Data/hora: 22/06/2024 19:35:03

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	24/08/2022	
Data da última atualização: 	25/08/2022	
Autoria: 	TORO, A. P. S. G. D.; WERNER, J. P. S.; REIS, A. A. dos; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A.	
Afiliação: 	FEAGRI/UNICAMP; FEAGRI/UNICAMP; UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA, FEAGRI/UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; UNICAMP; UNICAMP; FEAGRI/UNICAMP.	
Título: 	Evaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 1335-1340, 2022.	
DOI: 	https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1335-2022	
Idioma: 	Inglês	
Notas: 	Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.	
Conteúdo: 	ABSTRACT. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover.	
Palavras-Chave: 	Agricultura regenerativa; Aprendizado profundo; Crop identification; Floresta aleatória; Identificação de culturas; LSTM; Random forest; Regenerative agriculture.	
Thesagro: 	Sensoriamento Remoto.	
Thesaurus Nal: 	Remote sensing.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1145714/1/AP-Evalution-early-season-2022.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (21240-1) - UPC - AP - DD -  -  -  - DIS