Geospatial Simulation Model of Deforestation and Reforestation Using Multicriteria Evaluation

Abstract

Deforestation is an anthropic phenomenon that negatively affects the environment and therefore the climate, the carbon cycle, biodiversity and the sustainability of agriculture and drinking water sources. Deforestation is counteracted by reforestation processes, which is caused by the natural regeneration of forests or by the establishment of plantations. The present research is focused on generating a simulation model to predict the deforestation and reforestation for 2030 and 2050 using geospatial analysis techniques and multicriteria evaluation. The case study is the North Pacific Basin, which is one of the areas with the greatest loss of forest cover in Mexico. The results of the spatial analysis of forest dynamics determined that the forest area in 2030 would be 98,713.52 km2, while in 2050 would be 101,239.8 km2. The mean annual deforestation and reforestation expected in the study area is 115 and 193.84 km2, for the 2014–2030 period, while mean annual deforestation and reforestation values of 95 and 221.31 km2 are expected for the 2030–2050 period. Therefore, considering the forest cover predicted by the deforestation and reforestation model, a carbon capture of 16,209.67 ton/C was estimated for the 2014–2030 period and 587,596.01 ton/C for the 2030–2050.

Geospatial Simulation Model of Sustainable Mangrove Development Scenarios for the Years 2030 and 2050 in Marismas Nacionales, Mexico

“…After weighing the factors, they were normalized in the Idrisi selva software through the fuzzy logic function (fuzzy) using the scale of 0-255 and the monotonic increasing and decreasing linear functions according to the behavior of the factor, i.e., the factors of inverse proximity. In addition, the factors of remoteness to highways and roads were normalized by applying the linear decreasing function [12].…”

“…Once these factors were modeled, the suitability map was obtained, integrating the corresponding factors and employing the weighted linear summation that is based on adding the derivation of multiplying each element of the raster for each of the normalized factors by the established importance value for each factor [12]. Finally, the most suitable pixels corresponding to the surface demand were selected to generate the 2050 sustainable scenario.…”

“…Regarding the increasing damage to the environment, more and more future scenarios are being used to predict what is possible to happen by perceiving how the dynamic processes in a geographic space will be [11,12], comparing the consequences of each of them [13,14], and allowing the creation of new alternatives [15] by describing and generating coherent images [16]. In addition, they can be used as an early warning system by showing the effects of future changes in land use and vegetation cover [14], providing helpful information for professionals and decision makers of programs and public policies in environmental management; for this reason, they suggest greater use of predictive models [17].…”