Dynamic monitoring of rocky desertification utilizing a novel model based on Sentinel-2 images and KNDVI

Abstract

As a new vegetation index, kernel normalized difference vegetation index (KNDVI) has great advantages in monitoring regional land degradation and vegetation status. However, the research on the spatial monitoring of rocky desertification based on KNDVI and feature space model has not been reported. In this study, the KNDVI, MSAVI, NDVI, EVI and BLI were introduced to establish four feature space monitoring index. After accuracy validation and comparisons, the optimal rocky desertification monitoring model was proposed and the spatio-temporal changes of rocky desertification had been analyzed. The time scale of this study was from 2018 to 2022, and the study area was about 3412 km2. The results showed that: (1) The rocky desertification monitoring model of KNDVI-BLI had the highest accuracy with R2=0.909 and RMSE of 0.392. (2) The rocky desertification in the western, northern and southeastern parts of Qixingguan District was more severe than other parts. (3) During 2018–2022, the rocky desertification showed an exacerbating trend in Qixingguan District. The area of extremely severe rocky desertification increased by 8.24 km2, while that of severe rocky desertification increased by 20.46 km2. The research results could provide a scientific and effective monitoring method for the control of rocky desertification.

Keywords

• Sentinel-2 images
• rocky desertification
• feature space
• KNDVI
• BLI