Research

24 pages, 12270 KiB

Open AccessArticle

Qualitative Analysis of Tree Canopy Top Points Extraction from Different Terrestrial Laser Scanner Combinations in Forest Plots

by Sunni Kanta Prasad Kushwaha, Arunima Singh, Kamal Jain, Jozef Vybostok and Martin Mokros

ISPRS Int. J. Geo-Inf. 2023, 12(6), 250; https://doi.org/10.3390/ijgi12060250 - 19 Jun 2023

Cited by 1 | Viewed by 1309

Abstract

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree [...] Read more.

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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14 pages, 10720 KiB

Open AccessArticle

Importance of Protected Areas by Brazilian States to Reduce Deforestation in the Amazon

by Marcos V. L. Sousa, Silas N. Melo, Juciana C. B. Souza, Carlos F. A. Silva, Yuri Feitosa and Lindon F. Matias

ISPRS Int. J. Geo-Inf. 2023, 12(5), 190; https://doi.org/10.3390/ijgi12050190 - 04 May 2023

Cited by 1 | Viewed by 2025

Abstract

Protected areas (PAs) help in strategies for maintaining biodiversity and inhibiting deforestation of the Amazon rainforest. However, there are few studies that evaluate the effectiveness of lands protected by states (or federation units). Our goal was to compare land use change over 35 [...] Read more.

Protected areas (PAs) help in strategies for maintaining biodiversity and inhibiting deforestation of the Amazon rainforest. However, there are few studies that evaluate the effectiveness of lands protected by states (or federation units). Our goal was to compare land use change over 35 years in state-level PAs with another area of protection, both in the Amazon of the Maranhão state, Brazil. We employed remote sensing techniques, the geographic information system (GIS), and statistical analysis with the use of analyses of covariance (ANCOVAS) to analyze the presence of the classes of land use and change in the PA. The results indicate that the state PAs were effective in preserving forest cover and decelerating grazing. The implications of the results are discussed in the context of supporting public policies at the state level for the protection of the Amazon. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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25 pages, 18711 KiB

Open AccessArticle

Analysis of the Spatiotemporal Urban Expansion of the Rome Coastline through GEE and RF Algorithm, Using Landsat Imagery

by Francesco Lodato, Nicola Colonna, Giorgio Pennazza, Salvatore Praticò, Marco Santonico, Luca Vollero and Maurizio Pollino

ISPRS Int. J. Geo-Inf. 2023, 12(4), 141; https://doi.org/10.3390/ijgi12040141 - 25 Mar 2023

Cited by 6 | Viewed by 2715

Abstract

This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The [...] Read more.

This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the “Leonardo Da Vinci” Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform “Google Earth Engine” (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the “Urban” and “Woodland” classes over the 35-year time span that was considered, being 67.4 km² and 70.4 km², respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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17 pages, 5827 KiB

Open AccessArticle

Spatial Analysis of the Suitability of Hass Avocado Cultivation in the Cauca Department, Colombia, Using Multi-Criteria Decision Analysis and Geographic Information Systems

by Yesid Ediver Anacona Mopan, Andrés Felipe Solis Pino, Oscar Rubiano-Ovalle, Helmer Paz and Isabel Ramirez Mejia

ISPRS Int. J. Geo-Inf. 2023, 12(4), 136; https://doi.org/10.3390/ijgi12040136 - 23 Mar 2023

Cited by 2 | Viewed by 2438

Abstract

Avocado is an important export and consumption product in Colombia, and its economic importance is expected to increase in the coming years. With its vast potential territory for avocado cultivation, the department of Cauca is a crucial area for producing this variety. However, [...] Read more.

Avocado is an important export and consumption product in Colombia, and its economic importance is expected to increase in the coming years. With its vast potential territory for avocado cultivation, the department of Cauca is a crucial area for producing this variety. However, small producers in the region often need more knowledge of the most suitable locations for planting. This study seeks to determine the ideal areas for Hass avocado cultivation in Cauca using geographic information tools and multi-criteria decision analysis, using a set of official data from different governmental entities and the hierarchical analytical process that allows determining the intensity of the interrelation of factors in the cultivation of Hass avocado. The results indicate that the municipalities near the Popayán plateau have the most significant potential for Hass avocado production, using the analytical hierarchy process. Approximately 9.2% of the administrative territory of the region is classified as highly suitable for Hass avocado cultivation, and an additional 14.2% is considered moderately suitable, constituting about 700,000 hectares of arable land. This research provides decision-makers and producers with valuable knowledge to support and improve Hass avocado agriculture in the region by implementing agricultural engineering practices. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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16 pages, 3262 KiB

Open AccessArticle

Remote Sensing-Based Yield Estimation of Winter Wheat Using Vegetation and Soil Indices in Jalilabad, Azerbaijan

by Nilufar Karimli and Mahmut Oğuz Selbesoğlu

ISPRS Int. J. Geo-Inf. 2023, 12(3), 124; https://doi.org/10.3390/ijgi12030124 - 13 Mar 2023

Cited by 4 | Viewed by 1646

Abstract

Concerns about the expanding human population’s adequate supply of food draw attention to the field of Food Security. Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. [...] Read more.

