Yuzhen Shen

Context: Under global climate change and intensified human activities, the Yangtze River Basin (YZRB) and Yellow River Basin (YRB) face escalating challenges to ecosystem services (ESs), including water scarcity and biodiversity loss. Understanding ES dynamics is essential for sustainable landscape management. Objectives: This study aimed to (1) predict spatiotemporal changes in land cover and ESs from 2030 to 2100 under SSP1–2.6, SSP3–7.0, and SSP5–8.5 scenarios, and (2) quantify pixel-scale ES trade-offs/synergies and their driving factors. Methods: An integrated SSP–LUH2–PLUS–InVEST/CS framework was developed, combining climate scenarios, land use simulations, and ES assessments. The framework was applied to both basins to project future land cover changes and quantify ES responses under varying radiative forcing scenarios. Results: Forests expanded notably in the YZRB under SSP1–2.6 and SSP3–7.0, largely replacing grasslands and croplands, whereas grasslands remained in the YRB. Water yield (WY), soil conservation (SC), and habitat quality (HQ) improved the most under SSP1–2.6, whereas carbon sequestration (CS) peaked under SSP5–8.5. Synergies prevailed among WY–SC, CS–SC, and SC–HQ across all scenarios, whereas CS–HQ shifts to trade-offs under SSP5–8.5. ES interactions were scenario-dependent, with precipitation, temperature, potential evapotranspiration, land cover, and elevation as key drivers, shifting from land use-driven under low emissions to climate-driven under high emissions. Conclusions: Future ES management in the YZRB and YRB should adopt scenario-specific strategies, optimizing land use under low-emission pathways and applying climate-adaptive interventions under high-emission scenarios to sustain ES provision.

Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of XCS-LBP features is used as the change vector to improve the expression of change information. (3) A progressive Otsu method is developed for threshold segmentation of the change vector to reduce the false detection rate. Four datasets with different landscape complexities and seven state-of-the-art unsupervised change detection methods were used to test the performance of the proposed method. Quantitative results showed that the proposed method reduced the false detection rate and improved the accuracy of the detection of land cover changes. The F1 score achieved by the proposed method reached 0.8688, 0.8867, 0.7725, and 0.6634, respectively, which are higher than the highest corresponding F1 score achieved by the benchmark methods (0.8533, 0.8549, 0.6545, and 0.5895, respectively).

The registration of multimodal remote sensing (MMRS) images often suffers from local deformations and obvious nonlinear radiometric differences. Piecewise linear (PL) transformation handles local deformations well but requires a stricter position accuracy of feature point pairs (FPPs). After outlier removal, the inevitably remaining outliers lead to lower local registration accuracy. To fix this issue, we propose a new similarity metric MSOLGSSIM, which combines the multiscale and multiorientation Log-Gabor features with structural similarity. The metric is then used to optimize the FPPs by maximizing the similarity between the fixed and the registered images to improve the registration accuracy of the PL transformation. Experimental results based on eight pairs of MMRS images demonstrated the robustness of the proposed similarity metric and the effectiveness of the FPPs optimization method. The code of the proposed method can be downloaded from https://github.com/HoucaiGuo/FPPs-Optimization-Multimodal .

Yunnan Province in southwest China is characterized by a vast area, diverse climate types, rich ecosystem types, and unique biodiversity resources. With consideration of global climate change, there is an urgent need to evaluate the response of vegetation to drought in Yunnan. This study utilized the MOD13A3, MOD17A2, and Tropical Rainfall Measuring Mission (TRMM) 3B43 remote sensing products. The TRMM 3B43 downscaled monthly precipitation data were used to calculate the tropical rainfall condition index (TRCI) for Yunnan. The TRCI was used as a drought index, and the temporal and spatial changes in TRCI, gross primary productivity (GPP), and vegetation condition index (VCI) from 2009 to 2018 were explored. The response of vegetation to drought was evaluated under different time scales and varying land-use types. The results showed that during 2009–2018, (1) at an annual scale, the drought in Yunnan showed a weakening trend, and at a spatial scale, the drought showed a weakening trend from northwest to southeast. This weakening trend was more noticeable for cultivated land than for forest, grassland, and other land-use types. (2) GPP and VCI showed overall increasing trends at an annual scale, indicating gradual improvements in the GPP of vegetation and vegetation status, whereas the summer vegetation index showed a decreasing trend. (3) Although both the GPP and the growth state of vegetation were affected by drought, the responses of GPP and VCI to drought differed under different temporal scales and different land-use types. The responses of GPP and VCI to drought during spring were greater than those over other seasons, and the response of VCI to drought was more sensitive than that of GPP. Drought had a high impact on the GPP and vegetation growth of cultivated land and grassland with shallow root systems, whereas the impact of drought on forest was relatively stable.