Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland
Eurasian Steppes, the world’s largest grassland ecosystem, significantly contribute to our ecological environment. Grassland height is a crucial biophysical factor, offering insights into vertical vegetation structure and regional ecosystem dynamics. Therefore, investigating the trends in changes of...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014705 |
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| author | Wuhua Wang Jiakui Tang Na Zhang Xuefeng Xu Anan Zhang Yanjiao Wang Kaihui Li Yidan Wang |
| author_facet | Wuhua Wang Jiakui Tang Na Zhang Xuefeng Xu Anan Zhang Yanjiao Wang Kaihui Li Yidan Wang |
| author_sort | Wuhua Wang |
| collection | DOAJ |
| description | Eurasian Steppes, the world’s largest grassland ecosystem, significantly contribute to our ecological environment. Grassland height is a crucial biophysical factor, offering insights into vertical vegetation structure and regional ecosystem dynamics. Therefore, investigating the trends in changes of grassland height in Steppes is vital for sustainable land use and ecological balance. This study utilized machine learning models such as Random Forest (RF), AdaBoost, BP-Neural Network (BPNN), and Stacking Ensemble, combining them with topographic and meteorological data, MODIS reflectance data, and grassland height measurement data. The first Eurasian temperate grassland vegetation height dataset was finally generated. The results highlighted the superior performance of the Random Forest with an R2 of 0.54, RMSE of 7.39 cm, and MPAE of 49.25 %. Spatial and temporal analyses revealed a discernible spatial distribution in grassland heights across Eurasia, characterized by a consistent decrease from west to east and north to south. Throughout the year of 2001–2021, there were minor fluctuations in changes of grassland heights, ranging from 15.93 to 17.99 cm, with an overall incremental trend of 0.027 cm annually. Significance tests indicated that approximately 4.40 % of the surveyed regions, primarily concentrated in northern Central Asia, experiencing a noteworthy increase in grassland height dynamics. Conversely, about 2.10 % of the areas, predominantly located in Inner Mongolia, China, and southern Central Asia, have witnessed a reduction in grassland height. This study reveals both temporal and spatial dynamics by offering a comprehensive dataset on vegetation height in Eurasian temperate grassland. These discoveries bear significant ramifications for land management strategies and the advancement of ecological sustainability objectives. Additionally, they contribute significantly to improve the accuracy of estimates of aboveground biomass and carbon stocks in grassland ecosystems. |
| format | Article |
| id | doaj-art-8fe3eeca32af45aeac4d5e0150dfc56f |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-8fe3eeca32af45aeac4d5e0150dfc56f2025-01-31T05:10:29ZengElsevierEcological Indicators1470-160X2025-01-01170113013Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grasslandWuhua Wang0Jiakui Tang1Na Zhang2Xuefeng Xu3Anan Zhang4Yanjiao Wang5Kaihui Li6Yidan Wang7College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; Yanshan Earth Key Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, PR China; Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, 85354 Freising, GermanyCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; Yanshan Earth Key Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, PR China; Corresponding authors at: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China (J. Tang).College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; Yanshan Earth Key Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, PR China; Corresponding authors at: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China (J. Tang).College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; Yanshan Earth Key Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, PR China; Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, 85354 Freising, GermanyCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; Yanshan Earth Key Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, PR ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; Yanshan Earth Key Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, PR ChinaXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, PR ChinaPrecision Agriculture Lab, School of Life Sciences, Technical University of Munich, 85354 Freising, GermanyEurasian Steppes, the world’s largest grassland ecosystem, significantly contribute to our ecological environment. Grassland height is a crucial biophysical factor, offering insights into vertical vegetation structure and regional ecosystem dynamics. Therefore, investigating the trends in changes of grassland height in Steppes is vital for sustainable land use and ecological balance. This study utilized machine learning models such as Random Forest (RF), AdaBoost, BP-Neural Network (BPNN), and Stacking Ensemble, combining them with topographic and meteorological data, MODIS reflectance data, and grassland height measurement data. The first Eurasian temperate grassland vegetation height dataset was finally generated. The results highlighted the superior performance of the Random Forest with an R2 of 0.54, RMSE of 7.39 cm, and MPAE of 49.25 %. Spatial and temporal analyses revealed a discernible spatial distribution in grassland heights across Eurasia, characterized by a consistent decrease from west to east and north to south. Throughout the year of 2001–2021, there were minor fluctuations in changes of grassland heights, ranging from 15.93 to 17.99 cm, with an overall incremental trend of 0.027 cm annually. Significance tests indicated that approximately 4.40 % of the surveyed regions, primarily concentrated in northern Central Asia, experiencing a noteworthy increase in grassland height dynamics. Conversely, about 2.10 % of the areas, predominantly located in Inner Mongolia, China, and southern Central Asia, have witnessed a reduction in grassland height. This study reveals both temporal and spatial dynamics by offering a comprehensive dataset on vegetation height in Eurasian temperate grassland. These discoveries bear significant ramifications for land management strategies and the advancement of ecological sustainability objectives. Additionally, they contribute significantly to improve the accuracy of estimates of aboveground biomass and carbon stocks in grassland ecosystems.http://www.sciencedirect.com/science/article/pii/S1470160X24014705Grassland heightMulti-source dataMachine LearningEurasian steppe |
| spellingShingle | Wuhua Wang Jiakui Tang Na Zhang Xuefeng Xu Anan Zhang Yanjiao Wang Kaihui Li Yidan Wang Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland Ecological Indicators Grassland height Multi-source data Machine Learning Eurasian steppe |
| title | Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland |
| title_full | Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland |
| title_fullStr | Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland |
| title_full_unstemmed | Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland |
| title_short | Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland |
| title_sort | vegetation height estimation based on machine learning model driven by multi source data in eurasian temperate grassland |
| topic | Grassland height Multi-source data Machine Learning Eurasian steppe |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014705 |
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