In managing ecosystem services (ESs), it is vital to understand and effectively regulate the trade-offs and synergies (ToSs) involved. This study investigates the Tianshan Mountains (TSMs), utilizing the InVEST (Integrated Valuation of ESs and Tradeoffs) model to evaluate ecosystem service changes from 2000 to 2020, while employing univariate linear regression to examine their spatiotemporal dynamics. Pearson correlation analysis was also conducted to assess how climatic variables (temperature and precipitation) and vegetation indicators (NDVI, normalized difference vegetation index) influence the overall ecosystem service benefits. The findings reveal notable spatial heterogeneity and dynamic shifts in ESs across the TSMs, with strong synergies observed between carbon storage (CS) and other services (such as habitat quality, HQ; soil conservation, SC; and water yield, WY), especially in areas experiencing increased vegetation. However, the connection between HQ and WY was comparatively weaker and occasionally exhibited negative correlations during specific periods, highlighting the intricate trade-offs among various services. The correlation analysis further showed that climate and vegetation changes significantly impact ecosystem service benefits, with declining precipitation and rising temperatures reducing these benefits, whereas higher NDVI was associated with improved service functions. Ultimately, this study emphasizes the necessity of recognizing and managing ToSs in ESs to promote sustainable regional ecosystem development.

Quickly determining the metal content in plants and subsequently identifying geochemical anomalies can provide clues and guidance for predicting the location and scale of concealed ore bodies in vegetation-covered areas. Although visible, near-infrared and shortwave infrared (VNIR–SWIR) reflectance spectroscopy at wavelengths ranging from 400 to 2500 nm has been proven by many researchers to be a fast, accurate and nondestructive approach for estimating the contents of copper (Cu), lead (Pb), zinc (Zn) and other metal elements in plants, relatively few studies have been conducted on the estimation of lithium (Li) in plants. Therefore, the potential of applying VNIR–SWIR spectroscopy techniques for estimating the Li content in plants was explored in this study. The Jingerquan Li mining area in Hami, Xinjiang, China, was chosen. Three sampling lines were established near a pegmatite deposit and in a background region, canopy reflectance spectra were obtained for desert plants and Li contents were determined in the laboratory; then, quantitative relationships were established between nine different transformed spectra (including both integer and fractional orders) and the Li content was estimated using partial least squares regression (PLSR). The results showed that models constructed using high-order derivative spectra (with an order greater than or equal to 1) significantly outperformed those based on original and low-order derivative spectra (with an order less than 1). Notably, the model based on a 1.1-order derivative spectrum displayed the best performance. Furthermore, the performance of the model based on the two-layer wavelet coefficients of the 1.1-order derivative spectrum was further improved compared with that of the model based on only the 1.1-order derivative spectrum. The coefficient of determination ($R_{pre}^2$) and the ratio of performance to deviation (RPD) for the validation set increased from 0.6977 and 1.7656 to 0.7044 and 1.8446, respectively, and the root mean square error (RMSEpre) decreased from 2.5735 to 2.4633 mg/kg. These results indicate that quickly and accurately estimating the Li content in plants via the proposed spectroscopic analysis technique is feasible and effective; however, appropriate spectral preprocessing methods should be selected before hyperspectral estimation models are constructed. Overall, the developed hybrid spectral transformation approach, which combines wavelet coefficients and derivative spectra, displayed excellent application potential for estimating the Li content in plants.

Rapid and large-scale monitoring of soil copper levels enables the quick identification of areas where copper concentrations significantly exceed safe thresholds. It allows for selecting regions that require treatment and protection and is essential for safeguarding environmental and human health. Widely adopted monitoring models that utilize ground- and uncrewed-aerial-vehicle-based spectral data are superior to labor-intensive and time-consuming traditional methods that rely on point sampling, chemical analysis, and spatial interpolation. However, these methods are unsuitable for large-scale observations. Therefore, this study investigates the potential of utilizing spaceborne GF-5 hyperspectral data for monitoring soil copper content. Ninety-five soil samples were collected from the Kalatage mining area in Xinjiang, China. Three-band indices were constructed using fractional derivative spectra, and estimation models were developed using spectral indices highly correlated with the copper content. The results show that the proposed three-band spectral index accurately identifies subtle spectral characteristics associated with the copper content. Although the model is relatively simple, selecting the correct fractional order is critical in constructing spectral indices. The three-band spectral index based on fractional derivatives with orders of less than 0.6 provides higher accuracy than higher-order fractional derivatives. The index with spectral wavelengths of 426.796 nm, 512.275 nm, and 974.245 nm with 0.35-order derivatives exhibits the optimal performance (R² = 0.51, RPD = 1.46). Additionally, we proposed a novel approach that identifies the three-band indices exhibiting a strong correlation with the copper content. Subsequently, the selected indices were used as independent variables to develop new spectral indices for model development. This approach provides higher performance than models that use spectral indices derived from individual band values. The model utilizing the proposed spectral index achieved the best performance (R² = 0.56, RPD = 1.52). These results indicate that utilizing GF-5 hyperspectral data for large-scale monitoring of soil copper content is feasible and practical.

Research has widely validated three-band spectral index as a simple, valid, and highly accurate method of estimating the copper content of soil. However, selecting the best band combination from hundreds of thousands, even millions of candidate combinations in hyperspectral data, is a very complicated problem. To address this issue, this study collected a total of 170 soil samples from the Aktas copper-gold mining area in Fuyun County, Xinjiang, China. Then, two algorithms including Competitive Weighted Resampling (CARS) and Stepwise Regression Analysis (STE) were applied to pick the bands from the original and first-order derivative spectra, respectively. A three-band index model was developed using the selected feature bands to estimate soil copper content. Results showed the first-order derivative spectrum transforms the spectral curve into a sharper one, with more peaks and valleys, which is beneficial for increasing the correlation between bands and copper content compared with the original spectrum. Moreover, integrating first-order derivative spectroscopy with CARS makes it possible to precisely identify key spectral bands and outperforms the dimensionality-reduction capabilities compared with the integration of STE. This strategy drastically reduces the time spent screening and is proven to have similar model accuracy, as compared to the individual group lifting method. Specifically, it reduces the duration of an 8 h task down to a mere 2 s. An intelligent screening of three-band indices is proposed in this study as a method of rapidly estimating copper content in soil.