The human-induced changes of the Earth's land surfaces have been unprecedented, with outcomes often indicating degradation and loss of environmental quality. Mato Grosso State in Brazil, location of the study area, underwent extensive land-use and land-cover changes in recent decades with the rates, patterns and consequences poorly documented until now. In this context, the aim of the present research is to propose a multidisciplinary approach for quantifying historical land-use and environmental changes in the southeast part of this State, where the Cerrado biome (Brazilian savannas) has been intensively converted into agricultural lands. The methodology includes three parts: remote sensing change detection, land vulnerability mapping, and identification of key environmental indicators. Land-use/cover information was extracted from a temporal remote sensing dataset using an object-oriented classification approach, and the changes quantified employing a post-classification method. In addition, the study area was assessed for its vulnerabilities, focusing mainly on erosion risks, wetlands, and areas with limited or no suitability for crops. Finally, key environmental indicators were identified from the preceding steps and analyzed within the Organisation for Economic Co-operation and Development (OECD) Pressure-State-Response (PSR) framework. The results provided an improved mapping of the Cerrados natural vegetation conversion into crops and pastures, and indicate that the Cerrado vegetation was intensively converted and also became more fragmented in the time frame studied. Between 1985 and 2005 the area lost approximately 6491 km 2 of Cerrados (42 %). Modeling based on the Universal Soil Loss Equation indicated significant increase in erosion risk from 1985 to 2005 mainly related to the increase in crop areas and the crops' encroachment into more fragile lands.The identification of environmental indicators rendered complex environmental information more generally accessible by structuring it within the PSR framework.The indicators captured key information about land-use and environmental changes in the area, showing that agricultural expansion is the major human activity exerting pressure on natural resources at a landscape scale, and that the pattern of change included high rates of crop expansion and the use of fragile environments such as wetlands and sandy erodable soils.

The primary objective of this research was to evaluate the potential for monitoring forest change using Landsat ETM and Aster data. This was accomplished by performing eight change detection algorithms: pixel post-classification comparison (PCC), image differencing Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Transformed Difference Vegetation Index (TDVI), principal component analysis (PCA), multivariate alteration detection (MAD), change vector analysis (CVA) and tasseled cap analysis (TCA). Methods, Post-Classification Comparison and vegetation indices are straightforward techniques and easy to apply. In this study the simplified classification with only 4 forest classes namely close forest, open forest, bare land and grass land was used The overall classification accuracy obtained were 88.4%, 91.9% and 92.1% for the years 2000, 2003 and 2006 respectively. The Tasseled Cap green layer (GTC) composite of the three images was proposed to detect the change in vegetation of the study area. We found that the RBG-TCG worked better than RGBNDVI. For instance, the RBG-TCG detected some areas of changes that RGB-NDVI failed to detect them, moreover RBG-TCG displayed different changed areas with more strong colours. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The results support the CVA approach to change detection. The calculated date to date change vectors contained useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the principal components analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. Both methods found to offer the potential for monitoring forest change detection. A recently proposed approach, the multivariate alteration detection (MAD), in combination with a posterior maximum autocorrelation factor transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. The MAD transformation provides a way of combining different data types that found to be useful in change detection. Accuracy assessment is an important final step addressed in the study to evaluate the different change detection techniques. A quantitative accuracy assessment at level of change/no change pixels was performed to determine the threshold value with the highest accuracy. Among the various accuracy assessment methods presented the highest accuracy was obtained using the post-classification comparison based on supervised classification of each two time periods (2000 -2003 and 2003-2006), which were 90.6% and 87% consequently.