Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping

In the classification of images for land cover and land use mapping, several methods can be applied, however, they can present different results in relation to field truth. Therefore, the objective of this work was to test techniques for classifying high spatial digital images obtained from the Google Earth Pro® platform. The images refer to a section of the Federal University of Goias, campus Samambaia Goiania - GO, Brazil. Classification tests were performed on the images obtained, using two classifiers per region and two classifiers per pixel, both available free of charge, in the Spring software of the National Institute for Space Research (INPE / Brazil). For the analysis of the quality of the classifications, the results were compared to a survey by direct method, in this case the topographic one, seeking to identify which classifier came closest to the field truth. The classification that presented the best performance and class separability was the Bhattacharya, with Global Accuracy of 0.85. Bhattacharya was also the classifier that came closest in terms of measured areas, by the topographic survey, with the areas of the “zinc roofing” use class, analyzed and calculated

Agricultural mapping with remotely sensed images to support water resources management

Submitted by Erika Demachki ([email protected]) on 2015-03-20T20:31:52Z No. of bitstreams: 2 Dissertação - Nadyelle Curcino do Carmo - 2014.pdf: 11330040 bytes, checksum: d1ddeb18152a6fb46b421bfb97745ad9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Approved for entry into archive by Erika Demachki ([email protected]) on 2015-03-20T20:36:34Z (GMT) No. of bitstreams: 2 Dissertação - Nadyelle Curcino do Carmo - 2014.pdf: 11330040 bytes, checksum: d1ddeb18152a6fb46b421bfb97745ad9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Made available in DSpace on 2015-03-20T20:36:34Z (GMT). No. of bitstreams: 2 Dissertação - Nadyelle Curcino do Carmo - 2014.pdf: 11330040 bytes, checksum: d1ddeb18152a6fb46b421bfb97745ad9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-08-11The catchment areas of water are extremely important because they have the function of providing water for public supply, and its occupation should be monitored to avoid damage to its environmental quality. Currently, most of the mappings of use and occupation of land classified as agriculture evenly, not discriminate their crops, which could help more effectively in the planning and management of natural resources. This work aims at mapping the major crops of the state of Goiás, using GIS tools and remote sensing in order to provide support for managing water resources in the region. Vegetation indices produced from data collected by MODIS the years 2007-2013 to map cane sugar, corn and soybeans and winter corn were used. The methodology proved feasible, cost effective and promising for mapping corn and soybeans results, but the similarities obtained in relation to official data, for mapping of cane sugar and winter corn were not satisfactory. In a second step was quantified occupation of catchment basins of water for public supply for corn and soybeans in the state. The results showed that of the 183 catchment basins, 100 have these crops and that the basins of southern Goiás have densified occupation. The main conclusion of the paper is that the use and occupation of land in the catchment should be systematically monitored, which is quite feasible using images from satellite sensors with high temporal resolution, such as the MODIS images.As bacias de captação de água são extremamente importantes, pois possuem a função de fornecer água para o abastecimento público, e sua ocupação deve ser monitorada para evitar prejuízos à sua qualidade ambiental. Atualmente, a maior parte dos mapeamentos de uso e ocupação do solo classifica a agricultura de maneira homogênea, ou seja, não discrimina seus cultivos, o que poderia auxiliar de maneira mais efetiva no planejamento e gerenciamento dos recursos naturais. Este trabalho tem por objetivo mapear os principais cultivos agrícolas do estado de Goiás, utilizando ferramentas de geoprocessamento e sensoriamento remoto com o intuito de fornecer subsídios para o gerenciamento de recursos hídricos da região. Foram utilizados índices de vegetação produzidos a partir de dados coletados pelo sensor MODIS dos anos de 2007 a 2013 para o mapeamento da cana-de-açúcar, milho e soja e milho safrinha. A metodologia utilizada se mostrou viável, de baixo custo e com resultados promissores para o mapeamento do milho e soja, porém as similaridades obtidas, em relação aos dados oficiais, para o mapeamento da cana-de-açúcar e do milho safrinha não foram satisfatórios. Numa segunda etapa foi quantificada a ocupação das bacias de captação de água para abastecimento público por lavouras de milho e soja no Estado. Os resultados mostraram que das 183 bacias de captação, 100 possuem esses cultivos e que as bacias do sul goiano possuem ocupação densificada. A principal conclusão do trabalho é que o uso e ocupação do solo nas bacias de captação devem ser sistematicamente monitorados, o que é bastante viável utilizando imagens de sensores orbitais com alta resolução temporal, como é o caso das imagens do sensor MODIS