landsat 8, for 2002 and 2022, respectively. Two machine learning algorithms, support vector machine (SVM) and random forest (RF), were adopted, considering their power and robustness, among other factors, for supervised classification. Both algorithms provided an accuracy of over 92% and a kappa statistic exceeding 0.8. Further, we used image differencing techniques to track changes across two decades. The SVM suggested an increase of 85 km², and RF indicated an expansion of 52 km² in the surface water area. This study explicitly demonstrates how dynamic natural resources are, especially water sources. Thus, it can provide a foundation for research that further explores environmental assessments and sustainable water resource planning in Lake Okeechobee. Full article

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2385 KiB

Open AccessProceeding Paper

Downscaling the Resolution of the Rainfall Erosivity Factor in Soil Erosion Calculations in Watersheds in Atlantic Forest Biome, Brazil

by Saulo de Oliveira Folharini and Ana Maria Heuminski de Avila

Environ. Sci. Proc. 2024, 29(1), 8; https://doi.org/10.3390/ECRS2023-15842 - 28 Nov 2023

Viewed by 193

Abstract

The calculation of the R-factor (rainfall erosivity) for implementation in soil erosion models such as USLE (Universal Soil Loss Equation) and RUSLE (Revised Universal Soil Loss Equation) encounters substantial difficulties due to the scarcity of spatial databases with adequate resolution for territorial planning [...] Read more.

The calculation of the R-factor (rainfall erosivity) for implementation in soil erosion models such as USLE (Universal Soil Loss Equation) and RUSLE (Revised Universal Soil Loss Equation) encounters substantial difficulties due to the scarcity of spatial databases with adequate resolution for territorial planning actions at the local level. Otherwise, there is a spatial database available with a coarse resolution of themes that can be used to calculate the R-factor. We apply the spatial downscaling—based on the following regression models: linear (LN), general additive model (GAM), random forest (RF), cubist (CU)—to erosivity data (target variable) prepared for the State of São Paulo, Brazil, with a spatial resolution of 2500 m. We used DEM and slope data with 30 m fine resolution from the Atibaia watershed, located between the metropolitan regions of São Paulo (RMSP) and Campinas (RMC), to apply the downscaling. This framework improved the spatial resolution of the R-factor, which is necessary to calculate soil loss in the USLE and RUSLE equations in a territory where data with a fine resolution are still limited to the development of territorial planning projects at the local level. The RF model was better with R² 0.94. Full article

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519 KiB

Open AccessProceeding Paper

Machine Learning-Based Forest Type Mapping from Multi-Temporal Remote Sensing Data: Performance and Comparative Analysis

by Yusuf Ibrahim, Umar Yusuf Bagaye and Abubakar Ibrahim Muhammad

Environ. Sci. Proc. 2024, 29(1), 9; https://doi.org/10.3390/ECRS2023-15848 - 20 Dec 2023

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Abstract

This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, ranging from advanced (ensemble) machine learning (ML) methods to [...] Read more.

This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, ranging from advanced (ensemble) machine learning (ML) methods to several finely tuned support vector machine (SVM) variants, with a specific focus on Bayesian-optimized SVM with a radial basis function (RBF) kernel. Our findings highlight the robust performance of the Bayesian-optimized SVM, achieving a high accuracy of up to 94.27% and average precision and recall of 94.46% and 94.27%, respectively. Notably, this accuracy aligns with the levels attained by acclaimed ensemble techniques such as random forest and CatBoost while also surpassing those of XGBoost and LightGBM. These results highlight the potential of these methodologies to significantly enhance forest type mapping accuracy compared to traditional (linear) SVM and black-box neural networks. This, in turn, can enable the reliable identification and quantification of key services, including carbon storage and erosion protection, intrinsic to the forest ecosystem. The findings of our comparative study emphasize the profound impact of employing and fine-tuning ML approaches in the realm of remote sensing-based environmental analysis. Full article

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17091 KiB

Open AccessProceeding Paper

Mapping Seagrass Meadows and Assessing Blue Carbon Stocks Using Sentinel-2 Satellite Imagery: A Case Study in the Canary Islands, Spain

by Jorge Veiras-Yanes, Laura Martín-García, Enrique Casas and Manuel Arbelo

Environ. Sci. Proc. 2024, 29(1), 10; https://doi.org/10.3390/ECRS2023-15856 - 6 Dec 2023

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Abstract

This research evaluates the capability of Sentinel-2 satellite imagery for mapping Cymodocea nodosa meadows in El Médano (Tenerife, Canary Islands, Spain). A Level-1C image from 27 October 2022 was used. Atmospheric correction was addressed using the Sen2Cor tool, while Lyzenga’s method was employed [...] Read more.

This research evaluates the capability of Sentinel-2 satellite imagery for mapping Cymodocea nodosa meadows in El Médano (Tenerife, Canary Islands, Spain). A Level-1C image from 27 October 2022 was used. Atmospheric correction was addressed using the Sen2Cor tool, while Lyzenga’s method was employed to account for the water column effect. Three supervised classifications were performed using Random Forest, K-Nearest Neighbors (KNN) and KDTree-KNN algorithms. These classifications were complemented by an unsupervised classification and in situ data. Additionally, the amount of blue carbon sequestered by the C. nodosa in the study area was also estimated. Among the classifiers, the Random Forest algorithm produced the highest F1 scores, ranging from 0.96 to 0.99. The results revealed an average area of 237 ± 5 ha occupied by C. nodosa in the study region, translating to an average sequestration of 111,000 ± 2000 Mg CO₂. Notably, the seagrass meadows in this study area have the potential to offset the CO₂ emissions produced by the industrial combustion plant sector throughout the Canary Islands. This research represents a significant step forward in the protection and understanding of these invaluable ecosystems. It effectively underlines the potential of Sentinel-2 satellite data to map seagrass meadows and highlights their crucial role in achieving net zero carbon emissions on our planet. Full article

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1356 KiB

Open AccessProceeding Paper

Comparative Analysis of Remote Sensing via Drone and On-the-Go Soil Sensing via Veris U3: A Dynamic Approach

by Boris Boiarskii, Iurii Vaitekhovich, Shigefumi Tanaka, Doğan Güneş, Tsubasa Sato and Hideo Hasegawa

Environ. Sci. Proc. 2024, 29(1), 11; https://doi.org/10.3390/ECRS2023-15846 - 14 Dec 2023

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Abstract

The use of drones to gather remote data and soil sensors to collect ground information has become a powerful method for agricultural monitoring and analysis. However, integrating data from drone remote sensing and soil sensors in agricultural contexts can be problematic due to [...] Read more.

The use of drones to gather remote data and soil sensors to collect ground information has become a powerful method for agricultural monitoring and analysis. However, integrating data from drone remote sensing and soil sensors in agricultural contexts can be problematic due to variations in spatial and temporal resolutions. Ensuring precise synchronization and calibration is crucial for accurate comparative analysis. The objective of this study was to investigate the strengths and limitations of drone-based remote sensing and on-the-go Veris U3 sensor in agricultural contexts and explore the potential for data fusion. Through a series of field trials, data from drone-based remote sensing and ground-based soil sensing were collected in parallel. These data encompassed a range of factors, including vegetation health (vegetation indices), soil properties such as EC, pH, and optical measurements. The study delves into the challenges of data synchronization, calibration, and validation between the two methodologies. We discuss the potential for synergy in building a more holistic understanding of agriculture by fusing data from drones and in situ soil sensors. The findings of this research have implications for environmental monitoring, agriculture, and ecosystem management, suggesting that the combination of aerial and ground sensing offers a multi-dimensional perspective that can enhance decision-making processes and our grasp of intricate environmental processes. Full article

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2142 KiB

Open AccessProceeding Paper

Comparative Analysis of Summer Discomfort Index and Thermal Sensation Vote Using Remote Sensing Data in the Summer: A Case Study of the Mediterranean Cities Seville, Barcelona, and Tetuan

by Safae Ahsissene, Cristina Peña Ortiz and Naoufal Raissouni

Environ. Sci. Proc. 2024, 29(1), 12; https://doi.org/10.3390/ECRS2023-15832 - 15 Jan 2024

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Abstract

As urban areas expand, the focus on improving outdoor thermal comfort intensifies. This study generated Summer Discomfort Index (SDI) maps for Seville and Barcelona (Spain), as well as Tetuan (Morocco). SDI integrates temperature and humidity for an accurate comfort assessment. Calculations involved substituting [...] Read more.

As urban areas expand, the focus on improving outdoor thermal comfort intensifies. This study generated Summer Discomfort Index (SDI) maps for Seville and Barcelona (Spain), as well as Tetuan (Morocco). SDI integrates temperature and humidity for an accurate comfort assessment. Calculations involved substituting air temperature with land surface data from MODIS and incorporating humidity from weather stations, then comparing it to Thermal Sensation Votes (TSV) gathered through surveys. The objective was to assess thermal comfort levels and explore the relationship between remotely sensed SDI and residents’ reported perception. These detailed SDI maps offer crucial insights into summer thermal conditions, advancing urban climate studies and influencing urban planning, design, and well-being strategies. Full article

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2233 KiB

Open AccessProceeding Paper

Estimating Permafrost Active Layer Thickness (ALT) Biogeography over the Arctic Tundra

by Emiliana Valentini, Marco Salvadore, Serena Sapio, Roberto Salzano, Giovanni Bormidoni, Andrea Taramelli and Rosamaria Salvatori

Environ. Sci. Proc. 2024, 29(1), 13; https://doi.org/10.3390/ECRS2023-15843 - 11 Jun 2023

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Abstract

The geospatial model here presented estimates the permafrost active layer thickness (ALT) over the entire Arctic in the last 20 years, and it is based on the spatial and temporal oscillations measured by satellite-based essential variables associated with the thermal state of permafrost. [...] Read more.

The geospatial model here presented estimates the permafrost active layer thickness (ALT) over the entire Arctic in the last 20 years, and it is based on the spatial and temporal oscillations measured by satellite-based essential variables associated with the thermal state of permafrost. The model integrates the climate and soil components, such as the land surface temperature, the snow depth water equivalent, and the mid-summer albedo, with the structural and functional descriptors of Arctic tundra biome such as the fraction of absorbed photosynthetically active radiation. The distribution of estimated ALT varies according to the vegetation classes (mosses and lichens or grasses and shrubs), but a general increase has been estimated across the whole Arctic tundra region, with rates of up to 2 cm/year. Full article

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1720 KiB

Open AccessProceeding Paper

Evaluating Unmanned Aerial Vehicles vs. Satellite Imagery: A Case Study on Pistachio Orchards in Spain

by Raquel Martínez-Peña, Sara Álvarez, Rubén Vacas and Sergio Vélez

Environ. Sci. Proc. 2024, 29(1), 14; https://doi.org/10.3390/ECRS2023-15850 - 6 Nov 2023

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Abstract

Since the 20th century, satellites have been key in remote sensing, but the 21st century saw the rise of UAVs, especially in agriculture. While both are vital tools, their implications are often misunderstood. Precision agriculture requires an understanding of its strengths and weaknesses, [...] Read more.

Since the 20th century, satellites have been key in remote sensing, but the 21st century saw the rise of UAVs, especially in agriculture. While both are vital tools, their implications are often misunderstood. Precision agriculture requires an understanding of its strengths and weaknesses, especially with changing climate patterns affecting crops like pistachio in southern Europe. This study evaluates the effectiveness of satellites and UAVs in measuring NDVI for pistachio orchards in Spain, utilizing Sentinel 2 and a UAV equipped with a MicaSense Altum sensor. The results show that satellite data consistently underestimated NDVI values compared to UAV data, with a correlation of r-values ranging from 0.65 in July to 0.71 in September. The correlation values were consistent and very similar in all orchards. Despite the underestimation, satellites are deemed suitable for broader trend analysis, while UAVs provide more granular, precise agronomical assessments. An integrated utilization of both technologies is recommended for comprehensive and accurate precision agriculture practices. Full article

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3417 KiB

Open AccessProceeding Paper

Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach

by Gulam Mohiuddin and Jan-Peter Mund

Environ. Sci. Proc. 2024, 29(1), 15; https://doi.org/10.3390/ECRS2023-15836 - 28 Nov 2023

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Abstract

Rapid urbanization in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes. Such abrupt and significant land cover changes considerably affect the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST [...] Read more.

Rapid urbanization in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes. Such abrupt and significant land cover changes considerably affect the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential for effective urban planning and environmental management in agglomerations, particularly in the face of escalating climate change. This study aims to elucidate the spatiotemporal variations in LST in urban areas compared to LULC changes by applying remote sensing techniques. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development, using Landsat images from 2000 to 2021. The analysis employed an exploratory time series analysis of LST and examined areas with consistently higher LSTs (hotspots) regarding their specific LULC changes. The study revealed noticeable variability in LST (20 to 69 °C), predominantly influenced by seasonal variability and LULC changes. The hotspots provided insights into how LST varies within different LULCs at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes, warranting the attention of urban planners and policymakers. This study contributes to understanding the spatial relationship between LST and LULC changes, demonstrating the potential for developing new empirically rooted urban climate models that account for this complex physical interplay of changing land surfaces over time. While the study focused on a specific urban area, the methodology provides a replicable model for other similarly structured regions, potentially inspiring future research in various urban planning and monitoring contexts. Full article

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2289 KiB

Open AccessProceeding Paper

Surrogate Modeling of MODTRAN Physical Radiative Transfer Code Using Deep-Learning Regression

by Mohammad Aghdami-Nia, Reza Shah-Hosseini, Saeid Homayouni, Amirhossein Rostami and Nima Ahmadian

Environ. Sci. Proc. 2024, 29(1), 16; https://doi.org/10.3390/ECRS2023-16294 - 16 Nov 2023

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Abstract

Radiative Transfer Models (RTMs) are one of the major building blocks of remote-sensing data analysis that are widely used for various tasks such as atmospheric correction of satellite imagery. Although high-fidelity physical RTMs such as MODTRAN are considered to offer the best possible [...] Read more.

