Data/hora: 20/06/2024 07:51:24

Biblioteca(s): | Embrapa Roraima.
Data corrente: | 14/09/2021
Data da última atualização: | 14/09/2021
Autoria: | PRUDENTE, V. H. R.; SANCHES, I. D.; ADAMI, M.; SKAKUN, S.; OLDONI, L. V.; XAUD, H. A. M.; XAUD, M. R.; ZHANG, Y.
Afiliação: | HARON ABRAHIM MAGALHAES XAUD, CPAF-RR; MARISTELA RAMALHO XAUD, CPAF-RR.
Título: | SAR data for land use land cover classification in a tropical region with frequent cloud cover.
Ano de publicação: | 2020
Fonte/Imprenta: | IGARSS - INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020.
Idioma: | Inglês
Conteúdo: | This study aims at mapping Land Use and Land Cover (LULC) in the region of Roraima, Brazil, using time-series of Sentinel-1 Synthetic Aperture Radar (SAR) data. All available Sentinel-1 images covering the study area were used and classified using two machine learning algorithms, namely random forest and multilayer perceptron. LULC heterogeneity with the SAR process complexity makes the process challenging in distinguishing certain classes. Results show that SAR data could be used for LULC mapping, as rainforest, savannas, water, and sandbank/outcrop classes. But cannot provide accurate separation for all classes, mainly for those with similar geometrical structures, such as regeneration areas, perennial crops, and buritizais.
Palavras-Chave: | Machine learning; Sentinel.
Thesagro: | Radar.
Categoria do assunto: | --
Registro original: | Embrapa Roraima (CPAF-RR)
Exemplares: | CPAF-RR - (16178-1) - UPC - AA - DD -  -  -  - DIS