Machine Learning Models Based on UAV Oblique Images Improved Above‐Ground Biomass Estimation Accuracy Across Diverse Grasslands on the Qinghai–Tibetan Plateau

作者：Feida Sun, Dewei Chen, Linhao Li, Qiaoqiao Zhang, Xin Yuan, Zihong Liao, Chunlian Xiang, Lin Liu, Jiqiong Zhou, Mani Shrestha, Dong Xu, Yanfu Bai, A. Allan Degen · 发表于：Land Degradation and Development · 年份：2024 · DOI：10.1002/ldr.5381 · 被引用次数：7 · 研究领域：Remote Sensing in Agriculture、Land Use and Ecosystem Services、Remote Sensing and LiDAR Applications

ABSTRACT Unmanned aerial vehicles (UAVs) are becoming important tools for modern management and scientific research of grassland resources, especially in the dynamic monitoring of above‐ground biomass (AGB). However, current studies rely mostly on vertical images to construct models, with little consideration given to oblique images. Determination of image acquisition height often relies on experience and intuition, but there is limited comparison of models in estimating across different grassland types. To address this gap, this study selected 56 plots on the northern Qinghai–Tibetan Plateau (QTP), comprising 16 alpine meadows (AM), 14 alpine steppes (AS), 13 alpine meadow steppes (AMS), and 13 alpine desert steppes (ADS). We used the DJI Mavic 2 Pro to capture a total of 5040 images at six heights (5, 10, 20, 30, 40, and 50 m) and five angles (30°, 45°, 60°, 90°, and 180° panoramic shots). Based on RGB (red‐green‐blue) images, seven vegetation indices (normalized difference index (NDI), excess red vegetation index (EXR), modified green red vegetation index (MGRVI), visible atmospherically resistant index (VARI), excess green minus excess (EXG), green leaf index (GLI), and red–green–blue vegetation index (RGBVI)) were employed, displaying a trend in vegetation and biomass changes across different heights and angles, peaking at 20 m and 45°. Linear regression models and machine learning models (random forest, extreme gradient boosting, multilayer perceptron neural network, an...