Radiative Transfer Models (RTMs) are one of the major building blocks of remote-sensing data analysis that are widely used for various tasks such as atmospheric correction of satellite imagery. Although high-fidelity physical RTMs such as MODTRAN are considered to offer the best possible modeling of atmospheric procedures, they are computationally demanding and require a lot of parameters that should be tuned by an expert. Therefore, there is a need for surrogate models for the physical RTM codes that can mitigate these drawbacks while offering an acceptable performance. This study aimed to suggest surrogate models for the MODTRAN RTM using deep-learning models. For this purpose, the top of atmosphere (TOA) spectra calculated by the MODTRAN code as well as the bottom of atmosphere (BOA) input spectra and other atmospheric parameters such as temperature and water vapor content observations were collected and used as the training dataset. Two deep-learning regression models, including a fully connected network (FCN) and an auto-encoder (AE), as well as a random forest (RF) machine-learning regression model were trained. The results of these models were assessed using the three evaluation metrics root mean squared error (RMSE), regression coefficient (R²), and spectral angle mapper (SAM). The evaluations indicated that the AE offered the best performance in all the metrics, with RMSE, R², and SAM scores of 0.0087, 0.9906, and 1.4295 degrees, respectively, in the best-case scenarios. These results showed that deep-learning models can better reproduce results via high-fidelity physical RTMs. Full article

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1712 KiB

Open AccessProceeding Paper

Coastal Vegetation Change Detection Using a Remote Sensing Approach

by Usha Pandya, Ashwini N. Mudaliar and Switi Alvi

Environ. Sci. Proc. 2024, 29(1), 17; https://doi.org/10.3390/ECRS2023-15853 - 6 Nov 2023

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Abstract

Coastal zones represent varied and highly productive ecosystems such as mangroves, coral reefs, sea grasses, and sand dunes. However, as a result of globalization, anthropological activities have increased in coastal areas, putting these ecosystems under high pressure. This, in turn, has led to [...] Read more.

Coastal zones represent varied and highly productive ecosystems such as mangroves, coral reefs, sea grasses, and sand dunes. However, as a result of globalization, anthropological activities have increased in coastal areas, putting these ecosystems under high pressure. This, in turn, has led to the loss of valuable vegetation in the coastal areas of the world. This study was conducted to detect the changes occurring in the coastal vegetation in the Daman district of India. Daman is one of the Union territories of India, which have shown good development in recent years. As a result, the area covered by mangrove vegetation has changed at and near the coast of this district. A remote sensing approach was utilized in this study to detect the changes in vegetation that occurred between the years 2016 and 2021. Landsat ETM+ data were used to derive NDVI images of the study period using ERDAS imagine 2014. Field work covering the entire study area was carried out to classify and assess the accuracy of the vegetation categories, i.e., no vegetation, low vegetation, moderate vegetation, and dense vegetation. Vegetation maps for both the years were prepared using ArcGIS software 10.5. The results indicated that the area with no vegetation decreased during the study period, whereas the rest of the categories, i.e., low vegetation, moderate vegetation, and dense vegetation, showed an increase. This increase in vegetation can be attributed to the Daman official authorities’ efforts to conserve these coastal areas. This will lead to enhanced ecosystem services provided by these ecosystems. Full article

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8240 KiB

Open AccessProceeding Paper

Investigation of Thermal Heat Mapping and Vegetation Cooling Impact Using Landsat-5, -7, and -8 Imagery: A Case Study of Greater Beirut Area in Lebanon

by Ghaleb Faour, Joelle Hanna and Bilal Hammoud

Environ. Sci. Proc. 2024, 29(1), 18; https://doi.org/10.3390/ECRS2023-15863 - 6 Nov 2023

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Abstract

In this research study, we use Landsat-5, -7, and -8 thermal remote sensing technology to analyze the urban heat mapping of the Greater Beirut Area (GBA) in Lebanon. The investigation is conducted within a time frame that spans over three decades from 1990 [...] Read more.

In this research study, we use Landsat-5, -7, and -8 thermal remote sensing technology to analyze the urban heat mapping of the Greater Beirut Area (GBA) in Lebanon. The investigation is conducted within a time frame that spans over three decades from 1990 to 2020. For each year, we calculate the normalized difference vegetation index (NDVI) and land surface temperature (LST) statistics. Also, a spatial-temporal analysis is conducted to relate heat mapping in GBA to the topography based on the altitude. Overall results show that the temperature in GBA has increased over three decades, with an increase in the vegetation and urban LST by 1.10 °C and 1.26 °C, respectively. Results also show that green areas are cooler than urban areas. Local analyses show that vegetation and altitude have a cooling effect, with temperatures dropping in the high and green mountains. Full article

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Open AccessProceeding Paper

Analysis of Subglacial Lake Activity in Recovery Ice Stream with ICESat-2 Laser Altimetry

by Yangyang Chen

Environ. Sci. Proc. 2024, 29(1), 19; https://doi.org/10.3390/ECRS2023-15830 - 15 Nov 2023

Viewed by 180

Abstract

The latest laser altimetry technology employed by NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) enables the capture of denser and more precise spatial details. Here, we utilize ICESat-2 data from September 2018 to July 2022 to replicate and analyze the dynamics of [...] Read more.

The latest laser altimetry technology employed by NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) enables the capture of denser and more precise spatial details. Here, we utilize ICESat-2 data from September 2018 to July 2022 to replicate and analyze the dynamics of the recovery ice stream’s subglacial lake system. To investigate the pathways of subglacial water transfer and determine the outline of subglacial lakes, we employ the differential digital elevation model (DEM) method to depict the surface elevation changes of each subglacial lake at monthly intervals. Our findings indicate significant migration in the activity location of 4 lakes. Notably, Rec1, previously regarded as a single lake, performed as two distinct lakes during the study. Furthermore, we identify two large-scale lakes with subglacial water flux reaching 0.5 km³. Full article

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547 KiB

Open AccessProceeding Paper

Fast Computations of the Top-of-the-Atmosphere Radiance in a Spectral Range 400–2500 nm Using the PYDOME Tool

by Dmitry Efremenko, Bringfried Pflug, Rudolf Richter, Raquel de los Reyes and Thomas Trautmann

Environ. Sci. Proc. 2024, 29(1), 20; https://doi.org/10.3390/ECRS2023-15858 - 6 Nov 2023

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Abstract

Accurate computations of the radiance in the gaseous absorption bands typically require fine wavelength steps. In this paper, a fast technique for computing a radiance spectrum in the wavelength region of 400–2500 nm is proposed. Our approach draws inspiration from the established k [...] Read more.

Accurate computations of the radiance in the gaseous absorption bands typically require fine wavelength steps. In this paper, a fast technique for computing a radiance spectrum in the wavelength region of 400–2500 nm is proposed. Our approach draws inspiration from the established k-correlation distribution model, with k denoting the absorption coefficient. However, our method expands upon this by considering both the direct transmittance and the scattering coefficient as predictors. At selected spectral points, the full radiative transfer simulations are performed, and the mathematical relation between a predictor and the radiance is established. Then, the radiance is restored on a fine wavelength grid. This approach can be used to enhance the accuracy of the convolved spectrum computations based on precomputed approximately monochromatic lookup tables and reduce the size of lookup tables. Numerical analysis demonstrates the method’s applicability to scenarios characterized by aerosol optical thicknesses not exceeding two. Full article

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Open AccessProceeding Paper

Assessing the Impact of Landfills on Surrounding Vegetation: A Remote Sensing Analysis with Sentinel-2 and Landsat 8

by Rajesh Vanguri, Giovanni Laneve, Enrico Cadau, Silvia Scifoni and Martino Luca

Environ. Sci. Proc. 2024, 29(1), 21; https://doi.org/10.3390/ECRS2023-15865 - 6 Nov 2023

Viewed by 205

Abstract

This study investigates the impact of landfills on surrounding vegetation in Naples, Italy, utilizing Sentinel-2 and Landsat 8 imagery processed with Sen2like. Seventeen landfill sites were studied with indices NDVI and GCI using four years of data, revealing significant vegetation growth reduction near [...] Read more.

This study investigates the impact of landfills on surrounding vegetation in Naples, Italy, utilizing Sentinel-2 and Landsat 8 imagery processed with Sen2like. Seventeen landfill sites were studied with indices NDVI and GCI using four years of data, revealing significant vegetation growth reduction near two landfills compared to farther areas. Continuous monitoring is crucial, and satellite imagery offers an effective assessment tool. The study establishes a correlation between landfills and vegetation, underlining the need for ongoing monitoring and exploring additional factors influencing vegetation health within landfill environments. The findings contribute valuable insights to promote sustainable landfill management and protect surrounding ecosystems. Full article

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Open AccessProceeding Paper

Spatiotemporal Variations of Glacier Surface Facies (GSFs) in Svalbard: An Example of Midtre Lovénbreen

by Shridhar D. Jawak, Sagar F. Wankhede, Prashant H. Pandit and Keshava Balakrishna

Environ. Sci. Proc. 2024, 29(1), 22; https://doi.org/10.3390/ECRS2023-15840 - 6 Dec 2023

Viewed by 148

Abstract

Glacier surface facies (GSFs) are visible glaciological regions that can be distinguished and mapped at the end of summer using optical satellite data. GSF maps act as visual metrics of glacier health when assessed independently or correlated with in situ mass balance measurements. [...] Read more.

Glacier surface facies (GSFs) are visible glaciological regions that can be distinguished and mapped at the end of summer using optical satellite data. GSF maps act as visual metrics of glacier health when assessed independently or correlated with in situ mass balance measurements. The literature suggests that the spatiotemporal distribution of all accumulation and ablation facies are important inputs to 3D mass balance models because the GSF trends enhance the precision of models. For example, the progressive increase in the area and distribution of melting ice and decrease in the area and distribution of glacier ice, as estimated by satellite data, may signal potential mass loss without significant change in the overall area of the ablation zone. Tracking the evolution of GSFs in Svalbard is important for the predictive assessment of the cryosphere in the Arctic. This will further facilitate robust methods for monitoring GSFs on a planetary scale. In this context, we present a regional spatiotemporal analysis of GSFs of Midtre Lovénbreen, Ny Ålesund, Svalbard. We used openly available Landsat 8 Operational Land imager (OLI) and Sentinel 2A imagery taken in 2017–2022 to track the occurrence and variations of GSFs via machine learning. The current results suggest that ablation facies such as melting ice and dirty ice are increasing over time. Sentinel 2A provides finer resolution but is limited by its temporal coverage. Although Landsat is suitable for long-term trend analysis, its coarser resolution can lead to errors such as over/underestimation of smaller patches of facies on relatively smaller glaciers. As the spectral properties of GSFs are consistent over time, a robust set of spectra depicting variations in physical appearance of facies may be used to train machine learning algorithms, thereby improving efficacy. In forthcoming studies, our objective is to expand the temporal scope spanning decades and to trace facies evolution over longer time series. Full article

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790 KiB

Open AccessProceeding Paper

Split-Window Algorithm for Land Surface Temperature Retrieval from Joint Polar-Orbiting Satellite System JPSS-2/NOAA-21

by Fatima Zahrae Rhziel, Mohammed Lahraoua and Naoufal Raissouni

Environ. Sci. Proc. 2024, 29(1), 23; https://doi.org/10.3390/ECRS2023-16293 - 6 Nov 2023

Viewed by 132

Abstract

Land surface temperature (LST) plays a pivotal role in the dynamic exchange of energy between the Earth’s surface and the atmosphere. This research centers on the assessment of LST from satellite data acquired by the Joint Polar-orbiting Satellite System (JPSS), specifically JPSS-2/NOAA-21, employing [...] Read more.

Land surface temperature (LST) plays a pivotal role in the dynamic exchange of energy between the Earth’s surface and the atmosphere. This research centers on the assessment of LST from satellite data acquired by the Joint Polar-orbiting Satellite System (JPSS), specifically JPSS-2/NOAA-21, employing an innovative split-window algorithm (SWA). Atmospheric water vapor content (WVC) and surface emissivity are the two main input variables in the split-window technique. Therefore, the moderate resolution transmittance code, version 4.0 (MODTRAN 4.0), was used to simulate WVC and atmospheric transmittance. The performance of the SWA was rigorously assessed against standard atmospheric conditions, revealing its capacity to achieve an LST retrieval accuracy of 1.4 Kelvin (K), even in the presence of various errors. Moreover, the LST retrieval algorithm was validated using ground truth data sets from two Australian sites, and the RMSE value was 1.71 K. The achieved results demonstrate the algorithm’s capability to provide accurate LST estimation for NOAA-21 satellite data. Full article

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Open AccessProceeding Paper

Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms

by Lwandile Nduku, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, Wonga Masiza, Phathutshedzo Eugene Ratshiedana, Ahmed Mukalazi Kalumba and Johannes George Chirima

Environ. Sci. Proc. 2024, 29(1), 24; https://doi.org/10.3390/ECRS2023-15860 - 21 Nov 2023

Cited by 1 | Viewed by 226

Abstract

Crop growth and yield often vary, not only between farms, but also at the sub-field level. These variations can stem from sub-field heterogeneities of soil and plant biophysical parameters. This means that soil and plant biophysical data can be used to predict intra-field [...] Read more.

