Abstract
Abstract The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.
Highlights
As an important part of the ecosystem, the changes of vegetation in its quantity and population proportion will lead to changes in land surface energy, biogeochemical cycle, and hydrology, which is one of the most important links in global change [1,2,3]
The field measured fractional vegetation coverage (FVC) and the FVC estimated based on the pixel dichotomy model were regressed and fitted
The accuracy of the FVC estimated by remote sensing was evaluated by calculating the determination coefficient (R2) and root-mean-square error (RMSE)
Summary
As an important part of the ecosystem, the changes of vegetation in its quantity and population proportion will lead to changes in land surface energy, biogeochemical cycle, and hydrology, which is one of the most important links in global change [1,2,3]. To measure the surface vegetation coverage and its changes effectively and quantitatively, the researchers used the concept of fractional vegetation coverage (FVC) [4,5]. As a comprehensive quantitative index reflecting the surface conditions of vegetation community coverage, FVC is widely used in the ecological environment assessment [7], groundwater enrichment assessment [8], groundwater level monitoring [9], soil degradation, and desertification monitoring [10]. The traditional surface measurement methods for FVC include the photographic method, the sample strip method, the sample point method, the spatial quantitative meter, and so on [11]. With the development of remote sensing technology, remote sensing monitoring based on the relationship between vegetation spectral information and vegetation coverage has become the main technical means to obtain FVC in large areas [12]. The current data sources for remote sensing estimation of FVC mainly include Landsat, MODIS (Moderateresolution Imaging Spectroradiometer), GaoFen (GF), SPOT (Systeme Probatoire d’Observation de la Terre), and so on
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