Abstract
Desertification control and scientific evaluation of desert ecosystem sustainability are important issues for countries along the Silk Road Economic Belt. Fractional vegetation coverage (FVC) is used as a quantitative indicator to describe the vegetation coverage of desert ecosystems. Although satellite remote sensing technology has been widely used to retrieve FVC at the regional and global scale, the authenticity evaluation of the inversion results has been flawed. To gain insight into the composition, structure and changes of desert vegetation, it is important to assess the accuracy of FVC and explore the relationship between FVC and meteorological factors. Therefore, we adopted unmanned aerial vehicle remote sensing (UAVRS) technology to verify the inversion results and analyse the practicability of MODIS-NDVI (where NDVI = normalized difference vegetation index) products in desert areas. To provide a new method for the estimation of vegetation coverage in the natural state, the relationships between vegetation coverage and four meteorological factors, namely, land surface temperature, temperature, precipitation and evaporation were analysed. The results showed that using the original MODIS-NDVI data product with a spatial resolution of 250 m to invert vegetation coverage is practical in desert areas (coefficient of determination (R2) = 0.83, root mean square error (RMSE) = 0.052, normalized root mean square error (NRMSE) = 42.94%, mean absolute error (MAE) = 0.007) but underestimates vegetation coverage in the study area. MODIS-NDVI data products are different from the real NDVI in the study area. Correcting MODIS-NDVI data products can effectively improve the accuracy of the inversion. When extracting vegetation coverage in this area, the scale has little effect on the results. There is a significant correlation between precipitation, evaporation and FVC in the area, but the interaction of temperature and land surface temperature with precipitation and evaporation also has a considerable impact on FVC, and evaporation has a substantial impact on FVC values inverted from MODIS-NDVI data (FVCM), When exploring the relationship between vegetation coverage and meteorological elements, if vegetation coverage is retrieved from MODIS-NDVI data products or MODIS-NDVI data, when considering temperature and precipitation, the effect of evaporation should also be considered. In addition, meteorological factors can be used to predict FVC (R2 = 0.7364, RMSE = 0.0623), which provides a new method for estimating FVC in areas with less manual intervention.
Highlights
Vegetation, as an important part of terrestrial ecosystems, plays an important role in the processes of energy flow, ecohydrology, and the biogeochemical cycle but can effectively prevent soil erosion, purify air, and maintain the stability of the ecological environment [1,2]
The normalized difference vegetation index (NDVI) values of samples extracted from MODIS-NDVI images (NDVIM) were between 0.0689 and 0.3906, with an average of 0.1591 and a standard deviation of 0.0745
The use of the original MODIS-NDVI data product with a spatial resolution of 250 m to invert vegetation coverage is practical in desert areas (R2 = 0.83, root mean square error (RMSE) = 0.052, normalized root mean square error (NRMSE) = 42.94%, mean absolute error (MAE) = 0.007), but underestimated vegetation coverage in the study area
Summary
Vegetation, as an important part of terrestrial ecosystems, plays an important role in the processes of energy flow, ecohydrology, and the biogeochemical cycle but can effectively prevent soil erosion, purify air, and maintain the stability of the ecological environment [1,2]. Accurate evaluation of vegetation coverage on the land surface helps us to understand the dynamics of land ecosystems [3]. Fractional vegetation coverage (FVC) is a quantitative parameter for describing the vegetation coverage on the land surface and the characteristics of ecosystems [1]. FVC is an important input variable in weather forecast models, regional and global climate change models, and global climate change monitoring models that affect the accuracy of the prediction results [4,5]. The metabolic function of plants is being affected by global climate change, which may result in fluctuations in vegetation coverage [6]. Accurate evaluation of FVC and exploration of the relationship between meteorological factors and FVC have great importance for improving the accuracy of model predictions and understanding regional ecological changes
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