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Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarcity of field data and robust protocols to propagate uncertainty prevent a robust estimation through remote sensing. We upscaled AGB from field data to LiDAR, and to landscape scale using Sentinel-2 and ALOS-PALSAR through machine learning, propagated uncertainty using a Monte Carlo framework and explored the relative contributions of each sensor. Sentinel-2 outperformed ALOS-PALSAR (R2 = 0.66, vs 0.50), however, the combination provided the best fit (R2 = 0.70). The combined model explained 49% of the variation comparing against plots within the calibration area, and 17% outside, however, 94% of observations outside calibration area fell within the 95% confidence intervals. Finally, we partitioned the distribution of AGB in different management and conservation categories for evaluating the potential of different strategies for conserving carbon stock.

El diseño de estrategias exitosas para la conservación de los bosques demanda un entendimiento de las causas que originan el cambio de uso del suelo. En este trabajo se realizó un análisis histórico del cambio de uso del suelo en dos ejidos de la Selva Lacandona, en Chiapas. Se combinaron cartografía participativa y percepción remota para reconstruir los cambios ocurridos en el período 1986-2018, adicionalmente se realizaron talleres participativos y entrevistas semiestructuradas. Identificamos tres etapas en la historia de cambios en el uso del suelo, a) la colonización y adaptación (1986-1994), b) la intervención productiva (1994-2005) y c) la expansión ganadera y la conservación de los bosques (2005-2018). En cada una de ellas, los factores económicos y políticos causantes del cambio contribuyeron manera diferenciada y fueron mediados a nivel local por la organización específica en cada ejido. Se observó una fuerte disminución de las áreas dedicadas a la producción agrícola en favor de la ganadería. A pesar de que en la última etapa se han destinado recursos públicos para promover la conservación de los bosques, la infraestructura construida, la débil presencia institucional y la fuerte especialización en la producción bovina dificultan los esfuerzos por detener la deforestación.

A pesar de que a escala global se han estudiado las causas del cambio de uso de suelo, localmente existen pocos estudios detallados. En este trabajo se estudiaron los cambios de cobertura y uso de suelo de una comunidad agraria en Guerrero, México, en un periodo de 24 años. Se emplearon imágenes satelitales y ortofotos para elaborar mapas de vegetación y usos de suelo de alta resolución de los años 1995, 2009 y 2019. Adicionalmente se realizaron entrevistas a actores clave y se acopiaron y analizaron estadísticas productivas para identificar las causas del cambio en el uso del suelo. En los primeros años (1995–2009), se registró un aumento de la cobertura forestal de 259 ha (5.3%) y una disminución en la agricultura de 145 ha (3%). En la última etapa (2009–2019) la cobertura forestal aumentó 56 ha (1.2%) y la clase agricultura disminuyó 59 ha (1.2%). La recuperación de la cobertura forestal estuvo relacionada con un par de factores concomitantes, tales como el abandono de tierras agrícolas debido a la migración y el fortalecimiento de las reglas de acceso a los recursos forestales, este último en buena medida fue motivado por los beneficios obtenidos de la provisión de servicios ambientales hidrológicos de los bosques comunitarios.

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.

Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species were produced in order to identify areas that maximize carbon storage and biodiversity in the tropical forests of the Yucatan Peninsula, Mexico. We mapped carbon density and species richness of trees using L-band radar backscatter data as well as radar texture metrics, climatic and field data with the random forest regression algorithm. We reduced sources of errors in plot data of the national forest inventory by using correction factors to account for carbon stocks of small trees (

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.

We present a cost-effective statistical approach that integrates satellite imagery, environmental variables and ground inventory data to map the spatial distribution of aboveground woody biomass suitable for charcoal making. The study was conducted in the Cuitzeo basin located in central Mexico, where charcoal is produced from oak forests covering approximately 10% of the total area (4033 km 2 ). Diameters of trees and sprouts in 78 plots of 0.2 ha each was measured. Allometric equations previously developed locally that only require tree diameters were employed to estimate the amount of woody biomass suitable for charcoal making i.e. the amount of wood that is loaded into the kilns. The performance of two statistical techniques for the interpolation of field data was assessed by cross-validation; these techniques were linear regression and regression-kriging, the second taking into account the spatial autocorrelation of data. Spectral bands, vegetation indices, texture measurements and variables derived from a Digital Elevation Model were examined as explanatory variables. Accounting for spatial autocorrelation (regression-kriging) improved the model's R 2 from 0.61 to 0.69, representing a relative error reduction of 11.3% (from 11.01 to 9.77 t ha − 1 of wood suitable for charcoal). The available stock was compared to current estimates of charcoal demand in the Cuitzeo basin and insights were given on how this information can be used to estimate the annual sustainable production potential of oak in order to account for supply–demand balances.

