Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators

Abstract

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.

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

“…However, since many of these classifiers presented overlapping thresholds between very different classes, it is clear that they can not be configured as absolute descriptors of each class. For instance, analysts may need different class definition in their studies, or their definition may be based on criteria like above-ground biomass, Net Primary Productivity, forest stand volume, basal area, average stand height, diameter at breast height, age and/or many others, as used by studies such Lu et al [3], Vieira et al [69], Salomão et al [70] and Romero-Sanchez and Ponce-Hernandez [74] to justify the subdivision of secondary vegetation classes. Nonetheless, many of these classifiers are implemented and the users are free to further describe the classes or even to include new subclasses, without compromising the use of the proposed hierarchical class system.…”

Current and Potential Spatial Distribution of Six Endangered Pine Species of Mexico: Towards a Conservation Strategy

“…It is imperative to consider land use change and land cover dynamics to promote conservation strategies for each species. The Mexican territory suffers fragmentation, disturbances, and some degradation processes [59][60][61], causing changes in the structure, function, composition, productivity, and extent of forests that may contribute to the diminishing of forest species [62,63].…”

An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images

“…Therefore, for L = 1, SAVI approaches perpendicular vegetation index (PVI), which uses the perpendicular distance from each pixel coordinate to the soil line, while for L = 0, SAVI is equal to NDVI [37]. Many studies have identified the usefulness of long series of vegetation indices (mainly NDVI) derived from satellite data for monitoring purposes of forest degradation, but most have been based on low-to medium-resolution satellite images (e.g., [38][39][40]).…”