Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) optimization for multi-species coverage extraction. This study developed a universal method integrating four VIs—Excess Green Index (EXG), Visible Band Difference Vegetation Index (VDVI), Red-Green Ratio Index (RGRI), and Red-Green-Blue Vegetation Index (RGBVI)—to bridge UAV imagery with plant communities. By combining spectral separability analysis with machine learning (SVM), we established dynamic thresholds applicable to crops, trees, and shrubs, achieving cross-species compatibility without multispectral data. The results showed that all VIs achieved robust vegetation/non-vegetation discrimination (Kappa > 0.84), with VDVI being more suitable for distinguishing vegetation from non-vegetation. The overall classification accuracy for different vegetation types exceeded 92.68%, indicating that the accuracy is considerable. Crop coverage extraction showed a minimum segmentation error of 0.63, significantly lower than that of other vegetation types. These advances enable high-resolution vegetation monitoring, supporting biodiversity assessment and ecosystem service quantification. Our research findings track the impact of plant communities on the ecological environment and promote the application of UAVs in ecological restoration and precision agriculture.

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions.

Wheat stripe rust is an important fungal disease that threatens global wheat production, and precise monitoring in field environments is crucial for disease prevention and control. This study proposes a cross-scale monitoring method based on optimized hyperspectral vegetation index to address the issues of low efficiency of traditional monitoring methods and susceptibility of spectral signals to interference in field environments. Through comparative studies between experimental fields (n = 68) and large fields (n = 155), the performance of six vegetation indices was systematically evaluated, and optimized versions were designed. The study mainly found that the Yellow Rust Severity Index optimized (YRSI_O) index exhibited the best monitoring performance, with a field determination coefficient R² of 0.5713 (experimental field R² = 0.6118). The unmanned aerial vehicle (UAV) hyperspectral system combined with optimized vegetation index can effectively control spectral reflectance fluctuations, with a recognition accuracy of up to 85.2% in severely infected areas. This study also elucidated the three-stage physiological response mechanism of optimizing indicators on disease progression. This study provides key technical support for the practical application of hyperspectral technology in field monitoring of wheat stripe rust, and the proposed research method can be extended to other fields of crop disease monitoring.