Remote Sensing

26 pages, 12137 KiB

Open AccessArticle

GF-1/6 Satellite Pixel-by-Pixel Quality Tagging Algorithm

by Xin Fan, Hao Chang, Lianzhi Huo and Changmiao Hu

Remote Sens. 2023, 15(7), 1955; https://doi.org/10.3390/rs15071955 - 06 Apr 2023

Cited by 1 | Viewed by 1356

The Landsat and Sentinel series satellites contain their own quality tagging data products, marking the source image pixel by pixel with several specific semantic categories. These data products generally contain categories such as cloud, cloud shadow, land, water body, and snow. Due to [...] Read more.

The Landsat and Sentinel series satellites contain their own quality tagging data products, marking the source image pixel by pixel with several specific semantic categories. These data products generally contain categories such as cloud, cloud shadow, land, water body, and snow. Due to the lack of mid-wave and thermal infrared bands, the accuracy of traditional cloud detection algorithm is unstable when facing Chinese Gaofen-1/6 (GF-1/6) data. Moreover, it is challenging to distinguish clouds from snow. In order to produce GF-1/6 satellite pixel-by-pixel quality tagging data products, this paper builds a training sample set of more than 100,000 image pairs, primarily using Sentinel-2 satellite data. Then, we adopt the Swin Transformer model with a self-attention mechanism for GF-1/6 satellite image quality tagging. Experiments show that the model’s overall accuracy reaches the level of Fmask v4.6 with more than 10,000 training samples, and the model can distinguish between cloud and snow correctly. Our GF-1/6 quality tagging algorithm can meet the requirements of the “Analysis Ready Data (ARD) Technology Research for Domestic Satellite” project. Full article

(This article belongs to the Special Issue Gaofen 16m Analysis Ready Data)

► Show Figures

Figure 1

15 pages, 5218 KiB

Open AccessTechnical Note

Terrain Self-Similarity-Based Transformer for Generating Super Resolution DEMs

by Xin Zheng, Zelun Bao and Qian Yin

Remote Sens. 2023, 15(7), 1954; https://doi.org/10.3390/rs15071954 - 06 Apr 2023

Cited by 3 | Viewed by 1511

Abstract

High-resolution digital elevation models (DEMs) are important for relevant geoscience research and practical applications. Compared with traditional hardware-based methods, super-resolution (SR) reconstruction techniques are currently low-cost and feasible methods used for obtaining high-resolution DEMs. Single-image super-resolution (SISR) techniques have become popular in DEM [...] Read more.

High-resolution digital elevation models (DEMs) are important for relevant geoscience research and practical applications. Compared with traditional hardware-based methods, super-resolution (SR) reconstruction techniques are currently low-cost and feasible methods used for obtaining high-resolution DEMs. Single-image super-resolution (SISR) techniques have become popular in DEM SR in recent years. However, DEM super-resolution has not yet utilized reference-based image super-resolution (RefSR) techniques. In this paper, we propose a terrain self-similarity-based transformer (SSTrans) to generate super-resolution DEMs. It is a reference-based image super-resolution method that automatically acquires reference images using terrain self-similarity. To verify the proposed model, we conducted experiments on four distinct types of terrain and compared them to the results from the bicubic, SRGAN, and SRCNN approaches. The experimental results show that the SSTrans method performs well in all four terrains and has outstanding advantages in complex and uneven surface terrains. Full article

(This article belongs to the Section AI Remote Sensing)

► Show Figures

Figure 1

20 pages, 2510 KiB

Open AccessArticle

Parallel Computation of Multi-GNSS and Multi-Frequency Inter-Frequency Clock Biases and Observable-Specific Biases

by Linyang Li, Zhen Yang, Zhen Jia and Xin Li

Remote Sens. 2023, 15(7), 1953; https://doi.org/10.3390/rs15071953 - 06 Apr 2023

Cited by 1 | Viewed by 1249

Abstract

With the widespread application of GNSS, the delicate handling of biases among different systems and different frequencies is of critical importance, wherein the inter-frequency clock biases (IFCBs) and observable-specific signal biases (OSBs) should be carefully corrected. Usually, a serial approach is used to [...] Read more.

With the widespread application of GNSS, the delicate handling of biases among different systems and different frequencies is of critical importance, wherein the inter-frequency clock biases (IFCBs) and observable-specific signal biases (OSBs) should be carefully corrected. Usually, a serial approach is used to calculate these products. To accelerate the computation speed and reduce the time delay, a multicore parallel estimation strategy for IFCBs, code, and phase OSBs by utilizing task parallel library (TPL) is proposed, the parallel computations, including precise point positioning (PPP), IFCBs, and OSBs estimation, being carried out on the basis of data parallelisms and task-based asynchronous programming. Three weeks of observables from the multi-GNSS experiment campaign (MGEX) network is utilized. The result shows that the IFCB errors of GPS Block IIF and GLONASS M+ satellites are nonnegligible, in which the GLONASS M+ satellite R21 shows the largest IFCB of more than 0.60 m, while those of other systems and frequencies are marginal, and the code OSBs present excellent stability with a standard deviation (STD) of 0.10 ns for GPS and approximately 0.20 ns for other satellite systems. Besides, the phase OSBs of all systems show the stability of better than 0.10 ns, wherein the Galileo satellites show the best performance of 0.01 ns. Compared with the single-core serial computing method, the acceleration rates for IFCBs and OSBs estimation are 3.10, 5.53, 9.66, and 17.04 times higher using four, eight, sixteen, and thirty-two physical cores, respectively, through multi-core parallelized execution. Full article

(This article belongs to the Special Issue Precise Point Positioning with GPS, GLONASS, BeiDou, and Galileo II)

► Show Figures

Figure 1

22 pages, 8449 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China

by Menghua Li, Xiebing Yin, Bo-Hui Tang and Mengshi Yang

Remote Sens. 2023, 15(7), 1952; https://doi.org/10.3390/rs15071952 - 06 Apr 2023

Cited by 2 | Viewed by 1713

Abstract

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products [...] Read more.

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research. Full article

(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)

► Show Figures

Figure 1

29 pages, 16759 KiB

Open AccessArticle

A Novel Hybrid Intelligent SOPDEL Model with Comprehensive Data Preprocessing for Long-Time-Series Climate Prediction

by Zeyu Zhou, Wei Tang, Mingyang Li, Wen Cao and Zhijie Yuan

Remote Sens. 2023, 15(7), 1951; https://doi.org/10.3390/rs15071951 - 06 Apr 2023

Cited by 3 | Viewed by 1766

Abstract

Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately [...] Read more.

Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately forecast climate extremes. Sequence disorder, weak robustness, low characteristics and weak interpretability are four prevalent shortcomings in predicting long-time-series data. In order to resolve these deficiencies, our study gives a novel hybrid spatiotemporal model which offers comprehensive data preprocessing techniques, focusing on data decomposition, feature extraction and dimensionality upgrading. This model provides a feasible solution to the puzzling problem of long-term climate prediction. Firstly, we put forward a Period Division Region Segmentation Property Extraction (PD-RS-PE) approach, which divides the data into a stationary series (SS) for an Extreme Learning Machine (ELM) prediction and an oscillatory series (OS) for a Long Short-term Memory (LSTM) prediction to accommodate the changing trend of data sequences. Secondly, a new type of input-output mapping mode in a three-dimensional matrix was constructed to enhance the robustness of the prediction. Thirdly, we implemented a multi-layer technique to extract features of high-speed input data based on a Deep Belief Network (DBN) and Particle Swarm Optimization (PSO) for parameter searching of a neural network, thereby enhancing the overall system’s learning ability. Consequently, by integrating all the above innovative technologies, a novel hybrid SS-OS-PSO-DBN-ELM-LSTME (SOPDEL) model with comprehensive data preprocessing was established to improve the quality of long-time-series forecasting. Five models featuring partial enhancements are discussed in this paper and three state-of-the-art classical models were utilized for comparative experiments. The results demonstrated that the majority of evaluation indices exhibit a significant optimization in the proposed model. Additionally, a relevant evaluation system showed that the quality of “Excellent Prediction” and “Good Prediction” exceeds 90%, and no data with “Bad Prediction” appear, so the accuracy of the prediction process is obviously insured. Full article

(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)

► Show Figures

Figure 1

28 pages, 20227 KiB

Open AccessArticle

Long-Range Lightning Interferometry Using Coherency

by Xue Bai and Martin Füllekrug

Remote Sens. 2023, 15(7), 1950; https://doi.org/10.3390/rs15071950 - 06 Apr 2023

Viewed by 1472

Abstract

Traditional lightning detection and location networks use the time of arrival (TOA) technique to locate lightning events with a single time stamp. This contribution introduces a simulation study to lay the foundation for new lightning location concepts. Here, a novel interferometric method is [...] Read more.

Traditional lightning detection and location networks use the time of arrival (TOA) technique to locate lightning events with a single time stamp. This contribution introduces a simulation study to lay the foundation for new lightning location concepts. Here, a novel interferometric method is studied which expands the data use and maps lightning events into an area by using coherency. The amplitude waveform bank, which consists of averaged waveforms classified by their propagation distances, is first used to test interferometric methods. Subsequently, the study is extended to individual lightning event waveforms. Both amplitude and phase coherency of the analytic signal are used here to further develop the interferometric method. To determine a single location for the lightning event and avoid interference between the ground wave and the first skywave, two solutions are proposed: (1) use a small receiver network and (2) apply an impulse response function to the recorded waveforms, which uses an impulse to represent the lightning occurrence. Both methods effectively remove the first skywave interference. This study potentially helps to identify the lightning ground wave without interference from skywaves with a long-range low frequency (LF) network. It is planned to expand the simulation work with data reflecting a variety of ionospheric and geographic scenarios. Full article

(This article belongs to the Special Issue Advances in Instrumentation and Algorithms for Atmospheric Electricity Applications)

► Show Figures

Figure 1

26 pages, 15778 KiB

Open AccessArticle

A Comparative Assessment of Multi-Source Generation of Digital Elevation Models for Fluvial Landscapes Characterization and Monitoring

by Paweł Sudra, Luca Demarchi, Grzegorz Wierzbicki and Jarosław Chormański

Remote Sens. 2023, 15(7), 1949; https://doi.org/10.3390/rs15071949 - 06 Apr 2023

Cited by 4 | Viewed by 1806

Abstract

Imaging and measuring the Earth’s relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of [...] Read more.

Imaging and measuring the Earth’s relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of the land surface on digital elevation models is crucial. The main objective of the study was to assess and compare the accuracy of state-of-the-art remote sensing technologies in generating DEMs for riverscape characterization and fluvial monitoring applications. In particular, we were interested in DAP and LiDAR techniques comparison, and UAV applicability. We carried out field surveys, i.e., GNSS-RTK measurements, UAV and aircraft flights, on islands and sandbars within a nature reserve on a braided section of the Vistula River downstream from the city of Warsaw, Poland. We then processed the data into DSMs and DTMs based on four sources: ULS (laser scanning from UAV), UAV-DAP (digital aerial photogrammetry), ALS (airborne laser scanning), and satellite Pléiades imagery processed with DAP. The magnitudes of errors are represented by the cross-reference of values generated on DEMs with GNSS-RTK measurements. Results are presented for exposed sediment bars, riverine islands covered by low vegetation and shrubs, or covered by riparian forest. While the average absolute height error of the laser scanning DTMs oscillates around 8–11 cm for most surfaces, photogrammetric DTMs from UAV and satellite data gave errors averaging more than 30 cm. Airborne and UAV LiDAR measurements brought almost the perfect match. We showed that the UAV-based LiDAR sensors prove to be useful for geomorphological mapping, especially for geomorphic analysis of the river channel at a large scale, because they reach similar accuracies to ALS and better than DAP-based image processing. Full article

(This article belongs to the Special Issue Remote Sensing of Riparian Ecosystems)

► Show Figures

Figure 1

24 pages, 35825 KiB

Open AccessArticle

The Respondence of Wave on Sea Surface Temperature in the Context of Global Change

by Ru Yao, Weizeng Shao, Mengyu Hao, Juncheng Zuo and Song Hu

Remote Sens. 2023, 15(7), 1948; https://doi.org/10.3390/rs15071948 - 06 Apr 2023

Cited by 6 | Viewed by 1433

Abstract

Several aspects of global climate change, e.g., the rise of sea level and water temperature anomalies, suggest the advantages of studying wave distributions. In this study, WAVEWATCH-III (WW3) (version 6.07), which is a well-known numerical wave model, was employed for simulating waves over [...] Read more.