Crop growth and yield often vary, not only between farms, but also at the sub-field level. These variations can stem from sub-field heterogeneities of soil and plant biophysical parameters. This means that soil and plant biophysical data can be used to predict intra-field crop growth and yield variability. This study used soil properties and vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery as predictor variables, and monthly measurements of crop height (cm) as a response variable to predict crop growth rate in two winter wheat farms in South Africa. These datasets were analyzed using two regression models including Gaussian process regression (GPR) and ensemble learning that uses least-squares boosting (LSboost) and bagging (Bag) in MATLAB. The results showed that soil properties, particularly Ca, Mg, K and clay, were more important than VIs in predicting actual crop growth. Furthermore, GPR (R² = 0.68 to 0.75, RMSE = 15.85 to 18.38 cm) performed slightly better than LSboost-Bag-ER (R² = 0.64 to 0.70 and RMSE = 17.26 to 19.34 cm) in predicting crop growth. These findings are useful for crop agronomic management. Full article

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Open AccessProceeding Paper

Forest Cover Mapping Based on Remote Sensing Data

by Irina Danilova, Vera Ryzhkova and Mikhail Korets

Environ. Sci. Proc. 2024, 29(1), 25; https://doi.org/10.3390/ECRS2023-15864 - 6 Nov 2023

Viewed by 145

Abstract

This paper presents a technique for mapping the vegetation cover of mountainous areas based on seasonal satellite data from Landsat-OLI 8, using information about vegetation growth conditions. This mapping is based on the creation of a layer of relatively homogeneous areas in terms [...] Read more.

This paper presents a technique for mapping the vegetation cover of mountainous areas based on seasonal satellite data from Landsat-OLI 8, using information about vegetation growth conditions. This mapping is based on the creation of a layer of relatively homogeneous areas in terms of relief and climate. Training samples for the classification of images were formed within these areas. Satellite images were classified using the maximum likelihood method. The created map reflects the spatial distribution of 9 classes of forest vegetation and 10 classes of non-forest vegetation. Full article

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Open AccessProceeding Paper

Comparison between Classic Methods and Deep Learning Approach in Detecting Changes of Waterbodies from Sentinel-1 Images

by Sahand Tahermanesh, Behnam Asghari Beirami and Mehdi Mokhtarzade

Environ. Sci. Proc. 2024, 29(1), 26; https://doi.org/10.3390/ECRS2023-16186 - 28 Nov 2023

Viewed by 197

Abstract

Climate change has directly impacted Earth’s habitats, resulting in various adverse effects, such as the desiccation of water bodies. The process of identifying such changes through field observations is time-consuming and costly. By using remote sensing techniques, it has become easier than ever [...] Read more.

Climate change has directly impacted Earth’s habitats, resulting in various adverse effects, such as the desiccation of water bodies. The process of identifying such changes through field observations is time-consuming and costly. By using remote sensing techniques, it has become easier than ever to monitor changes in the environment. Radar satellites, unlike optics, can acquire data in all weather conditions, regardless of the time of day. These data can provide valuable information about the environment and surface roughness. Various methods have been proposed for detecting changes, which can be divided into classic and deep learning methods. Classic methods only use image information, such as radar backscatter, which cannot extract spatial information. Sentinel-1 (S1) is an Earth observation radar sensor that provides free access to SAR (Synthetic Aperture Radar) images. This study aims to compare the performance of two classic methods, a ratio index (RI) and Markov random field (MRF), with deep learning networks in detecting changes. As a deep network, Inception CNN (convolutional neural network) is presented as an enhancement of the original CNN to detect the changes. To evaluate methods, two instances of S1 images from Lake Poopó, located in the Altiplano Mountains in Oruro Department, Bolivia, are used as a primary dataset. The results of the comparison models were assessed using three evaluation metrics: Overall Accuracy (O.A), Missed Error (M.E), and Kappa Coefficient (K). Based on the evaluations, the Inception CNN performed exceptionally in all metrics, with O.A, K, and M.E rates of 97.35%, 90.28%, and 9%, respectively. Meanwhile, the ratio index had poor performance, with 83.27%, 29.05%, and 75.03%, respectively, for O.A, K, and M.E. These results indicated that the Inception CNN could provide better performance in detecting changes from S1 images. Full article

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Open AccessProceeding Paper

Assessing ALOS-2/PALSAR-2 Data’s Potential in Detecting Forest Volume Losses from Selective Logging in a Section of Tapajós National Forest

by Natalia C. Wiederkehr, Fábio F. Gama and Polyanna da C. Bispo

Environ. Sci. Proc. 2024, 29(1), 27; https://doi.org/10.3390/ECRS2023-15984 - 21 Nov 2023

Viewed by 161

Abstract

This study assesses ALOS-2/PALSAR-2 (ALOS2) polarimetric images for detecting forest volume losses due to selective logging in a region in the Brazilian Amazon. Two logging-intensive areas, APU 2016, and APU 2017, were studied. ALOS2 imagery attributes, including backscatter and phase data, were analyzed [...] Read more.

This study assesses ALOS-2/PALSAR-2 (ALOS2) polarimetric images for detecting forest volume losses due to selective logging in a region in the Brazilian Amazon. Two logging-intensive areas, APU 2016, and APU 2017, were studied. ALOS2 imagery attributes, including backscatter and phase data, were analyzed for differences between logged and unlogged regions using Wilcoxon’s nonparametric test at a 95% confidence level. The Radar Normalized Difference Vegetation Index proved effective in detecting selective logging-induced forest volume losses, with consistent results (p-values of 0.003 for APU 2016 and 0.037 for APU 2017). These findings provide insights for monitoring and mitigating ecological impacts of logging in complex forest ecosystems. Full article

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Open AccessProceeding Paper

Comparing the Water Storage Changes in Iran and Its Six Neighboring Countries with Gravity Recovery and Climate Experiment Satellite Data on Google Earth Engine

by Sahand Tahermanesh, Behnam Asghari Beirami and Mehdi Mokhtarzade

Environ. Sci. Proc. 2024, 29(1), 28; https://doi.org/10.3390/ECRS2023-16295 - 6 Nov 2023

Viewed by 167

Abstract

Water plays a vital role in sustaining life and meeting the water needs of various sectors, such as agriculture, industries, and households. As water resources continue to be depleted, several hazards arise for communities. Declining water quality, a declining water table, reduced plant [...] Read more.

Water plays a vital role in sustaining life and meeting the water needs of various sectors, such as agriculture, industries, and households. As water resources continue to be depleted, several hazards arise for communities. Declining water quality, a declining water table, reduced plant growth, droughts, diminished agricultural productivity, and the underutilization of power generation stations are some of the hazards associated with these conditions. Therefore, it is crucial to monitor the changes in water resources. The traditional methods used for measuring water storage face various challenges, including limited spatial coverage, low temporal resolution, high cost and resource requirements, and accuracy limitations. To address these challenges, remote sensing sensors such as the GRACE satellite provide rich sources of information that can be used to evaluate water reserves. Moreover, the Google Earth Engine provides access to a wide range of satellite imagery and geospatial data for various applications, making geospatial information more accessible and enabling informed decisions. This study analyzed the GRACE satellite time series data from 2002 to 2017 to investigate and compare water storage changes in Iran and six neighboring countries: Turkey, Iraq, Saudi Arabia, Turkmenistan, Pakistan, and Afghanistan. The final statistical analyses indicate a decrease in water reserves in almost all mentioned countries. The analysis of the results shows that Iran ranks second in terms of water reserve consumption after Iraq, which had the worst performance. Our study concludes with a concerning outlook on water storage in Iran, primarily attributed to inefficient water resource management, reduced rainfall, drought, and excessive withdrawals. Full article

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Open AccessProceeding Paper

Temporal Change Dynamics of the Hydrometeorological Conditions of Upper Subarnarekha River Basin (SRB) Using Geospatial Techniques

by Farhin Tabassum and Akhouri Pramod Krishna

Environ. Sci. Proc. 2024, 29(1), 29; https://doi.org/10.3390/ECRS2023-16364 - 6 Nov 2023

Viewed by 129

Abstract

Understanding the dynamics of any river basin requires a comprehensive analysis of factors such as urbanization, socioeconomic growth, deforestation, agricultural practices, and mining activities. This study aims to investigate the climatic and land use variations and their implications on the hydrometeorological conditions of [...] Read more.

Understanding the dynamics of any river basin requires a comprehensive analysis of factors such as urbanization, socioeconomic growth, deforestation, agricultural practices, and mining activities. This study aims to investigate the climatic and land use variations and their implications on the hydrometeorological conditions of the upper Subarnarekha River Basin (SRB). Decadal Land Use and Land Cover (LULC) alterations were assessed for the years 2001, 2010, and 2020. Further, climatic variations were studied using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) precipitation data and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) temperature data (2001–2020). Conventional groundwater level data from the India-Water Resource Information System (WRIS) for the same timeframe were also integrated to explore groundwater level fluctuations. Such temporal variations were examined using Theil Sen’s Median Trend and Mann-Kendall tests. The study also determines how LULC changes and climate variability influence groundwater level in the upper SRB during pre-monsoon, monsoon, and post-monsoon seasons. Results showed higher precipitation and temperature in the southeastern basin region. A strong connection between rainfall and groundwater levels was inferred, with rainfall exhibiting a non-significant upward trend (9.83 mm/year), while temperature shows a persistently significant increasing trend. These observations emphasize the importance of monitoring the hydrometeorological behavior of the basin, underlining its critical role in ensuring the long-term sustainability of water resources. Full article

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Open AccessProceeding Paper

Temporal Changes in Vegetation around Open Cast Quarries: Milas–Ören Lignite Coal Quarries

by Ceren Ozcan-Tatar, Bilge Bingul and Saye Nihan Cabuk

Environ. Sci. Proc. 2024, 29(1), 30; https://doi.org/10.3390/ECRS2023-15983 - 6 Nov 2023

Viewed by 206

Abstract

Due to the rising coal demand for energy, open cast coal mines, as well as concerns about their environmental impact, have increased globally. Such mines, often situated in forested areas outside cities, raise apprehensions. This study evaluates the changes in vegetation around a [...] Read more.

Due to the rising coal demand for energy, open cast coal mines, as well as concerns about their environmental impact, have increased globally. Such mines, often situated in forested areas outside cities, raise apprehensions. This study evaluates the changes in vegetation around a Milas–Ören open cast lignite coal mining site, Türkiye, between 1984 and 2023, using Landsat images and Google Earth Engine. The results reveal a loss of around 1950 ha of forests, 570 ha of olive groves, and an expansion of mining areas by over 1700 ha. The study found that mining activities have environmental impacts outside as well as inside the mining area, and the study provides a long-term and systematic analysis of the current situation. Full article

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Open AccessProceeding Paper

Extraction of Surface Water Extent: Automated Thresholding Approaches

by Meghaa Sathish Kumar

Environ. Sci. Proc. 2024, 29(1), 31; https://doi.org/10.3390/ECRS2023-15861 - 6 Nov 2023

Viewed by 250

Abstract

Inland water bodies play a crucial role in both ecological and sociological contexts. The distribution of these water bodies can change over time due to natural or human-induced factors. Monitoring the extent of surface water is vital to understand extreme events such as [...] Read more.

Inland water bodies play a crucial role in both ecological and sociological contexts. The distribution of these water bodies can change over time due to natural or human-induced factors. Monitoring the extent of surface water is vital to understand extreme events such as floods and droughts. The availability of dense temporal Earth observation data from sensors like Landsat and Sentinel, coupled with advancements in cloud computing, has enabled the analysis of surface water extent over extended periods. In this study, automated thresholding approaches were applied within the Google Earth Engine platform to extract the surface water extent of the Chembarampakkam reservoir in Tamil Nadu, India. Sentinel-2 data spanning from 2019 to 2023 were used to derive two key indices, namely, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). These indices were then thresholded to determine the presence of water. The performance of two different global thresholding techniques, namely, the deterministic thresholding and Otsu thresholding methods, was compared to achieve better results. To enhance the accuracy of the deterministic technique, an iterative method was implemented. While the threshold values were generally similar for both techniques, the Otsu algorithm slightly outperformed the iterated deterministic technique in water classification. Furthermore, a surface water dynamics image was obtained using temporal images, providing insights into the temporal surface dynamism of the reservoir. Overall, this study highlights the significance of surface water monitoring using remote sensing and cloud computing techniques. Full article

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Open AccessProceeding Paper

A Methodological Approach for Assessing the Resilience of Pinus halepensis Mill. Plant Communities Using UAV-LiDAR Data

by Fernando Pérez-Cabello, Cristian Iranzo, Raúl Hoffrén, María Adell, Antonio Montealegre, Raquel Montorio, Alberto García-Martín and Luis A. Longares

Environ. Sci. Proc. 2024, 29(1), 32; https://doi.org/10.3390/ECRS2023-15855 - 6 Nov 2023

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Abstract

The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burned areas. This study presents a methodology using UAV-LiDAR data to quantify malleability in three burned areas (1970, 1995, 2008) through the statistical analysis of tree height [...] Read more.

The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burned areas. This study presents a methodology using UAV-LiDAR data to quantify malleability in three burned areas (1970, 1995, 2008) through the statistical analysis of tree height and Profile Area Change (PAC) metrics. Significant differences in vegetation height (99th percentile) among the three fires, with specific maximum absolute differences (D) depending on the fire year, have been identified. Positive PAC values in 2008 indicate deeper LiDAR penetration, resulting in lower regeneration, while values close to 0 in 1970 suggest more uniform regeneration. The use of LiDAR metrics and uni-temporal sampling between burned sectors and controls aids in understanding community resilience and identifying recovery stages in P. halepensis forests. Full article

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Open AccessProceeding Paper

Long-Term Surface Water Variability in Chilika Lake Using Archival Remote Sensing Data

by Vivek Ganesh, Santonu Goswami and Harini Nagendra

Environ. Sci. Proc. 2024, 29(1), 33; https://doi.org/10.3390/ECRS2023-16706 - 6 Nov 2023

Viewed by 193

Abstract

Asia’s largest lake and the world’s foremost tropical lagoon, Chilika, stands as a testament to ecological diversity. The lake is diverse in biodiversity and is a sanctuary for over 400 distinct brackish and freshwater species. However, the lake is confronted by ceaseless pressures [...] Read more.

Asia’s largest lake and the world’s foremost tropical lagoon, Chilika, stands as a testament to ecological diversity. The lake is diverse in biodiversity and is a sanctuary for over 400 distinct brackish and freshwater species. However, the lake is confronted by ceaseless pressures from a confluence of natural forces and anthropogenic activities. These challenges threaten to unleash ecological transformations that could reshape this ecological marvel. This study examined the spatial–temporal variation in the lake for the years 1988 to 2017 using archival remote sensing data. The Normalized Difference Water Index (NDWI) derived from Landsat 5-TM and Landsat 8-OLI was used to understand the expansion and contraction happening in the extent of the lake. To determine the water spread area, from each NDWI image, the minimum (Min.) pixel values, maximum (Max.) pixel values, and mean pixel values were extracted, and a yearly composite for was created the aforementioned years. Full article

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Open AccessProceeding Paper

Estimation of Air Temperature at Sites in Maritime Antarctica Using MODIS LST Collection 6 Data

by Alejandro Corbea-Pérez, Carmen Recondo and Javier F. Calleja

Environ. Sci. Proc. 2024, 29(1), 34; https://doi.org/10.3390/ECRS2023-15866 - 6 Dec 2023

Viewed by 119

Abstract

It is known that changes in temperature could cause changes in the Antarctic Ice Sheet, which would have an immediate and long-term impact on the global mean sea level. For this reason, the monitoring of air temperature (T_a) is of [...] Read more.

It is known that changes in temperature could cause changes in the Antarctic Ice Sheet, which would have an immediate and long-term impact on the global mean sea level. For this reason, the monitoring of air temperature (T_a) is of great interest to the scientific community. On the other hand, Antarctica constitutes an area of difficult access, which makes it difficult to obtain in situ data. Because of this, Land Surface Temperature (LST) remote sensing data have become an important alternative for estimating T_a. In this work, we estimated T_a from daytime and nighttime LST data at maritime Antarctic sites in the South Shetland Archipelago using empirical models, based on the addition of spatiotemporal variables. We used T_a data from the Spanish Antarctic stations and from the PERMASNOW project stations. MOD11A1 and MYD11A1 (Collection 6) Moderate Resolution Imaging Spectroradiometer (MODIS) LST products were downloaded from the Google Earth Engine platform and only the highest quality data were selected. Outliers associated with clouds were removed with filters. Two different multilinear regression models were tested: models for each individual station and global models based on the data from all the stations. The simple regression analysis LST against T_a showed that a better fit is always achieved with daytime LST data (R² average = 0.73) than with nighttime LST data (R² average = 0.56). The performance of the models was improved with the addition of spatiotemporal variables as predictive variables, with which we obtained an average R² = 0.75 for daytime data and an average R² = 0.60 for nighttime data. The global models allowed for improving the correlation and reducing the errors with respect to the models obtained using individual stations. Global models provide a precise description of the behavior of the temperature in maritime Antarctica, where it is not possible to install and maintain a dense network of weather stations. Full article

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Open AccessProceeding Paper

Change Detection from Landsat-8 Images Using a Multi-Scale Convolutional Neural Network (Case Study: Sahand City) ^†

by Sahand Tahermanesh, Behnam Asghari Beirami and Mehdi Mokhtarzade

Environ. Sci. Proc. 2024, 29(1), 35; https://doi.org/10.3390/ECRS2023-16611 - 6 Nov 2023

Viewed by 182

Abstract

Identifying changes in the Earth’s phenomena is vital for understanding and mitigating the impacts of environmental issues. Monitoring the Earth’s surface phenomena can be carried out effectively using satellite images acquired at different times. In addition to spectral features, spatial features play a [...] Read more.

Identifying changes in the Earth’s phenomena is vital for understanding and mitigating the impacts of environmental issues. Monitoring the Earth’s surface phenomena can be carried out effectively using satellite images acquired at different times. In addition to spectral features, spatial features play a significant role in detecting precise changes. However, classical change detection (CD) methods rarely consider spatial information and fail to account for scale variations within images. The present study introduces a novel deep learning-based CD method that hierarchically extracts spatial–spectral features at various scales to address these issues. The proposed deep neural network generates a binary change map by employing a multi-scale approach that integrates the information of patches of varied sizes at the decision level. We conducted experiments using Landsat-8 images from Sahand City, East Azarbaijan, Iran, because of their remarkable capacity to represent the Earth’s surface details. Tabriz’s population growth has led to rapid development in Sahand City to accommodate citizens. Studying these changes can offer valuable insights into urban planning. The performance of the proposed deep model is evaluated in comparison to two classical methods, the change vector analysis (CVA) method and a random forest (RF) algorithm. Based on the change detection results, the proposed deep learning network demonstrates a significant improvement in the kappa coefficient (KC) compared to the RF and CVA methods, with increases of approximately 11.86% and 29.36%, respectively. Furthermore, in terms of overall accuracy (O.A.), the proposed network outperforms both the RF and CVA methods by approximately 17.08% and 29.16%, respectively. The proposed multi-scale deep network performs better at detecting changes across all metrics. As a result, the CVA method fails to identify changes with sufficient accuracy. Full article

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Open AccessProceeding Paper

Detection and Clustering of Grapevine Varieties via Multispectral Aerial Imagery and Vegetation Indices Analysis

by Olympia Kourounioti and Emmanouil Oikonomou

Environ. Sci. Proc. 2024, 29(1), 36; https://doi.org/10.3390/ECRS2023-16705 - 6 Nov 2023

Viewed by 165

Abstract

Viticulture requires constant and meticulous care throughout the year to ensure a high-quality harvest. Precision agriculture has significantly advanced, enabling the early detection of potential threats or diseases without harming the crops. Understanding the distinct features of various grapevine varieties, such as chlorophyll [...] Read more.

Viticulture requires constant and meticulous care throughout the year to ensure a high-quality harvest. Precision agriculture has significantly advanced, enabling the early detection of potential threats or diseases without harming the crops. Understanding the distinct features of various grapevine varieties, such as chlorophyll content, canopy growth, stress, and interactions with specific soil elements, is crucial. To address these challenges, this study employs multispectral images captured by unmanned aerial vehicles (UAVs), providing a method for exploiting the spectral features of the vine canopies on a field with numerous vine varieties. The primary objective is to cluster different grapevine varieties in the same area based on common spectral characteristics. Full article

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Open AccessProceeding Paper

Assessment of Outdoor Thermal Comfort during the Last Decade Using Landsat 8 Imagery with Machine Learning Tools over the Three Metropolitan Cities of India

by Peri Subrahmanya Hari Prasad and A. N. V. Satyanarayana

Environ. Sci. Proc. 2024, 29(1), 37; https://doi.org/10.3390/ECRS2023-15838 - 16 Nov 2023

Viewed by 132

Abstract

Due to rapid urban growth and population increase, accurately tracking land use and cover changes (LULC) is vital for predicting outdoor thermal comfort. We used high-res Landsat 8 imagery and on-site weather data, employing a Support Vector Machine (SVM) with PCA to estimate [...] Read more.

Due to rapid urban growth and population increase, accurately tracking land use and cover changes (LULC) is vital for predicting outdoor thermal comfort. We used high-res Landsat 8 imagery and on-site weather data, employing a Support Vector Machine (SVM) with PCA to estimate thermal comfort. The PCA addressed variable multicollinearity, and LULC was classified using decision trees. Notable LULC trends emerged. In Hyderabad, built-up areas rose from 37% to 48% (2009–2019) and barren lands fell from 42% to 18%. In Bangalore, built-up areas surged from 25% to 80%, causing vegetation loss (25% to 2%) and reduced barren land (50% to 18%). Jaipur saw a 12% built-up area increase with a slight vegetation uptick. The thermal comfort analysis highlighted Bangalore’s intense urbanization and Jaipur’s limited expansion. Discomfort ranked highest in barren lands, followed by urban, vegetation, and water areas. Full article

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513 KiB

Open AccessProceeding Paper

Estimation of Land Surface Temperature from the Joint Polar-Orbiting Satellite System Missions: JPSS-1/NOAA-20 and JPSS-2/NOAA-21

by Fatima Zahrae Rhziel, Mohammed Lahraoua and Naoufal Raissouni

Environ. Sci. Proc. 2024, 29(1), 38; https://doi.org/10.3390/ECRS2023-15847 - 6 Nov 2023

Viewed by 141

Abstract

The accurate estimation of land surface temperature (LST) is a vital parameter in various fields, such as hydrology, meteorology, and surface energy balance analysis. This study focuses on the estimation of LST using data acquired from Joint Polar-Orbiting Satellite System (JPSS) satellites, specifically [...] Read more.

The accurate estimation of land surface temperature (LST) is a vital parameter in various fields, such as hydrology, meteorology, and surface energy balance analysis. This study focuses on the estimation of LST using data acquired from Joint Polar-Orbiting Satellite System (JPSS) satellites, specifically JPSS-1/NOAA-20 and JPSS-2/NOAA-21. The methodology for this research centers on the utilization of the split-window algorithm, a well-established and recognized technique renowned for its proficiency in extracting accurate land surface temperature (LST) values from remotely sensed data. This algorithm leverages the differential behavior of thermal infrared (TIR) radiance measured in two adjacent spectral channels to estimate LST, effectively mitigating the influence of atmospheric distortions on the acquired measurements. To establish the accuracy of the proposed approach, the coefficients of the split-window algorithm were determined using linear regression analysis, utilizing a dataset generated via extensive radiative transfer modeling. The calculated LST values were subsequently compared with LST products provided by the National Oceanic and Atmospheric Administration (NOAA). The evaluation process encompassed the computation of root mean square error (RMSE) values, offering insights into the performance of the algorithm for both JPSS-1/NOAA-20 and JPSS-2/NOAA-21 missions. LST retrieval validation with standard atmospheric simulation indicates that the JPSS-1/NOAA-20 and The JPSS-1/NOAA-21 algorithms have demonstrated an accuracy of 1.4 K in retrieval of LST with different errors. The obtained results demonstrate the potential of the split-window algorithm to effectively estimate LST from JPSS satellite data. The RMSE values, 2.05 and 1.71 for JPSS-1/NOAA-20 and JPSS-2/NOAA-21, respectively, highlight the algorithm’s capability to provide accurate LST estimates for different mission datasets. This research contributes to enhancing our understanding of land surface temperature dynamics using remote sensing technology and showcases the valuable insights that can be gained from JPSS missions in monitoring and studying Earth’s surface processes. Full article

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Open AccessProceeding Paper

Drone-Based Smart Weed Localization from Limited Training Data and Radiometric Calibration Parameters

by Mehdi Khoshboresh-Masouleh and Reza Shah-Hosseini

Environ. Sci. Proc. 2024, 29(1), 39; https://doi.org/10.3390/ECRS2023-15854 - 14 Nov 2023

Viewed by 133

Abstract

The most efficient tool for practical uses, like weed monitoring in smart farming, is presently small object localization from drone images. While most object detection models indicate competency in localization when trained on large datasets, applying a few-shot learning technique can enhance scene [...] Read more.

