Data/hora: 05/06/2024 14:03:05

Biblioteca(s): 	Embrapa Amazônia Oriental.	
Data corrente: 	25/10/2022	
Data da última atualização: 	25/10/2022	
Autoria: 	BRICEÑO CASTILLO, G. V.; FREITAS, L. J. M. de; CORDEIRO, V. A.; ORELLANA, J. B. P.; REATEGUI-BETANCOURT, J. L.; NAGY, L.; MATRICARDI, E. A. T.	
Afiliação: 	GUIDO VICENTE BRICEÑO CASTILLO, UNIVERSIDADE DE BRASÍLIA; LUCAS JOSE MAZZEI DE FREITAS, CPATU; VICTOR ALMEIDA CORDEIRO, UNIVERSIDADE DE BRASÍLIA; JORGE BRENO PALHETA ORELLANA, UNIVERSIDADE DE BRASÍLIA; JORGE LUIS REATEGUI-BETANCOURT, UNIVERSIDADE DE BRASÍLIA; LASZLO NAGY, UNIVERSIDADE ESTADUAL DE CAMPINAS; ERALDO APARECIDO TRONDOLI MATRICARDI, UNIVERSIDADE DE BRASÍLIA.	
Título: 	Assessment of selective logging impacts using UAV, Landsat, and Sentinel data in the Brazilian Amazon rainforest.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	Journal of Applied Remote Sensing, v. 16, n. 1, 014526, Mar. 2022.	
DOI: 	https://doi.org/10.1117/1.JRS.16.014526	
Idioma: 	Inglês	
Conteúdo: 	Several studies have assessed forest disturbance in tropical forests using Landsat imagery. However, the spatial resolution (30 m) of Landsat images has often been considered too coarse to accurately detect the extent and impacts of selective logging. The Sentinel-2 satellite launched in 2015 has been providing images at spatial resolutions of 10 to 20 m and those images have shown an improved potential for detecting forest disturbances in tropical regions. We compared Landsat-8 and Sentinel-2 imagery for detecting selective logging in a rain forest site in the Brazilian Amazon. The aerosol-free modified soil adjusted vegetation index (MSAVI_af) was retrieved from the satellite images acquired in August 2020 immediately following logging. A robust reference dataset of very-high-resolution imagery (0.5 m) acquired using a complementary metal oxide semiconductor sensor (visible bands) onboard of an unmanned aerial vehicle was used to image the area of interest and a map derived from it was used to assess the classification accuracies made using satellite-derived data. The overall accuracy of the classified Sentinel-2 and Landsat-8 images varied between 54% and 83%, depending on the applied classification parameters for distinguishing undisturbed from disturbed forest canopy. Images acquired using the UAV allowed us to detect subtle impacts of canopy openings by selective logging activities. Images acquired using the UAV allowed the detection of small canopy openings, but not Sentinel-2 or Landsat-8. Sentinel-2 provided more details of canopy disturbances than Landsat image. Our classification approach is fully implementable on the Google Earth Engine platform and is a promising technique to monitor selective logging impacts in tropical forests.	
Palavras-Chave: 	Drone; Imagem de satélite; Veículo aéreo não tripulado.	
Thesagro: 	Degradação Ambiental; Floresta Tropical; Impacto Ambiental.	
Thesaurus Nal: 	Unmanned aerial vehicles.	
Categoria do assunto: 	K Ciência Florestal e Produtos de Origem Vegetal	
Registro original: 	Embrapa Amazônia Oriental (CPATU)	
Exemplares: 	CPATU - (58437-1) - UPC - AP - DD -  -  -  - DIS