Concerns about the expanding human population’s adequate supply of food draw attention to the field of Food Security. Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. However, given the breadth and number of these regions, field research would be an expensive and time-consuming endeavour. With the advent of remote sensing and optical sensors, it is now possible to acquire diverse data remotely, quickly, and inexpensively. This study investigated the limitations and capabilities of remote sensing data application in the field of planning Food Security. As a result, Sentinel 2 and Shuttle Radar Topography Mission (SRTM) data were used to estimate winter wheat yields with a high degree of accuracy (98.03%) using the Mamatkulov technique and the MEDALUS model, which was both free and widely available. This method can make it possible to make predictions about the productivity of newly created crop fields or for which we do not have information about the productivity of previous years, without the need to wait for building regression models or any field studies. Considering the outcome, wide-range and larger analyses on this topic can be carried through. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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19 pages, 12361 KiB

Open AccessArticle

Point Cloud Data Processing Optimization in Spectral and Spatial Dimensions Based on Multispectral Lidar for Urban Single-Wood Extraction

by Shuo Shi, Xingtao Tang, Bowen Chen, Biwu Chen, Qian Xu, Sifu Bi and Wei Gong

ISPRS Int. J. Geo-Inf. 2023, 12(3), 90; https://doi.org/10.3390/ijgi12030090 - 23 Feb 2023

Cited by 3 | Viewed by 1629

Abstract

Lidar can effectively obtain three-dimensional information on ground objects. In recent years, lidar has developed rapidly from single-wavelength to multispectral hyperspectral imaging. The multispectral airborne lidar Optech Titan is the first commercial system that can collect point cloud data on 1550, 1064, and [...] Read more.

Lidar can effectively obtain three-dimensional information on ground objects. In recent years, lidar has developed rapidly from single-wavelength to multispectral hyperspectral imaging. The multispectral airborne lidar Optech Titan is the first commercial system that can collect point cloud data on 1550, 1064, and 532 nm channels. This study proposes a method of point cloud segmentation in the preprocessed intensity interpolation process to solve the problem of inaccurate intensity at the boundary during point cloud interpolation. The entire experiment consists of three steps. First, a multispectral lidar point cloud is obtained using point cloud segmentation and intensity interpolation; the spatial dimension advantage of the multispectral point cloud is used to improve the accuracy of spectral information interpolation. Second, point clouds are divided into eight categories by constructing geometric information, spectral reflectance information, and spectral characteristics. Accuracy evaluation and contribution analysis are also conducted through point cloud truth value and classification results. Lastly, the spatial dimension information is enhanced by point cloud drop sampling, the method is used to solve the error caused by airborne scanning and single-tree extraction of urban trees. Classification results showed that point cloud segmentation before intensity interpolation can effectively improve the interpolation and classification accuracies. The total classification accuracy of the data is improved by 3.7%. Compared with the extraction result (377) of single wood without subsampling treatment, the result of the urban tree extraction proved the effectiveness of the proposed method with a subsampling algorithm in improving the accuracy. Accordingly, the problem of over-segmentation is solved, and the final single-wood extraction result (329) is markedly consistent with the real situation of the region. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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24 pages, 9616 KiB

Open AccessArticle

Mapping Cropland Extent in Pakistan Using Machine Learning Algorithms on Google Earth Engine Cloud Computing Framework

by Rana Muhammad Amir Latif, Jinliao He and Muhammad Umer

ISPRS Int. J. Geo-Inf. 2023, 12(2), 81; https://doi.org/10.3390/ijgi12020081 - 20 Feb 2023

Cited by 3 | Viewed by 2731

Abstract

An actual cropland extent product with a high spatial resolution with a precision of up to 60 m is believed to be particularly significant in tackling numerous water security concerns and world food challenges. To advance the development of niche, advanced cropland goods [...] Read more.

An actual cropland extent product with a high spatial resolution with a precision of up to 60 m is believed to be particularly significant in tackling numerous water security concerns and world food challenges. To advance the development of niche, advanced cropland goods such as crop variety techniques, crop intensities, crop water production, and crop irrigation, it is necessary to examine how cropland products typically span narrow or expansive farmlands. Some of the existing challenges are processing by constructing precision-high resolution cropland-wide items of training and testing data on diverse geographical locations and safe frontiers, computing capacity, and managing vast volumes of geographical data. This analysis includes eight separate Sentinel-2 multi-spectral instruments data from 2018 to 2019 (Short-wave Infrared Imagery (SWIR 2), SWIR 1, Cirrus, the near infrared, red, green, blue, and aerosols) have been used. Pixel-based classification algorithms have been employed, and their precision is measured and scrutinized in this study. The computations and analyses have been conducted on the cloud-based Google Earth Engine computing network. Training and testing data were obtained from the Google Earth Engine map console at a high spatial 10 m resolution for this analysis. The basis of research information for testing the computer algorithms consists of 855 training samples, culminating in a manufacturing field of 200 individual validation samples measuring product accuracy. The Pakistan cropland extent map produced in this study using four state-of-the-art machine learning (ML) approaches, Random Forest, SVM, Naïve Bayes & CART shows an overall validation accuracy of 82%, 89% manufacturer accuracy, and 77% customer accuracy. Among these four machine learning algorithms, the CART algorithm overperformed the other three, with an impressive classification accuracy of 93%. Pakistan’s average cropland areas were calculated to be 370,200 m², and the cropland’s scale of goods indicated that sub-national croplands could be measured. The research offers a conceptual change in the development of cropland maps utilizing a remote sensing multi-date. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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25 pages, 3833 KiB

