AUTHOR=Guan Qiang , Jiang Mingyang , Du Wen , Chen Xueyan , Yan Baolong 

TITLE=Integrating UAV visible and multispectral imagery to assess grazing-induced vegetation responses in sandy grasslands

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1730583

DOI=10.3389/fpls.2025.1730583

ISSN=1664-462X

ABSTRACT=IntroductionMonitoring grazing intensity is crucial for maintaining ecological balance and promoting the sustainable management of sandy grasslands. Traditional ground surveys and single-source remote sensing often lack the spatial resolution, spectral richness, and robustness required to accurately characterize heterogeneous grazing impacts. Unmanned aerial vehicle (UAV)-based multi-source remote sensing provides fine-scale, repeatable observations that can overcome the limitations of traditional field surveys.MethodsGrazing experiments were conducted in the sandy grasslands of Inner Mongolia, China, using UAVs to capture visible and multispectral imagery across plots subjected to different grazing intensities. Spectral responses were analyzed using mean–variance statistics and Tukey’s multiple comparison tests. A series of novel spectral indices were constructed based on separability analysis and integrated with traditional vegetation indices to address the limited sensitivity of conventional indices and multi-index feature redundancy. An automatic incremental feature selection (AIFS) algorithm was developed to adaptively optimize the feature subset and enhance model robustness, with a support vector machine classifier, k-nearest neighbor, and random forest used for grazing intensity recognition.ResultsDistinct spectral responses to grazing disturbance were observed: visible bands increased with grazing intensity due to enhanced soil background effects, while red-edge and near-infrared bands effectively captured reductions in chlorophyll content and canopy structure under moderate to severe grazing. Traditional vegetation indices were sensitive to extreme grazing, whereas the proposed indices showed superior performance in distinguishing moderate grazing levels. The AIFS-optimized feature subset reduced redundancy and improved model accuracy, achieving the highest recognition performance (OA=92.13%, Kappa=88.99%)—outperforming models using all features or single-source data.DiscussionIntegrating UAV visible and multispectral imagery with intelligent feature selection enhances the detection of grazing-induced vegetation responses. This approach provides a robust framework for high-precision grassland monitoring and sustainable ecological management in arid and semi-arid regions.