Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1136079
Research center of Embrapa/Collection: Embrapa Agricultura Digital - Artigo em anais de congresso (ALICE)
Date Issued: 2021
Type of Material: Artigo em anais de congresso (ALICE)
Authors: REIS, A. A. dos
WERNER, J. P. S.
SILVA, B. C. da
ANTUNES, J. F. G.
ESQUERDO, J. C. D. M.
FIGUEIREDO, G. K. D. A.
COUTINHO, A. C.
LAMPARELLI, R. A. C.
MAGALHÃES, P. S. G.
Additional Information: ALINY APARECIDA DOS REIS, UNIVERSITY OF CAMPINAS; JOÃO PAULO SAMPAIO WERNER, UNIVERSITY OF CAMPINAS; BRUNA CAROLINE DA SILVA, UNIVERSITY OF CAMPINAS; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNIVERSITY OF CAMPINAS; ALEXANDRE CAMARGO COUTINHO, CNPTIA; RUBENS AUGUSTO CAMARGO LAMPARELLI, UNICAMP; PAULO SERGIO GRAZIANO MAGALHÃES, UNICAMP.
Title: Can canopy height of mixed pastures in integrated crop-livestock systems be estimated using planetscope imagery?
Publisher: In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. Proceedings reference. Brasília, DF: Embrapa, 2021. p. 658-663.
Language: Ingles
Notes: WCCLF 2021. Evento online.
Keywords: Sistemas integrados
Nanossatélites
Medidas de textura
Integração lavoura pecuária
Canopy height
Integrated systems
Nano-satellites
Texture measures
Integrated crop-livestock systems
Description: ABSTRACT. Canopy height (CH) is one of the key parameters used to evaluate forage biomass production and support grazing management decisions in intensively managed fields. In this study, we demonstrate the potential of using textural information derived from PlanetScope (PS) imagery to estimate CH of intensively managed mixed pastures in an Integrated Crop-Livestock Systems (ICLS) in the western region of São Paulo State, Brazil. PS images and field data of CH were acquired during the forage growing season of 2019 (from May to November) to calibrate and validate the CH prediction models using the Random Forest (RF) regression algorithm. We used as predictor variables eight second-order texture measures derived from the green, red, near-infrared spectral bands of PS images using the grey level co-occurrence matrix (GLCM) statistical texture approach. Pasture CH varied from 0.12 to 1.20 m with a coefficient of variation equal to 63.34%. Our best RF model was able to predict the spatiotemporal changes in pasture CH with high accuracy (R2 = 0.88) even with the high variability of the pasture CH through the forage growing season, mainly due to forage composition (different proportions of millet and ruzi grass) and grazing activities.
Thesagro: Pastagem
Data Created: 2021-11-11
ISBN: 978-65-994135-4-4
Appears in Collections:Artigo em anais de congresso (CNPTIA)

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