Journal Article

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Van de Voorde, Tim, Azadi, Hossein, Ablekim, Abdimijit, Wu, Shupu, Kurban, Alishir, Maeyer, Philippe De, Dufatanye Umwali, Edovia, Guo, Zengkun

2021

Overview

Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

• Publisher: MDPI AG
• Subject: Agriculture & agronomie / Agriculture & agronomy / Arid regions / Correlation coefficient / Correlation coefficients / Data acquisition / Desertification / Development characteristics / Earth and Planetary Sciences (all) / Ecological function / Environmental changes / Environmental monitoring / fuels (fire ecology) / GEMI-DFI model / Grasslands / Growth conditions / Life sciences / Linear regression models / Mathematical analysis / Monitoring / Non-photosynthetic vegetation / Normalized difference indices / Normalized difference vegetation index / Normalized difference vegetative index / Normalized differences / Photosynthesis / Photosynthetic vegetation / Ratio vegetation indices / Regression analysis / Regression models / Remote sensing / Rivers / Sciences du vivant / Seasonal variation / Seasonal variations / Short wave radiation / soil / Soils / Tillage / Time series / Vegetation / Vegetation growth / Water shortages