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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Discriminating Land Use and Land Cover Classes in Brazil Based on the Annual PROBA-V 100 m Time Series

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Autor(es):
Shimabukuro, Yosio Edemir [1] ; Arai, Egidio [1] ; Duarte, Valdete [1] ; Dutra, Andeise Cerqueira [1] ; Godinho Cassol, Henrique Luis [1] ; Sano, Edson Eyji [2] ; Hoffmann, Tania Beatriz [1]
Número total de Autores: 7
Afiliação do(s) autor(es):
[1] Brazilian Natl Inst Space Res, Remote Sensing Div, BR-12227010 Sao Paulo, SP - Brazil
[2] Embrapa Cerrados, BR-73301970 Planaltina, DF - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING; v. 13, p. 3409-3420, 2020.
Citações Web of Science: 0
Resumo

Brazil, with more than 8 million km(2), presents six different biomes, ranging from natural grasslands (Pampa biome) to tropical rainfall forests (Amazonia biome), with different land-use types (mostly pasturelands and croplands) and pressures (mainly in the Cerrado biome). The objective of this article is to present a new method to discriminate the most representative land use and land cover (LULC) classes of Brazil, based on the PROBA-V images. The images were converted into vegetation, soil, and shade fraction images by applying the linear spectral mixing model. Then, the pixel-based, highest proportion, annual mosaics of the fraction images, and their corresponding standard deviation images were derived and classified using the random forest algorithm. The following LULC classes were considered: forestlands, shrublands, grasslands, croplands, pasturelands, water bodies, and others. An agreement analysis was conducted with two available LULC maps derived from the Landsat satellite, the MapBiomas, and the Finer Resolution Observation and Monitoring-Global Land Cover (FROM-GLC) projects. Forestlands (412 million ha) and pasturelands (242 million ha) were the two most representative LULC classes; and croplands accounted for 30 million ha. The results presented an overall agreement of 69% and 58% with the MapBiomas and FROM-GLC projects, respectively. The proposed method is a good alternative to support operational projects of LULC map production that are important for planning biodiversity conservation or environmentally sustainable land occupation. (AU)

Processo FAPESP: 16/19806-3 - Mapeamento e monitoramento da degradação florestal utilizando dados de sensores remotos com resolução espacial média e moderada
Beneficiário:Yosio Edemir Shimabukuro
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 18/14423-4 - Modelagem decenal das emissões brutas de carbono derivadas de incêndios florestais na Amazônia
Beneficiário:Henrique Luis Godinho Cassol
Linha de fomento: Bolsas no Brasil - Pós-Doutorado