Assessing and monitoring forest degradation under national Monitoring, Verification and Reporting (MRV) systems in developing countries have been difficult to implement due to the lack of adequate technical and operational capacities. This study aims at providing methodological options for monitoring forest degradation in developing countries by using freely available remote sensing, forest inventory and ancillary data. We propose using Canopy Cover to separate, through a time series analysis approach using Landsat Imagery, forest areas with changes over time from sectors that report a “stable condition”. Above ground Biomass and Net Primary Productivity derived from remote sensing data were used to define thresholds for areas considered degraded. The approach was tested in a semi-deciduous tropical forest in the Southeast of Mexico. The results showed that higher rates of forest degradation, 1596 to 2865 ha year⁻¹, occur in areas with high population densities. The results also showed that 43% of the forests of the study area remain with no evident signs of degradation, as determined by the indicators used. The approach and procedures followed allowed for the identification and mapping of the temporal and spatial distribution of forest degradation, based on the indicators selected, and they are expected to serve as the basis for operations of the Reduction of Emissions from Deforestation and Forest Degradation (REDD+) initiative in Mexico and other developing countries, provided appropriate adaptations of the methodology are made to the conditions of the area in turn.

Locating potential tree-planting sites and analyzing tree canopy cover is important in the planning and management of urban forests. This paper reports the quantification of potential planting sites as well as tree canopy cover in the urban area of Mexico City, estimated by means of SPOT (Satellite Pour l’Observation de la Terre) 6 satellite images and a supervised pixel-based classification approach. Results showed an estimated area of 3100.7 ha of potentially useful sites, including places with bare soil and grass-covered areas such as median strips, roundabouts and parks. An average tree canopy cover of 10.6% and an average impervious surface of 79.2% for the 15 boroughs were also analyzed. The area of potential planting sites would represent a 5% gain for the current tree canopy cover if it were to be planted. With an overall accuracy of 92.4%, the use of both images from the SPOT 6 sensor and the classification approach proved to be appropriate for obtaining thematic covers in the urban environment of Mexico City.

A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper ETM+ scan-line corrector SLC are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: 1 local linear histogram matching LLHM; 2 neighbourhood similar pixel interpolator NSPI; 3 geostatistical neighbourhood similar pixel interpolator GNSPI; and 4 weighted linear regression WLR. All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients κ, and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis ISODATA land-cover classification maps. In addition, Pearson correlation coefficients r and root mean squares of the error RMSEs were employed for estimates agreement with fractional land cover. The best results visually overall accuracy > 85%, κ r > 0.84 and RMSE

Mexico is home to the highest species diversity of pines: 46 species out of 113 reported around the world. Within the great diversity of pines in Mexico, Pinus culminicola Andresen et Beaman, P. jaliscana Perez de la Rosa, P. maximartinenzii Rzed., P. nelsonii Shaw, P. pinceana Gordon, and P. rzedowskii Madrigal et M. Caball. are six catalogued as threatened or endangered due to their restricted distribution and low population density. Therefore, they are of special interest for forest conservation purposes. In this paper, we aim to provide up-to-date information on the spatial distribution of these six pine species according to different historical registers coming from different herbaria distributed around the country by using spatial modeling. Therefore, we recovered historical observations of the natural distribution of each species and modelled suitable areas of distribution according to environmental requirements. Finally, we evaluated the distributions by contrasting changes of vegetation in the period 1991–2016. The results highlight areas of distribution for each pine species in the northeast, west, and central parts of Mexico. The results of this study are intended to be the basis of in situ and ex situ conservation strategies for the endangered Mexican pines.

En la actualidad algunos pinos endémicos de México están en riesgo, debido a diversos factores, tanto antrópicos como relacionados con alteraciones climáticas. En este trabajo se modeló la distribución potencial actual y bajo escenarios de cambio climático de cuatro especies de pinos piñoneros. Para ello, se utilizaron coberturas bioclimáticas y datos de presencia de los taxones considerados, como variables predictoras. Los resultados sugieren una buena predicción de los patrones de distribución geográfica (AUC> 0.9 en todos los casos). La distribución potencial alta generada con el modelo GFDL-CM3, para el horizonte 2049-2065 escenarios RCP 4.5 tuvo modificaciones especificas por taxon en su superficie. Las especies más sensibles fueron Pinus culminicola, P. johannis y P. pinceana que evidenciaron reducciones de 83.6, 59.5 y 80.0 %, respectivamente; comparados con la superficie actual. En el escenario RCP 8.5, las reducciones de estos taxa fueron de 83.7, 56.9 y 79.3 %, para cada una. Los modelos de distribución de P. nelsonii mostraron, para ambos escenarios climáticos (RCP 4.5 y 8.5), aumentos en la extensión potencial alta con respecto a la actual hasta de 50.8 % y 49.5 %, respectivamente. Los escenarios de cambio climático indican que la distribución de los pinos piñoneros será afectada en el futuro cercano por los cambios en los patrones de precipitación y temperatura.