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Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV
Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779174/ https://www.ncbi.nlm.nih.gov/pubmed/36554681 http://dx.doi.org/10.3390/ijerph192416793 |
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author | Xu, Xue Liu, Luyao Han, Peng Gong, Xiaoqian Zhang, Qing |
author_facet | Xu, Xue Liu, Luyao Han, Peng Gong, Xiaoqian Zhang, Qing |
author_sort | Xu, Xue |
collection | PubMed |
description | Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5–20%), moderate (FVC: 21–50%), slight (FVC: 51–70%), and non-desertification (FVC: 71–100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient (k), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM(2)) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment. |
format | Online Article Text |
id | pubmed-9779174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97791742022-12-23 Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV Xu, Xue Liu, Luyao Han, Peng Gong, Xiaoqian Zhang, Qing Int J Environ Res Public Health Article Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5–20%), moderate (FVC: 21–50%), slight (FVC: 51–70%), and non-desertification (FVC: 71–100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient (k), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM(2)) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment. MDPI 2022-12-14 /pmc/articles/PMC9779174/ /pubmed/36554681 http://dx.doi.org/10.3390/ijerph192416793 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Xue Liu, Luyao Han, Peng Gong, Xiaoqian Zhang, Qing Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV |
title | Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV |
title_full | Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV |
title_fullStr | Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV |
title_full_unstemmed | Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV |
title_short | Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV |
title_sort | accuracy of vegetation indices in assessing different grades of grassland desertification from uav |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779174/ https://www.ncbi.nlm.nih.gov/pubmed/36554681 http://dx.doi.org/10.3390/ijerph192416793 |
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