Este estudo foi conduzido em uma área localizada no município de Lagoa da Confusão, Estado do Tocantins, com os objetivos de diagnosticar fragmentos florestais naturais, denominados regionalmente de "ipucas", e mapear as diferentes feições fisionômicas e o uso antrópico da área. Para realização deste estudo utilizou-se um sistema de informações geográficas, IDRISI 2.0. O principal resultado obtido foi o histórico de perturbação que se intensificou a partir da criação do Estado do Tocantins e da implantação do Projeto Rio Formoso para o cultivo de arroz irrigado; em relação à classificação fisionômica e ao uso antrópico foram individualizadas 73 "ipucas". A partir das variáveis consideradas verificou-se que, em relação à área, 56,16% dos fragmentos possuem áreas de até 5,00 ha e apenas quatro apresentaram áreas superiores a 100,00 ha. Aproximadamente 50% destas possuem formas alongadas, o que indica alta relação perímetro/área. Apenas três "ipucas" apresentaram índice de circularidade (C) próximo de 1. Foram identificadas oito feições circunvizinhas às "ipucas". Destas, cinco são ambientes naturais (varjão-sujo, varjão-limpo, pastagem natural, corpos d'água e afloramento rochoso) e as demais resultantes de ações antrópicas (área agrícola, pastagem plantada e rede viária).

Global efforts to avoid anthropogenic conversion of natural habitat rely heavily on the establishment of protected areas. Studies that evaluate the effectiveness of these areas with a focus on preserving the natural habitat define effectiveness as a measure of the influence of protected areas on total avoided conversion. Changes in the estimated effectiveness are related to local and regional differences, evaluation methods, restriction categories that include the protected areas, and other characteristics. The overall objective of this study was to evaluate the effectiveness of protected areas to prevent the advance of the conversion of natural areas in the core region of the Brazil’s Cerrado Biome, taking into account the influence of the restriction degree, governmental sphere, time since the establishment of the protected area units, and the size of the area on the performance of protected areas. The evaluation was conducted using matching methods and took into account the following two fundamental issues: control of statistical biases caused by the influence of covariates on the likelihood of anthropogenic conversion and the non-randomness of the allocation of protected areas throughout the territory (spatial correlation effect) and the control of statistical bias caused by the influence of auto-correlation and leakage effect. Using a sample design that is not based on ways to control these biases may result in outcomes that underestimate or overestimate the effectiveness of those units. The matching method accounted for a bias reduction in 94–99% of the estimation of the average effect of protected areas on anthropogenic conversion and allowed us to obtain results with a reduced influence of the auto-correlation and leakage effects. Most protected areas had a positive influence on the maintenance of natural habitats, although wide variation in this effectiveness was dependent on the type, restriction, governmental sphere, size and age group of the unit.

Termos para indexação: classes de solo, declividade, uso atual das terras. CHARACTERIZATION OF THE PHYSICAL ENVIRONMENT IN THE QUATRO BOCAS WATERSHED IN ANGELIM, PE, AND ITS QUANTIFICATION BY A GEOGRAPHIC INFORMATION SYSTEMABSTRACT -The objectives of this paper were to characterize the physical environment of a watershed and to use a Geographic Information System (GIS) to evaluate: area by soil class; area by soil class and slope; area by soil class, slope and current land use. Additionally some considerations about the watershed planning are also made. The basic information layers used were: soils map, slope map and current land use map (year of 1989). The soils presented a high deficiency of natural fertility with the Red-Yellow Podzolic soil (Abruptic Plinthaqult) occupying 87% of the area, mostly in accentuated slopes favoring, therefore, the erosion process, posing difficulties to the mechanization and requiring special care regarding its use and management. The Dystrophic Regosol (Arenic Entisol) and the Dystrophic Alluvial (Fluvic Entisol) soils represent 13% of the area in a relief suitable to the establishment of annual crops.

The constant increase in population in conjunction with unplanned and irregular urban growth, typical problems in developing countries, can promote a rapid increase in population density and related public infrastructure demand that may be hard to bear with the available economic resources. Efficient monitoring of urban development is thus a key instrument for planners and public policy makers that have to cope with this scenario. This work aims at developing a tool to aid monitoring urban growth from very-high resolution remote sensing images, focussing on the integration of available open-source software and the application of OBIA methods. Specifically, we created a method for detection of urban, land use/land cover classes based on the integration of the InterIMAGE and the Orange Canvas software packages. The image interpretation model for the particular application was constructed with the aid of dataflow building blocks (widgets) for data analysis, structured in the visual programming environment of Orange Canvas. The Classification Tree and the Classification Tree Graph widgets were used to design a decision tree that was later translated in InterIMAGE Decision Rules. The study was conducted over an image from the GeoEye-1 sensor, covering a central area of the city of Goianésia, in the Midwestern region of Brazil. Ten land use/land cover classes were the target of the supervised classification. The results obtained in the experiments confirm that the integration of the two open-source packages can provide for accurate remote sensing image analysis, while facilitating data exploration and the construction of automatic image interpretation models.

In this work we introduce an object-based method, applied to urban land cover mapping. The method is implemented with two open-source tools: SIPINA, a data mining software package; and InterIMAGE, an object-based image analysis system. Initially, segmentation, feature extraction and sample selection procedures are performed with InterIMAGE. In order to reduce the time and subjectivity involved to develop the decision rules in InterIMAGE, a data mining step is then carried out with SIPINA. In sequence, the decision trees delivered by SIPINA are analysed and encoded into InterIMAGE decision rules for the final classification step. Experiments were conducted using a subset of a GeoEye image, acquired in January 01, 2013, covering the urban portion of the municipality of Goianésia, Brazil. Five decision tree induction algorithms, available in SIPINA, were tested: ID3, C45, GID3, Assistant86 and CHAID. The TAU and Kappa coefficients were used to evaluate the results. The TAU values obtained were in the range of 0.66 and 0.70, while those for Kappa varied from 0.65 to 0.69.