Open AccessArticle

A Spatial and Temporal Evaluation of Broad-Scale Yield Predictions Created from Yield Mapping Technology and Landsat Satellite Imagery in the Australian Mediterranean Dryland Cropping Region

by Greg Lyle, Kenneth Clarke, Adam Kilpatrick, David McCulloch Summers and Bertram Ostendorf

ISPRS Int. J. Geo-Inf. 2023, 12(2), 50; https://doi.org/10.3390/ijgi12020050 - 03 Feb 2023

Cited by 2 | Viewed by 3341

Abstract

Contemplation of potential strategies to adapt to a changing and variable climate in agricultural cropping areas depends on the availability of geo-information that is at a sufficient resolution, scale and temporal length to inform these decisions. We evaluated the efficacy of creating high-resolution, [...] Read more.

Contemplation of potential strategies to adapt to a changing and variable climate in agricultural cropping areas depends on the availability of geo-information that is at a sufficient resolution, scale and temporal length to inform these decisions. We evaluated the efficacy of creating high-resolution, broad-scale indicators of yield from simple models that combine yield mapping data, a precision agriculture tool, with the normalised difference vegetation index (NDVI) from Landsat 5 and 7 ETM+ imagery. These models were then generalised to test its potential operationalisation across a large agricultural region (>1/2 million hectares) and the state of South Australia (>8 million hectares). Annual models were the best predictors of yield across both areas. Moderate discrimination accuracy in the regional analysis meant that models could be extrapolated with reasonable spatial precision, whereas the accuracy across the state-wide analysis was poor. Generalisation of these models to further operationalise the methodology by removing the need for crop type discrimination and the continual access to annual yield data showed some benefit. The application of this approach with past and contemporary datasets can create a long-term archive that fills an information void, providing a powerful evidence base to inform current management decisions and future on-farm land use in cropping regions elsewhere. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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15 pages, 2804 KiB

Open AccessEditor’s ChoiceArticle

Imputation of Missing Parts in UAV Orthomosaics Using PlanetScope and Sentinel-2 Data: A Case Study in a Grass-Dominated Area

by Francisco R. da S. Pereira, Aliny A. Dos Reis, Rodrigo G. Freitas, Stanley R. de M. Oliveira, Lucas R. do Amaral, Gleyce K. D. A. Figueiredo, João F. G. Antunes, Rubens A. C. Lamparelli, Edemar Moro and Paulo S. G. Magalhães

ISPRS Int. J. Geo-Inf. 2023, 12(2), 41; https://doi.org/10.3390/ijgi12020041 - 28 Jan 2023

Cited by 1 | Viewed by 1905

Abstract

The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight [...] Read more.

The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight restrictions are common in agricultural monitoring, especially in large areas. In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop–livestock system (ICLS) covered by grass at the time. We validated the proposed framework by simulating and imputing artificial missing parts in a UAV orthomosaic and then comparing the original data with the model predictions. Spectral bands and the normalized difference vegetation index (NDVI) derived from PS, as well as S2 images (separately and combined), were used as predictor variables of the UAV spectral bands and NDVI in developing the RF-based imputation models. The proposed framework produces highly accurate results (RMSE = 6.77–17.33%) with a computationally efficient and robust machine-learning algorithm that leverages the wealth of empirical information present in optical satellite imagery (PS and S2) to impute up to 50% of missing parts in a UAV orthomosaic. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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12 pages, 3688 KiB

Open AccessArticle

A Comparison of Several UAV-Based Multispectral Imageries in Monitoring Rice Paddy (A Case Study in Paddy Fields in Tottori Prefecture, Japan)

by Muhammad Dimyati, Supriatna Supriatna, Ryota Nagasawa, Fajar Dwi Pamungkas and Rizki Pramayuda

ISPRS Int. J. Geo-Inf. 2023, 12(2), 36; https://doi.org/10.3390/ijgi12020036 - 21 Jan 2023

Cited by 7 | Viewed by 2239

Abstract

In recent years, unmanned aerial vehicles (UAVs) have been actively applied in the agricultural sector. Several UAVs equipped with multispectral cameras have become available on the consumer market. Multispectral data are informative and practical for evaluating the greenness and growth status of vegetation [...] Read more.

In recent years, unmanned aerial vehicles (UAVs) have been actively applied in the agricultural sector. Several UAVs equipped with multispectral cameras have become available on the consumer market. Multispectral data are informative and practical for evaluating the greenness and growth status of vegetation as well as agricultural crops. The precise monitoring of rice paddy, especially in the Asian region, is crucial for optimizing profitability, sustainability, and protection of agro-ecological services. This paper reports and discusses our findings from experiments conducted to test four different commercially available multispectral cameras (Micesense RedEdge-M, Sentera Single NDVI, Mapir Survey3, and Bizworks Yubaflex), which can be mounted on a UAV in monitoring rice paddy. The survey has conducted in the typical paddy field area located in the alluvial plain in Tottori Prefecture, Japan. Six different vegetation indices (NDVI, BNDVI, GNDVI, VARI, NDRE and MCARI) captured by UAVs were also compared and evaluated monitoring contribution at three different rice cropping phases. The results showed that the spatial distribution of NDVI collected by each camera is almost similar in paddy fields, but the absolute values of NDVI differed significantly from each other. Among them, the Sentera camera showed the most reasonable NDVI values of each growing phase, indicating 0.49 in the early reproductive phase, 0.62 in the late reproductive stage, and 0.38 in the ripening phase. On the other hand, compared to the most commonly used NDVI, VARI which can be calculated from only visible RGB bands, can be used as an easy and effective index for rice paddy monitoring. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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26 pages, 5994 KiB

Open AccessArticle

Spatial Application of Southern U.S. Pine Water Yield for Prioritizing Forest Management Activities

by Jordan Vernon, Joseph St. Peter, Christy Crandall, Olufunke E. Awowale, Paul Medley, Jason Drake and Victor Ibeanusi

ISPRS Int. J. Geo-Inf. 2023, 12(2), 34; https://doi.org/10.3390/ijgi12020034 - 19 Jan 2023

Viewed by 2038

Abstract

Forest management depends on forest condition data and the ability to quantify the impacts of management activities to make informed decisions. Spatially quantifying water yield (WY) from forests across large landscapes enables managers to consider potential WY changes when designing forest management plans. [...] Read more.

Forest management depends on forest condition data and the ability to quantify the impacts of management activities to make informed decisions. Spatially quantifying water yield (WY) from forests across large landscapes enables managers to consider potential WY changes when designing forest management plans. Current forest water yield datasets are either spatially coarse or too restricted to specific sites with in situ monitoring to support some project-level forest management decisions. In this study, we spatially apply a stand-level southern pine WY model over a forested landscape in the Florida panhandle. We informed the WY model with pine leaf area index inputs created from lidar remote sensing and field data, a spatial and temporal aridity index from PRISM and MODIS data, and a custom depth to groundwater dataset. Baseline WY conditions for the study area were created using the Esri and Python tools we developed to automate the WY workflow. Several timber thinning scenarios were then used to quantify water yield increases from forest management activities. The results of this methodology are detailed (10 m spatial resolution) forest WY raster datasets that are currently being integrated with other spatial datasets to inform forest management decisions. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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19 pages, 3885 KiB

Open AccessArticle

Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery

by Wuttichai Boonpook, Yumin Tan, Attawut Nardkulpat, Kritanai Torsri, Peerapong Torteeka, Patcharin Kamsing, Utane Sawangwit, Jose Pena and Montri Jainaen

ISPRS Int. J. Geo-Inf. 2023, 12(1), 14; https://doi.org/10.3390/ijgi12010014 - 07 Jan 2023

Cited by 10 | Viewed by 5018

Abstract

Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem [...] Read more.

Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use dataset is proposed for automatic land use classification using Landsat 8 imagery. The experimental results illustrate that deep learning semantic segmentation using the baseline network (SegNet, U-Net) outperforms pixel-based machine learning algorithms (MLE, SVM, RF) for land use classification. Furthermore, the LoopNet network, which comprises a convolutional loop and convolutional block, is superior to other baseline networks (SegNet, U-Net, PSPnet) and improvement networks (ResU-Net, DeeplabV3+, U-Net++), with 89.84% overall accuracy and good segmentation results. The evaluation of multispectral bands in the land use dataset demonstrates that Band 5 has good performance in terms of extraction accuracy, with 83.91% overall accuracy. Furthermore, the combination of different spectral bands (Band 1–Band 7) achieved the highest accuracy result (89.84%) compared to individual bands. These results indicate the effectiveness of LoopNet and multispectral bands for land use classification using Landsat 8 imagery. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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23 pages, 8859 KiB

Open AccessArticle

Billion Tree Tsunami Forests Classification Using Image Fusion Technique and Random Forest Classifier Applied to Sentinel-2 and Landsat-8 Images: A Case Study of Garhi Chandan Pakistan

by Shabnam Mateen, Narissara Nuthammachot, Kuaanan Techato and Nasim Ullah

ISPRS Int. J. Geo-Inf. 2023, 12(1), 9; https://doi.org/10.3390/ijgi12010009 - 29 Dec 2022

Cited by 5 | Viewed by 2705

Abstract

In order to address the challenges of global warming, the Billion Tree plantation drive was initiated by the government of Khyber Pakhtunkhwa, Pakistan, in 2014. The land cover changes as a result of Billion Tree Tsunami project are relatively unexplored. In particular, the [...] Read more.

