Annual cropland mapping using data mining and OLI Landsat-8.
ABSTRACT: In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports....
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Recursos: | Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
| Repositorio: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
| Idioma: | inglés |
| OAI Identifier: | oai:www.alice.cnptia.embrapa.br:doc/1114915 |
| Acesso em linha: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1114915 http://dx.doi.org/10.1590/1807-1929/agriambi.v23n12p952-958 |
| Access Level: | acceso abierto |
| Palavra-chave: | Árvore de decisão Métricas temporais de NDVI Mineração de dados Séries temporais Decision tree NDVI temporal metrics Random forest Data mining Normalized difference vegetation index Time series analysis |
| Resumo: | ABSTRACT: In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC). At first, seven classes were mapped (water bodies, sugarcane, urban area, annual crops, forest, pasture and reforestation areas); then, only two classes were considered (annual crops and other targets). When classifying the seven targets, both methods had corresponding results, showing global accuracy near 84%. NDVI temporal metrics showed producer?s and user?s accuracy for the annual crop class of 86 and 100%, respectively. However, if considering only two classes, the NDVI temporal metrics reached global accuracy of near 98% and producer?s and user?s accuracy above 94%. |
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