%0 Conference Proceedings
%4 sid.inpe.br/marte2/2017/10.27.12.41.33
%2 sid.inpe.br/marte2/2017/10.27.12.41.34
%@isbn 978-85-17-00088-1
%F 59855
%T Classificação digital de imagens aplicada à produção de mapas de cobertura e uso da terra do estado de Goiás, ano base 2015
%D 2017
%A Sousa, Silvio Braz de,
%A Coelho, Robson Vieira,
%A Cunha, Felipe Silva,
%@electronicmailaddress sousasb@gmail.com
%E Gherardi, Douglas Francisco Marcolino,
%E Aragão, Luiz Eduardo Oliveira e Cruz de,
%B Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)
%C Santos
%8 28-31 maio 2017
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 2439-2445
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%X . Digital image-processing techniques are a recurrent theme in studies on the use of remote sensing data for the landscape ecology monitoring and assessment. From the 1970''s, USA Landsat serie images (Land Remote Sensing Satellite) became the main data source on land cover and land use, with 44 years of data being used by researchers around the world for environmental modeling. This paper aims to present results related to application of digital classification techniques for large geographic areas, developed with few computational and human resources. Specifically, a land cover and land use map was made for state of Goiás in 2015 base year. For this research, OLI Landsat scenes from dry season (preferably August), supervised classification (SAM) and SRTM digital elevation data (to filter out shadows mistakenly classified as water) were used. The whole methodology relied on the use of free data and Geographic Information System (GIS), a fact that reduces costs for mapping land use and land cover. The results indicate that the mapping developed is in accordance with official mappings (Probio, 2002 and TerraClass Cerrado 2013), as well as confirms the advanced stage of environment degradation of the native vegetation, which, in turn, according to the model, occupies approximately 30% of the territory of Goiás.
%9 Monitoramento e modelagem ambiental
%@language pt
%3 59855.pdf