Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)

Abstract

It is significant to study the vegetation of protected areas in rugged mountains where the vegetation grows naturally with minimal eco-society environmental stress from anthropogenic activities. The shadow-eliminated vegetation index (SEVI) was used to monitor the vegetation of protected areas, since it successfully removes topographic shadow effects. In order to auto achieve the best adjustment factor for SEVI calculation from regional area images, we developed a new calculation algorithm using block information entropy (BIE-algorithm). The BIE-algorithm auto-detected typical blocks (subareas) from slope images and achieved the best adjustment factor from a block where the SEVI obtained the highest information entropy in an entire scene. Our obtained regional SEVI result from two scenes of Landsat 8 OLI images using the BIE-algorithm exhibited an overall flat feature with the impression of the relief being drastically removed. It achieved balanced values among three types of samples: Sunny area, self-shadow, and cast shadow, with SEVI means of 0.73, 0.77, and 0.75, respectively, and the corresponding SEVI relative errors of self-shadow and cast shadow were only 4.99% and 1.84%, respectively. The linear regression of SEVI vs. the cosine of the solar incidence angle was nearly horizontal, with an inclination of −0.0207 and a coefficient of determination of 0.0042. The regional SEVI revealed that the vegetation growth level sequence of three protected areas was Wuyishan National Park (SEVI mean of 0.718) > Meihuashan National Nature Reserve (0.672) > Minjiangyuan National Nature Reserve (0.624) > regional background (0.572). The vegetation growth in the protected areas was influenced by the terrain slope and years of establishment of the protected area and by the surrounding buffer zone. The homogeneous distribution of vegetation in a block is influenced by many factors, such as the actual vegetation types, block size, and shape, which need consideration when the proposed BIE-algorithm is used.

Evaluating canopy water content in grasslands by using in situ hyperespectral data

“…Diverse ecological conditions across different elevation gradient significantly influence the biophysical and biochemical structures of vegetation (Bora et al, 2020;Dechimo Jr & Buot Jr, 2023;Pescador et al, 2015;Zou et al, 2023). In this context, there is an effort to test existing indices (Yang and Guo, 2014;Wu et al, 2008) or to produce new vegetation indices in studies conducted at the hyperspectral level (Chang-Hua et al, 2010;He et al, 2006;Jiang et al, 2022). The main reason for this is that vegetation indices are affected by many external factors and there is no linear relationship between the biophysical and biochemical properties of vegetation and vegetation indices (Xie et al, 2009).…”

Vegetation Monitoring for Mountainous Regions Using a New Integrated Topographic Correction (ITC) of the SCS + C Correction and the Shadow-Eliminated Vegetation Index

“…As a key parameter, the adjustment factor balances the underelimination or over-elimination of topographic shadow in rugged mountains. We used a calculation algorithm based on the block information entropy (the BIE-algorithm) to obtain the best adjustment factor [40]. First, the slope calculated from the ASTER GDEM V2 of the 30 m spatial resolution was resampled to a 6 km resolution.…”

“…Recently, a type of method in the form of vegetation indices, based on the band-ratio model, has been developed to obtain the vegetation information without the topographic shadow, such as the modified enhanced vegetation index [38] and the shadow-eliminated vegetation index (SEVI) [39]. Some of them can rectify the cast shadow as well as the self-shadow, with the value in the shadows approximately corrected to that in sunny areas, e.g., the SEVI achieved a high performance in the removal of topographic shadow and was used in vegetation monitoring of protected areas in rugged mountains [40]. However, these methods lose spectral resolution [24], obtaining a single-band grayscale image where the topographic shadow was eliminated.…”