{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:01:09Z","timestamp":1775055669654,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T00:00:00Z","timestamp":1644624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Science and Technology Plan Leading Project of Fujian Province, China","award":["2021Y0005."],"award-info":[{"award-number":["2021Y0005."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 \u22120.0207 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) &gt; Meihuashan National Nature Reserve (0.672) &gt; Minjiangyuan National Nature Reserve (0.624) &gt; 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.<\/jats:p>","DOI":"10.3390\/rs14040882","type":"journal-article","created":{"date-parts":[[2022,2,13]],"date-time":"2022-02-13T20:34:45Z","timestamp":1644784485000},"page":"882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1105-5205","authenticated-orcid":false,"given":"Hong","family":"Jiang","sequence":"first","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China"}]},{"given":"Maolin","family":"Yao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China"}]},{"given":"Zhaoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Wenting","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China"}]},{"given":"Zhengyuan","family":"Mao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1126\/science.1196624","article-title":"Scenarios for global biodiversity in the 21st century","volume":"330","author":"Pereira","year":"2010","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/506153a","article-title":"Carbon sequestration: Managing forests in uncertain times","volume":"506","author":"Bellassen","year":"2014","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.5194\/essd-10-2141-2018","article-title":"Global carbon budget 2018","volume":"10","author":"Andrew","year":"2018","journal-title":"Earth Syst. 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