Remote Sensing Estimation Methods for Determining FVC in Northwest Desert Arid Low Disturbance Areas Based on GF-2 Imagery

Abstract

Fractional vegetation cover (FVC) is a vital indicator of surface vegetation. Studies of regional vegetation cover are helpful for understanding the status of the regional ecological environment and can provide important references for the formulation of ecological restoration plans and the evaluation of restoration effects. In vegetation cover related research, studies on extraction methods have attracted much attention. Studies have shown that the universality of vegetation cover extraction methods is poor, as well as the existing studies were mostly conducted on agricultural and forest land in wet, semi-humid and semi-arid areas, while few have investigated arid areas with sparse vegetation that is mainly shrubs and grass. To investigate the accuracy and applicability of different methods for estimating vegetation cover in the near-natural zone of the northwest arid desert, this study extracted six vegetation indices (NDVI, SAVI, MSAVI, ARVI, EVI, and MVI), which could effectively exclude soil and meteorological information to obtain pure vegetation information based on GF-2 multispectral-panchromatic fusion images. Two types of models were then established, including the single VI models (DP model) and multi-VI models (R model, RF model and PCA model), three statistics (SSE, R 2 , RMSE) were introduced to validate model accuracy and four-fold cross-validation was used to probe the models for overfitting. After filtering the models through these methods, the selected model was applied to invert the vegetation coverage in the study area. The results show three key aspects of this system. (1) Among the various models, the DP model constructed using the EVI and the RF model are more suitable for FVC extraction in the study area. This conclusion was further verified by the significant correlation between the inversion results of the FVC for the entire study area by applying these two models.(2) The values of pure bare soil and vegetation pixels (VIs and VIv) in the DP model will obviously affect the accuracy of the model. Thus, the empirical values should not be blindly adopted in actual research. (3) The vegetation distributions in the figures of the FVC results are similar to the outline of the mountains in the study area, indicating that the coverage distribution may be greatly affected by topographic factors. It is recommended that this aspect should be introduced in subsequent studies.