Data/hora: 12/06/2024 19:28:24

Biblioteca(s): 	Embrapa Agricultura Digital.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	A - 1	
Data corrente: 	18/09/2020	
Data da última atualização: 	14/12/2021	
Autoria: 	REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; ROCHA, J. V.; MAGALHÃES, P. S. G.	
Afiliação: 	ALINY A. DOS REIS, Nipe, Feagri/Unicamp; JOÃO P. S. WERNER, Feagri/Unicamp; BRUNA C. SILVA, Feagri/Unicamp; GLEYCE K. D. A. FIGUEIREDO, Feagri/Unicamp; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; RUBENS A. C. LAMPARELLI, Nipe/Unicamp; JANSLE V. ROCHA, Feagri/Unicamp; PAULO S. G. MAGALHÃES, Nipe/Unicamp.	
Título: 	Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020.	
DOI: 	10.3390/rs12162534	
Idioma: 	Inglês	
Notas: 	Article number: 2534.	
Conteúdo: 	Abstract: Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions oered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.	
Palavras-Chave: 	Extreme gradient boosting; Floresta aleatória; Integrated systems; Mixed pastures; Pastagem tropical; Pasto; Random forest; Texture measures.	
Thesagro: 	Biomassa; Pastagem Mista; Sensoriamento Remoto.	
Thesaurus Nal: 	Aboveground biomass; Biomass; Pastures; Remote sensing; Tropical pastures.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/216114/1/AP-Monitoring-pasture-aboveground-2020.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (20498-1) - UPC - AP - DD -  -  -  - DIS