Several aspects of global climate change, e.g., the rise of sea level and water temperature anomalies, suggest the advantages of studying wave distributions. In this study, WAVEWATCH-III (WW3) (version 6.07), which is a well-known numerical wave model, was employed for simulating waves over global seas from 1993–2020. The European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus Marine Environment Monitoring Service (CMEMS), current and sea level were used as the forcing fields in the WW3 model. The validation of modelling simulations against the measurements from the National Data Buoy Center (NDBC) buoys and Haiyang-2B (HY-2B) altimeter yielded a root mean square error (RMSE) of 0.49 m and 0.63 m, with a correlation (COR) of 0.89 and 0.90, respectively. The terms calculated by WW3-simulated waves, i.e., breaking waves, nonbreaking waves, radiation stress, and Stokes drift, were included in the water temperature simulation by a numerical circulation model named the Stony Brook Parallel Ocean Model (sbPOM). The water temperature was simulated in 2005–2015 using the high-quality Simple Ocean Data Assimilation (SODA) data. The validation of sbPOM-simulated results against the measurements obtained from the Array for Real-time Geostrophic Oceanography (Argo) buoys yielded a RMSE of 1.12 °C and a COR of 0.99. By the seasonal variation, the interrelation of the currents, sea level anomaly, and significant wave heights (SWHs) were strong in the Indian Ocean. In the strong current areas, the distribution of the sea level was consistent with the SWHs. The monthly variation of SWHs, currents, sea surface elevation, and sea level anomalies revealed that the upward trends of SWHs and sea level anomalies were consistent from 1993–2015 over the global ocean. In the Indian Ocean, the SWHs were obviously influenced by the SST and sea surface wind stress. The rise of wind stress intensity and sea level enlarges the growth of waves, and the wave-induced terms strengthen the heat exchange at the air–sea layer. It was assumed that the SST oscillation had a negative response to the SWHs in the global ocean from 2005–2015. This feedback indicates that the growth of waves could slow down the amplitude of water warming. Full article

(This article belongs to the Special Issue Advances in the Ocean Surface Dynamics: Ocean Waves, Wind, and Air-Sea Interaction - in Memory of Professor Shengchang Wen)

► Show Figures

Figure 1

22 pages, 12963 KiB

Open AccessArticle

Multi-Resolution Population Mapping Based on a Stepwise Downscaling Approach Using Multisource Data

by Yan Jin, Rui Liu, Haoyu Fan, Pengdu Li, Yaojie Liu and Yan Jia

Remote Sens. 2023, 15(7), 1947; https://doi.org/10.3390/rs15071947 - 06 Apr 2023

Cited by 1 | Viewed by 1625

Abstract

The distribution of the population is an essential aspect of addressing social, economic, and environmental problems. Gridded population data can provide more detailed information than census data, and multisource data from remote sensing and geographic information systems have been widely used for population [...] Read more.

The distribution of the population is an essential aspect of addressing social, economic, and environmental problems. Gridded population data can provide more detailed information than census data, and multisource data from remote sensing and geographic information systems have been widely used for population estimation studies. However, due to spatial heterogeneity, the population has different distribution characteristics and variation patterns at different scales, while the relationships between multiple variables also vary with scale. This article presents a stepwise downscaling approach in that the random forest regression kriging technique is used to downscale census data to multi-resolution gridded population datasets. Using Nanjing, China, as the experimental case, population distribution maps were generated at 100 m, 500 m, and 1 km spatial resolution, and compared with the other three downscaling methods and three population products. The results demonstrated the produced gridded population maps by the proposed approach have higher accuracy and more accurate details of population distribution with the smallest mean absolute error (MAE) and root mean squared error (RMSE) values of 1.590 and 2.189 ten thousand people (over 40% reduction). The artificial land and road data are the two most important indicators of population distribution for the regional random forest modeling in Nanjing. Our proposed method can be a valuable tool for population mapping and has the potential to monitor sustainable development goals. Full article

(This article belongs to the Section Remote Sensing and Geo-Spatial Science)

► Show Figures

Figure 1

18 pages, 6835 KiB

Open AccessArticle

The Development of a Low-Cost Hydrophone for Passive Acoustic Monitoring of Dolphin’s Vocalizations

by Rocco De Marco, Francesco Di Nardo, Alessandro Lucchetti, Massimo Virgili, Andrea Petetta, Daniel Li Veli, Laura Screpanti, Veronica Bartolucci and David Scaradozzi

Remote Sens. 2023, 15(7), 1946; https://doi.org/10.3390/rs15071946 - 06 Apr 2023

Cited by 1 | Viewed by 2616

Abstract

Passive acoustics are widely used to monitor the presence of dolphins in the marine environment. This study aims to introduce a low-cost and homemade approach for assembling a complete underwater microphone (i.e., the hydrophone), employing cheap and easy to obtain components. The hydrophone [...] Read more.

Passive acoustics are widely used to monitor the presence of dolphins in the marine environment. This study aims to introduce a low-cost and homemade approach for assembling a complete underwater microphone (i.e., the hydrophone), employing cheap and easy to obtain components. The hydrophone was assembled with two piezo disks connected in a balanced configuration and encased in a plastic container filled with plastic foam. The hydrophone’s performance was validated by direct comparison with the commercially available AS-1 hydrophone (Aquarian Hydrophones, Anacortes, U.S.) on different underwater acoustic signals: artificial acoustic signals (ramp and multitone signals) and various dolphin vocalizations (whistle, echolocation clicks, and burst pulse signals). The sensitivity of the device’s performance to changes in the emission source position was also tested. The results of the validation procedure on both artificial signals and real dolphin vocalizations showed that the significant cost savings associated with cheap technology had a minimal effect on the recording device’s performance within the frequency range of 0–35 kHz. At this stage of experimentation, the global cost of the hydrophone could be estimated at a few euros, making it extremely price competitive when compared to more expensive commercially available models. In the future, this effective and low-cost technology would allow for continuous monitoring of the presence of free-ranging dolphins, significantly lowering the total cost of autonomous monitoring systems. This would permit broadening the monitored areas and creating a network of recorders, thus improving the acquisition of data. Full article

(This article belongs to the Special Issue Remote Sensing and Other Geomatics Techniques for Marine Applications)

► Show Figures

Graphical abstract

16 pages, 13281 KiB

Open AccessArticle

Convolutional Neural Network Maps Plant Communities in Semi-Natural Grasslands Using Multispectral Unmanned Aerial Vehicle Imagery

by Maren Pöttker, Kathrin Kiehl, Thomas Jarmer and Dieter Trautz

Remote Sens. 2023, 15(7), 1945; https://doi.org/10.3390/rs15071945 - 06 Apr 2023

Viewed by 2153

Abstract

Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in [...] Read more.

Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

► Show Figures

Figure 1

16 pages, 2343 KiB

Open AccessArticle

Decline of Late Spring and Summer Snow Cover in the Scottish Highlands from 1984 to 2022: A Landsat Time Series

by Benedict D. Spracklen and Dominick V. Spracklen

Remote Sens. 2023, 15(7), 1944; https://doi.org/10.3390/rs15071944 - 06 Apr 2023

Cited by 2 | Viewed by 1424

Abstract

Late spring and summer snow cover, the remnants of winter and early spring snowfall, not only possess an intrinsic importance for montane flora and fauna, but also act as a sensitive indicator for climate change. The variability and potential trends in late spring [...] Read more.

Late spring and summer snow cover, the remnants of winter and early spring snowfall, not only possess an intrinsic importance for montane flora and fauna, but also act as a sensitive indicator for climate change. The variability and potential trends in late spring and summer (snowmelt season) snow cover in mountain regions are often poorly documented. May to mid-September Landsat imagery from 1984 to 2022 was used to quantify changes in the snow-covered area of upland regions in the Scottish Highlands. There was substantial annual variability in the area of May to mid-September snow cover combined with a significant decline over the 39-year study period (p = 0.02). Long-term climate data used to show variability in May to mid-September snow cover was positively related to winter snowfall and negatively related to winter and April temperatures. The results from a long-running field survey counting the number of snow patches that survive until the following winter were used to check the veracity of the study. Further, accuracy was estimated through comparison with higher resolution Sentinel-2 imagery, giving a user and producer accuracy rate of 99.8% and 87%, respectively. Projected future warming will further diminish this scarce, valuable habitat, along with its associated plant communities, thus threatening the biodiversity and scenic value of the Scottish Highlands. Full article

► Show Figures

Figure 1

12 pages, 817 KiB

Open AccessCommunication

Design of Extensible Structured Interferometric Array Utilizing the “Coarray” Concept

by Qiang Wang, Cong Xue, Shurui Zhang, Renli Zhang and Weixing Sheng

Remote Sens. 2023, 15(7), 1943; https://doi.org/10.3390/rs15071943 - 05 Apr 2023

Viewed by 1333

Abstract

The optimum placement of receiving telescope antennas is a central topic for designing radio interferometric arrays, and this determines the performance of the obtained information. A variety of arrays are designed for different purposes, and they perform poorly in scalability. In this paper, [...] Read more.

The optimum placement of receiving telescope antennas is a central topic for designing radio interferometric arrays, and this determines the performance of the obtained information. A variety of arrays are designed for different purposes, and they perform poorly in scalability. In this paper, we consider a subclass of structured sparse arrays, namely nested arrays, and examine the important role of “coarray” in interferometric synthesis imaging, which is utilized to design nested array configurations for a complete uniform Fourier plane coverage in both supersynthesis and instantaneous modes. Both nested arrays and the theory of the coarray have rich research achievements, and we apply them to astronomy to design arrays with good scalability and imaging performance. Simulated celestial source image retrieval performance validates the effectiveness of nested interferometric arrays. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

► Show Figures

Figure 1

23 pages, 9006 KiB

Open AccessEditor’s ChoiceArticle

Terrestrial Laser Scanning for Non-Destructive Estimation of Aboveground Biomass in Short-Rotation Poplar Coppices

by María Menéndez-Miguélez, Guillermo Madrigal, Hortensia Sixto, Nerea Oliveira and Rafael Calama

Remote Sens. 2023, 15(7), 1942; https://doi.org/10.3390/rs15071942 - 05 Apr 2023

Cited by 2 | Viewed by 1587

Abstract

Poplar plantations in high-density and short-rotation coppices (SRC) are a suitable way for the fast production of wood that can be transformed into bioproducts or bioenergy. Optimal management of these coppices requires accurate assessment of the total standing biomass. However, traditional field inventory [...] Read more.

Poplar plantations in high-density and short-rotation coppices (SRC) are a suitable way for the fast production of wood that can be transformed into bioproducts or bioenergy. Optimal management of these coppices requires accurate assessment of the total standing biomass. However, traditional field inventory is a challenging task, given the existence of multiple shoots, the difficulty of identifying terminal shoots, and the extreme high density. As an alternative, in this work, we propose to develop individual stool and plot biomass models using metrics derived from terrestrial laser scanning (TLS) as predictors. To this aim, we used data from a SRC poplar plantation, including nine plots and 154 individual stools. Every plot was scanned from different positions, and individual stools were felled, weighed, and dried to compute aboveground biomass (AGB). Individual stools were segmented from the cloud point, and different TLS metrics at stool and plot level were derived following processes of bounding box, slicing, and voxelization. These metrics were then used, either alone or combined with field-measured metrics, to fit biomass models. Our results indicate that at individual-stool level, the biomass models combining TLS metrics and easy to measure in field metrics (stool diameter) perform similarly to the traditional allometric models based on field inventories, while at plot scales, TLS-derived models show superiority over traditional models. Our proposed methodology permits accurate and non-destructive estimates of the biomass fixed in SRC plantations. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

► Show Figures

Figure 1

18 pages, 7948 KiB

Open AccessArticle

Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images

by Changjun Gu, Suju Li, Ming Liu, Kailong Hu and Ping Wang

Remote Sens. 2023, 15(7), 1941; https://doi.org/10.3390/rs15071941 - 05 Apr 2023

Cited by 5 | Viewed by 2504

Abstract

Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool [...] Read more.

Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool for monitoring. Lake Mercbacher, an ice-dammed lake in the central Tian Shan mountain range, poses a significant threat downstream due to its relatively high frequency of outbursts. In this study, we first monitored the daily changes in the lake area before the 2022 Lake Mercbacher outburst. Additionally, based on historical satellite images from 2014 to 2021, we calculated the maximum lake area (MLA) and its changes before the outburst. Furthermore, we extracted the proportion of floating ice and water area during the period. The results show that the lake area of Lake Mercbacher would first increase at a relatively low speed (0.01 km²/day) for about one month, followed by a relatively high-speed increase (0.04 km²/day) until reaching the maximum, which would last for about twenty days. Then, the lake area would decrease slowly until the outburst, which would last five days and is significant for early warning. Moreover, the floating ice and water proportion provides more information about the outburst signals. In 2022, we found that the floating ice area increased rapidly during the early warning stage, especially one day before the outburst, accounting for about 50% of the total lake area. Historical evidence indicates that the MLA shows a decreasing trend, and combining it with the outburst date and climate data, we found that the outburst date shows an obvious advance trend (6 days per decade) since 1902, caused by climate warming. Earlier melting results in an earlier outburst. This study provides essential references for monitoring Lake Mercbacher GLOFs and building an effective early warning system. Full article

(This article belongs to the Special Issue Space-Based Earth Observations for Disaster Risk and Emergency Monitoring)

► Show Figures

Figure 1

18 pages, 18279 KiB

Open AccessArticle

On Surface Waves Generated by Extra-Tropical Cyclones—Part I: Multi-Satellite Measurements

by Vahid Cheshm Siyahi, Vladimir Kudryavtsev, Maria Yurovskaya, Fabrice Collard and Bertrand Chapron

Remote Sens. 2023, 15(7), 1940; https://doi.org/10.3390/rs15071940 - 05 Apr 2023

Cited by 1 | Viewed by 1375

Abstract

Surface waves generated by Extra-Tropical Cyclones (ETCs) can significantly affect shipping, fishing, offshore oil and gas production, and other marine activities. This paper presents the results of a satellite data-based investigation of wind waves generated by two North Atlantic ETCs. These ETCs were [...] Read more.

