TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications

Câmara, Gilberto; Vinhas, Lúbia; Ferreira, Karine Reis; Queiroz, Gilberto Ribeiro de; Souza, Ricardo Cartaxo Modesto de; Monteiro, Antônio Miguel Vieira; Carvalho, Marcelo Tílio De; Casanova, Marco A.; Freitas, Ubirajara Moura De

doi:10.1007/978-3-540-74831-1_12

Cited by 55 publications

(52 citation statements)

References 32 publications

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“…Para alcançar esses efeitos, uma função kernel quártica (Druck et al, 2004), com raio adaptativo segundo os centroides dos polígonos dos municípios, foi gerada (Câmara et al, 2008). O somatório anual de diagnósticos laboratorialmente positivos de raiva bovina em cada município foi utilizado para a realização da interpolação da função.…”

Section: Methodsunclassified

Raiva bovina segundo os mosaicos de uso e cobertura da terra no estado de São Paulo entre 1992 e 2003

Gomes

Monteiro

Escada

2011

Arq. Bras. Med. Vet. Zootec.

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O objetivo deste trabalho foi descrever a influência dos mosaicos de uso e de cobertura da terra na distribuição espacial da raiva bovina na região de maior incidência do Estado de São Paulo, entre 1992 e 2003. Geotecnologias foram utilizadas para gerar três camadas sobrepostas: a enfermidade caracterizada por uma função kernel, os tipos de uso e classes de cobertura da terra obtidos por classificação de imagens de satélites e a altitude. A enfermidade esteve fortemente moldada pelas áreas de vegetação rasteira (pastagens). O relevo pareceu formar os mosaicos de uso e cobertura da terra, os quais acabaram determinando os locais de grassamento ou não da enfermidade. Grandes áreas contínuas do mesmo tipo de uso e classe de cobertura da terra tenderam a ser protetoras da raiva bovina, ou favorecedoras em áreas vizinhas.

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Section: Methodsunclassified

Raiva bovina segundo os mosaicos de uso e cobertura da terra no estado de São Paulo entre 1992 e 2003

Gomes

Monteiro

Escada

2011

Arq. Bras. Med. Vet. Zootec.

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“…Subsequently, a fully automated multi-date image segmentation and classification algorithm was employed to produce thematic LULC maps for the area. The image segmentation was done employing the Baazt segmenter [45] available in Inpe's TerraLib Operators [46]. The generated objects (polygons) were automatically labelled according to a class majority derived from a pixel based classification [42].…”

Section: Land Use and Land Cover Changes (Lulcc)mentioning

confidence: 99%

Indicators for Assessing Habitat Values and Pressures for Protected Areas—An Integrated Habitat and Land Cover Change Approach for the Udzungwa Mountains National Park in Tanzania

et al. 2016

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Assessing the status and monitoring the trends of land cover dynamics in and around protected areas is of utmost importance for park managers and decision makers. Moreover, to support the Convention on Biological Diversity (CBD)'s Strategic Action Plan including the Aichi Biodiversity Targets, such efforts are necessary to set a framework to reach the agreed national, regional or global targets. The integration of land use/cover change (LULCC) data with information on habitats and population density provides the means to assess potential degradation and disturbance resulting from anthropogenic activities such as agriculture and urban area expansion. This study assesses the LULCC over a 20 year (1990-2000-2010) period using freely available Landsat imagery and a dedicated method and toolbox for the Udzungwa Mountains National Park (UMNP) and its surroundings (20 km buffer) in Tanzania. Habitat data gathered from the Digital Observatory for Protected Areas (DOPA)'s eHabitat+ Web service were used to perform ecological stratification of the study area and to develop similarity maps of the potential presence of comparable habitat types outside the protected area. Finally, integration of the habitat similarity maps with the LULCC data was applied in order to evaluate potential pressures on the different habitats within the national park and on the linking corridors between UMNP and other protected areas in the context of wildlife movement and migration. The results show that the UMNP has not suffered from relevant human activities during the study period. The natural vegetation area has remained stable around 1780 km 2 . In the surrounding 20 km buffer area and the connecting corridors, however, the anthropogenic impact has been strong. Artificially built up areas increased by 14.24% over the last 20 years and the agriculture area increased from 11% in 1990 to 30% in the year 2010. The habitat functional types and the similarity maps confirmed the importance of the buffer zone and the connecting corridors for wildlife movements, while the similarity maps detected other potential corridors for wildlife.

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“…The system works as a plugin to the software TerraView [41], which provides the interface to the user (hereby called the interpreter), with visualization of geographic information data stored in databases. GeoDMA is coded in C++, using the QT cross-platform application development framework [5], and the free GIS and image processing library TerraLib [13]. Figure 2 shows a general diagram of the system.…”

Section: Geodma Descriptionmentioning

confidence: 99%

GeoDMA—Geographic Data Mining Analyst

Körting

Fonseca

Câmara

2013

Computers & Geosciences

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Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in one year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote databases. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.

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TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications

Cited by 55 publications

References 32 publications

Raiva bovina segundo os mosaicos de uso e cobertura da terra no estado de São Paulo entre 1992 e 2003

Raiva bovina segundo os mosaicos de uso e cobertura da terra no estado de São Paulo entre 1992 e 2003

Indicators for Assessing Habitat Values and Pressures for Protected Areas—An Integrated Habitat and Land Cover Change Approach for the Udzungwa Mountains National Park in Tanzania

GeoDMA—Geographic Data Mining Analyst

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