EVALUATION OF EARLY SEASON MAPPING OF INTEGRATED CROP LIVESTOCK SYSTEMS USING SENTINEL-2 DATA

Autor: 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.
Přispěvatelé: FEAGRI/UNICAMP, UNICAMP, JULIO CESAR DALLA MORA ESQUERDO, CNPTIA, FEAGRI/UNICAMP, JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA, ALEXANDRE CAMARGO COUTINHO, CNPTIA, FEAGRI/UNICAMP.
Rok vydání: 2022
Předmět:
Zdroj: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice)
Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
ISSN: 2194-9034
1201-6659
Popis: 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. Made available in DSpace on 2022-08-24T19:26:01Z (GMT). No. of bitstreams: 1 AP-Evalution-early-season-2022.pdf: 12016659 bytes, checksum: 3fc8d4c05a9162610d9bccca3210e837 (MD5) Previous issue date: 2022 Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.
Databáze: OpenAIRE
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