Search

Abstract: The present work is committed to simulate the expansion of agricultural and cattle raising activities within a watershed located in the fringes of the Xingu National Park, Brazilian Amazon. A spatially explicit dynamic model of land cover and land use change was used to provide both past and future scenarios of forest conversion into such rural activities, aiming to identify the role of driving forces of change in the study area. The employed modeling platform – Dinamica EGO – consists in a cellular automata environment that embodies neighborhood-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables and legal restrictions drove this simulation model, and statistical validation tests were then conducted for the generated past simulations (from 2000 to 2005), by means of multiple resolution fitting methods. Based on optimal calibration of past simulations, future scenarios were conceived, so as to figure out trends and spatial patterns of forest conversion in the study area for the year 2015. In all simulated scenarios, pasturelands remained nearly stable throughout the analyzed period, while a large expansion in croplands took place. The most optimistic scenario indicates that more than 50% of the natural forest will be replaced by either cropland or pastureland by 2015. This modeling experiment revealed the suitability of the adopted model to simulate processes of forest conversion. It also indicates its possible further applicability in generating simulations of deforestation for areas with expanding rural activities in the Amazon and in tropical forests worldwide. [Copyright &y& Elsevier]

Abstract: Fires in the tropical forests are the main source of greenhouse gas emissions in Brazil. Current methods aimed at the detection and monitoring of fire events in the Brazilian Amazon are frequently insufficient for policy decisions which aim to prevent new fire events and identify the previous land cover of the affected areas. This research applies remote sensing and GIS technique to areas with high occurrence of forest fires in the Brazilian Amazon, with the aiming to recognize land use changes that could: 1) Identify areas with high risk of being burnt and 2) Improve current fire scars mapping methods by enabling the discrimination of fires in primary forests and fires in previously burnt areas. The Change Vector Analysis method was applied to the Red and NIR bands of two MODIS/Terra images from key dates prior to the 2005 forest fire season, resulting in one change vector image with two components: direction and magnitude of changes. A Decision Tree (DT) was designed and evaluated through the C 4.5 algorithm to classify 2400 sample pixels extracted from four selected classes inside the change vector images: A) Forest; B) Agricultural areas; C) Fire risk in primary forest; and D) Fire risk in already degraded areas. The DT achieved a global accuracy of 90.21%. Samples from classes B and D were the main contributors to the DT confusion, with omission errors of 9.5% and 24.5%, respectively. The method was tested in 14 municipalities for the year of 2005, 2006 and 2007 and compared with hotspots from the MODIS active fire product, resulting in a correlation coefficient of 0.84. [ABSTRACT FROM AUTHOR]