The Grassland Ecology Remote Sensing Team at the Institute of Agricultural Resources and Regional Planning of the Chinese Academy of Agricultural Sciences has made progress in the estimation of aboveground biomass (AGB) on natural grasslands using unmanned aerial vehicles (UAVs). The research findings, titled "The performance of a canopy relative height model (CRHM) in natural grassland aboveground biomass estimation using unmanned aerial vehicle data", have been published in the journal Computers and Electronics in Agriculture.

AGB on natural grasslands is a crucial indicator reflecting the health status of grassland ecosystems. Accurate monitoring is essential for the rational utilization and management of grassland resources, as well as for optimizing livestock production. In recent years, the rapid development of UAV remote sensing technology has provided new tools and methods and methods for estimating vegetation AGB. Natural grassland vegetation exhibits complex vertical structural information, and the introduction of spatial structural information plays a significant role in accurately estimating AGB. However, existing methods still face challenges in obtaining vegetation height information and addressing vegetation index saturation issues, which limit the improvement of AGB estimation accuracy.

To address this scientific challenge, the research team proposed a Canopy Relative Height Model (CRHM) based on UAV LiDAR data. By combining UAV multispectral data, they developed an AGB estimation model based on relative vegetation volume and the Reconstructed Vegetation Index (ReVI). The research results demonstrate that CRHM performs well on harvested grasslands, effectively capturing relative changes in vegetation height. Compared to traditional methods, the AGB estimation model based on ReVI exhibits superior predictive capability. Additionally, the research team found that ReVI can effectively mitigate vegetation index saturation issues in high AGB areas of harvested grasslands. The research outcomes not only provide technical support for obtaining vegetation height information on natural grasslands but also offer new methods to enhance AGB estimation accuracy.

Flowchart of Natural Grassland Aboveground Biomass (AGB) Monitoring Technology based on UAV LiDAR and Multispectral Data[Photo/IARRP]

Yang Yifeng, a research assistant at the Institute of Agricultural Resources and Regional Planning of the Chinese Academy of Agricultural Sciences, is the first author of the paper, with associate researcher Xu Dawei as the corresponding author. This research received support and funding from the State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the National Key Research and Development Program of China, the National Natural Science Foundation of China, and the Agricultural Science and Technology Innovation Program (ASTIP).

Original Link: https://doi.org/10.1016/j.compag.2025.110137