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.
Highlights
We developed a new calculation algorithm using block information entropy (BIEWe developed a new calculation algorithm using block information entropy (BIEalgorithm) to calculate an improved that includes slope resampling, block detection, algorithm) to calculate an improved shadow-eliminated vegetation index (SEVI) that includes slope resampling, block detection, SEVISEVI
We determined that the best adjustment factors were image was achieved from the block that obtained the highest information entropy of the
We developed a new calculation algorithm using block information entropy to autodetect typical subareas of rugged terrain and achieve the best adjustment factor for the calculation of the shadow-eliminated vegetation index
Summary
Protected areas are useful for natural vegetation growth and mitigating vegetation degradation and deforestation [5,6,7,8,9,10,11,12], since they suffer minimal eco-society environmental stress from human beings [13,14]. It is significant to monitor the vegetation growth of protected areas. In addition to field surveys, remotely sensed measures have become an effective technology in vegetation monitoring [15,16,17,18,19,20,21,22,23]. Remote sensing of vegetation in mountain environments is notoriously difficult, due to the anisotropic nature of spectral variation caused
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