A critical discussion was included of further historical factors responsible for deforestation. Economic analysis of deforestation in Mexico* - Volume 1 Issue 2 - Edward B. Barbier, Joanne C. Burgess
Also Carlos is going to perform a degradation analysis using these satellite images using spectral mixture analysis. 2006; 37(6): 802–815. However, for the lower basin, direct beam radiation explained more of the total deviance than slope. The difference between deforestation and degradation analysis is that deforestation will detect forest cover elimination (FIGURE 3) whereas deforestation can detect partial vegetation removal (FIGURE 4). cost of access to available land from rural settlements) and regional relative accessibility or accessibility to regional markets (i.e. In addition, this data could also be used to evaluate changes in habitat suitability for different wildlife species across Mexico. Bands show two standard errors around the response.Response of vegetation cover in the middle basin to each term in a GAM model including local relative accessibility (Access100), population density (PopDens), and hydroperiod. Small farmers in the upper basin live in a highly scattered settlement pattern and are predominantly subsistence farmers. pmid:16555027 . Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Water levels near the surface maybe associated with the probability of accumulating water and unfavourable moisture conditions for cropping.Finally, the analysis confirmed the importance of dry season insolation as an important factor in deforestation, particularly in the lower basin. Socioeconomic drivers of deforestation in the northern Ecuadorian Amazon. The study area consists of the complete area of the Usumacinta River Basin located in Mexican territory. [We believe that policies directed at promoting the diversified and sustainable use of forests as well as alternative agricultural techniques that maintain and/or increase the productivity of cultivated land, would be more effective in reducing the likelihood of deforestation in To date, almost all of the strategies focused on reducing deforestation are directed at small-scale farmers. The methodology probes to be successful for interpreting the relative importance of a series of physical and socio-economic factors responsible for the pattern of historical deforestation and for providing contextual explanations for this pattern. This suggests that direct beam radiation may be associated with some driving factor not captured by slope. This finding is in accordance with the results from other research (e.g. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. In addition, off road cost of access to available land, which is modulated by terrain and topography, may be a critical factor; therefore, areas of steep slope are not likely to be deforested. Land-use changes in Mexico are a response to regional, national, or international market pressures for the extraction of timber products, mining, converting forests to agricultural production areas, tourism, urban and industrial developments, and infrastructure projects (e.g., dams, roads, and highways). The annual average is 2143 mm. The use of fire is a common feature in this region. The adopted methodology in this study was useful for providing insight into the comparative importance of these drivers in different parts of the region. The ideal would probably be an approach in which this kind of spatial analysis is enriched with such data.For more information about PLOS Subject Areas, click This information will be also shared with Carlos’ collaborators in Mexico, which include the National Forest Commission (CONAFOR), the National Commission for Protected Areas (CONANP) and NGO Grupo Ecologico Sierra Gorda. Once the images has been cloud corrected, a high performance classification algorithm will be applied to a range of satellite images covering a 20 year span from 1990 to 2010 to identify forested vs. non-forested areas across the 13 regions at temporal intervals of approximately three years. An accuracy assessment of the classification results suggested a Kappa index of agreement of 0.85, and an overall agreement of 90.2 percent.
The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. Click through the PLOS taxonomy to find articles in your field.For more information about PLOS Subject Areas, click