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The Ebinur Lake Basin (ELB), which is a typical watershed in an arid region, has an extremely delicate natural ecosystem. Rapid urbanisation and economic growth have triggered substantial ecological and environmental transformations in this key economic hub of Xinjiang. However, a comprehensive and systematic knowledge of the evolving ecological conditions in the ELB remains limited. Therefore, this study modelled the landscape ecological risk index (LERI) using land use/land cover (LULC) data from 1985 to 2022 and assessed the drivers of landscape ecological risk (LER) using a geographical detector model (GDM). The findings revealed that (1) from 1985 to 2022, the construction land, cropland, and forestland areas in the ELB increased, whereas those of water bodies, grasslands, and barren land decreased. (2) Between 1985 and 2022, LER in the ELB showed a downward trend. Spatially, LER was predominantly characterised by lower and lowest risk levels. The higher and highest risk status has been around Ebinur lake and has continued to improve each year. (3) Climatic factors, particularly temperature and precipitation, were identified as the most significant drivers of the LER change from 1985 to 2022. The findings provide crucial scientific knowledge for advancing sustainable development and maintaining ecological security in the ELB. [ABSTRACT FROM AUTHOR]

Considering climate change and increasing human impact, ecological quality and its assessment have also received increasing attention. Taking the Irtysh River Basin as an example, we utilize multi-period MODIS composite imagery to obtain five factors (greenness, humidity, heat, dryness, and salinity) to construct the model for the amended RSEI (ARSEI) based on the Google Earth Engine platform. We used the Otsu algorithm to generate dynamic thresholds to improve the accuracy of ARSEI results, performed spatiotemporal pattern and evolutionary trend analysis on the results, and explored the influencing factors of ecological quality. Results indicate that: (1) The ARSEI demonstrates a correlation exceeding 0.88 with each indicator, offering an efficient approach to characterizing ecological quality. The ecological quality of the Irtysh River Basin exhibits significant spatial heterogeneity, demonstrating a gradual enhancement from south to north. (2) To evaluate the ecological quality of the Irtysh River Basin, the ARSEI was utilized, exposing a stable condition with slight fluctuations. In the current research context, the ecological quality of the Irtysh River Basin watershed area is projected to continuously enhance in the future. This is due to the constant ecological protection and management initiatives carried out by countries within the basin. (3) Precipitation, soil pH, elevation, and human population are the main factors influencing ecological quality. Due to the spatial heterogeneity, the driving factors for different ecological quality classes vary. Overall, the ARSEI is an effective method for ecological quality assessment, and the research findings can provide references for watershed ecological environment protection, management, and sustainable development. [ABSTRACT FROM AUTHOR]

As a transboundary river with rich and unique wetland types, the Irtysh River faces various challenges and threats from human activities and climate change, which affect area, type, and function of wetland. To accurately obtain information on the spatial and temporal distribution of wetlands in this basin, this study compares and evaluates the consistency and accuracy of a total of eleven remote sensing (RS) based land use/land cover (LULC), and wetland products. The information extraction effect of each RS product was examined through methods such as wetland area and type description, thematic map comparison, and similarity coefficient and Kappa coefficient calculations, which can reflect the wetland distribution characteristics and differences among the RS products in the Irtysh River Basin. The results show that although there is a consensus among the products in the major wetland distribution areas, there are still obvious deviations in detail depiction due to differences in factors such as data sources and methods. The products of Global 30 m Wetland Fine Classification Data (GWL_FCS30) and Global 30 m Land Cover Data (GLC_FCS30-2020) released by the Institute of Space and Astronautical Information Innovation (ISAI) of the Chinese Academy of Sciences (CAS) have a clear advantage in extracting spatial morphology features of wetlands due to the use of multi-source data, while the Esri Global 10 m Land Cover Data (ESRI_Global-LULC_10m) and products such as the global 10 m land cover data (FROM_GLC10_2017) from Tsinghua University have higher classification consistency. Moreover, data resolution, classification scheme design, and validation methods are key factors affecting the quality of wetland information extraction in the Irtysh River Basin. In practical terms, the findings of this study hold significant implications for informed decision-making in wetland conservation and management within the Irtysh River Basin. By advancing wetland monitoring technologies and addressing critical considerations in information extraction, this research effectively bridges the gap between remote sensing technology and practical applications, offering valuable insights for regional wetland protection efforts. [ABSTRACT FROM AUTHOR]