The water source area of the middle route of the South-to-North Water Diversion Project is an important water conservation and ecological protection area in China. Based on remote sensing data, this paper analyzed the evolution process of land use/cover change in water source region in the past 35 years. Then, based on the InVEST model, the spatial-temporal patterns of water yield in the water source region were calculated with land use cover, meteorology and soil data as inputs. The impacts of climate factors such as precipitation and temperature and land use change on water yield were discussed, and the responses of water yield to these two changes were also discussed. The results show that from 1985 to 2020, the average water yield depth in the middle route of the South-to-North Water Diversion Project increases first and then decreases, from 615 mm in 1985 to 738 mm in 2000, and then decreases to 521 mm in 2020. The spatial heterogeneity of the water-producing capacity is obvious. The high value of the water-producing capacity is concentrated in the Daba Mountain area in the south, while the low values are concentrated in the Hanzhong Basin, Ankang Basin and the eastern plain area. The spatial pattern of water producing depth has no obvious change. The average water yield depth of forest, grassland and shrub in the region was the largest, and forest and cultivated land were the main contributors to the total water yield of the region, providing 82% and 14% of the total water yield in 2020. Precipitation has a significant effect on water yield, while land use/cover change has a small effect on water yield.

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The biogeography research of orchids through species distribution models (SDMs), a vital tool in the biogeography field, is critical to understanding the fundamental geographic distribution patterns and identifying conservation priorities. The correspondence between species occurrence and environmental information is crucial to the model’s performance. However, ecological preferences unique to different orchid species, such as their life forms, are often overlooked during the modeling process. This oversight can introduce bias and increase model uncertainty. Additionally, human activities, as an important potential predictor, have not been quantified in any orchid SDMs. Taking the Hengduan Mountains as an example, we preprocessed all orchid species’ occurrences based on physiological characteristics. Choosing five spatial factors related to human activities to quantify the interference and enter into models as HI factor. Using different modeling methods (GLM, MaxEnt, and RF) and evaluation indices (AUC, TSS, and Kappa), diverse modeling strategies have been constructed in the study. A double-ranking method has been adopted to select the critical orchid distribution regions. The results showed that classification models based on physiological characteristics significantly improved the model’s accuracy while adding the HI factor had the same effect but the absence of enough significance. Suitability maps indicated that highly heterogeneous mountainous areas were vital for the distribution of orchids in the Hengduan Mountains. Different distribution patterns and critical regions existed between various orchid life forms geographically - terrestrial orchids were dominant in the mountain, and mycoherterophical orchids were primarily located in the north, more influenced by vegetation and temperature. Critical regions of epiphytic orchids were in the south due to a greater dependence on precipitation and temperature. These studies are informative for understanding the orchids’ geographic distribution patterns in the Hengduan Mountains, promoting conservation, and providing references for similar research beyond orchids.

Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests.