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

机构：[1] Qinghai Provincial Key Laboratory of Physical Geography and Environmental Process, College of Geographical Science, Qinghai Normal University, Xining, 810008, China; [2] Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation [Ministry of Education], Qinghai Normal University, Xining, 810008, China; [3] Southern Qilian Mountain Forest Ecosystem Observation and Research Station of Qinghai Province, Huzhu, 810500, China; [4] Qinghai Forestry Engineering Supervision Center Co., Ltd., Xining, 810008, China; [5] Service and Support Center of Qilian Mountain National Park in Qinghai, Xining, 810008, China; [6] Yeniugou Forest Farm, Qilian, 810499, China; [7] The College of Forestry, Beijing Forestry University, Beijing, 100083, China; [8] Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing, 100091, 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)

语种：英文

外文关键词：Adaptive boosting - Antennas - Deep neural networks - Ecosystems - Mean square error - Parameter estimation - Pixels - Unmanned aerial vehicles (UAV) - Vegetation

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 ? 2025 by the authors.