Surface waves generated by Extra-Tropical Cyclones (ETCs) can significantly affect shipping, fishing, offshore oil and gas production, and other marine activities. This paper presents the results of a satellite data-based investigation of wind waves generated by two North Atlantic ETCs. These ETCs were fast-moving systems, inhibiting resonance (synchronism) between the group velocity of the generated waves and the ETC translation velocity. In these cases, wave generation begins when the front boundary of the storm appears at a given ocean location point. Since developing waves are slow, they move backward relative to the storm, grow in time, and then leave the ETC stormy area through the rear sector. Multi-satellite observations confirm such a paradigm, revealing that the storm regions are filled with young developing wind waves, the most developed in the rear-right sector. As observed, the energy of these waves grew in time during the ETC life span. It is demonstrated that the extended-fetch concept (inherent for Tropical Cyclones) does not apply to ETC. Instead, by analogy, the concept of extended-duration wave growth is more relevant. Satellite observations confirmed the validity of duration-laws for waves generated by ETCs, and demonstrated that extended-fetch solutioncan be valid at time scales exceeding the lifespan of considered ETCs. Full article

(This article belongs to the Special Issue Remote Sensing of Wave Fields under Extreme Weather Conditions (in Tropical and Extra-Tropical Cyclones and Polar Lows))

► Show Figures

Figure 1

32 pages, 2644 KiB

Open AccessReview

A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment

by Mahyat Shafapourtehrany, Maryna Batur, Farzin Shabani, Biswajeet Pradhan, Bahareh Kalantar and Haluk Özener

Remote Sens. 2023, 15(7), 1939; https://doi.org/10.3390/rs15071939 - 05 Apr 2023

Cited by 5 | Viewed by 6178

Abstract

The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies [...] Read more.

The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies offer a means by which earthquake occurrence can be predicted or foreshadowed; managed in terms of levels of preparation related to land use planning; availability of emergency shelters, medical resources, and food supplies; and assessment of damage and remedial priorities. This literature review paper surveys the geospatial technologies employed in earthquake research and disaster management. The objectives of this review paper are to assess: (1) the role of the range of geospatial data types; (2) the application of geospatial technologies to the stages of an earthquake; (3) the geospatial techniques used in earthquake hazard, vulnerability, and risk analysis; and (4) to discuss the role of geospatial techniques in earthquakes and related disasters. The review covers past, current, and potential earthquake-related applications of geospatial technology, together with the challenges that limit the extent of usefulness and effectiveness. While the focus is mainly on geospatial technology applied to earthquake research and management in practice, it also has validity as a framework for natural disaster risk assessments, emergency management, mitigation, and remediation, in general. Full article

► Show Figures

Figure 1

35 pages, 1464 KiB

Open AccessReview

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

by Godson Ebenezer Adjovu, Haroon Stephen, David James and Sajjad Ahmad

Remote Sens. 2023, 15(7), 1938; https://doi.org/10.3390/rs15071938 - 04 Apr 2023

Cited by 24 | Viewed by 8034

Abstract

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among [...] Read more.

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

► Show Figures

Graphical abstract

27 pages, 42556 KiB

Open AccessArticle

Space Target Material Identification Based on Graph Convolutional Neural Network

by Na Li, Chengeng Gong, Huijie Zhao and Yun Ma

Remote Sens. 2023, 15(7), 1937; https://doi.org/10.3390/rs15071937 - 04 Apr 2023

Cited by 3 | Viewed by 1986

Abstract

Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a [...] Read more.

Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85–90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

► Show Figures

Graphical abstract

30 pages, 19069 KiB

Open AccessEditor’s ChoiceArticle

Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling

by Fuensanta G. Montesinos, Sergio Sainz-Maza, David Gómez-Ortiz, José Arnoso, Isabel Blanco-Montenegro, Maite Benavent, Emilio Vélez, Nieves Sánchez and Tomás Martín-Crespo

Remote Sens. 2023, 15(7), 1936; https://doi.org/10.3390/rs15071936 - 04 Apr 2023

Cited by 6 | Viewed by 2734

Abstract

This study used spatiotemporal land gravity data to investigate the 2021 eruption that occurred in the Cumbre Vieja volcano (La Palma, Canary Islands). First, we produced a density model by inverting the local gravity field using data collected in July 2005 and July [...] Read more.

This study used spatiotemporal land gravity data to investigate the 2021 eruption that occurred in the Cumbre Vieja volcano (La Palma, Canary Islands). First, we produced a density model by inverting the local gravity field using data collected in July 2005 and July 2021. This model revealed a low-density body beneath the western flank of the volcano that explains a highly fractured and altered structure related to the active hydrothermal system. Then, we retrieved changes in gravity and GNSS vertical displacements from repeated measurements made in a local network before (July 2021) and after (January 2022) the eruption. After correcting the vertical surface displacements, the gravity changes produced by mass variation during the eruptive process were used to build a forward model of the magmatic feeding system consisting of dikes and sills based on an initial model defined by the paths of the earthquake hypocenters preceding the eruption. Our study provides a final model of the magma plumbing system, which establishes a spatiotemporal framework tracing the path of magma ascent from the crust–mantle boundary to the surface from 11–19 September 2021, where the shallowest magma path was strongly influenced by the low-density body identified in the inversion process. Full article

(This article belongs to the Special Issue Investigation of Volcanic Activity Using Potential and Deformation Fields Retrieved from Proximal and Remote Sensing Techniques)

► Show Figures

Figure 1

17 pages, 4983 KiB

Open AccessArticle

Dynamics of Forest Vegetation in an Urban Agglomeration Based on Landsat Remote Sensing Data for the Period 1990–2022: A Case Study

by Elena Petrovna Yankovich, Ksenia Stanislavovna Yankovich and Nikolay Viktorovich Baranovskiy

Remote Sens. 2023, 15(7), 1935; https://doi.org/10.3390/rs15071935 - 04 Apr 2023

Cited by 1 | Viewed by 1455

Abstract

In recent years, the vegetation cover in urban agglomerations has been changing very rapidly due to technogenic influence. Satellite images play a huge role in studying the dynamics of forest vegetation. Special programs are used to process satellite images. The purpose of the [...] Read more.

