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  • 标题:Uso e ocupação da superfície a partir do Índice de Vegetação Ajustado ao Solo (IVAS) na Área de Proteção Permanente (APP) do açude Santa Teresa em Soledade-PB
  • 本地全文:下载
  • 作者:Lázaro Avelino de Sousa ; Janaína Barbosa da Silva ; Sérgio Murilo Santos de Araújo
  • 期刊名称:Research, Society and Development
  • 电子版ISSN:2525-3409
  • 出版年度:2022
  • 卷号:11
  • 期号:14
  • 页码:1-12
  • DOI:10.33448/rsd-v11i14.36489
  • 语种:English
  • 出版社:Grupo de Pesquisa Metodologias em Ensino e Aprendizagem em Ciências
  • 摘要:The classification of the use and occupation of the terrestrial surface through remote sensing is extremely important for the analysis of vegetation cover and can be done from the spectral response from a given vegetation index that best responds to the objectives of the intended study. In this sense, this study used the Soil-Adjusted Vegetation Index (SA-VI) to carry out the supervised classification of the Permanent Preservation Area (PPA) of the Santa Teresa Reservoir, located in the municipality of Soledade, in the semi-arid region of Paraíba. From the digital processing of Sentinel-2 satellite images from the years 2016 and 2018, 5 classes of use and occupation of the surface were mapped using the ArcGis-Pro software. These classes were delimited within the perimeter of the polygon surrounding the weir, defined by a buffer generated from the edge of the water body, 100 meters wide (PPA strip). Based on the spectral response provid-ed by the application of the SAVI, training samples were collected and the supervised classification by maximum likeli-hood (MAXVER) was carried out, reaching the percentages of covered area of 60.44% of vegetation and 39 .57% of exposed soil, for the year 2016; and 68.48% of vegetation, 9.80% of water and 21.72% of exposed soil, for the year 2018. The vegetation classes were called Herbaceous Semidense, Subshrub Sparse and Shrub Semidense. It was con-cluded that the gains and losses of each identified class, in terms of coverage area, were determined by the average rain-fall of each year.
  • 关键词:Supervised classification;Remote sensing;Semiarid.
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