Título:	Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery.
Autor:	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.
Afiliación:	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.
Año:	2020
Referencia:	Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020.
Descripción:	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.
Thesagro:	Biomassa Pastagem Mista Sensoriamento Remoto
NAL Thesaurus:	Pastures Tropical pastures Biomass Aboveground biomass Remote sensing
Palabras clave:	Pasto Pastagem tropical Floresta aleatória Random forest Mixed pastures Integrated systems Texture measures Extreme gradient boosting
DOI:	10.3390/rs12162534
Notas:	Article number: 2534.
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPTIA)