AUTHOR=Dong Chunyuan , Qiao Rongrong , Yang Zhicheng , Luo Lihui , Chang Xueli TITLE=Eco-environmental quality assessment of the artificial oasis of Ningxia section of the Yellow River with the MRSEI approach JOURNAL=Frontiers in Environmental Science VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.1071631 DOI=10.3389/fenvs.2022.1071631 ISSN=2296-665X ABSTRACT=

Remote sensing ecological index (RSEI) has the advantages of rapid, repeatable and relatively accurate in regional eco-environment quality assessment. Due to the lack of consideration of the interaction of adjacent analysis units in RSEI calculation, there is a few uncertainties in the assessment results. Based on RSEI, the landscape diversity index (LDI) was introduced, which considered the heterogeneity caused by the difference between the assessment unit and the adjacent one, and rebuilt modified remote sensing ecological index (MRSEI) to evaluate the eco-environment quality in the artificial oasis of Ningxia section of Yellow River. The results showed that the area of Fair and Poor grades in the low MRSEI year (2000) was greater than that of other grades, and the area of Moderate and Fair grades was greater than that of other grades in the high MRSEI year (2020). The conversion characteristics of different grades were Poor and Fair grades to adjacent high grades. During the study period, the eco-environment quality of the study area was improved, and the composition and pattern of land use types had a significant impact on MRSEI. Introduction of LDI-improved MRSEI can not only include the heterogeneous effect between the analysis unit and the adjacent one, but also consider the spatial scale effect of LDI to make the evaluation results more credible. However, some evaluation factors of RSEI and MRSEI (e.g., LDI, NDVI, and NDBSI) represent the accumulation of surface status over long-time scales, while others (e.g., Wet and LST) reflects only short-time scale features of the land surface. Therefore, how to eliminate the uncertainty caused by temporal scale mismatch is a challenge for RSEI and MRSEI applications.