La leña es fuente principal de energía para numerosas comunidades indígenas de México. Se estudió la relación entre consumo y abasto de leña y las estrategias que emprenden las familias para enfrentar el déficit de abasto de leña en un grupo de cafetaleros de la Sociedad Cooperativa Maya Vinic distribuidos en ocho comunidades de Chenalhó, Chiapas. El análisis incluyó talleres participativos, entrevistas, medición directa del consumo, colecta e identificación botánica de material vegetativo de especies usadas para leña. El consumo de leña para la época seca fue 3.7 ± 1.5 kg persona-1 día-1 y 3.9 ± 2.7 kg persona-1 día-1 en la temporada lluviosa. En general, las familias con mayor número de integrantes optimizan el uso de la leña al reducir la tasa de consumo per capita; familias con 10 a 12 integrantes consumen hasta 55% menos en relación con familias de 2-4 personas. La pérdida de la cobertura vegetal original ha resultado en una escasez de especies tradicionalmente usadas para leña (por ej., encinos) por lo que ahora se emplean especies provenientes de los sistemas agroforestales para satisfacer la demanda de leña. Las acciones para reducir el consumo se limitan a mantener apagada la fogata cuando no se preparan alimentos y usar prensa para disminuir los tiempos de preparación de las tortillas. Aunque algunas familias han adoptado el uso de estufas ahorradoras como estrategia para disminuir el consumo (4.1 ± 1.7 kg persona -1 día-1 ) no se encontraron diferencias significativas (p>0.05) con relación al consumo mediante el fogón tradicional (3.7 ± 1.5 kg persona -1 día-1 ). Aunque los cafetales son fuente continua de aprovisionamiento de leña, éstos no satisfacen las demandas, por lo que algunas familias deben comprarla con una inversión promedio de $1631 ± 1203 pesos al año. Se vislumbra que el déficit de leña se agudizará en los próximos años ante el incremento en la densidad poblacional, a la continua deforestación y empobrecimiento florístico de los fragmentos forestales que sirven como fuente de abastecimiento de leña. Hay bajas posibilidades de establecer plantaciones dendroenergéticas en las áreas actuales de producción de café, mientras éstas no compensen los beneficios económicos que se obtienen de los cafetales

Predictions of tropical forest structure at the landscape level still present relatively high levels of uncertainty. In this study we explore the capabilities of high-resolution Satellite Pour l'Observation de la Terre (SPOT)-5 XS images to estimate basal area, tree volume and tree biomass of a tropical rainforest region in Chiapas, Mexico. SPOT-5 satellite images and forest inventory data from 87 sites were used to establish a multiple linear regression model. The 87 0.1-ha plots covered a wide range of forest structures, including mature forest, with values from 74.7 to 607.1 t ha-1. Spectral bands, image transformations and texture variables were explored as independent variables of a multiple linear regression model. The R2s of the final models were 0.58 for basal area, 0.70 for canopy height, 0.73 for bole volume, and 0.71 for biomass. A leave-one-out cross-validation produced a root mean square. error (RMSE) of 5.02 m2 ha-1 (relative RMSE of 22.8%) for basal area; 3.22 m (16.1%) for canopy height; 69.08 m3 ha-1 (30.7%) for timber volume, and 59.3 t ha-1 (21.2%) for biomass. In particular, the texture variable 'variance of near-infrared' turned out to be an excellent predictor for forest structure variables.

This study examines the correlation between deforestation, carbon dioxide emissions and potential causal factors of land-use change within an area of 2.7 million ha in Chiapas, southern Mexico between 1975 and 1996. Digitized land-use maps and interpreted satellite images were used to quantify land-use changes. Geo-referenced databases of population and digitized maps of roads and topography were used to determine which factors could be used to explain observed changes in land-use. The study analyzed the relationship between carbon emissions during this period and two types of possible causal factors: “predisposing” factors that determine the susceptibility of a particular area of forest to change (slope, distance to agriculture and roads, land tenure) and “driving” factors representing the pressures for change (population density, poverty). The correlated factors were combined in risk matrices, which show the proportion of vulnerable carbon stocks lost in areas with defined social, economic and environmental characteristics. Such matrices could be used to predict future deforestation rates and provide a verifiable evidence-base for defining baseline carbon emissions for forest conservation projects. Based on the results of the analysis, two matrices were constructed, using population density as the single most important driving factor and distance from roads and distance from agriculture as the two alternatives for the predisposing factors of deforestation.

