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Region-growing segmentation algorithms are useful for remote sensing image segmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. This letter proposes an objective function for selecting suitable parameters for region-growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.

Today, there is a huge amount of data gathered about the Earth, not only from new spatial information systems, but also from new and more sophisticated data collection technologies. This scenario leads to a number of interesting research challenges, such as how to integrate geographic information of different kinds. The basic motivation of this paper is to introduce a GIS architecture that can enable geographic information integration in a seamless and flexible way based on its semantic value and regardless of its representation. The proposed solution is an ontology-driven geographic information system that acts as a system integrator. In this system, an ontology is a component, such as the database, cooperating to fulfill the system's objectives. By browsing through ontologies the users can be provided with information about the embedded knowledge of the system. Special emphasis is given to the case of remote sensing systems and geographic information systems. The levels of ontologies can be used to guide processes for the extraction of more general or more detailed information. The use of multiple ontologies allows the extraction of information in different stages of classification.The semantic integration of aerial images and GIS is a crucial step towards better geospatial modeling.

A major recent trend in remote sensing research is the analysis of satellite image time series for land use and land cover monitoring and mapping. In this paper, we describe the Time-Weighted Dynamic Time Warping algorithm, which improves on previously proposed methods for land cover and land use classification. The method is based on the dynamic time warping method that measures similarity between two temporal sequences. We modified this method to account for seasonality of land cover types. The resulting algorithm is flexible to account for different cropland systems, tropical forests, and pasture using few training samples. The algorithm had better results than other Dynamic Time Warping variations for land classification. The method is suitable to make land use and land cover maps and has potential for large-scale analysis at country or continental scale, using global data sets such as the EVI time series from the MODIS sensor.

Regionalization is a classification procedure applied to spatial objects with an areal representation, which groups them into homogeneous contiguous regions. This paper presents an efficient method for regionalization. The first step creates a connectivity graph that captures the neighbourhood relationship between the spatial objects. The cost of each edge in the graph is inversely proportional to the similarity between the regions it joins. We summarize the neighbourhood structure by a minimum spanning tree (MST), which is a connected tree with no circuits. We partition the MST by successive removal of edges that link dissimilar regions. The result is the division of the spatial objects into connected regions that have maximum internal homogeneity. Since the MST partitioning problem is NP-hard, we propose a heuristic to speed up the tree partitioning significantly. Our results show that our proposed method combines performance and quality, and it is a good alternative to other regionalization methods found in the literature.

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The role of improving the enforcement of Brazil's Forest Code in reducing deforestation in the Amazon has been highlighted in many studies. However, in a context of strong political pressure for loosening environmental protections, the future impacts of a nationwide implementation of the Forest Code on both environment and agriculture remain poorly understood. Here, we present a spatially explicit assessment of Brazil's 2012 Forest Code through the year 2050; specifically, we use a partial equilibrium economic model that provides a globally consistent national modeling framework with detailed representation of the agricultural sector and spatially explicit land-use change. We test for the combined or isolated impacts of the different measures of the Forest Code, including deforestation control and obligatory forest restoration with or without environmental reserve quotas. Our results show that, if rigorously enforced, the Forest Code could prevent a net loss of 53.4 million hectares (Mha) of forest and native vegetation by 2050, 43.1 Mha (81%) of which are in the Amazon alone. The control of illegal deforestation promotes the largest environmental benefits, but the obligatory restoration of illegally deforested areas creates 12.9 Mha of new forested area. Environmental reserve quotas further protect 5.8 Mha of undisturbed natural vegetation. Compared to a scenario without the Forest Code, by 2050, cropland area is only reduced by 4% and the cattle herd by 8%. Our results show that compliance with the Forest Code requires an increase in cattle productivity of 56% over four decades, with a combination of a higher use of supplements and an adoption of semi-intensive pasture management. We estimate that the enforcement of the Forest Code could contribute up to 1.03 PgCO 2 e to the ambitious GHG emissions reduction target set by Brazil for 2030.