文献类型：期刊文献

英文题名：Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery

作者：Du, Kai[1,2,3] Shao, Yi[1] Yao, Naixin[4] Yu, Hongyan[5] Ma, Shaozhong[6] Mao, Xufeng[1,2] Wang, Litao[7] Wang, Jianjun[8]

第一作者：Du, Kai

通信作者：Wang, JJ[1]

机构：[1]Qinghai Normal Univ, Coll Geog Sci, Qinghai Prov Key Lab Phys Geog & Environm Proc, Xining 810008, Peoples R China;[2]Qinghai Normal Univ, Key Lab Tibetan Plateau Land Surface Proc & Ecol, Minist Educ, Xining 810008, Peoples R China;[3]Southern Qilian Mt Forest Ecosyst Observat & Res S, Huzhu 810500, Peoples R China;[4]Qinghai Forestry Engn Supervis Ctr Co Ltd, Xining 810003, Peoples R China;[5]Serv & Support Ctr Qilian Mt Natl Pk Qinghai, Xining 810008, Peoples R China;[6]Yeniugou Forest Farm, Qilian 810499, Peoples R China;[7]Beijing Forestry Univ, Coll Forestry, Beijing 100083, Peoples R China;[8]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China

年份：2025

卷号：25

期号：14

外文期刊名：SENSORS

收录：;EI(收录号：20253018862957);Scopus(收录号：2-s2.0-105011644402);WOS:【SCI-EXPANDED(收录号:WOS:001554253500001)】；

基金：This research was funded by the National Key R&D Program of the Science and Technology of China (2024YFD2201105), the Natural Science Foundation of Qinghai Province, China (2024-ZJ-960), and the Qinghai Normal University Young and Middle-aged Researchers Fund (KJQN2022002).

语种：英文

外文关键词：Qinghai-Tibet Plateau; fractional vegetation cover; machine learning; pixel dichotomy model; shapley additive explanations

摘要：Fractional Vegetation Cover (FVC) is a crucial indicator describing vegetation conditions and provides essential data for ecosystem health assessments. However, due to the low and sparse vegetation in alpine meadows, it is challenging to obtain pure vegetation pixels from Sentinel-2 imagery, resulting in errors in the FVC estimation using traditional pixel dichotomy models. This study integrated Sentinel-2 imagery with unmanned aerial vehicle (UAV) data and utilized the pixel dichotomy model together with four machine learning algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Deep Neural Network (DNN), to estimate FVC in an alpine meadow region. First, FVC was preliminarily estimated using the pixel dichotomy model combined with nine vegetation indices applied to Sentinel-2 imagery. The performance of these estimates was evaluated against reference FVC values derived from centimeter-level UAV data. Subsequently, four machine learning models were employed for an accurate FVC inversion, using the estimated FVC values and UAV-derived reference FVC as inputs, following feature importance ranking and model parameter optimization. The results showed that: (1) Machine learning algorithms based on Sentinel-2 and UAV imagery effectively improved the accuracy of FVC estimation in alpine meadows. The DNN-based FVC estimation performed best, with a coefficient of determination of 0.82 and a root mean square error (RMSE) of 0.09. (2) In vegetation coverage estimation based on the pixel dichotomy model, different vegetation indices demonstrated varying performances across areas with different FVC levels. The GNDVI-based FVC achieved a higher accuracy (RMSE = 0.08) in high-vegetation coverage areas (FVC > 0.7), while the NIRv-based FVC and the SR-based FVC performed better (RMSE = 0.10) in low-vegetation coverage areas (FVC < 0.4). The method provided in this study can significantly enhance FVC estimation accuracy with limited fieldwork, contributing to alpine meadow monitoring on the Qinghai-Tibet Plateau.