Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125026
Title: Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery.
Authors: 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.
Affiliation: 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.
Date Issued: 2020
Citation: Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020.
Description: 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
Keywords: Pasto
Pastagem tropical
Floresta aleatória
Random forest
Mixed pastures
Integrated systems
Texture measures
Extreme gradient boosting
Language: Ingles
DOI: 10.3390/rs12162534
Notes: Article number: 2534.
Type of Material: Artigo de periódico
Access: openAccess
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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