Data/hora: 16/06/2024 05:17:43

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	21/09/2020	
Data da última atualização: 	14/12/2021	
Autoria: 	REIS, A. A. dos; SILVA, B. C.; WERNER, J. P. S.; SILVA, Y. F.; ROCHA, J. V.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C; MAGALHÃES, P. S. G.	
Afiliação: 	Feagri, Nipe/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; Nipe/Unicamp; Nipe/Unicamp.	
Título: 	Exploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42-3, W12, p. 419-424, 2020.	
DOI: 	https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020	
Idioma: 	Inglês	
Notas: 	Publicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020.	
Conteúdo: 	ABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.	
Palavras-Chave: 	Aprendizado de máquina; Dove satellites; Floresta aleatória; Índice de vegetação; Integração lavoura-pecuária; Integrated crop-livestock system; Machine Learning; Nano-Satellites; Pastureland; Random Forest; Vegetation Indices.	
Thesagro: 	Biomassa; Pastagem.	
Thesaurus Nal: 	Biomass; Pasture management; Vegetation index.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (20499-1) - UPC - AP - DD -  -  -  - DIS