In recent years, the vegetation cover in urban agglomerations has been changing very rapidly due to technogenic influence. Satellite images play a huge role in studying the dynamics of forest vegetation. Special programs are used to process satellite images. The purpose of the study is to analyze forest vegetation within the territory of the Tomsk agglomeration based on Landsat remote sensing data for the period from 1990 to 2022. The novelty of the study is explained by the development of a unique program code for the analysis of Landsat satellite data on the previously unexplored territory of the Tomsk agglomeration with the prospect of moving to the scale of the entire state in the future. In this study, the authors present an algorithm implemented in Python to quantify the change in the area of vegetation in an urban agglomeration using Landsat multispectral data. The tool allows you to read space images, calculate spectral indices (NDVI, UI, NDWI), and perform statistical processing of interpretation results. The created tool was applied to study the dynamics of vegetation within the Tomsk urban agglomeration during the period 1990–2022. Key findings and conclusions: (1) The non-forest areas increased from 1990 to 1999 and from 2013 to 2022. It is very likely that this is due to the deterioration of the standard of living in the country during these periods. The first time interval corresponds to the post-Soviet period and the devastation in the economy in the 1990s. The second period corresponds to the implementation and strengthening of sanctions pressure on the Russian Federation. (2) The area of territories inhabited by people has been steadily falling since 1990. This is due to the destruction of collective agriculture in the Russian Federation and the outflow of the population from the surrounding rural settlements to Tomsk and Seversk. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

► Show Figures

Figure 1

13 pages, 3077 KiB

Open AccessCommunication

Inversion of Wind and Temperature from Low SNR FPI Interferograms

by Yafei Wei, Sheng-Yang Gu, Zhenlin Yang, Cong Huang, Na Li, Guoyuan Hu and Xiankang Dou

Remote Sens. 2023, 15(7), 1934; https://doi.org/10.3390/rs15071934 - 04 Apr 2023

Cited by 1 | Viewed by 924

Abstract

The temperature and wind in the middle and upper atmosphere can be obtained by recording the Doppler shift and broadening of the airglow emission, which is reflected by the interference ring from a ground-based Fabry–Perot interferometer (FPI) system. FPI observations are highly susceptible [...] Read more.

The temperature and wind in the middle and upper atmosphere can be obtained by recording the Doppler shift and broadening of the airglow emission, which is reflected by the interference ring from a ground-based Fabry–Perot interferometer (FPI) system. FPI observations are highly susceptible to weather and the external environment, which seriously affect the signal-to-noise ratio (SNR) of FPI interferograms. An SNR can significantly increase errors in determining the center of the interferogram, leading to inaccurate wind and temperature inversions. The calculation shows that the wind inversion from the interferogram decreases and the temperature increases for larger central errors. In this paper, we propose the maximum standard deviation method (MSDM) with high accuracy and robustness to determine the interference ring center. The performance of the MSDM is better achieved by using more than 100 1D interferogram bins to determine the center of interferograms. The robustness of the MSDM is investigated by computing numerous simulated interferograms with white Gaussian noise and Poisson noise, and compared with the two algorithms of binarization and peak fitting, which are usually used to invert wind and temperature from the interference ring of FPI. The results show that MSDM has higher accuracy and robustness than the other two algorithms. We also simulate the distortion interferogram when the FPI may be illuminated by inhomogeneous background light, which can introduce additional errors in wind and temperature, and the MSDM still performs better. Finally, we invert the wind and temperature from the real airglow interferogram by the Kelan (38.7°N, 111.6°E) FPI, which shows that both the wind and temperature inverted by MSDM better agree well with the FPI product than the other two algorithms. Therefore, the MSDM helps to improve the accuracy and stability to invert the wind and temperature. Full article

► Show Figures

Figure 1

18 pages, 8822 KiB

Open AccessArticle

Data Comparison and Cross-Calibration between Level 1 Products of DPC and POSP Onboard the Chinese GaoFen-5(02) Satellite

by Xuefeng Lei, Zhenhai Liu, Fei Tao, Hao Dong, Weizhen Hou, Guangfeng Xiang, Lili Qie, Binghuan Meng, Congfei Li, Feinan Chen, Yanqing Xie, Miaomiao Zhang, Lanlan Fan, Liangxiao Cheng and Jin Hong

Remote Sens. 2023, 15(7), 1933; https://doi.org/10.3390/rs15071933 - 04 Apr 2023

Cited by 1 | Viewed by 1605

Abstract

The Polarization CrossFire (PCF) suite onboard the Chinese GaoFen-5(02) satellite has been sophisticatedly composed by the Particulate Observing Scanning Polarimeter (POSP) and the Directional Polarimetric Camera (DPC). Among them, DPC is a multi-angle sequential measurement polarization imager, while POSP is a cross-track scanning [...] Read more.

The Polarization CrossFire (PCF) suite onboard the Chinese GaoFen-5(02) satellite has been sophisticatedly composed by the Particulate Observing Scanning Polarimeter (POSP) and the Directional Polarimetric Camera (DPC). Among them, DPC is a multi-angle sequential measurement polarization imager, while POSP is a cross-track scanning simultaneous polarimeter with corresponding radiometric and polarimetric calibrators, which can theoretically be used for cross comparison and calibration with DPC. After the data preprocessing of these two sensors, we first select local homogeneous cluster scenes by calculating the local variance-to-mean ratio in DPC’s Level 1 product projection grids to reduce the influence of scale differences and geometry misalignment between DPC and POSP. Then, taking the observation results after POSP data quality assurance as the abscissa and taking the DPC observation results under the same wavelength band and geometric conditions as the same ordinate, a two-dimensional radiation/polarization feature space is established. Results show that the normalized top of the atmosphere (TOA) radiances of DPC and POSP processed data at the nadir are linearly correlated. The normalized TOA radiance root mean square errors (RMSEs) look reasonable in all common bands. The DPC and POSP normalized radiance ratios in different viewing zenith angle ranges at different times reveal the temporal drift of the DPC relative radiation response. The RMSEs, mean absolute errors (MAEs), relative errors (REs), and scatter percentage of DPC degree of linear polarization (DoLP) falling within the expected error (EE = ±0.02) of POSP measured DoLP are better than 0.012, 0.009, 0.066, and 91%, respectively. Full article

(This article belongs to the Special Issue Advances in Satellite and Ground-Based Polarimetric Remote Sensing and Applications in Atmosphere, Ocean and Land Surface Detections)

► Show Figures

Figure 1

19 pages, 2390 KiB

Open AccessEditor’s ChoiceTechnical Note

Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery

by Piyush S. Agram, Michael S. Warren, Scott A. Arko and Matthew T. Calef

Remote Sens. 2023, 15(7), 1932; https://doi.org/10.3390/rs15071932 - 04 Apr 2023

Cited by 3 | Viewed by 1770

Abstract

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata [...] Read more.

