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dc.contributor.authorTORO, A. P. S. G. D.
dc.contributor.authorWERNER, J. P. S.
dc.contributor.authorREIS, A. A. dos
dc.contributor.authorESQUERDO, J. C. D. M.
dc.contributor.authorANTUNES, J. F. G.
dc.contributor.authorCOUTINHO, A. C.
dc.contributor.authorLAMPARELLI, R. A. C.
dc.contributor.authorMAGALHÃES, P. S. G.
dc.contributor.authorFIGUEIREDO, G. K. D. A.
dc.date.accessioned2022-08-24T19:26:01Z-
dc.date.available2022-08-24T19:26:01Z-
dc.date.created2022-08-24
dc.date.issued2022
dc.identifier.citationThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 1335-1340, 2022.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1145714-
dc.descriptionABSTRACT. 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAgricultura regenerativa
dc.subjectIdentificação de culturas
dc.subjectFloresta aleatória
dc.subjectAprendizado profundo
dc.subjectLSTM
dc.subjectRegenerative agriculture
dc.subjectCrop identification
dc.subjectRandom forest
dc.titleEvaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data.
dc.typeArtigo de periódico
dc.subject.thesagroSensoriamento Remoto
dc.subject.nalthesaurusRemote sensing
dc.description.notesEdition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.
riaa.ainfo.id1145714
riaa.ainfo.lastupdate2022-08-24
dc.identifier.doihttps://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1335-2022
dc.contributor.institutionFEAGRI/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.
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