We present a methodology for testing and applying a regional baseline for carbon (C) emissions from land-use change, using a spatial modelling approach (hereafter called the Climafor approach). The methodology is based on an analysis of causal factors of previous land-use change (Castillo et al. 2005). Carbon risk matrices constructed from the spatial correlation analysis between observed deforestation and driving factors (Castillo et al. 2005), are used to estimate future carbonemissions within acceptable limits for a forest conservation project. The performance of two risk matrices were tested by estimating carbon emissions between 1975 and 1996 from randomly selected sample plots of sizes varying from 1,600 to 10,000 ha and comparing the results of the observed emissions from these sample plots with the model estimations. Expected emissions from continued land-use change was estimated for the community applying the risk matrices to the current land cover. The methodology provides an objective means of constructing baseline scenarios including confidence intervals, using the sum of variances of the various data sources, such as measured carbon densities, classification errors, errors in the risk matrices, and differences between the model prediction and observed emissions of sample plots due to sample size. The procedures applied in this study also give an indication of the impact of the variance in the various data sources on the size of the confidence intervals, which allows project developers to decide what data sources are essential to improve his baseline. The modelling approach to estimate the deforestation pattern is based on readily available cartographic and census data, whereas data on carbon densities are required to assess the potential for forest conservation projects to offset carbon emissions.

We evaluated the impacts of land-cover changes of evergreen cloud forests on the distribution of quetzals in the four mountain regions of Chiapas, Mexico. The land-cover changes were estimated comparing satellite images of 1970 and 2000. We also simulated the amount of remnant forests 50 years in the future. The past and the current distributions of quetzals were based on literature records and recent field surveys. Our results showed that in 1970 the forests occupied 973 km 2 , and in 2000, 312 km 2 , and annual loss from 3.34 to 6.85%. Our simulation suggests that 50 years from now, only one region will maintain evergreen cloud forests. In addition, we documented literature reports of 39 forests inhabited by quetzals, but in 2001 there remained 11 smallisolated forests. In order to guarantee the survival of this species, conversation efforts must be made to protect the quetzal and its habitat throughout the entire Mesoamerica region. # 2003 Elsevier Ltd. All rights reserved.

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼40,000 and ∼53,000, i.e. at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa., 0, SCOPUS: ar.j, info:eu-repo/semantics/published

Con el propósito de conocer que los factores promueven el cambio en la cobertura del suelo en la cuenca del río Sabinal, Chiapas, este trabajo analiza la relación entre mapas de cambio en la cobertura del suelo y estadísti- cas socioeconómicas. Se elaboraron mapas de la cobertura del suelo de 1992 y 2009 con imágenes satelitales de alta resolución; también se colectaron y analizaron datos socioeconómicos relacionados con el uso del suelo. La comparación de los mapas muestra que la tasa de deforestación es del 0.5 %, la cual es más baja que la reportada en otras zonas de bosque tropical seco; la población rural se ha mantenido a niveles similares de 1990, pero la población urbana creció más del doble. La super cie promedio de potreros por productor se ha duplicado, pero la de cultivos se mantiene similar a 1992, ya que los campesinos pre rieron sistemas de producción extensivos. Los terrenos agrícolas han disminuido en extensión y se han desplazado a zonas de mayor pendiente debido a la expansión de las áreas urbanas. Se encontró una alta dependencia de insumos de otras regiones del país, pero la producción de alimentos básicos en la cuenca se ha mantenido a niveles de 1991.

The transformation of natural forest to secondary forest and pastures has been the most common process of land use change in tropical countries in recent decades (FAO, 2010). The main causes of deforestation include institutional factors, markets, public policies and global forces, which often act synergistically (Deininger and Minten, 1999; Bocco et al. 2001; Lambin et al., 2001). Mexico is a country with 64,802x103 ha of forested land, and it is one of the ten countries with the largest area of primary forest (3% of total). The annual net loss of deforestation in Mexico has been estimated to be 0.52% for the period of 1990-2010, but the net loss, on average, has decreased over the past few years (FAO, 2010). The highest deforestation rates are concentrated in the south and central regions of the country, as documented elsewhere: 8.4% in el Nevado de Toluca, state of Mexico (1972-2000) (Maass et al., 2006); 8% in Patzcuaro, Michoacan (1960-1990) (Klooster, 2000); 6.9% in some areas of Campeche (ReyesHernandez et al., 2003); 6.1% in the highlands of the state of Chiapas (Cayuela et al., 2006; Echeverria et al., 2007); and 2-6.7% in Selva Lacandona, also in Chiapas (Ortiz-Espejel & Toledo, 1998; de Jong et. al., 2000). Precisely, the states of Chiapas and Yucatan have registered the highest rate of forest conversion to grasslands and slash-and-burn cultivation over the past two decades, and Chiapas alone has contributed towards 12% of national deforestation during the period 1993-2007 (De Jong et al, 2010; Diaz-Gallegos et al., 2010). In Mexico, deforestation occurs because forests become converted to agriculture, livestock and urban areas. But also because logging activities fail to meet the requirements of forest management plans. All these processes result in the loss of forest goods and services (Lambin et al. 2003), and they contribute to ecosystem fragmentation (Ochoa-Gaona & Gonzalez Espinosa, 2000; Cayuela et al., 2006), biological invasions (Hobbs, 2000), greenhouse gas emissions (Watson et al., 2000), biodiversity loss (Lugo et al., 1993), soil degradation (Lal, 2004) and water siltation (Sweeney et al., 2004).