The main objective of this research is to analyze deforestation in State Sinaloa during the period 1990-2014. For this, “deforestationhot-spot areas” were identified, by crossing maps of 1993 and 2011 at a 1:250,000...The main objective of this research is to analyze deforestation in State Sinaloa during the period 1990-2014. For this, “deforestationhot-spot areas” were identified, by crossing maps of 1993 and 2011 at a 1:250,000 scale with knowledge from environmental and forest experts from each region. Landsat images from 1990 and 2014 and Terra Amazon System were used to monitor the most critical hot spot area, applying Linear Spectral Mixture Analysis and Image Segmentation Ground Product. In order to generate the map deforestation year zero (1990), every segmented object of ground product was visually assigned to “Forest” and “No-Forest” categories. Therefore, gains and losses were interpreted for the map deforestation year one (2014). Those products were validated with the help of experts on the subject and applying a confusion matrix. Results obtained indicated that the highest forest loss was located in North-Central Sinaloa (hot spot area number two) by establishing the average annual rate of deforestation of 4741.90 ha/year with an average rate of 0.60%, being higher than the national average rate (0.37%). This result affects directlyon calculation of carbonfluxes at nationallevel.展开更多
In any model, Sensitivity Analysis (SA) is a fundamental process to improve the robustness and credibility of the results, as part of validation procedure. Generally, SA determined how the variation in the model outpu...In any model, Sensitivity Analysis (SA) is a fundamental process to improve the robustness and credibility of the results, as part of validation procedure. Generally, SA determined how the variation in the model output can be apportioned to different sources of variations, and how the given model depends upon the information fed into it. Many complex techniques of SA have been developed within the field of numerical modeling;however, they have limited applications for spatial models, as they do not consider variations in the spatial distributions of the variables included. In this research, a variation in the implementation of a Global Sensitivity Analysis (E-FAST) is proposed in order to include the spatial level. For this purpose the conventional tools available in a raster Geographical Information System (GIS) are used. The procedure has been tested in a simulation of urban growth for the Madrid Region (Spain) based on Multi-Criteria Evaluation (MCE) techniques. The results suggest that the inclusion of the spatial perspective in the application of the SA is necessary, because it can modify the factors that have a decisive influence on the results.展开更多
文摘The main objective of this research is to analyze deforestation in State Sinaloa during the period 1990-2014. For this, “deforestationhot-spot areas” were identified, by crossing maps of 1993 and 2011 at a 1:250,000 scale with knowledge from environmental and forest experts from each region. Landsat images from 1990 and 2014 and Terra Amazon System were used to monitor the most critical hot spot area, applying Linear Spectral Mixture Analysis and Image Segmentation Ground Product. In order to generate the map deforestation year zero (1990), every segmented object of ground product was visually assigned to “Forest” and “No-Forest” categories. Therefore, gains and losses were interpreted for the map deforestation year one (2014). Those products were validated with the help of experts on the subject and applying a confusion matrix. Results obtained indicated that the highest forest loss was located in North-Central Sinaloa (hot spot area number two) by establishing the average annual rate of deforestation of 4741.90 ha/year with an average rate of 0.60%, being higher than the national average rate (0.37%). This result affects directlyon calculation of carbonfluxes at nationallevel.
文摘In any model, Sensitivity Analysis (SA) is a fundamental process to improve the robustness and credibility of the results, as part of validation procedure. Generally, SA determined how the variation in the model output can be apportioned to different sources of variations, and how the given model depends upon the information fed into it. Many complex techniques of SA have been developed within the field of numerical modeling;however, they have limited applications for spatial models, as they do not consider variations in the spatial distributions of the variables included. In this research, a variation in the implementation of a Global Sensitivity Analysis (E-FAST) is proposed in order to include the spatial level. For this purpose the conventional tools available in a raster Geographical Information System (GIS) are used. The procedure has been tested in a simulation of urban growth for the Madrid Region (Spain) based on Multi-Criteria Evaluation (MCE) techniques. The results suggest that the inclusion of the spatial perspective in the application of the SA is necessary, because it can modify the factors that have a decisive influence on the results.