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We have quantified the loss of precision due to the application of static flattening factors, and show that these are well below the stated requirements of change-detection algorithms. Finally, we have discussed the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth-Observation Satellites (CEOS) Analysis-Ready Dataset (ARD) families—Geocoded Single-Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework. Full article

(This article belongs to the Section Engineering Remote Sensing)

► Show Figures

Figure 1

25 pages, 18095 KiB

Open AccessArticle

Improving Semantic Segmentation of Roof Segments Using Large-Scale Datasets Derived from 3D City Models and High-Resolution Aerial Imagery

by Florian L. Faltermeier, Sebastian Krapf, Bruno Willenborg and Thomas H. Kolbe

Remote Sens. 2023, 15(7), 1931; https://doi.org/10.3390/rs15071931 - 04 Apr 2023

Cited by 1 | Viewed by 2664

Abstract

Advances in deep learning techniques for remote sensing as well as the increased availability of high-resolution data enable the extraction of more detailed information from aerial images. One promising task is the semantic segmentation of roof segments and their orientation. However, the lack [...] Read more.

Advances in deep learning techniques for remote sensing as well as the increased availability of high-resolution data enable the extraction of more detailed information from aerial images. One promising task is the semantic segmentation of roof segments and their orientation. However, the lack of annotated data is a major barrier for deploying respective models on a large scale. Previous research demonstrated the viability of the deep learning approach for the task, but currently, published datasets are small-scale, manually labeled, and rare. Therefore, this paper extends the state of the art by presenting a novel method for the automated generation of large-scale datasets based on semantic 3D city models. Furthermore, we train a model on a dataset 50 times larger than existing datasets and achieve superior performance while applying it to a wider variety of buildings. We evaluate the approach by comparing networks trained on four dataset configurations, including an existing dataset and our novel large-scale dataset. The results show that the network performance measured as intersection over union can be increased from 0.60 for the existing dataset to 0.70 when the large-scale model is applied on the same region. The large-scale model performs superiorly even when applied to more diverse test samples, achieving 0.635. The novel approach contributes to solving the dataset bottleneck and consequently to improving semantic segmentation of roof segments. The resulting remotely sensed information is crucial for applications such as solar potential analysis or urban planning. Full article

(This article belongs to the Special Issue Semantic Segmentation of High-Resolution Images with Deep Learning, Volume II)

► Show Figures

Figure 1

11 pages, 5709 KiB

Open AccessCommunication

A New Algorithm for Ill-Posed Problem of GNSS-Based Ionospheric Tomography

by Debao Wen, Kangyou Xie, Yinghao Tang, Dengkui Mei, Xi Chen and Hanqing Chen

Remote Sens. 2023, 15(7), 1930; https://doi.org/10.3390/rs15071930 - 04 Apr 2023

Cited by 1 | Viewed by 1127

Abstract

Ill-posedness of GNSS-based ionospheric tomography affects the stability and the accuracy of the inversion results. Truncated singular value decomposition (TSVD) is a common algorithm of ionospheric tomography reconstruction. However, the TSVD method usually has low inversion accuracy and reconstruction efficiency. To resolve the [...] Read more.

Ill-posedness of GNSS-based ionospheric tomography affects the stability and the accuracy of the inversion results. Truncated singular value decomposition (TSVD) is a common algorithm of ionospheric tomography reconstruction. However, the TSVD method usually has low inversion accuracy and reconstruction efficiency. To resolve the above problem, a truncated mapping singular value decomposition (TMSVD) algorithm is presented to improve the reconstructed accuracy and computational efficiency. To authenticate the effectiveness and the advantages of the TMSVD algorithm, a numerical test scheme is devised. Finally, ionospheric temporal–spatial variations of the selected reconstructed region are studied using the GNSS observations under different geomagnetic conditions. The reconstructed results of TMSVD can accurately reflect semiannual anomalies, diurnal variations, and geomagnetic storm effects. In contrast with the ionosonde data, it is found that the reconstructed profiles of the TMSVD method are more consistent with than those of the IRI 2016. The study suggests that TMSVD is an efficient algorithm for the tomographic reconstruction of ionospheric electron density (IED). Full article

(This article belongs to the Special Issue Ionosphere Monitoring with Remote Sensing II)

► Show Figures

Graphical abstract

17 pages, 4485 KiB

Open AccessArticle

Recovery of Water Quality and Detection of Algal Blooms in Lake Villarrica through Landsat Satellite Images and Monitoring Data

by Lien Rodríguez-López, Iongel Duran-Llacer, Lisandra Bravo Alvarez, Andrea Lami and Roberto Urrutia

Remote Sens. 2023, 15(7), 1929; https://doi.org/10.3390/rs15071929 - 03 Apr 2023

Cited by 4 | Viewed by 3858

Abstract

Phytoplankton is considered a strong predictor of the environmental quality of lakes, while Chlorophyll-a is an indicator of primary productivity. In this study, 25 LANDSAT images covering the 2014–2021 period were used to predict Chlorophyll-a in the Villarrica lacustrine system. A Chlorophyll-a recovery [...] Read more.