In order to address the challenges of global warming, the Billion Tree plantation drive was initiated by the government of Khyber Pakhtunkhwa, Pakistan, in 2014. The land cover changes as a result of Billion Tree Tsunami project are relatively unexplored. In particular, the utilization of remote sensing techniques and satellite image classification has not yet been done. Recently, the Sentinel-2 (S2) satellite has found much utilization in remote sensing and land cover classification. Sentinel-2 (S2) sensors provide freely available images with a spatial resolution of 10, 20 and 60 m. The higher classification accuracy is directly dependent on the higher spatial resolution of the images. This research aims to classify the land cover changes as a result of the Billion Tree plantation drive in the areas of our interest using Random Forest Classifier (RFA) and image fusion techniques applied to Sentinel-2 and Landsat-8 satellite images. A state-of-the-art, model-based image-sharpening technique was used to sharpen the lower resolution Sentinel-2 bands to 10 m. Then the RFA classifier was used to classify the sharpened images and an accuracy assessment was performed for the classified images of the years 2016, 2018, 2020 and 2022. Finally, ground data samples were collected using an unmanned aerial vehicle (UAV) drone and the classified image samples were compared with the real data collected for the year 2022. The real data ground samples were matched by more than 90% with the classified image samples. The overall classification accuracies [%] for the classified images were recorded as 92.87%, 90.79%, 90.27% and 93.02% for the sample data of the years 2016, 2018, 2020 and 2022, respectively. Similarly, an overall Kappa hat classification was calculated as 0.87, 0.86, 0.83 and 0.84 for the sample data of the years 2016, 2018, 2020 and 2022, respectively. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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16 pages, 5831 KiB

Open AccessArticle

Characteristics of False-Positive Active Fires for Biomass Burning Monitoring in Indonesia from VIIRS Data and Local Geo-Features

by Parwati Sofan, Fajar Yulianto and Anjar Dimara Sakti

ISPRS Int. J. Geo-Inf. 2022, 11(12), 601; https://doi.org/10.3390/ijgi11120601 - 01 Dec 2022

Cited by 4 | Viewed by 2149

Abstract

In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire data (VNP14IMGML), which provides [...] Read more.

In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire data (VNP14IMGML), which provides attributes of thermal anomalies every month from 2012 to 2020 in Indonesia. The characteristics of thermal anomalies other than biomass burning were explored using fire radiative power (FRP) values, confidence levels of active fire, fire pixel areas, and their allocations to permanent geographical features (i.e., volcano, river, lake, coastal line, road, and industrial/settlement areas). The Tukey test showed that there was a significant difference between the mean FRP values of the other thermal anomalies, type-1 (active volcano), type-2 (other static land sources), and type-3 (detection over water/offshore), at a confidence level of 95%. Most thermal anomalies other than biomass burning were in the nominal confidence level with a fire pixel area of 0.21 km². High spatial images validated these thermal anomaly types as false positives of biomass burning. A zone map of potential false-positive active fire for biomass burning was established in this study by referring to the allocation of thermal anomalies from permanent geographical features. Implementing the zone map removed approximately 13% of the VIIRS active fires as the false positive of biomass burning. Insights gleaned through this study will support efficient ground validation of actual forest/land fires. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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18 pages, 4101 KiB

Open AccessArticle

Spatiotemporal Changes and Driving Factors of Ecosystem Health in the Qinling-Daba Mountains

by Ting Xiang, Xiaoliang Meng, Xinshuang Wang, Jing Xiong and Zelin Xu

ISPRS Int. J. Geo-Inf. 2022, 11(12), 600; https://doi.org/10.3390/ijgi11120600 - 29 Nov 2022

Cited by 2 | Viewed by 1812

Abstract

Rapid industrialization and urbanization have accelerated land-use changes in mountainous areas, with dramatic impacts on ecosystem health. In particular, the Qinling-Daba Mountains, as China’s central water tower, ecological green lung, and biological gene bank, have rich resource endowments and extremely high ecological value [...] Read more.

Rapid industrialization and urbanization have accelerated land-use changes in mountainous areas, with dramatic impacts on ecosystem health. In particular, the Qinling-Daba Mountains, as China’s central water tower, ecological green lung, and biological gene bank, have rich resource endowments and extremely high ecological value and are an important protective wall to China’s ecological security. Therefore, understanding the level of ecosystem health and its drivers in the research area contributes to the conservation and restoration of the mountain ecosystem. Based on remote sensing image data and land-use data from 2000 to 2020, we explored the spatial characteristics of ecosystem health, and supplemented with socio-economic data to explore its driving factors. The results show that (1) the ecosystem health in the study area has been continuously improved during the study period, and the regional differences in ecological organization are the most prominent; (2) the level of ecosystem health in the Qinling-Daba Mountains has been spatially improved from the peripheral areas to the central area, showing significant spatial autocorrelation and local spatial aggregation; (3) the ecosystem health is influenced by a combination of natural and anthropogenic factors, among which the negative effect of GRDP is mainly concentrated in the eastern region, the negative effect of the proportion of built-up land gradually spreads to the western region, and the positive effect of the proportion of forest land has a large scale. This study contributes to a better understanding of ecosystem health in mountainous counties in China and provides useful information for policymakers to formulate ecological and environmental management policies. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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17 pages, 8625 KiB

Open AccessArticle

Effects of Atmospheric Correction and Image Enhancement on Effective Plastic Greenhouse Segments Based on a Semi-Automatic Extraction Method

by Yao Yao and Shixin Wang

ISPRS Int. J. Geo-Inf. 2022, 11(12), 585; https://doi.org/10.3390/ijgi11120585 - 23 Nov 2022

Cited by 2 | Viewed by 1575

Abstract

To improve the multi-resolution segmentation (MRS) quality of plastic greenhouses (PGs) in GaoFen-2 (GF-2) images, the effects of atmospheric correction and image enhancement on effective PG segments (EPGSs) were evaluated. A new semi-automatic method was also proposed to extract EPGSs in an accurate [...] Read more.

To improve the multi-resolution segmentation (MRS) quality of plastic greenhouses (PGs) in GaoFen-2 (GF-2) images, the effects of atmospheric correction and image enhancement on effective PG segments (EPGSs) were evaluated. A new semi-automatic method was also proposed to extract EPGSs in an accurate and efficient way. Firstly, GF-2 images were preprocessed via atmospheric correction, orthographical correction, registration, fusion, linear compression, or spatial filtering, and, then, boundary-removed point samples with adjustable density were made based on reference polygons by taking advantage of the characteristics of chessboard segmentation. Subsequently, the point samples were used to quickly and accurately extract segments containing 70% or greater of PG pixels in each MRS result. Finally, the extracted EPGSs were compared and analyzed via intersection over union (IoU), over-segmentation index (OSI), under-segmentation index (USI), error index of total area (ETA), and composite error index (CEI). The experimental results show that, along with the change in control variables, the optimal scale parameter, time of segmentation, IoU, OSI, USI, and CEI all showed strong changing trends, with the values of ETA all close to 0. Furthermore, compared with the control group, all the CEIs of the EPGSs extracted from those corrected and enhanced images resulted in lower values, and an optimal CEI involved linearly compressing the DN value of the atmospheric-corrected fusion image to 0–255, and then using Fast Fourier Transform and a circular low-pass filter with a radius of 800 pixels to filter from the spatial frequency domain; in this case, the CEI had a minimum value of 0.159. The results of this study indicate that the 70% design in the experiment is a reasonable pixel ratio to determine the EPGSs, and the OSI-USI-ETA-CEI pattern can be more effective than IoU when it is needed to evaluate the quality of EPGSs. Moreover, taking into consideration heterogeneity and target characteristics, atmospheric correction and image enhancement prior to MRS can improve the quality of EPGSs. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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19 pages, 5301 KiB

Open AccessArticle

Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey

by Nooshin Mashhadi and Ugur Alganci

ISPRS Int. J. Geo-Inf. 2022, 11(11), 573; https://doi.org/10.3390/ijgi11110573 - 16 Nov 2022

Cited by 5 | Viewed by 1917

Abstract

Time series analysis combined with remote sensing data allows for the study of abrupt changes in the environment due to significant and severe disturbances such as deforestation, agricultural activities, fires, and urban expansion, as well as gradual changes such as climate variability and [...] Read more.

Time series analysis combined with remote sensing data allows for the study of abrupt changes in the environment due to significant and severe disturbances such as deforestation, agricultural activities, fires, and urban expansion, as well as gradual changes such as climate variability and forest degradation in the ecosystem. The precision of any change detection analysis is highly dependent upon its ability to separate actual changes and fluctuations on a seasonal scale. One of the efficient methods in this context is using the Breaks for Additive Seasonal and Trend (BFAST) set of algorithms. This study aims to perform a comprehensive and comparative evaluation of different Vis’ performance in forest degradation with the Landsat 8 images and BFASTMonitor approach. Through evaluation, the study also considers the potential effects of different forest types and deforestation scales in the Marmara region of Turkey. For this purpose, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Normalized Burn Ratio (NBR) vegetation indices (VI) were selected for a comparative evaluation. The overall accuracy of VIs in deciduous forests was around 85% for NDVI, NDMI, and NBR, and 78.80% for EVI, while in coniferous forests, the overall accuracy demonstrated higher values of about 88% for NDVI, NDMI, and EVI, and 87.28% for NBR. Consequently, water-sensitive VIs that utilize shortwave infrared bands proved to be slightly more sensitive in detecting forest disturbances while chlorophyll-sensitive VIs represented lower accuracy for both forest types. Overall, all VIs faced an underestimation error in deforested area detection that was observable through negative BIAS. The results illuminate that BFASTMonitor can be considered as a tool in monitoring forest environments due to its acceptable deforestation determination capability in deciduous and coniferous forests, with slightly higher performance for small-scale deforestation patterned regions. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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19 pages, 4857 KiB

Open AccessArticle

Spatiotemporal Change in Livestock Population and Its Correlation with Meteorological Disasters during 2000–2020 across Inner Mongolia

by Hui Bai, Baizhu Wang, Yuanjun Zhu, Semyung Kwon, Xiaohui Yang and Kebin Zhang

ISPRS Int. J. Geo-Inf. 2022, 11(10), 520; https://doi.org/10.3390/ijgi11100520 - 16 Oct 2022

Cited by 3 | Viewed by 1882

Abstract

Inner Mongolia (IM) is one of the five major pastoral areas in China, and animal husbandry is its traditional industry. The population of livestock is an important factor affecting the sustainable development of livestock and grassland. Due to the special geographical location of [...] Read more.

