Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan
Climate change and human activities have severely impacted Central Asia's mountainous grasslands' health status, making monitoring aboveground biomass (AGB) crucial for grassland protection. However, a high-resolution and low-cost solution for AGB monitoring is lacking for Central Asia...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Environmental and Sustainability Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972724000138 |
| _version_ | 1797338202904723456 |
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| author | Tianli Pan Huping Ye Xinyu Zhang Xiaohan Liao Dongliang Wang Dalai Bayin Mustafo Safarov Mekhrovar Okhonniyozov Gulayozov Majid |
| author_facet | Tianli Pan Huping Ye Xinyu Zhang Xiaohan Liao Dongliang Wang Dalai Bayin Mustafo Safarov Mekhrovar Okhonniyozov Gulayozov Majid |
| author_sort | Tianli Pan |
| collection | DOAJ |
| description | Climate change and human activities have severely impacted Central Asia's mountainous grasslands' health status, making monitoring aboveground biomass (AGB) crucial for grassland protection. However, a high-resolution and low-cost solution for AGB monitoring is lacking for Central Asia's grassland. This research proposes an unmanned aerial vehicle (UAV) based AGB monitoring framework using consumer-level cameras. Texture features from UAV RGB images and vegetation indices from UAV multispectral images are used to predict AGB. As one of the typical mountainous countries in Central Asia, Tajikistan is chosen as the study area. Four different grassland types were chosen to investigate the performance of applying UAV for AGB monitoring. Firstly, correlation analysis was performed to identify important features for AGB estimation. Subsequently, the ground-measured AGB and UAV image features were utilized in multiple linear regression (MLR) and generalized additive model (GAM) to develop an AGB prediction model. The results show that the angular second moment (ASM) of the green band and green normalized difference vegetation index (GNDVI) are the two most important features for AGB estimation in the study area. Furthermore, the GAM-based model demonstrated higher accuracy (R2 = 0.87, RMSE = 127.53 g/m2, rRMSE = 0.17) compared to MLR (R2 = 0.74, RMSE = 181.68 g/m2, rRMSE = 0.24), highlighting the nonlinear relationship between AGB and UAV image features. This research uses multispectral and RGB images to achieve a high accuracy and efficient AGB monitoring framework for Central Asia grasslands and provides a reference to validate satellite images for providing long-term and large-scale monitoring of grassland ecosystems and reasonable utilization of grassland resources. |
| first_indexed | 2024-03-08T09:27:35Z |
| format | Article |
| id | doaj.art-2286e5861cd24dcaa130508157526ab6 |
| institution | Directory Open Access Journal |
| issn | 2665-9727 |
| language | English |
| last_indexed | 2024-03-08T09:27:35Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj.art-2286e5861cd24dcaa130508157526ab62024-01-31T05:45:35ZengElsevierEnvironmental and Sustainability Indicators2665-97272024-06-0122100345Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in TajikistanTianli Pan0Huping Ye1Xinyu Zhang2Xiaohan Liao3Dongliang Wang4Dalai Bayin5Mustafo Safarov6Mekhrovar Okhonniyozov7Gulayozov Majid8Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing, 100101, China; Corresponding author. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing, 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, ChinaXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, 830011, ChinaResearch Center for Ecology and Environment of Central Asia, Dushanbe, 734024, Tajikistan; Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, ChinaResearch Center for Ecology and Environment of Central Asia, Dushanbe, 734024, TajikistanResearch Center for Ecology and Environment of Central Asia, Dushanbe, 734024, Tajikistan; Institute of Water Problems, Hydropower and Ecology of the National Academy of Sciences of Tajikistan, Dushanbe, 734024, TajikistanClimate change and human activities have severely impacted Central Asia's mountainous grasslands' health status, making monitoring aboveground biomass (AGB) crucial for grassland protection. However, a high-resolution and low-cost solution for AGB monitoring is lacking for Central Asia's grassland. This research proposes an unmanned aerial vehicle (UAV) based AGB monitoring framework using consumer-level cameras. Texture features from UAV RGB images and vegetation indices from UAV multispectral images are used to predict AGB. As one of the typical mountainous countries in Central Asia, Tajikistan is chosen as the study area. Four different grassland types were chosen to investigate the performance of applying UAV for AGB monitoring. Firstly, correlation analysis was performed to identify important features for AGB estimation. Subsequently, the ground-measured AGB and UAV image features were utilized in multiple linear regression (MLR) and generalized additive model (GAM) to develop an AGB prediction model. The results show that the angular second moment (ASM) of the green band and green normalized difference vegetation index (GNDVI) are the two most important features for AGB estimation in the study area. Furthermore, the GAM-based model demonstrated higher accuracy (R2 = 0.87, RMSE = 127.53 g/m2, rRMSE = 0.17) compared to MLR (R2 = 0.74, RMSE = 181.68 g/m2, rRMSE = 0.24), highlighting the nonlinear relationship between AGB and UAV image features. This research uses multispectral and RGB images to achieve a high accuracy and efficient AGB monitoring framework for Central Asia grasslands and provides a reference to validate satellite images for providing long-term and large-scale monitoring of grassland ecosystems and reasonable utilization of grassland resources.http://www.sciencedirect.com/science/article/pii/S2665972724000138Unmanned aerial vehicle (UAV)Central AsiaGrassland aboveground biomass (AGB)MultispectralRGB |
| spellingShingle | Tianli Pan Huping Ye Xinyu Zhang Xiaohan Liao Dongliang Wang Dalai Bayin Mustafo Safarov Mekhrovar Okhonniyozov Gulayozov Majid Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan Environmental and Sustainability Indicators Unmanned aerial vehicle (UAV) Central Asia Grassland aboveground biomass (AGB) Multispectral RGB |
| title | Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan |
| title_full | Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan |
| title_fullStr | Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan |
| title_full_unstemmed | Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan |
| title_short | Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan |
| title_sort | estimating aboveground biomass of grassland in central asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures a case study of typical grassland in tajikistan |
| topic | Unmanned aerial vehicle (UAV) Central Asia Grassland aboveground biomass (AGB) Multispectral RGB |
| url | http://www.sciencedirect.com/science/article/pii/S2665972724000138 |
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