Phytoplankton is considered a strong predictor of the environmental quality of lakes, while Chlorophyll-a is an indicator of primary productivity. In this study, 25 LANDSAT images covering the 2014–2021 period were used to predict Chlorophyll-a in the Villarrica lacustrine system. A Chlorophyll-a recovery algorithm was calculated using two spectral indices (FAI and SABI). The indices that presented the best statistical indicators were the floating algal index (R² = 0.87) and surface algal bloom index (R² = 0.59). A multiparametric linear model for Chlorophyll-a estimation was constructed with the indices. Statistical indicators were used to validate the multiple linear regression model used to predict Chlorophyll-a by means of spectral indices, with the following results: a MBE of −0.136 μ, RMSE of 0.055 μ, and NRMSE of 0.019%. All results revealed the strength of the model. It is necessary to raise awareness among the population that carries out activities around the lake in order for them to take policy actions related to water resources in this Chilean lake. Furthermore, it is important to note that this study is the first to address the detection of algal blooms in this Chilean lake through remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

► Show Figures

Graphical abstract

23 pages, 28261 KiB

Open AccessArticle

Spatio-Temporal Changes of Mangrove-Covered Tidal Flats over 35 Years Using Satellite Remote Sensing Imageries: A Case Study of Beibu Gulf, China

by Ertao Gao and Guoqing Zhou

Remote Sens. 2023, 15(7), 1928; https://doi.org/10.3390/rs15071928 - 03 Apr 2023

Viewed by 2063

Abstract

Tidal flats provide ecosystem services to billions of people worldwide; however, their changing status is largely unknown. Several challenges in the fine extraction of tidal flats using remote sensing techniques, including tide-level and water-edge line changes, exist at present, especially regarding the spatial [...] Read more.

Tidal flats provide ecosystem services to billions of people worldwide; however, their changing status is largely unknown. Several challenges in the fine extraction of tidal flats using remote sensing techniques, including tide-level and water-edge line changes, exist at present, especially regarding the spatial and temporal distribution of mangroves. This study proposed a tidal flats extraction method using a combination of threshold segmentation and tidal-level correction, considering the influence of mangrove changes. We extracted the spatial distribution of tidal flats in Beibu Gulf, Southwest China, from 1987 to 2021 using time-series Landsat and Sentinel-2 images, and further analyzed the dynamic variation characteristics of the total tidal flats, each coastal segment, and the range of erosion and silting. To quantitatively investigate the interaction between tidal flats and mangroves, this study established a regression model based on multi-temporal tidal flats and mangrove data. The results indicated that the overall accuracy of the tidal flat extraction results was 93.9%, and the kappa coefficient was 0.82. The total area of tidal flats in Beibu Gulf decreased by 130 km² from 1987 to 2021, with an average annual change of −3.7 km²/a. In addition, a negative correlation between the tidal flat change area and mangrove change area in Shankou, Maowei Sea, and Pearl Bay was observed, with correlation coefficients of −0.28, −0.30 and −0.64, respectively. These results demonstrate that the distribution of tidal flats provides a good environment and expansion space for the rapid growth of mangroves. These results can provide references for tidal flats’ resource conservation, ecological health assessment, and vegetation changes in coastal wetlands in China and other countries in Southeast Asia. Full article

(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)

► Show Figures

Figure 1

23 pages, 30351 KiB

Open AccessArticle

Comparing the Capability of Sentinel-2 and Landsat 9 Imagery for Mapping Water and Sandbars in the River Bed of the Lower Tagus River (Portugal)

by Romeu Gerardo and Isabel P. de Lima

Remote Sens. 2023, 15(7), 1927; https://doi.org/10.3390/rs15071927 - 03 Apr 2023

Cited by 3 | Viewed by 2776

Abstract

Mapping river beds to identify water and sandbars is a crucial task for understanding the morphology and hydrodynamics of rivers and their ecological conditions. The main difficulties of this task so far have been the limitations of conventional approaches, which are generally costly [...] Read more.

Mapping river beds to identify water and sandbars is a crucial task for understanding the morphology and hydrodynamics of rivers and their ecological conditions. The main difficulties of this task so far have been the limitations of conventional approaches, which are generally costly (e.g., equipment, time- and human resource-demanding) and have poor flexibility to deal with all river conditions. Currently, alternative approaches rely on remote sensing techniques, which offer innovative tools for mapping water bodies in a quick and cost-effective manner based on relevant spectral indices. This study aimed to compare the capability of using imagery from the Sentinel-2 and newly launched Landsat 9 satellite to achieve this goal. For a segment of the Lower Tagus River (Portugal) with conditions of very low river discharge, comparison of the Normalized Difference Water Index, Modified Normalized Difference Water Index, Augmented Normalized Difference Water Index, and Automated Water Extraction Index calculated from the imagery of the two satellites shows that the two satellites’ datasets and mapping were consistent and therefore could be used complementarily. However, the results highlighted the need to classify satellite imagery based on index-specific classification decision values, which is an important factor in the quality of the information extracted. Full article

(This article belongs to the Special Issue Initial Understanding of Landsat-9 Capabilities and Applications)

► Show Figures

Figure 1

20 pages, 9703 KiB

Open AccessArticle

Temporal and Spatial Change in Vegetation and Its Interaction with Climate Change in Argentina from 1982 to 2015

by Qi Long, Fei Wang, Wenyan Ge, Feng Jiao, Jianqiao Han, Hao Chen, Fidel Alejandro Roig, Elena María Abraham, Mengxia Xie and Lu Cai

Remote Sens. 2023, 15(7), 1926; https://doi.org/10.3390/rs15071926 - 03 Apr 2023

Cited by 2 | Viewed by 3108

Abstract

Studying vegetation change and its interaction with climate change is essential for regional ecological protection. Previous studies have demonstrated the impact of climate change on regional vegetation in South America; however, studies addressing the fragile ecological environment in Argentina are limited. Therefore, we [...] Read more.

Studying vegetation change and its interaction with climate change is essential for regional ecological protection. Previous studies have demonstrated the impact of climate change on regional vegetation in South America; however, studies addressing the fragile ecological environment in Argentina are limited. Therefore, we assessed the vegetation dynamics and their climatic feedback in five administrative regions of Argentina, using correlation analysis and multiple regression analysis methods. The Normalized Difference Vegetation Index 3rd generation (NDVI3g) from Global Inventory Monitoring and Modeling Studies (GIMMS) and climatic data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) were processed. The NDVI of the 1982–2015 period in Argentina showed a downward trend, varying from −1.75 to 0.69/decade. The NDVI in Northeast Argentina (NEA), Northwest Argentina (NWA), Pampas, and Patagonia significantly decreased. Precipitation was negatively correlated with the NDVI in western Patagonia, whereas temperature and solar radiation were positively correlated with the NDVI. Extreme precipitation and drought were essential causes of vegetation loss in Patagonia. The temperature (73.09%), precipitation (64.02%), and solar radiation (73.27%) in Pampas, Cuyo, NEA, and NWA were positively correlated with the NDVI. However, deforestation and farming and pastoral activities have caused vegetation destruction in Pampas, NEA, and NWA. Environmental protection policies and deforestation regulations should be introduced to protect the ecological environment. The results of this study clarify the reasons for the vegetation change in Argentina and provide a theoretical reference for dealing with climate change. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate Ⅱ)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sens., Volume 15, Issue 7 (April-1 2023) – 237 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI