Inversion of Vegetation Components Based on the Spectral Mixture Analysis Using Hyperion Data

Wang, Mingchang; Niu, Xuefeng; Yang, Qian; Chen, Shengbo; Yang, Guodong; Wang, Fengyan

doi:10.1007/s12524-017-0661-2

Cited by 10 publications

(1 citation statement)

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“…In addition, because of the complexity of vegetation cover and the influence of topography (e.g., shadows), VI cannot be directly used to evaluate the degree of rocky desertification in karst regions (Yue et al, 2011). One assumption of SMA is that pixels are a composite of different and finite features (end‐members) with relatively invariable spectral properties (Adams et al, 1995; Wang, et al, 2018). However, SMA is often constrained by the variability of end‐members, especially in karst regions with high heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Assessment of karst rocky desertification from the local to regional scale based on unmanned aerial vehicle images: A case‐study of Shilin County, Yunnan Province, China

Dai

Sun²,

Bin

et al. 2021

Land Degrad Dev

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Karst rocky desertification is a major ecologic and geologic problem in Southwest China that has restricted the sustainable development of society and the economy. Many methods have been used to evaluate rocky desertification based on satellite remote sensing, but the results of these methods are affected by the heterogeneous surroundings of the karst region and the low resolution of sensors. In this study, a new method that combines satellite images and unmanned aerial vehicle (UAV) images was used to quantitatively extract information and evaluate rocky desertification. First, we extracted the bare rock ratio from the local high‐resolution UAV images, and then the regression models were established between the bare rock ratio of UAV images and the band reflectivity and eight rock indices of satellite images to invert the bare rock ratio at the county scale. The results showed that the overall accuracy and F1 of the classification of UAV images was 97% and 90%, respectively. The linear regression model between the reflectivity of the pixels in band 2 of the LANDSAT image and the bare rock ratio extracted from the UAV images was the best (R2 = 0.86). In addition, our method in which rocky desertification was assessed by the inversion model based on UAV images and LANDSAT images was superior to the traditional approach based on vegetation coverage. These results suggested that we can extract information on rocky desertification based on high‐resolution UAV images and assess rocky desertification by the inversion model from the local to regional scale.

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Section: Introductionmentioning

confidence: 99%