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

This study presents for the first time in Venezuela a joint analysis of deforestation and forest degradation processes, including its effects on carbon emissions. The Caparo Forest Reserve, located in the Western Plains ecoregion, in one of the national hot spots of deforestation, served as a case study using three different periods: 1990-2000, 2000-2010 and 2010-2015. In the context of the United Nations Framework Convention on Climate Change (UNFCCC) framework, the Practice Guidance for Land Use, Land-Use Change and Forestry from the Intergovernmental Panel on Climate Change (IPCC) was followed. These guidelines combine the activity data for the estimation of deforestation and degradation rates, in this case using open access Landsat imagery in conjunction with the TerraAmazon system with the emission factors, and these based on aboveground biomass (AGB) estimations using field data from permanent plots monitored during the study period. Deforestation was responsible of a net loss of −53,461 ha, while close to −3667 ha were classified as degraded forests during the 1990-2000 decade (−4.9% annual deforestation rate). An estimated area of −36,447 ha and −515 ha between 2000 and 2010 was affected by both processes (−4.3% annual forest loss), and −8111 ha and −737 ha between 2010 and 2015 (−3.2% per year). These processes were responsible for an estimated equivalent in carbon emissions of 2.21 ± 0.32 (SEM-Standard Error of the Mean) Mt CO 2 year −1 (1990-2000), 1.56 ± 0.19 Mt CO 2 per year between 2000 and 2010, while 0.80 ± 0.11 Mt CO 2 year −1 during the 2010-2015 period. Between 92.9% and 98.63% (mean 94.9%) of these emissions came from deforestation, and between 1.37% and 7.79% (mean 5.1%) from forest degradation. Using available data, at national scale, deforestation and forest degradation in Caparo represented, on average, 0.49% of the total CO 2 emissions and about 1.79% of land use change related emissions for the same period in Venezuela. Finally, we briefly outline a set of elements so these results can serve as a baseline for the potential establishment of a Reducing Emissions from Deforestation and Forest Degradation (REDD+) strategy in the area.

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.

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.

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+).

The present study focuses on identifying and describing the possible proximate and underlying causes of deforestation and its factors using the combination of two techniques: (1) specialized consultation and (2) spatial logistic regression modeling. These techniques were implemented to characterize the deforestation process qualitatively and quantitatively, and then to graphically represent the deforestation process from a temporal and spatial point of view. The study area is the North Pacific Basin, Mexico, from 2002 to 2014. The map difference technique was used to obtain deforestation using the land-use and vegetation maps. A survey was carried out to identify the possible proximate and underlying causes of deforestation, with the aid of 44 specialized government officials, researchers, and people who live in the surrounding deforested areas. The results indicated total deforestation of 3,938.77 km2 in the study area. The most important proximate deforestation causes were agricultural expansion (53.42%), infrastructure extension (20.21%), and wood extraction (16.17%), and the most important underlying causes were demographic factors (34.85%), economics factors (29.26%), and policy and institutional factors (22.59%). Based on the spatial logistic regression model, the factors with the highest statistical significance were forestry productivity, the slope, the altitude, the distance from population centers with fewer than 2500 inhabitants, the distance from farming areas, and the distance from natural protected areas.