Inner Mongolia (IM) is one of the five major pastoral areas in China, and animal husbandry is its traditional industry. The population of livestock is an important factor affecting the sustainable development of livestock and grassland. Due to the special geographical location of IM, various meteorological disasters occur frequently, which have a significant impact on the local livestock population. In this study, principal component analysis (PCA) and geographically weighted principal component analysis (GWPCA) were used to explore the spatial and temporal patterns of small livestock and large livestock populations in county-level administrative units from 2000 to 2020, and the effects of meteorological disasters on livestock populations were also considered. We found that the cumulative proportion of total variance (CPTV) of the first two principal components of global PCA for small livestock and the first principal component for large livestock reached 94.54% and 91.98%, respectively, while the CPTV of GWPCA was in the range of 93.23–96.45% and 88.47–92.49%, respectively, which showed stronger spatial explanation; the small livestock population was significantly correlated with spring drought, summer drought, spring–summer drought and snow disaster. However, the correlation between large livestock and summer drought and spring–summer drought is greater. We conclude that GWPCA can better explain the spatial change of livestock populations; meteorological disasters have both advantages and disadvantages on the livestock population, and the drought types that have a greater impact on livestock are summer drought and spring–summer drought. There are geographical differences in the impact of meteorological disasters, with drought affecting most of IM and snow disaster mainly affecting the eastern region; large livestock were mainly affected by drought, while small livestock were affected by both drought and snow disaster. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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14 pages, 5906 KiB

Open AccessArticle

Identification of Paddy Varieties from Landsat 8 Satellite Image Data Using Spectral Unmixing Method in Indramayu Regency, Indonesia

by Iqbal Maulana Cipta, Lalu Muhamad Jaelani and Hartanto Sanjaya

ISPRS Int. J. Geo-Inf. 2022, 11(10), 510; https://doi.org/10.3390/ijgi11100510 - 30 Sep 2022

Cited by 2 | Viewed by 2431

Abstract

Indramayu Regency is the highest rice producer in West Java province, Indonesia. According to the Central Statistics Agency (BPS), in 2021, rice production in 2020 reached 1,365,435.39 tons of GKG (milled dry grain). Technological developments in the food sector produce various kinds of [...] Read more.

Indramayu Regency is the highest rice producer in West Java province, Indonesia. According to the Central Statistics Agency (BPS), in 2021, rice production in 2020 reached 1,365,435.39 tons of GKG (milled dry grain). Technological developments in the food sector produce various kinds of premium quality rice and rice varieties resistant to climate change, such as Ciherang, Inpari 32 HDB and IR 64. The regular monitoring of specific rice varieties over large areas effectively maintains the quality and quantity of rice production. This study used remote sensing data to monitor rice conditions and distribution based on the spectral unmixing method. The spectral unmixing method was used to identify the percentage of the presence of a pure object in a pixel. The results obtained in this study were images of the endmember fractions of rice varieties and areas of dominant rice varieties used in the Indramayu district. The dominant variety detected with the processing results was the Inpari 32 HDB variety, with an area of 30,738.64 hectares. In comparison, varieties other than Inpari 32 HDB were also detected in several areas in the Indramayu district, with an area of 12,192.68 hectares. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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22 pages, 41372 KiB

Open AccessArticle

Object-Based Automatic Mapping of Winter Wheat Based on Temporal Phenology Patterns Derived from Multitemporal Sentinel-1 and Sentinel-2 Imagery

by Limei Wang, Guowang Jin, Xin Xiong, Hongmin Zhang and Ke Wu

ISPRS Int. J. Geo-Inf. 2022, 11(8), 424; https://doi.org/10.3390/ijgi11080424 - 26 Jul 2022

Cited by 5 | Viewed by 1974

Abstract

Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on [...] Read more.

Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on winter wheat identification remains unclear. To overcome these limitations, this study developed an object-based automatic approach to map winter wheat using multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. First, after S1 and S2 images were preprocessed, the Simple Non-Iterative Clustering (SNIC) algorithm was used to conduct image segmentation to obtain homogeneous spatial objects with a fusion of S1 and S2 bands. Second, the temporal phenology patterns (TPP) of winter wheat and other typical land covers were derived from object-level S1 and S2 imagery based on the collected ground truth samples, and two improved distance measures (i.e., a composite of Euclidean distance and Spectral Angle Distance, (ESD) and the difference–similarity factor distance (DSF)) were built to evaluate the similarity between two TPPs. Third, winter wheat objects were automatically identified from the segmented spatial objects by the maximum between-class variance method (OTSU) with distance measures based on the unique TPP of winter wheat. According to ground truth data, the DSF measure was superior to other distance measures in winter wheat mapping, since it achieved the best overall accuracy (OA), best kappa coefficient (Kappa) and more spatial details for each feasible band (i.e., NDVI, VV, and VH/VV), or it obtained results comparable to those for the best one (e.g., NDVI + VV). The resultant winter wheat maps derived from the NDVI band with the DSF measure achieved the best accuracy and more details, and had an average OA and Kappa of 92% and 84%, respectively. The VV polarization with the DSF measure produced the second best winter wheat maps with an average OA and Kappa of 91% and 80%, respectively. The results indicate the great potential of the proposed object-based approach for automatic winter wheat mapping for both optical and Synthetic Aperture Radar (SAR) imagery. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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15 pages, 1358 KiB

Open AccessArticle

Multipurpose GIS Portal for Forest Management, Research, and Education

by Martin Zápotocký and Milan Koreň

ISPRS Int. J. Geo-Inf. 2022, 11(7), 405; https://doi.org/10.3390/ijgi11070405 - 15 Jul 2022

Cited by 3 | Viewed by 2962

Abstract

The main objective of this research was to develop a web-based geographic information system (GIS) based on a detailed analysis of user preferences from the perspective of forest research, management and education. An anonymous questionnaire was used to elicit user preferences for a [...] Read more.