The most efficient tool for practical uses, like weed monitoring in smart farming, is presently small object localization from drone images. While most object detection models indicate competency in localization when trained on large datasets, applying a few-shot learning technique can enhance scene comprehension, even when provided with limited training data. This investigation introduces a few-shot model for localizing weed grasses in multispectral drone images. The model encompasses a reflectance calibration factor, enabling it to perform well on tasks that it has yet to be specifically trained. An inductive transfer system enhances the model’s ability to generalize and accurately localize weeds. The research results demonstrate the potential of the suggested approach to detect weed grasses in drone-based multispectral images and calibration reflectance factor with a mIoU score of 71.45% and an accuracy of 84.3%, despite several difficulties in practical implementation. Full article

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Open AccessProceeding Paper

An Attempt: A Modified Semi-Empirical Approach Based on Retrieving Soil Fluoride from Agricultural Patches Using Sentinel-1 SAR Data

by Vijayasurya Krishnan and Manimaran Asaithambi

Environ. Sci. Proc. 2024, 29(1), 40; https://doi.org/10.3390/ECRS2023-16318 - 17 Nov 2023

Viewed by 154

Abstract

Plant growth and health are affected by 0.06–0.09% of crustal fluoride. A semi-empirical model estimated wet soil fluoride using Sentinel-1 5.405 GHz data as dependent on dielectric components and loss angles. Mineral surface charges and electrical potential limited clay soil ion mobility via [...] Read more.

Plant growth and health are affected by 0.06–0.09% of crustal fluoride. A semi-empirical model estimated wet soil fluoride using Sentinel-1 5.405 GHz data as dependent on dielectric components and loss angles. Mineral surface charges and electrical potential limited clay soil ion mobility via moisture and permeability. Real and imaginary dielectric components approximated a 3° to 4° loss angle in lab soil samples with high and low fluoride electrical conductivity. An estimated percentage of dielectric component loss over wide areas could have implied fluoride. Finally, linear regression between field fluoride value and conductance loss was used to estimate fluoride. The statistical differences (R² = 0.86, RMSE = 1.90, and Bias = 0.35) between predicted and simulated fluoride levels over clay soil and soil with different vegetation development suggest that C-band SAR data may detect fluoride. Full article

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Open AccessProceeding Paper

Drone-Based Spatio-Temporal Assessment of a Seagras Meadow: Insights into Anthropogenic Pressure

by Dorothée James, Antoine Collin and Agathe Bouet

Environ. Sci. Proc. 2024, 29(1), 41; https://doi.org/10.3390/ECRS2023-15851 - 6 Nov 2023

Viewed by 135

Abstract

Zostera marina L. is a flowering plant of great ecological interest as a breeding, nursery, and feeding place for many species. However, its spatial location implies strong competition with human uses (boating, fishing, etc.). Regular monitoring at a very high spatial and temporal [...] Read more.

Zostera marina L. is a flowering plant of great ecological interest as a breeding, nursery, and feeding place for many species. However, its spatial location implies strong competition with human uses (boating, fishing, etc.). Regular monitoring at a very high spatial and temporal resolution by a drone has been initiated to study the spatio-temporal and ecological dynamics of the seagrass meadow. Three drone campaigns per year were carried out in 2021 and 2022, totaling six spatial models. A pixel-oriented classification was performed to determine the overall envelope and to analyze the fragmentation of the meadow, which is likely caused by anchorage. A yearly loss of 465.18 m² was measured (envelope area) and a difference of 12.15 m² was observed between 2021 and 2022 (fragmented envelope area). Full article

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Open AccessProceeding Paper

Time Series Analysis of Sea Ice Production in Polynyas in the Amery Ice Shelf in Antarctica

by Miao Gu

Environ. Sci. Proc. 2024, 29(1), 42; https://doi.org/10.3390/ECRS2023-16368 - 28 Nov 2023

Viewed by 147

Abstract

The Amery Ice Shelf is a major source of sea ice, whose production is linked to the global climate. In 2019, a collapse event occurred in the Amery Ice Shelf; sea ice production before and during this collapse needs to be studied. In [...] Read more.

The Amery Ice Shelf is a major source of sea ice, whose production is linked to the global climate. In 2019, a collapse event occurred in the Amery Ice Shelf; sea ice production before and during this collapse needs to be studied. In this study, polynyas in the Amery Ice Shelf were identified according to ice thickness, and sea ice production was obtained by calculating the heat flux during winter (March–October) in 2013–2020. It was found that the sea ice production in the polynyas fluctuated greatly, and the maximum annual ice production occurred in 2018, which reached 225.4 km³. As for the collapse event in 2019, it is assumed that it may have exacerbated the volatility and instability of sea ice production. Full article

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2011 KiB

Open AccessProceeding Paper

Characteristics of the Snow Cover in East and West Antarctica and Their 20-Year Trends Retrieved from Satellite Remote Sensing Data

by Aleksey Malinka, Yauheni Ilkevich, Alexander Prikhach, Eleonora Zege, Iosif Katsev, Burcu Özsoy, Mahmut Oğuz Selbesoğlu, Özgün Oktar, Mustafa Fahri Karabulut, Esra Günaydın and Bahadır Çelik

Environ. Sci. Proc. 2024, 29(1), 43; https://doi.org/10.3390/ECRS2023-15862 - 6 Nov 2023

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Abstract

The aim of this study was to make a comparative analysis of the state of the snow surface in East and West Antarctica, including changes in snow cover characteristics during the past two decades. To do so, we used the ASAR (Antarctic Snow [...] Read more.

The aim of this study was to make a comparative analysis of the state of the snow surface in East and West Antarctica, including changes in snow cover characteristics during the past two decades. To do so, we used the ASAR (Antarctic Snow Albedo Retriever) algorithm, which processes satellite data and retrieves an effective snow grain size and a fraction of rocks not covered by snow, to process the MODIS data throughout the entire period of its operation (up to now). We have chosen several test areas (approximately 30 × 30 km²) to study the state of the snow cover on Enderby Land (East Antarctica), on the coast of the Ross Sea (the Transantarctic Mountains), and the Antarctic Peninsula (West Antarctica). As a result, we have plotted and analyzed the time series of the effective snow grain size and rock fraction in these areas across the last 20 years. We have found weak negative trends for the effective grain size on the coast of Enderby Land and the Ross Sea. The rock fraction does not demonstrate any trend. The study of snow cover trends on a continental scale can contribute to the investigation of environmental changes in Antarctica. Full article

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Open AccessProceeding Paper

YOLO-Based Fish Detection in Underwater Environments

by Mohammed Yasser Ouis and Moulay Akhloufi

Environ. Sci. Proc. 2024, 29(1), 44; https://doi.org/10.3390/ECRS2023-16315 - 22 Dec 2023

Viewed by 262

Abstract

In this work, we present a comprehensive study on fish detection in underwater environments using sonar images from the Caltech Fish Counting Dataset (CFC). We use the CFC dataset, initially designed for tracking purposes, to optimize and evaluate the performance of YOLO v7 [...] Read more.

In this work, we present a comprehensive study on fish detection in underwater environments using sonar images from the Caltech Fish Counting Dataset (CFC). We use the CFC dataset, initially designed for tracking purposes, to optimize and evaluate the performance of YOLO v7 and YOLO v8 models in fish detection. Our findings demonstrate the high performance of these deep learning models in accurately detecting fish species in sonar images. In our evaluation, YOLO v7 achieved an average precision of 68.3% (AP50) and 62.15% (AP75), while YOLO v8 demonstrated an even better performance with an average precision of 72.47% (AP50) and 66.21% (AP75) across the test dataset of 334,017 images. These high-precision results underscore the effectiveness of these models in fish detection tasks under various underwater conditions. With a dataset of 162,680 training images and 334,017 test images, our evaluation provides valuable insights into the models performance and generalization across diverse underwater conditions. This study contributes to the advancement of underwater fish detection by showcasing the suitability of the CFC dataset and the efficacy of YOLO v7 and YOLO v8 models. These insights can pave the way for further advancements in fish detection, supporting conservation efforts and sustainable fisheries management. Full article

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Open AccessProceeding Paper

Urban Effects on Cloud Base Height and Cloud Persistence over Sofia, Bulgaria

by Ventsislav Danchovski and Danko Ivanov

Environ. Sci. Proc. 2024, 29(1), 45; https://doi.org/10.3390/ECRS2023-16317 - 28 Nov 2023

Cited by 1 | Viewed by 126

Abstract

Cities may have local weather and climates that are significantly different from their surrounding rural areas due to the different physical characteristics of urban surfaces and emissions of substances, with the latter being modulated by the rhythm of the urban ecosystem. Radiative, thermal, [...] Read more.

Cities may have local weather and climates that are significantly different from their surrounding rural areas due to the different physical characteristics of urban surfaces and emissions of substances, with the latter being modulated by the rhythm of the urban ecosystem. Radiative, thermal, moisture and aerodynamic properties of the urban surface influence cloud formation as well as their characteristics. By using in situ measurements as well as data from remote sensing instruments (ceilometers) located in the city center and its outskirts, urban impact on cloudiness over the city of Sofia is evaluated. It is found that the cloud base height over the city center reaches more than 200 m higher than that over the rural area. It is shown that clouds over the rural area are more persistent in cold months as well as in the afternoon in spring. Full article

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Open AccessProceeding Paper

Impact of Land Use and Land Cover Change on Agricultural Production in District Bahawalnagar, Pakistan

by Aamir Raza, Muhammad Adnan Shahid, Muhammad Safdar, Muhammad Zaman, Rehan Mehmood Sabir, Hafsa Muzammal and Mian Muhammad Ahmed

Environ. Sci. Proc. 2024, 29(1), 46; https://doi.org/10.3390/ECRS2023-16644 - 15 Jan 2024

Viewed by 294

Abstract

Land use and land cover (LULC) change is a major driver of environmental change in District Bahawalnagar, Punjab. LULC change can lead to changes in soil quality, water availability, and climate, all of which can affect crop yields. LULC change can also lead [...] Read more.

Land use and land cover (LULC) change is a major driver of environmental change in District Bahawalnagar, Punjab. LULC change can lead to changes in soil quality, water availability, and climate, all of which can affect crop yields. LULC change can also lead to the loss of agricultural land, forest land, water bodies, and an increment in urban land that causes climate change and affects the agricultural sector. The study area showed that in the last thirty years, the population increased, built-up land increased, and agricultural land dropped by 30%. The present status of knowledge is reviewed in this paper on the impact of LULC on agricultural production in District Bahawalnagar. The conversion of agricultural land to urban development in District Bahawalnagar has led to a decline in crop yields of an average of 10%. The production of wheat and rice, the two major crops grown in District Bahawalnagar, is influenced by LULC changes. This study also found that the loss of agricultural land has resulted in an increase in soil salinity, which has further reduced crop yields. The detrimental effects of LULC change on agricultural output in District Bahawalnagar can be mitigated by adopting sustainable land management practices. These practices include reforestation, conservation agriculture, and water conservation. The government of Pakistan can also play a role in mitigating the negative impacts of LULC change on agricultural production by developing and implementing land use plans that protect agricultural land from conversion to other uses. More research is required to fully comprehend the effects of LULC and develop effective management strategies. However, LULC is a major challenge that must be addressed if we are to ensure food security in the future. Full article

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Open AccessProceeding Paper

A Methodological Approach to Identify Thermal Anomaly Hotspots Misclassified as Fire Pixels in Fire Radiative Power (FRP) Products

by Federico Filipponi and Alessandro Mercatini

Environ. Sci. Proc. 2024, 29(1), 47; https://doi.org/10.3390/ECRS2023-16316 - 21 Nov 2023

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Abstract

Thermal anomalies detected by Earth observation satellites have been widely used to identify active fires, even though there has been a high percentage of misclassified fire pixels. A total of about 75,000 Fire Radiative Power (FRP) pixels have been spatially and temporally combined [...] Read more.

Thermal anomalies detected by Earth observation satellites have been widely used to identify active fires, even though there has been a high percentage of misclassified fire pixels. A total of about 75,000 Fire Radiative Power (FRP) pixels have been spatially and temporally combined with the EFFIS Burned Areas Database, distributed under the Copernicus Emergency Management Service, in order to identify thermal anomaly hotspots misclassified as fire pixels. The proposed approach uses a cluster analysis to partition the FRP pixels dataset into discrete subsets, based on defined distance measures like the spatial distance of the pixel centroids and the temporal frequencies. Later, zonal statistics were performed in order to evaluate fractional land cover within each identified hotspot. Results demonstrate that misclassified large surfaces, like industrial areas, can be identified from both spatial and temporal patterns, while other FRP false alarms are smaller in size. Full article

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Open AccessProceeding Paper

Efficient Assessment of Crop Spatial Variability Using UAV Imagery: A Geostatistical Approach

by Sergio Vélez, Mar Ariza-Sentís and João Valente

Environ. Sci. Proc. 2024, 29(1), 48; https://doi.org/10.3390/ECRS2023-16643 - 6 Nov 2023

Viewed by 137

Abstract

Precision agriculture has seen significant advancements with the integration of remote-sensing technologies. However, challenges such as real-time data availability and computing limitations persist. This study aimed to develop a standardized method for generating spatial variability maps for vineyard management using UAV (unmanned aerial [...] Read more.

Precision agriculture has seen significant advancements with the integration of remote-sensing technologies. However, challenges such as real-time data availability and computing limitations persist. This study aimed to develop a standardized method for generating spatial variability maps for vineyard management using UAV (unmanned aerial vehicle) imagery. Using IDW (inverse distance weight), nadir images with geotagged locations were processed to extract spectral information. The results were analyzed using the NGRDI (normalized green-red difference index) and demonstrated that geo-interpolation methods are effective compared to traditional photogrammetry-based methods but 90% faster, highlighting their potential in real-time applications and edge computing. In addition, IDW correlation with Sentinel-2 imagery reached values as high as r = 0.8. This method offers a faster, less resource-intensive alternative to existing techniques for crop mapping, addressing the current challenges in precision agriculture. Full article

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Open AccessProceeding Paper

Trainable Noise Model as an Explainable Artificial Intelligence Evaluation Method: Application on Sobol for Remote Sensing Image Segmentation

by Hossein Shreim, Abdul Karim Gizzini and Ali J. Ghandour

Environ. Sci. Proc. 2024, 29(1), 49; https://doi.org/10.3390/ECRS2023-16609 - 6 Nov 2023

Viewed by 163

Abstract

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of [...] Read more.

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of deep learning algorithms is essential. Most AI-based computer vision models are often black boxes; hence, providing the explainability of deep neural networks in image processing is crucial for their wide adoption and deployment in medical image analysis, autonomous driving, and remote sensing applications. Existing XAI methods aim to provide insights about the methodology used by the black-box model in making decisions by highlighting the most relevant regions within the input image that contribute to the model’s prediction. Recently, several XAI methods for image classification tasks have been introduced. In contrast, image segmentation has received comparatively less attention in the context of explainability, although it is a fundamental task in computer vision applications, especially in remote sensing. Only some research proposes gradient-based XAI algorithms for image segmentation. This paper adapts the recent gradient-free Sobol XAI method for semantic segmentation. To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model. The main objective of this model is to induce noise on the explanation maps, where a higher induced noise signifies low accuracy and vice versa. A benchmark analysis is conducted to evaluate and compare the performances of three XAI methods, Seg-Grad-CAM, Seg-Grad-CAM++ and Seg-Sobol, using the proposed noise-based evaluation technique. This constitutes the first attempt to run and evaluate XAI methods using high-resolution satellite images. Our code is publicly available at GitHub. Full article

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Open AccessProceeding Paper

Empirical Study of PEFT Techniques for Winter-Wheat Segmentation

by Mohamad Hasan Zahweh, Hasan Nasrallah, Mustafa Shukor, Ghaleb Faour and Ali J. Ghandour

Environ. Sci. Proc. 2024, 29(1), 50; https://doi.org/10.3390/ECRS2023-15833 - 6 Nov 2023

Viewed by 136

Abstract

Parameter Efficient Fine-Tuning (PEFT) techniques have recently experienced significant growth and have been extensively employed to adapt large vision and language models to various domains, enabling satisfactory model performance with minimal computational needs. Despite these advances, more research has yet to delve into [...] Read more.

Parameter Efficient Fine-Tuning (PEFT) techniques have recently experienced significant growth and have been extensively employed to adapt large vision and language models to various domains, enabling satisfactory model performance with minimal computational needs. Despite these advances, more research has yet to delve into potential PEFT applications in real-life scenarios, particularly in the critical domains of remote sensing and crop monitoring. In the realm of crop monitoring, a key challenge persists in addressing the intricacies of cross-regional and cross-year crop type recognition. The diversity of climates across different regions and the need for comprehensive large-scale datasets have posed significant obstacles to accurately identifying crop types across varying geographic locations and changing growing seasons. This study seeks to bridge this gap by comprehensively exploring the feasibility of cross-area and cross-year out-of-distribution generalization using the state-of-the-art (SOTA) wheat crop monitoring model. The aim of this work is to explore efficient fine-tuning approaches for crop monitoring. Specifically, we focus on adapting the SOTA TSViT model, recently proposed in CVPR 2023, to address winter-wheat field segmentation, a critical task for crop monitoring and food security, especially following the Ukrainian conflict, given the economic importance of wheat as a staple and cash crop in various regions. This adaptation process involves integrating different PEFT techniques, including BigFit, LoRA, Adaptformer, and prompt tuning, each designed to streamline the fine-tuning process and ensure efficient parameter utilization. Using PEFT techniques, we achieved notable results comparable to those achieved using full fine-tuning methods while training only a mere 0.7% parameters of the entire TSViT architecture. More importantly, we achieved the claimed performance using a limited subset of remotely labeled data. The in-house labeled data-set, referred to as the Lebanese Wheat dataset, comprises high-quality annotated polygons for wheat and non-wheat classes for the study area in Beqaa, Lebanon, with a total surface of 170 km², over five consecutive years from 2016 to 2020. Using a time series of multispectral Sentinel-2 images, our model achieved a 84% F1-score when evaluated on the test set, shedding light on the ability of PEFT to drive accurate and efficient crop monitoring, designed mainly for developing countries characterized by limited data availability. We intend to publicly release the Lebanese winter-wheat data set, code repository, and model weights. Full article

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Open AccessProceeding Paper

Assessing the Impact of Climate Change on Seasonal Variation in Agricultural Land Use Using Sentinel-2 and Machine Learning

by Musa Mustapha and Mhamed Zineddine

Environ. Sci. Proc. 2024, 29(1), 51; https://doi.org/10.3390/ECRS2023-16365 - 6 Nov 2023

Viewed by 154

Abstract

The Fez region in Morocco has experienced changes in agricultural land use as a result of climate change. These changes include erratic rainfall, rising temperatures, and evapotranspiration. The objective of this research is to investigate the impact of these changes on agricultural land [...] Read more.

The Fez region in Morocco has experienced changes in agricultural land use as a result of climate change. These changes include erratic rainfall, rising temperatures, and evapotranspiration. The objective of this research is to investigate the impact of these changes on agricultural land use between 2018 and 2022 using remote sensing data (sentinel-2 and MODIS), climate data, drought index (Vegetation Condition Index (VCI)) and two machine learning algorithms (Random Forest (RF) and Gradient Tree Boost (GTB). The RF and GTB algorithms were trained and tested, and their performance was analyzed, revealing that the GTB algorithm is more efficient than the RF, with a Kaffa coefficient of 91% and overall accuracy of 93%. The analysis of climate change on land use and land cover (LULC) variations revealed a significant (54%) reduction in rainfall. Furthermore, agricultural land use and water were reduced by 41% and 17%, respectively. Conversely, barren land and built-up areas increased by 58% and 4%, respectively, and the annual mean VCI decreased from 39.72 in 2018 to 19.9 in 2022. The study concluded that climate change had a significant impact on the region’s agricultural land cover, and decreases in rainfall directly affect agricultural land use. Full article

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Open AccessProceeding Paper

Integration of Object-Oriented Remote Sensing and Machine Learning to Create Field Model for Optimized Regional Agricultural Management

by Emine Senkardesler

Environ. Sci. Proc. 2024, 29(1), 52; https://doi.org/10.3390/ECRS2023-15834 - 6 Nov 2023

Viewed by 133

Abstract

In an era marked by tools like Artificial Intelligence (AI), Machine Learning (ML) and remote sensing (RS), agriculture is a primary beneficiary. These technologies help to optimize agricultural productivity, by improving resource usage and increasing yield. They not only optimize resource use but [...] Read more.

In an era marked by tools like Artificial Intelligence (AI), Machine Learning (ML) and remote sensing (RS), agriculture is a primary beneficiary. These technologies help to optimize agricultural productivity, by improving resource usage and increasing yield. They not only optimize resource use but also adapt to climate change, necessitating the management of risks associated with agricultural practices. Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI) are relatively simple yet useful algorithms that can be used to implement precision agriculture (PA). Optical satellite images can sense the reflected lights coming from leaves which can provide various crop development information used to implement PA. This study involves monitoring agricultural production both seasonally and daily using Sentinel-2 multi-spectral time-series data. Time-series images from 2017 to 2022 are analyzed to estimate phenological dates of crops. To understand these stages, a combination of MSAVI (Modified Soil-Adjusted Vegetation Index) and NDVI is used. First, the mean MSAVI is calculated by the year, depending on thresholds, NDVI values are replaced with MSAVI values for certain dates, and phenological dates are determined according to the merged mean Vegetation Index (VI) values. The results are compared with a Crop Progress Report (CPR) published by the United States Department of Agriculture (USDA) with Root-Mean-Square Error (RMSE). After finding the stages, the field is mosaicked for each stage for each year. For the bare soil dates, a Normalized Difference Salinity Index (NDSI) is calculated to understand the change in soil salinity. For the dates of emergence and silking, MSAVI is used. For the dough, dent, mature and harvest stages, NDVI is used. To understand daily changes, object-oriented and pixel-based methods (land segmentation) for field models are used to detect trends in the field. The standard deviation of every pixel is calculated, and clusters are created with the k-means clustering algorithm. The field model includes the characteristics of the field. In PA, site-specific solutions are extremely important to get the optimum results. Since meteorological events have a great effect on agricultural applications, using meteorological data is the main milestone to improve this study. Overall, this research aims to contribute to regional agricultural production and management modules by using remote sensing and machine learning technology. Full article

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Open AccessProceeding Paper

Enhancing Photon Transport Simulation in Earth’s Atmosphere: Acceleration of Python Monte Carlo Model Using Vectorization and Parallelization Techniques

by Jona Brügmann, Dmitry Efremenko and Thomas Trautmann

Environ. Sci. Proc. 2024, 29(1), 53; https://doi.org/10.3390/ECRS2023-15841 - 11 Dec 2023

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Abstract

Photon transport within Earth’s atmosphere is a vital aspect of atmospheric science. The accurate modeling of radiative transfer is crucial for remote sensing data analysis. Yet, simulating the photon transport in multi-dimensional models poses a significant computational challenge. Monte Carlo simulations are a [...] Read more.

Photon transport within Earth’s atmosphere is a vital aspect of atmospheric science. The accurate modeling of radiative transfer is crucial for remote sensing data analysis. Yet, simulating the photon transport in multi-dimensional models poses a significant computational challenge. Monte Carlo simulations are a common approach, but they demand a large number of photons for reliable results. Parallelization techniques can be employed to accelerate Monte Carlo computations by using multi-core CPUs and GPUs. This research delves into a comparative analysis of different parallelization techniques for the Python version of the Monte Carlo model. We consider conventional photon transport simulations that rely on iterative loops, the multithreading technique, NumPy’s vectorization, and GPU acceleration via the CuPy library. It is shown that CuPy, harnessing GPU parallelism, significantly accelerates simulations, making them suitable for large-scale scenarios. It is shown that as the number of photons grows, the overhead from reading and retrieving data to the GPU decreases, making the CuPy library an effective and easy-to-use option for Monte Carlo simulations. Full article

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Open AccessProceeding Paper

Monitoring Forest Dynamics in the Palmira Area of Ecuador Using the Land Trendr and Continuous Change Detection Algorithms

by Marco Castelo, Jorge López, Edgar Merino, Gustavo Buñay, Mayra Peñafiel, Rene Villa, Johanna Santana and Edwin Tipán

Environ. Sci. Proc. 2024, 29(1), 54; https://doi.org/10.3390/ECRS2023-16703 - 6 Nov 2023

Viewed by 180

Abstract

Deforestation is a significant global concern, as forests are vital for climate balance, water conservation, and rainfall. In Palmira, Chimborazo, Ecuador, a pattern of afforestation followed by deforestation has been observed, influenced by both public and private activities. Some areas, due to prolonged [...] Read more.

Deforestation is a significant global concern, as forests are vital for climate balance, water conservation, and rainfall. In Palmira, Chimborazo, Ecuador, a pattern of afforestation followed by deforestation has been observed, influenced by both public and private activities. Some areas, due to prolonged erosion, have even turned into deserts. This study utilized the Google Earth Engine platform and algorithms like LandTrendr and CCDC to analyze satellite imagery from 2000 to 2020, aiming to understand the forest dynamics in four specific Palmira locations. The results were consistent with documented patterns of afforestation and deforestation in the region. For instance, the Galte Laime area experienced an increase in forest cover until 2006, after which significant deforestation occurred. In contrast, Palmira Dávalos, often referred to as the Palmira Desert, consistently showed minimal vegetation, a result of centuries of erosion. Galte Cuatro Esquinas presented a decline in forest cover until 2009, after which regrowth was observed. Jatun Loma initially maintained its forest cover but eventually experienced deforestation, followed by a reforestation phase. In conclusion, this research offers a comprehensive insight into Palmira’s forest dynamics using advanced algorithms and satellite-based time series. The findings emphasize the importance of remote sensing tools in monitoring forest changes, which can be pivotal for informed decision making in forest management and conservation in the region. Full article

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33102 KiB

Open AccessProceeding Paper

The Use of Ultra-High Resolution UAV Lidar Infrared Intensity for Enhancing Coastal Cover Classification

by Antoine Collin, Dorothée James, Régis Gallon, Emmanuel Poizot and Eric Feunteun

Environ. Sci. Proc. 2024, 29(1), 55; https://doi.org/10.3390/ECRS2023-16610 - 6 Dec 2023

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Abstract

Coastal areas gather increasing hazards, exposures, and vulnerabilities in the context of anthropogenic changes. Understanding their spatial responses to acute and chronic drivers requires ultra-high spatial resolution that can only be achieved by UAV-based sensors. UAV lasergrammetry constitutes, to date, the best observation [...] Read more.

Coastal areas gather increasing hazards, exposures, and vulnerabilities in the context of anthropogenic changes. Understanding their spatial responses to acute and chronic drivers requires ultra-high spatial resolution that can only be achieved by UAV-based sensors. UAV lasergrammetry constitutes, to date, the best observation of the xyz variables in terms of resolution, precision, and accuracy, allowing coastal areas to be reliably mapped. However, the use of lidar reflectivity (or intensity) remains poorly examined for mapping purposes. The added value of the lidar-derived near-infrared (NIR) was estimated by comparing the classification results of nine coastal habitats based on the blue–green–red (BGR) passive and BGR-NIR passive–active datasets. A gain of 4.14% was found at the landscape level, while habitat-scaled improvements were highlighted for the “salt marsh” and “soil” habitats (4 and 4.56% for producer’s accuracy, PA, and user’s accuracy, UA; and 8.95 and 9.48% for PA and UA, respectively). Full article

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4544 KiB

Open AccessProceeding Paper

Quantification of Coastal Erosion Rates Using Landsat 5, 7, and 8 and Sentinel-2 Satellite Images from 1986–2022—Case Study: Cartagena Bay, Valparaíso, Chile

by Idania Briceño de Urbaneja, Waldo Pérez-Martínez, Carolina Martínez, Josep Pardo-Pascual, Jesús Palomar-Vázquez, Catalina Aguirre and Raimundo Donoso-Garcés

Environ. Sci. Proc. 2024, 29(1), 56; https://doi.org/10.3390/ECRS2023-16300 - 21 Nov 2023

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Abstract

Coastal erosion has become one of the many natural hazards affecting Chile’s sandy coastlines. Currently, more than 90% of the sandy coasts of Valparaíso show high erosion rates. Cartagena Bay is one of the coastal areas with the greatest transformations caused by extreme [...] Read more.

Coastal erosion has become one of the many natural hazards affecting Chile’s sandy coastlines. Currently, more than 90% of the sandy coasts of Valparaíso show high erosion rates. Cartagena Bay is one of the coastal areas with the greatest transformations caused by extreme events and anthropogenic activities. Satellite imagery is seen as an invaluable resource for following these coastal changes. This study combines optical satellite imagery, a simulation-derived wave climate, in situ data, the SHOREX system developed in Python, and GIS-based tools such as DSAS to quantify rates of change in the Bay from 1986 to 2022. Satellite-derived shorelines were used to identify erosion hotspot areas in the Bay, differentiating the impact of erosive processes associated with ENSO hydrometeorological phenomena, the 27-F 2010 earthquake, and tidal waves from 2015–2022, which led to major transformations in the morphodynamics of the beach. The results show that the Bay is currently undergoing high erosional processes in 20% of the coastline with values <− 1.5 m/year and 60% with erosion rates ranging from [−0.2 to −1.5 m/year]. Since 2015, these processes have been accentuated, due to increased swells throughout the year. Full article

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479 KiB

Open AccessProceeding Paper

Improving Up-Close Remote Sensing of Occluded Areas in Vineyards through Customized Multiple-Unmanned-Aerial-Vehicle Path Planning

by Mar Ariza-Sentís, Sergio Vélez, Roberto G. Valenti and João Valente

Environ. Sci. Proc. 2024, 29(1), 57; https://doi.org/10.3390/ECRS2023-15857 - 6 Nov 2023

Viewed by 116

Abstract

This study presents a novel approach to address challenges regarding data acquisition for object detection and tracking purposes by enhancing UAV path planning specifically designed for fruit detection in woody crops trained on vertical trellises, considering the biophysical environment of the field. The [...] Read more.

This study presents a novel approach to address challenges regarding data acquisition for object detection and tracking purposes by enhancing UAV path planning specifically designed for fruit detection in woody crops trained on vertical trellises, considering the biophysical environment of the field. The proposed method implements the Ant Colony Optimization (ACO) algorithm and enables single and multiple UAVs to fly synchronously while ensuring a safe distance between platforms. The results highlight that ACO is able to generate optimal and safe routes, considering the vegetation and covering the whole agricultural area. Moreover, it shows potential to solve partial leaf occlusion for fruit identification. Full article

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3078 KiB

Open AccessProceeding Paper

Studying Correlation between Precipitation and NDVI/MODIS for Time Series (2012–2022) in Arid Region in Syria

by Rukea Al-hasn

Environ. Sci. Proc. 2024, 29(1), 58; https://doi.org/10.3390/ECRS2023-16704 - 15 Jan 2024

Viewed by 170

Abstract

Vegetation degradation is correlated with drought. The more drought intensifies, the more degraded vegetation increases. Therefore, this study aimed to assess the correlation between rainfall and changes in the Normalized Difference Vegetation Index (NDVI) under arid and semi-arid conditions in Syria. This study [...] Read more.

Vegetation degradation is correlated with drought. The more drought intensifies, the more degraded vegetation increases. Therefore, this study aimed to assess the correlation between rainfall and changes in the Normalized Difference Vegetation Index (NDVI) under arid and semi-arid conditions in Syria. This study was carried out using annual rainfall data for 2012–2022, obtained from the Agricultural cloud seeding Project, to determine the average rainfall in the study area and to link it to the NDVI of MODIS image data processed using the Google Earth Engine (GEE) for April of each year for the same time series. The results showed that the lowest NDVI value (0.098) was in 2016, representing the driest year during the studied series, while the highest NDVI value (0.24) was in 2019, which coincided with the highest rainfall rate of 206.67 mm, thus representing the least arid year during the same series. It also found a strong correlation (R = 0.7) between the overall average rainfall and the overall NDVI values of the studied time series. This study shows that changes in the NDVI are associated with changes in rainfall, indicating that they can be used to estimate and study drought as a simple method derived from satellite data in isolation from ground data. Full article

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1024 KiB

Open AccessProceeding Paper

Comparison of Supervised Classification Algorithms Using a Hyperspectral Image for Land Use/Land Cover Classification

by Sonia Sharma Banjade, Nitant Rai and Bipana Subedi

Environ. Sci. Proc. 2024, 29(1), 59; https://doi.org/10.3390/ECRS2023-16702 - 11 Dec 2023

Viewed by 144

Abstract

Hyperspectral imaging is becoming popular in land use/land cover classification because of its ability to capture detailed information through higher spatial resolution and contagious spectral bands. Using the hyperspectral image from G-LiHT (Goddard’s LiDAR, Hyperspectral, and Thermal) Airborne Imager covering a study area [...] Read more.

Hyperspectral imaging is becoming popular in land use/land cover classification because of its ability to capture detailed information through higher spatial resolution and contagious spectral bands. Using the hyperspectral image from G-LiHT (Goddard’s LiDAR, Hyperspectral, and Thermal) Airborne Imager covering a study area in Tennessee, Knoxville, we compared the performance of Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Support Vector Machine (SVM) for land use/land cover classification. We used a confusion matrix for the accuracy assessment of the classifiers. Among the three classifiers, SVM showed the highest accuracy with 92.03%. Our results also show that some classes, such as water and forests, are consistently distinguishable across all classification methods, while others, such as built-up areas, vary depending on the technique used. Full article

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4353 KiB

Open AccessProceeding Paper

Creating a Comprehensive Landslides Inventory Using Remote Sensing Techniques and Open Access Data

by Constantinos Nefros, Constantinos Loupasakis, Stavroula Alatza and Charalampos Kontoes

Environ. Sci. Proc. 2024, 29(1), 60; https://doi.org/10.3390/ECRS2023-15849 - 15 Jan 2024

Viewed by 167

Abstract

Landslides are natural disasters with a high socio-economic impact on human societies due to the considerable number of fatalities and the destruction of infrastructure that they cause. A comprehensive landslides inventory is vital for reducing this impact as it can be used in [...] Read more.

Landslides are natural disasters with a high socio-economic impact on human societies due to the considerable number of fatalities and the destruction of infrastructure that they cause. A comprehensive landslides inventory is vital for reducing this impact as it can be used in landslides susceptibility studies for the identification of the subregions of an area that are most prone to landslides for the evaluation of the landslide precipitation activation thresholds, and subsequently for the determination of the most suitable precautionary measures. Nowadays, remote sensing techniques are widely used by scientists for creating landslide inventories as they can be rapidly applied to identify landslides along with their spatial characteristics. Nevertheless, besides these characteristics, a comprehensive inventory must also include the time of their activation and the factors that led to their activation. These elements can be quite difficult to specify, especially in areas where official landslide data do not exist, such as in countries that do not have a published national landslides inventory. The objective of this research study is to provide a framework for the creation of a comprehensive landslides inventory by combining open access or publicly available data with remote sensing data and techniques. The Chania regional unit in the western part of Crete Island, Greece, was selected as the study area. Our study presents how a complete landslides inventory, consisting of 236 landslides, was established based on differential interferometric synthetic aperture radar (DInSAR) techniques and open access or publicly available data. This framework can significantly contribute to scientific research on landslide susceptibility in countries that lack a comprehensive landslides inventory. Moreover, it highlights the potential of remote sensing techniques and open access data in improving our understanding of landslide activation mechanisms. Full article

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3258 KiB

Open AccessProceeding Paper

Deep-Learning-Based Edge Detection for Improving Building Footprint Extraction from Satellite Images

by Nima Ahmadian, Amin Sedaghat, Nazila Mohammadi and Mohammad Aghdami-Nia

Environ. Sci. Proc. 2024, 29(1), 61; https://doi.org/10.3390/ECRS2023-16615 - 20 Dec 2023

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Abstract

Buildings are objects of great importance that need to be observed continuously. Satellite and aerial images provide valuable resources nowadays for building footprint extraction. Since these images cover large areas, manually detecting buildings will be a time-consuming task. Recent studies have proven the [...] Read more.

Buildings are objects of great importance that need to be observed continuously. Satellite and aerial images provide valuable resources nowadays for building footprint extraction. Since these images cover large areas, manually detecting buildings will be a time-consuming task. Recent studies have proven the capability of deep learning algorithms in building footprint extraction automatically. But these algorithms need vast amounts of data for training and they may not perform well under the low-data conditions. Digital surface models provide height information, which helps discriminate buildings from their surrounding objects. However, they may suffer from noises, especially on the edges of buildings, which may result in low boundary resolution. In this research, we aim to address this problem by using edge bands detected by a deep learning model alongside the digital surface models to improve the building footprint extraction when training data are low. Since satellite images have complex backgrounds, using conventional edge detection methods like Canny or Sobel filter will produce a lot of noisy edges, which can deteriorate the model performance. For this purpose, first, we train a U-Net model for building edge detection with the WHU dataset and fine-tune the model with our target training dataset, which contains a low quantity of satellite images. Then, the building edges of the target test images are predicted using this fine-tuned U-Net and concatenated with our RGB-DSM test images to form 5-band RGB-DSM-Edge images. Finally, we train a U-Net with 5-band training images of our target dataset, which contain precise building edges in their fifth band. Then, we use this model for building footprint extraction from 5-band test images, which contain building edges in their fifth band that are predicted by a deep learning model in the first stage. We compared the results of our proposed method with 4-band RGB-DSM and 3-band RGB images. Our method obtained 82.88% in IoU and 90.45% in F1-score metrics, which indicates that, by using edge bands alongside the digital surface models, the performance of the model improved 2.57% and 1.59% in IoU and F1-score metrics, respectively. Also, the predictions made by 5-band images have sharper building boundaries than RGB-DSM images. Full article

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4892 KiB

Open AccessProceeding Paper

Performance Evaluation of Urban Canopy Parameters Derived from VHR Optical Stereo Data

by Kshama Gupta, Shweta Khatriker and Ashutosh Bhardwaj

Environ. Sci. Proc. 2024, 29(1), 62; https://doi.org/10.3390/ECRS2023-16646 - 6 Nov 2023

Viewed by 122

Abstract

Urban canopy parameters (UCPs) are parameters which are utilized to define the thermal, radiative, and roughness properties of urban areas, which have a significant impact on the urban microclimate. The rapidly growing urbanization, especially in developing regions, leads to the modification of urban [...] Read more.

Urban canopy parameters (UCPs) are parameters which are utilized to define the thermal, radiative, and roughness properties of urban areas, which have a significant impact on the urban microclimate. The rapidly growing urbanization, especially in developing regions, leads to the modification of urban geometry, which calls for the characterization of UCPs in the countries of such regions to account for high population pressure, heterogeneous urban environments, and the subsequent impacts on global climate change. A research study conducted in Delhi, India, found that very-high-resolution (VHR) optical satellite stereo datasets provide reasonable accuracy with respect to the extraction of building heights and footprints, which are further employed for the computation of UCPs. However, the study evaluates only the key input parameters due to the non-availability of the 3D geodatabase. Hence, in this study, an attempt has been made to evaluate all UCPs derived from VHR optical stereo data, along with the key input parameters, against reference data collected from the field in the city of Bhubaneshwar, India. Performance evaluation with reference-data-derived UCPs shows that all the UCPs retrieved from VHR optical stereo data have a high prediction accuracy. Overall bias, overall mean absolute error (MAE), and root mean square error (RMSE) from satellite-derived UCPs were found to be better than 1 m for most of the UCPs, except for building-surface-area-to-plan-area ratio, height-to-width ratio, and complete aspect ratio, which were found to be less than 2.7 m. The correlation coefficient values were also observed to be more than 0.7 for most of the UCPs, except plan area density, roughness length, and frontal area density. This study concludes that UCPs derived from VHR optical stereo data have high accuracy, even in the low-to-medium-rise urban environments of the study area. The study has a high potential to be replicated in countries in developing regions which have similar development characteristics and face resource and policy constraints with respect to the availability of airborne LiDAR and SAR data. Full article

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1705 KiB

Open AccessProceeding Paper

GEOSAT 2 Atmospherically Corrected Images: Algorithm Validation

by César Fernández, Carolina de Castro, María Elena Calleja, Rafael Sousa, Rubén Niño, Lucía García, Silvia Fraile and Iñigo Molina

Environ. Sci. Proc. 2024, 29(1), 64; https://doi.org/10.3390/ECRS2023-16296 - 6 Nov 2023

Viewed by 113

Abstract

Solar radiation reflected by the Earth’s surface to satellite sensors is modified by its interaction with the atmosphere. The application of atmospheric correction of optical satellite imagery is an essential and needed pre-processing tool for modeling biophysical variables, multi-temporal analysis, and digital classification [...] Read more.

Solar radiation reflected by the Earth’s surface to satellite sensors is modified by its interaction with the atmosphere. The application of atmospheric correction of optical satellite imagery is an essential and needed pre-processing tool for modeling biophysical variables, multi-temporal analysis, and digital classification processes. As a result, true surface reflectance values are obtained without atmosphere influence. To assess this process, GEOSAT (part of the ESA’s Third-Party Mission Programme) performs an optimization of the GEOSAT 2 very high resolution (VHR) multispectral imagery adapting the well-known 6S model to the different wavelengths covered by the GEOSAT 2 spectral bands (VHR, PAN). The 6S model predicts surface reflectance (BOA) using information from the apparent reflectance (TOA) captured by the satellite sensor and the corresponding atmospheric conditions. To perform the atmospheric correction (AC), both the configuration of the atmosphere at the time of capture and the conditions of scene pointing and luminosity, must be considered. The first is mainly determined by three values: water vapor, ozone, and the number of air-suspended particles (aerosols). For the latter, the geometry of the scene, as well as the respective sun and sensor observation positions are the values to be considered. To validate the resultant GEOSAT 2 AC images, obtained from applying the GEOSAT atmospheric correction algorithm, different common areas between these and Sentinel-2 L2A products have been selected. Then, band-by-band (R, G, B, and NIR) operations were performed, such as the calculation of the mean square error (RMSE) and a regression analysis. Then, spectral profiles for the three generic land coverages (vegetation, soil, and water) were also gathered over the spectral range of GEOSAT 2 and S2 corresponding bands. The outcomes, once analyzed, lead us to conclude that the results obtained by applying the promising GEOSAT AC algorithm are satisfactory and seem to correctly estimate BOA reflectance values for vegetation and water coverages. To extend the study and improve the result, ground reflectance values will be required. Full article

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861 KiB

Open AccessProceeding Paper

Enhancing Winter Wheat Yield Estimation Using Machine Learning and Fusion of Radar and Optical Satellite Imagery

by Shabnam Asgari, Mahdi Hasanlou and Saeid Homayouni

Environ. Sci. Proc. 2024, 29(1), 65; https://doi.org/10.3390/ECRS2023-16645 - 6 Nov 2023

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Abstract

Accurate crop yield Mapping is paramount in agricultural monitoring and food security. In this study, we present a comprehensive investigation into estimating winter wheat yield in the Qazvin plane of Iran, leveraging the synergy between machine learning algorithms and the fusion of remote [...] Read more.

Accurate crop yield Mapping is paramount in agricultural monitoring and food security. In this study, we present a comprehensive investigation into estimating winter wheat yield in the Qazvin plane of Iran, leveraging the synergy between machine learning algorithms and the fusion of remote sensing data from radar and optical satellite sensors. The research is based on the availability of high-quality in situ yield data gathered by the Ministry of Agriculture in collaboration with the Food and Agriculture Organization (FAO), collected during the 2019–2020 crop year. The study area encompasses the Qazvin plane, an agriculturally significant region renowned for winter wheat production in Iran. In-situ data from various agricultural fields and seed types as reference measurements enabled us to conduct rigorous validation of the performance of machine learning algorithms and the effectiveness of the fused remote sensing data. The primary objective of this study is to assess and compare the performance of seven prominent machine learning algorithms for accurate estimation of the annual winter wheat yields. Furthermore, we investigate the individual and synergistic capabilities of radar and optical satellite sensors in estimating winter wheat yield. Through rigorous analysis of the pixel-level confusion matrices, we identify the most effective model for yield estimation, evaluating the complementarity and information redundancy between the two types of remote sensing data. In this study, we conducted an extensive comparison of various machine learning algorithms for winter wheat crop yield estimation in the Qazvin plane of Iran. Among the four best-performing algorithms examined, namely polynomial regression (RMSE = 0.5657

t / {h a}^{- 1}

), random forest (RMSE = 0.1632

t / {h a}^{- 1}

), XGBoost (RMSE = 0.3153

t / {h a}^{- 1}

), and the proposed Multi-Layer Perceptron (MLP) (RMSE = 0.1324

t / {h a}^{- 1}

), the MLP demonstrated superior performance. The MLP’s yield estimation exceeded the total yearly agricultural statistics of Qazvin by 0.19 percent. However, this discrepancy can be attributed to various factors, including errors in wheat and barley field mapping, miscalculation in cumulative statistics, and the inherent limitations of yield estimation algorithms in capturing the dynamic nature of agricultural systems. The findings of this research provide valuable insights into the potential of machine learning algorithms and remote sensing data fusion for accurate crop yield estimation, paving the way for enhanced agricultural monitoring and decision-making processes in the region. Full article

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2861 KiB

Open AccessProceeding Paper

Simulation of DEM Based on ICESat-2 Data Using Openly Accessible Topographic Datasets

by Shruti Pancholi, A. Abhinav, Sandeep Maithani and Ashutosh Bhardwaj

Environ. Sci. Proc. 2024, 29(1), 66; https://doi.org/10.3390/ECRS2023-16189 - 11 Dec 2023

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Abstract

The digital elevation model (DEM) is a three-dimensional digital representation of the terrain or the Earth’s surface. For determining topography, DEMs are the most used and ideal method with (i.e., the digital surface model) or without the objects (i.e., the digital terrain model). [...] Read more.

The digital elevation model (DEM) is a three-dimensional digital representation of the terrain or the Earth’s surface. For determining topography, DEMs are the most used and ideal method with (i.e., the digital surface model) or without the objects (i.e., the digital terrain model). Various techniques are used to create DEMs, including traditional surveying methods, photogrammetry, InSAR, lidar, clinometry, and radargrammetry. DEMs generated by LiDAR tend to be the most accurate except for the VHR datasets acquired from UAVs with spatial resolution of a few centimeters. In many parts of the region, LiDAR data are not available, which limits researchers’ access to high-resolution and accurate DEMs. With a beam footprint of 13 m and a pulse interval of 0.7 m, ICESat-2 promises high orbital precision and high accuracy. ICESat-2 can produce high-accuracy DEMs in complex topographies with an accuracy of a few centimeters. The Earth’s surface elevations are provided by discrete photon data from ICESat-2. It is difficult to justify the continuity of the topographical data using traditional interpolation techniques since they over-smooth the estimated space. Geospatial data can be analyzed with machine learning algorithms to extract patterns and spatial extents. To estimate a DEM from LiDAR point data from ICESat-2 using CartoDEM, machine learning regression algorithms are used in this study V3 R1. This study was conducted over a hilly terrain of the Dehradun region in the foothills of the Himalayas in India. The applicability and robustness of these algorithms has been tested for a plain region of Ghaziabad, India, in an earlier study. The interpolation of DEM from ICESat-2 data was analyzed using regression-based machine learning techniques. Interpolated DEMs were evaluated against the TANDEM-X DEM of the same region with RMSEs of 7.13 m, 7.01 m, 7.15 m, and 3.76 m respectively, using gradient boosting regressors, random forest regressors, decision tree regressors, and multi-layer perceptron (MLP) regressors. Based on the four algorithms tested, the MLP regressor shows the best performance in the previous study. The accuracy of the simulated ICESat-2 DEM using the MLP regressor was assessed in this study using the DGPS points over the Dehradun region. The RMSE was of the order of 6.58 m for the DGPS reference data. Full article

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37561 KiB

Open AccessProceeding Paper

CoastSnap Valparaíso Region: An Experience of Citizen Science in Chile

by Idania Briceño de Urbaneja, Waldo Pérez-Martínez and Natalia Tapia-Pineda

Environ. Sci. Proc. 2024, 29(1), 67; https://doi.org/10.3390/ECRS2023-16700 - 6 Nov 2023

Viewed by 105

Abstract

The coastal areas of Chile are undergoing major transformations associated with changes in the frequency and intensity of coastal storms, alterations in the rainfall regime with a megadrought of more than 10 years, and variations in ocean currents, which have generated a series [...] Read more.

The coastal areas of Chile are undergoing major transformations associated with changes in the frequency and intensity of coastal storms, alterations in the rainfall regime with a megadrought of more than 10 years, and variations in ocean currents, which have generated a series of impacts related to beach erosion, the alteration of coastal wetlands and the effects on coastal cities. Faced with this problem, it is necessary to incorporate multiple monitoring methodologies that are complementary to existing ones, and that promote the incorporation of communities as fundamental axes in data collection. In this paper, we present the CoastSnap initiative implemented in Chile, whose results have been notorious, despite the short implementation time. Up to July 2023, the communities had shared 350 photographs that have allowed for analysis of the variability of the beaches, allowing for the quantification of variations on average up to 45 m wide in some of their beaches. Full article

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1236 KiB

Open AccessProceeding Paper

Pléiades Neo-Derived Bathymetry in Coastal Temperate Waters: The Case Study of Bay of Saint-Malo

by Antoine Collin, Dorothée James and Eric Feunteun

Environ. Sci. Proc. 2024, 29(1), 68; https://doi.org/10.3390/ECRS2023-16366 - 11 Dec 2023

Viewed by 9

Abstract

Satellite-derived bathymetry is increasingly attracting stakeholders’ attention tasked with remote and/or shallow depths, given its affordability compared to airborne lidar and waterborne sonar surveys. The 6-band 1.2 m Pléiades Neo (PNEO) multispectral imagery has not yet been evaluated for such a purpose. The [...] Read more.

Satellite-derived bathymetry is increasingly attracting stakeholders’ attention tasked with remote and/or shallow depths, given its affordability compared to airborne lidar and waterborne sonar surveys. The 6-band 1.2 m Pléiades Neo (PNEO) multispectral imagery has not yet been evaluated for such a purpose. The contribution of the novel PNEO bands to the depth retrieval was assessed over unclear coastal seawaters (0.2 m⁻¹ of vertical light attenuation in the bay of Saint-Malo, France). The relevance of the radiometric level was also tested: top-of-atmosphere (TOA) digital number (DN), TOA radiance, TOA reflectance, bottom-of-atmosphere (BOA) maritime-modeled reflectance, and BOA tropospheric-modeled reflectance. The lidar response, ranging from 0 to 20 m depth, was stratified by 90 random samples per bathymetric slice of 1 m. The model was based on an easy-to-transfer neural network (one hidden layer and three neurons). The best predictions, reaching R²_test of 0.81, were equally obtained for the full PNEO dataset at TOA DN, radiance, and reflectance. For both BOA full-dataset products, the results were slightly less satisfactory: R²_test of 0.75 (maritime) and 0.76 (tropospheric). Full article

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6208 KiB

Open AccessProceeding Paper

Integrated Approach for Tree Health Prediction in Reforestation Using Satellite Data and Meteorological Parameters

by Gijs van den Dool and Deepali Bidwai

Environ. Sci. Proc. 2024, 29(1), 15982; https://doi.org/10.3390/ECRS2023-15982 - 6 Nov 2023

Viewed by 176

Abstract

This study introduces a holistic methodology that synergizes high-resolution satellite imagery from Planet and historical data from Sentinel 2 with meteorological insights extracted from ERA5 data. By computing vital vegetation indices (NDVI, NDWI, mSAVI-2) and meteorological indices (SPI, KBDI), we establish customized growing [...] Read more.

This study introduces a holistic methodology that synergizes high-resolution satellite imagery from Planet and historical data from Sentinel 2 with meteorological insights extracted from ERA5 data. By computing vital vegetation indices (NDVI, NDWI, mSAVI-2) and meteorological indices (SPI, KBDI), we establish customized growing conditions, enabling the prediction and continuous monitoring of tree health and stress. This approach integrates time series models for temperature, precipitation, and vegetation indices, augmenting the understanding of growing conditions and facilitating informed site selection for reforestation initiatives. Satellite data are sourced from Copernicus (Sentinel 2 using GEE) and Planet imagery (via QGIS plugin). The Copernicus Climate Data Store (ERA5) provides meteorological and climate assimilation data, complemented by reforestation specifics such as tree counts and planting timelines. Full article

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