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Article

Verification of Fractional Vegetation Coverage and NDVI of Desert Vegetation via UAVRS Technology

by 1,2, 1,2,* and 1,2
1
Shapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China
2
Key Laboratory of Stress Physiology and Ecology in Cold and Arid Regions, Gansu Province, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1742; https://doi.org/10.3390/rs12111742
Received: 28 April 2020 / Revised: 22 May 2020 / Accepted: 22 May 2020 / Published: 28 May 2020
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. View Full-Text
Keywords: fractional vegetation coverage (FVC); NDVI; unmanned aerial vehicle; temperature; precipitation; desert vegetation fractional vegetation coverage (FVC); NDVI; unmanned aerial vehicle; temperature; precipitation; desert vegetation
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MDPI and ACS Style

Tang, L.; He, M.; Li, X. Verification of Fractional Vegetation Coverage and NDVI of Desert Vegetation via UAVRS Technology. Remote Sens. 2020, 12, 1742. https://doi.org/10.3390/rs12111742

AMA Style

Tang L, He M, Li X. Verification of Fractional Vegetation Coverage and NDVI of Desert Vegetation via UAVRS Technology. Remote Sensing. 2020; 12(11):1742. https://doi.org/10.3390/rs12111742

Chicago/Turabian Style

Tang, Liang, Mingzhu He, and Xinrong Li. 2020. "Verification of Fractional Vegetation Coverage and NDVI of Desert Vegetation via UAVRS Technology" Remote Sensing 12, no. 11: 1742. https://doi.org/10.3390/rs12111742

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