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Knowledge of the spatial distribution of aboveground biomass (AGB) is crucial to guide forest conservation and management to maintain carbon stocks. LiDAR has been highly successful for this purpose, but has limited availability. Very-high resolution (

Background Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. Results Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R2 = 0.44 vs R2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R2 = 0.26). Conclusions This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

Many studies have considered community-based forestry enterprises to be the best option for development of rural Mexican communities with forests. While some of Mexico's rural communities with forests receive significant economic and social benefits from having a community forestry enterprise, the majority have not formed such enterprises. The purpose of this article is to identify and describe factors limiting the formation of community forestry enterprise in rural communities with temperate forests in the Southern Mixteca region of Oaxaca, Mexico. The study involved fieldwork, surveys applied to Community Board members, and maps developed from satellite images in order to calculate the forested surface area. It was found that the majority of Southern Mixteca communities lack the natural and social conditions necessary for developing community forestry enterprise; in this region, commercial forestry is limited due to insufficient precipitation, scarcity of land or timber species, community members' wariness of commercial timber extraction projects, ineffective local governance, lack of capital, and certain cultural beliefs. Only three of the 25 communities surveyed have a community forestry enterprise; however, several communities have developed other ways of profiting from their forests, including pine resin extraction, payment for environmental services (PES), sale of spring water, and ecotourism. We conclude that community forestry enterprise are not the only option for rural communities to generate income from their forests; in recent years a variety of forest-related economic opportunities have arisen which are less demanding of communities' physical and social resources.

The biodiversity data typically available for fitting distributional models in the tropics come from museum and scientific collections which are often incomplete and prone to sampling and environmental biases. Nevertheless, most studies undertaken in tropical regions assume that collection data offers a satisfactory environmental coverage without any quantitative assessment. In this study, we investigate the effects of differences in environmental bias and coverage provided by distributional data when aggregated into different grid cell sizes, on the performance of species richness-environment models and predictions. We use an extensive data compilation, including national and regional collections, on the distribution of amphibians, reptiles and fishes in the hydrologic region of the Usumacinta River as a case study. General additive models and environmental variables are used to construct predictive models at 40, 20, 10 and 5 km grid resolutions, based on well-sampled cells. The best multivariate models included nonparametric interaction terms for the effects of precipitation and temperature and suggested an altitudinal shift in the relative importance of energy and water in determining the distribution of species richness. For fishes, geomorphology accounted for fine scale variation in species richness along the hydrologic network, indicated by peaks in species diversity at the junction of the major rivers where major accumulation of water and sediments occurs. For all taxonomic groups, we found that sampling biases deviated most from the mean bias at the extremes of gradients accounting for important environmental factors. The pattern of environmental bias changed with grid size, with the form and amount of change being case-specific. Biases affected distribution predictions when compared with unbiased datasets. Moreover, not all models resulted best at coarser resolution as it is commonly assumed. Our results demonstrate that bias in the available data must be evaluated before mapping biodiversity distributions, irrespective of the choice of scale.

Quantifying patterns of deforestation and linking these patterns to potentially influencing variables is a key component of modelling and projecting land use change. Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Simplifications of cause-consequence relationships that are difficult to support empirically may influence environment and development policies because they suggest simple solutions to complex problems. Deforestation is a complex process driven by multiple proximate and underlying factors and a range of scales. In this study we use a multivariate statistical analysis to provide contextual explanation for deforestation in the Usumacinta River Basin based on partial pattern matching. Our approach avoided testing trivial null hypotheses of lack of association and investigated the strength and form of the response to drivers. As not all factors involved in deforestation are easily mapped as GIS layers, analytical challenges arise due to lack of a one to one correspondence between mappable attributes and drivers. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. We developed a series of informative generalised additive models based on combinations of layers that corresponded to hypotheses regarding processes. The importance of the variables representing accessibility was emphasised by the analysis. We provide evidence that land tenure is a critical factor in shaping the decision to deforest and that direct beam insolation has an effect associated with fire frequency and intensity. The effect of winter insolation was found to have many applied implications for land management. The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. It was an informative approach, thus allowing the construction of a comprehensive understanding of its causes.

Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m2 to 1000 m2 (R2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.

Gebhardt et al. (2014) presented the Monitoring Activity Data for the Mexican REDD+ program (MAD-MEX), an automatic nation-wide land cover monitoring system for the Mexican REDD+ MRV. Though MAD-MEX represents a valuable first effort toward establishing a national reference emissions level for the implementation of REDD+ in Mexico, in this paper, we argue that this land cover system has important limitations that may prevent it from becoming operational for REDD+ MRV. Specifically, we show that (1) the accuracy assessment of MAD-MEX land cover maps is optimistically biased; (2) the ability of MAD-MEX to monitor land cover change, including deforestation and forest degradation; is poor and (3) the use of an entirely automatic classification approach, such as that followed by MAD-MEX, is highly problematic in the case of a large and heterogeneous country like Mexico. We discuss these limitations and call into question the ability of a land cover monitoring system, such as MAD-MEX, both to elaborate a national reference emissions level and to monitor future forest cover change, as part of a REDD+ MRV system. We provide some insights with the aim of improving the development of nation-wide land cover monitoring systems in Mexico and elsewhere.