Classification of Grassland Desertification in China Based on vis-NIR UAV Hyperspectral Remote Sensing

High-precision statistics on desertification of grassland features are an important part of grassland ecosystem research. The traditional manual survey is inefficient, and satellite remote sensing has very limited statistical precision, so high-spectral remote sensing by low-altitude drones is preferable. Here, we report a hyperspectral remote-sensing system for unmanned aerial vehicles (UAVs). We used the vegetation and soil of typical desertified grassland in Inner Mongolia as research objects to collect vis-NIR hyperspectral data on desertification using a deep belief network (DBN), 2D convolutional neural network (2D-CNN), and 3D convolutional neural network (3D-CNN). The results show that these typical deep learning models can effectively classify hyperspectral data on desertified grassland features. The highest classification accuracy was achieved by 3D-CNN, with an overall accuracy of 86.36%. This study enriches the spatial scale of remote sensing research on grassland desertification, and provides a basis for further high-precision statistics and inversion of remote sensing of grassland desertification.