The main objective of this research was to develop a web-based geographic information system (GIS) based on a detailed analysis of user preferences from the perspective of forest research, management and education. An anonymous questionnaire was used to elicit user preferences for a hardware platform and evaluations of web-mapping applications, geographic data, and GIS tools. Mobile GIS was used slightly more often than desktop GIS. Web-mapping applications that provide information to the public and the present research results were rated higher than the forest management application. Orthophotos for general purposes and thematic layers such as forest stand maps, soils, protected areas, cadastre, and forest roads were preferred over highly specialized layers. Tools for data searching, map printing, measuring, and drawing on digital maps were rated higher than tools for online map editing and geographic analysis. The analysis of user preferences was used to design a new multipurpose GIS portal for the University Forest Enterprise. The GIS portal was designed with a three-tier architecture on top of the software library for managing user access, working interactively with digital maps, and managing web map applications. The web map applications focus on tools and geographic information not available elsewhere, specifically timber harvest and logistics, research plots, and hunting game management. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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19 pages, 9412 KiB

Open AccessArticle

Assessing the Spectral Information of Sentinel-1 and Sentinel-2 Satellites for Above-Ground Biomass Retrieval of a Tropical Forest

by Dimitris Stratoulias, Narissara Nuthammachot, Tanita Suepa and Khamphe Phoungthong

ISPRS Int. J. Geo-Inf. 2022, 11(3), 199; https://doi.org/10.3390/ijgi11030199 - 16 Mar 2022

Cited by 3 | Viewed by 2719

Abstract

Earth Observation (EO) spectral indices have been an important tool for quantifying and monitoring forest biomass. Nevertheless, the selection of the bands and their combination is often realized based on preceding studies or generic assumptions. The current study investigates the relationship between satellite [...] Read more.

Earth Observation (EO) spectral indices have been an important tool for quantifying and monitoring forest biomass. Nevertheless, the selection of the bands and their combination is often realized based on preceding studies or generic assumptions. The current study investigates the relationship between satellite spectral information and the Above Ground Biomass (AGB) of a major private forest on the island of Java, Indonesia. Biomass-related traits from a total of 1517 trees were sampled in situ and their AGB were estimated from species-specific allometric models. In parallel, the exhaustive band combinations of the Ratio Spectral Index (RSI) were derived from near-concurrently acquired Sentinel-1 and Sentinel-2 images. By applying scenarios based on the entire dataset, the prevalence and monodominance of acacia, mahogany, and teak tree species were investigated. The best-performing index for the entire dataset yielded R² = 0.70 (R² = 0.78 when considering only monodominant plots). An application of eight traditional vegetation indices provided, at best, R² = 0.65 for EVI, which is considerably lower compared to the RSI best combination. We suggest that an investigation of the complete band combinations as a proxy of retrieving biophysical parameters may provide more accurate results than the blind application of popular spectral indices and that this would take advantage of the amplified information obtained from modern satellite systems. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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20 pages, 6788 KiB

Open AccessArticle

Optimizing the Sampling Area across an Old-Growth Forest via UAV-Borne Laser Scanning, GNSS, and Radial Surveying

by Sebastiano Sferlazza, Antonino Maltese, Gino Dardanelli and Donato Salvatore La Mela Veca

ISPRS Int. J. Geo-Inf. 2022, 11(3), 168; https://doi.org/10.3390/ijgi11030168 - 04 Mar 2022

Cited by 6 | Viewed by 3144

Abstract

Aboveground biomass, volume, and basal area are among the most important structural attributes in forestry. Direct measurements are cost-intensive and time-consuming, especially for old-growth forests exhibiting a complex structure over a rugged topography. We defined a methodology to optimize the plot size and [...] Read more.

Aboveground biomass, volume, and basal area are among the most important structural attributes in forestry. Direct measurements are cost-intensive and time-consuming, especially for old-growth forests exhibiting a complex structure over a rugged topography. We defined a methodology to optimize the plot size and the (total) sampling area, allowing for structural attributes with a tolerable error to be estimated. The plot size was assessed by analyzing the semivariogram of a CHM model derived via UAV laser scanning, while the sampling area was based on the calculation of the absolute relative error as a function of allometric relationships. The allometric relationships allowed the structural attributes from trees’ height to be derived. The validation was based on the positioning of a number of trees via total station and GNSS surveys. Since high trees occlude the GNSS signal transmission, a strategy to facilitate the positioning was to fix the solution using the GLONASS constellation alone (showing the highest visibility during the survey), and then using the GPS constellation to increase the position accuracy (up to PDOP~5−10). The tree heights estimated via UAV laser scanning were strongly correlated (r² = 0.98, RMSE = 2.80 m) with those measured in situ. Assuming a maximum absolute relative error in the estimation of the structural attribute (20% within this work), the proposed methodology allowed the portion of the forest surface (≤60%) to be sampled to be quantified to obtain a low average error in the calculation of the above mentioned structural attributes (≤13%). Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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29 pages, 4740 KiB

Open AccessArticle

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

by Mirka Mobilia and Antonia Longobardi

ISPRS Int. J. Geo-Inf. 2021, 10(3), 192; https://doi.org/10.3390/ijgi10030192 - 23 Mar 2021

Cited by 17 | Viewed by 2987

Abstract

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses [...] Read more.

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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