A New Remote Sensing Desert Vegetation Detection Index
Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/24/5742 |
| _version_ | 1797379511060267008 |
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| author | Zhenqi Song Yuefeng Lu Ziqi Ding Dengkuo Sun Yuanxin Jia Weiwei Sun |
| author_facet | Zhenqi Song Yuefeng Lu Ziqi Ding Dengkuo Sun Yuanxin Jia Weiwei Sun |
| author_sort | Zhenqi Song |
| collection | DOAJ |
| description | Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification. |
| first_indexed | 2024-03-08T20:24:20Z |
| format | Article |
| id | doaj.art-a2f8184569454e7bbd7eb0991b0e200d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T20:24:20Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a2f8184569454e7bbd7eb0991b0e200d2023-12-22T14:39:12ZengMDPI AGRemote Sensing2072-42922023-12-011524574210.3390/rs15245742A New Remote Sensing Desert Vegetation Detection IndexZhenqi Song0Yuefeng Lu1Ziqi Ding2Dengkuo Sun3Yuanxin Jia4Weiwei Sun5School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaAcademy of Forestry Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, ChinaShandong Zhengyuan Digital City Construction Co., Ltd., Yantai 264670, ChinaLand desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.https://www.mdpi.com/2072-4292/15/24/5742HSV color spacechannel enhancementUAV visible imagerydesert vegetation extractionland desertification monitoringHSVGVI |
| spellingShingle | Zhenqi Song Yuefeng Lu Ziqi Ding Dengkuo Sun Yuanxin Jia Weiwei Sun A New Remote Sensing Desert Vegetation Detection Index Remote Sensing HSV color space channel enhancement UAV visible imagery desert vegetation extraction land desertification monitoring HSVGVI |
| title | A New Remote Sensing Desert Vegetation Detection Index |
| title_full | A New Remote Sensing Desert Vegetation Detection Index |
| title_fullStr | A New Remote Sensing Desert Vegetation Detection Index |
| title_full_unstemmed | A New Remote Sensing Desert Vegetation Detection Index |
| title_short | A New Remote Sensing Desert Vegetation Detection Index |
| title_sort | new remote sensing desert vegetation detection index |
| topic | HSV color space channel enhancement UAV visible imagery desert vegetation extraction land desertification monitoring HSVGVI |
| url | https://www.mdpi.com/2072-4292/15/24/5742 |
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