Over the last few years, urban expansion has occurred without much control or urban planning, causing both regional and global impacts on the natural ecosystems. This situation has spurred reflection on current planning policies and has also stimulated research into new tools and methodologies for the simulation of alternative territorial scenarios for the future. In this respect, the use of multicriteria evaluation techniques within a GIS environment has proved to be an efficient option for an optimal allocation of land uses. However, those techniques have not been explored thoroughly in the field of future scenario simulation. The aim of our research is to show and to reinforce the possibilities of these techniques in developing three different scenarios of urban growth (business as usual, crisis, and innovation and sustainability). All three scenarios discriminate between three land uses (residential, commercial, and industrial), and have been developed for the Madrid region. The base-line year of these scenarios is 2000 and the time horizon is 2020. In that process, fourteen spatial factors, which encompass environmental, economic, and social issues, have been considered. The results obtained show that some discrepancies exist between the business as usual scenario and European policies for transport and the environment. This situation has an impact on the territory and causes irreversible damage to the environment. The crisis scenario, in contrast, alleviates those pressures, if not because of the application of environmental policies to the planning process, at least because the situation of crisis causes the metropolitan model to change. Finally, innovation has been considered a driver of economic development and good planning practice, the positive effects of which have been highlighted in the third scenario.

ResumenEl objetivo de esta investigación fue estudiar la deforestación y sus causas en el estado de Sinaloa, México. Para ello, se utilizó la cartografía de Uso de Suelo y Vegetación del año 1993 y 2011 a escala 1:250 000, con esta se estimó la deforestación mediante una técnica de detección de cambios; posteriormente, se caracterizó la deforestación mediante la consulta a expertos. Por último, se aplicó la matriz de cambios para analizar las pérdidas, ganancias y transiciones y corroborar cartográficamente lo obtenido por los expertos y la detección de cambios. Los resultados indican una deforestación de 126.50 km 2 /año y una tasa media anual de 0.41%. De la consulta a expertos se determinó que las principales causas de estos procesos son la expansión agrícola y la extensión de infraestructura con un impacto de 49.40% y 18.8%, respectivamente. En cuanto a la matriz de cambios, se determinó que especialmente la categoría

The last ten years have shown that Climate Change (CC) is a major global issue to attend to. The integration of its effects into coastal impact assessments and adaptation plans has gained great attention and interest, focused on avoiding or minimizing human lives and asset losses. Future scenarios of mean sea level rises and wave energy increase rates have then been computed, but downscaling still remains necessary to assess the possible local effects in small areas. In this context, the effects of CC on the wave climate in the Gulf of California (GC), Mexico, have received little attention, and no previous studies have tackled the long-term trend of wave climate at a regional scale. In this paper, the long-term trends of the wave height, wave period and wave energy in the GC were thus investigated, using the fifth-generation climate reanalysis dataset (ERA5). The long-term shoreline evolution was also examined from historical Landsat images, so as to identify erosional hotspots where intervention can be prioritized. The results indicate that both the mean and extreme wave regimes in the GC are getting more energetic and that two-thirds of the coast is suffering chronic erosion. A discrepancy between the trends of the wave period and wave height in some regions of the Gulf was also found. Finally, the importance of natural processes, human activity and CC in the shoreline change is highlighted, while addressing the need for future permanent field observations and studies in the GC.

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.

Coda waves were analyzed from events recorded by NARS seismic network deployed along both margins of the Gulf of California, Mexico, to estimate coda attenuation Qc. Sato’s (1977) single scattering model was used for a coda window of 20 to 25 s beginning at twice the S-wave travel time. Events recorded from 2003 to 2007 located in the central region of the Gulf of California were analyzed. Source-to-receiver distances are between 40 and 500 km. Assuming a power law of the form QC (f) = QO f a, QC values were averaged and a value of QO = 83±3 and a frequency-dependence α value of 1.06±0.03 in the frequency range from 1 to 7 Hz was obtained. QO value and the high frequency dependency agree with the values of other regions characterized by a high tectonic activity. Based on source-station distribution two subregions (north and south) were defined. QC values were calculated and correlated with tectonics and morphology of each area. The observed higher attenuation in the south region can be attributed to the fact that south region is more fractured since the greater earthquakes occur in central to south Gulf of California and the oceanic crust is reported to be thinner in the southern region.

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.

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. <p>   </p>

Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due to selective logging in a Forest Reserve of the Venezuelan Amazon. Our work was conducted in two phases: the first, by means of a direct method we detected the infrastructure related to logging at the sub-pixel level, and for the second, we used an indirect approach using buffer areas applied to the results of the selective logging mapping. Pre- and post-logging forest inventory data, combined with the mapping analysis were used to quantify the effects of logging on aboveground carbon emissions for three different sources: hauling, skidding and tree felling. With an overall precision of 0.943, we demonstrate the potential of this method to efficiently map selective logging and forest degradation with commission and omission errors of +7.6 ± 4.5 (Mean ± SD %) and −7.5% ± 9.1 respectively. Forest degradation due to logging directly affected close to 24,480 ha, or about ~1% of the total area of the Imataca Forest Reserve. On average, with a relatively low harvest intensity of 2.8 ± 1.2 trees ha−1 or 10.5 ± 4.6 m3 ha−1, selective logging was responsible for the emission of 61 ± 21.9 Mg C ha−1. Lack of reduced impact logging guidelines contributed to pervasive effects reflected in a mean reduction of ~35% of the aboveground carbon compared to unlogged stands. This research contributes to further improve our understanding of the relationships between selective logging and forest degradation in tropical managed forests and serves as input for the potential implementation of projects for reducing emissions from deforestation and forest degradation (REDD+).