A novel remote sensing ecological vulnerability index on large scale: A case study of the China-Pakistan Economic Corridor region

[Display omitted] •A novel remote sensing ecological vulnerability index (RSEVI) was proposed.•The RSEVI had better applicability with the precision of 89.75%.•The increment and increasing rate of ecological vulnerability in the northeastern parts were larger.•The precipitation had negative relationships with ecological vulnerability.•The dominant factor and dominant interactive factors differed greatly in different sub-regions. There were dramatic changes in the ecological vulnerability (EV) of the China-Pakistan Economic Corridor (CPEC) region due to the effects of climate change and human activity. Obtaining field observation and statistical data for the evaluation of the EV of the CPEC region is difficult due to its transnational status. This study proposes a novel remote sensing ecological vulnerability index (RSEVI) based on the Moderate Resolution Imaging Spectroradiometer (MODIS), STRM3, DMSP-OLS, and NPP-VIIRS products. The RSEVI index was applied to the CPEC region to investigate the spatiotemporal changes in EV and the influencing factors during 2000–2019. The results showed that: (1) the large-scale application of the RSEVI index showed good applicability to the CPEC region, with a precision of 89.75%; (2) the average RSEVI value for the CPEC region was 0.83, thereby falling into a category of “intensive vulnerability”; (3) a stable trend in RSEVI was observed for the entire CPEC region during the study period, except for the Indus Basin where there was a significant change; (4) the increasing rate of the RSEVI in the northeastern parts exceeded that of other parts during 2000–2019; (5) precipitation, temperature, and vegetation coverage showed negative relationships with RSEVI, whereas RSEVI showed a positive relationship with slope. The RSEVI results indicated that ice and bare land were the most vulnerable, whereas cropland was the least vulnerable; (6) there were differences in the dominant factor and dominant interactive factor among different sub-regions. The results of this study could provide important decision support for the protection of the ecological environment and for economic development.