Search

Abstract Spatial information on the timing of forest cover loss is important to identify and map stand age, which is a key factor driving the recovery of carbon pools and can also be used to estimate aboveground biomass (AGB) based on its relationship with stand age. Here, we estimated the spatial distribution of stand age and AGB of young forest (

. 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 estadísticas 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 supercie promedio de potreros por productor se ha duplicado, pero la de cultivos se mantiene similar a 1992, ya que los campesinos prerieron 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.

We assess the additional forest cover protected by 13 rural communities located in the southern state of Chiapas, Mexico, as a result of the economic incentives received through the country's national program of payments for biodiversity conservation. We use spatially explicit data at the intra-community level to define a credible counterfactual of conservation outcomes. We use covariate-matching specifications associated with spatially explicit variables and difference-in-difference estimators to determine the treatment effect. We estimate that the additional conservation represents between 12 and 14.7 percent of forest area enrolled in the program in comparison to control areas. Despite this high degree of additionality, we also observe lack of compliance in some plots participating in the PES program. This lack of compliance casts doubt on the ability of payments alone to guarantee long-term additionality in context of high deforestation rates, even with an augmented program budget or extension of participation to communities not yet enrolled.

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.

Antecedentes: Los mapas de bosques perturbados son útiles para identificar afectaciones sobre la biodiversidad y los servicios ecosistémicos. Los métodos que emplean únicamente datos espectrales para detectar las perturbaciones a nivel regional tienen limitaciones. El conocimiento de expertos y el análisis de fragmentación puede mejorar la estimación. Preguntas: ¿Cuál es la distribución de los bosques perturbados en una región de alta biodiversidad? ¿Qué tipos de vegetación y regiones son las más afectadas? Descripción de los datos: imágenes satelitales SPOT 2015, Sentinel-2 de 2019. Se colectó información de la vegetación en 653 sitios. Además, se usaron datos de herbario, censos agrícolas y del Inventario Nacional Forestal. Lugar y fecha del estudio: Estado de Chiapas, durante 2018-2022. Métodos: Se elaboró un mapa híbrido de los tipos de vegetación enfatizando la identificación de bosques secundarios, también se realizó un análisis de fragmentación y se calculó la biomasa leñosa por tipo de bosque. Resultados: El 40 % de la superficie del Estado mantiene una cobertura arbórea; pero solo en el 12 % no se aprecia perturbación; la mayor parte de los bosques no perturbados se encuentran en tres regiones: Selva Lacandona, Sierra Madre y Planicie del Golfo. En general la biomasa de los bosques perturbados es significativamente menor que la de su contraparte madura. Conclusiones: En Chiapas la distribución de los bosques en buen estado de conservación está restringida; casi la mitad de ellos se encuentran fuera de las ANP, por lo que es imperativo promover estrategias adicionales para su manejo y conservación.

Abstract Payments for Ecosystem Services (PES) provide conditional incentives for forest conservation. PES short-term effects on deforestation are well-documented, but we know less about program effectiveness when participation is sustained over time. Here, we assess the impact of consecutive renewals of PES contracts on deforestation and forest degradation in three municipalities of the Selva Lacandona (Chiapas, Mexico). PES reduced deforestation both after a single 5-year contract and after two consecutive contracts, but the impacts are only detectable in higher deforestation-risk parcels. Enrollment duration increases PES impact in these parcels, which suggests a positive cumulative effect over time. These findings suggest that improved spatial targeting and longer-term enrollment are key enabling factors to improve forest conservation outcomes in agricultural frontiers.

Coffee is one of the most important agricultural commodities. Agroforestry systems (AFS) are increasingly used in coffee cultivation because of environmental benefits, adaptability of the systems, and economic profits. However, identifying the spatial distribution of AFS through remote sensing continues to be challenging. The current systematic review focuses on the accuracies obtained and the computational methods and satellite data used in mapping coffee AFS between 2000 and 2020. To facilitate the analysis, we ordered the mapped AFS into five classes according to their density and species composition of shade trees. The Kruskal-Wallis test was applied to evaluate significative differences among classes. Both shade-tree densities and species composition affected the accuracy level. The worst results were obtained in AFS retaining many woody species from the original forest and high tree density (user accuracy [Formula: see text]0.5). About the methods, maximum likelihood was the most widely used with very variable results; some non-parametric methods such as CART, ISODATA, RF, SMA, and SVM presented consistently high accuracy ([Formula: see text]0.75). High spatial resolution multispectral imagery was suitable for mapping AFS; very few studies were found with radar imagery, so it would be desirable to increase its use combined with optical data.

Antecedentes: La identificación de los usos de suelo particulares (e.g., tipo de cultivo) que generan la pérdida de los diferentes tipos de vegetación, las tendencias de degradación y la pérdida de biodiversidad ha sido limitadamente explorada en estudios sobre dinámica del uso de suelo en paisajes agropecuarios. Preguntas: ¿Cuáles son las superficies ocupadas por los diferentes usos de suelo y vegetación? ¿cuál es la permanencia, tasa de cambio y tasa de pérdida de los diferentes usos de suelo y vegetación? ¿qué usos de suelo dirigen los cambios observados? Área de estudio y fechas: Soconusco, Chiapas, México. Período: 2000-2017. Métodos: Se usaron imágenes de Google Earth, el método de fotointerpretación y la verificación en campo para generar mapas de cambio de uso de suelo y vegetación. Se crearon matrices de permanencia/transición y se calcularon tasas de cambio relativo y de pérdida de superficie para los diferentes usos de suelo y vegetación. Resultados: La zona de actividad agropecuaria y la vegetación natural fueron las categorías con mayor superficie y permanencia. La mayor tasa de ganancia anual de superficie se observó en la zona de actividad agropecuaria, influenciada por el incremento de cultivos de importancia económica (mango y palma africana). La mayor tasa de pérdida anual se registró en la vegetación natural, y fue dirigida principalmente por la transición de tular y de vegetación secundaria hacia zonas agropecuarias. Conclusiones: Ante la limitada formación de vegetación secundaria y la alta permanencia/incremento de zonas agropecuarias, es importante considerar acciones que diversifiquen estos paisajes agropecuarios.

Abstract 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.

Coffee is one of the most important agricultural commodities of Mexico. Mapping coffee land cover is still a challenge because it is grown mainly on small areas in agroforestry systems (AFS), which are located in hard-to-access mountainous regions. The objective of this research was to map coffee AFS types in a mountainous region using the changing spectral response patterns over the dry season as well as supplementary data. We employed Sentinel-1, Sentinel-2 and ALOS-Palsar images, a digital elevation model, soil moisture layers, and 150 field plots. First, we defined three coffee AFS types based on their structural and spectral characteristics. Then, we performed a recursive feature elimination analysis to identify the most relevant predictor variables for each land use/cover class in the region. Next, we constructed a predictor variable dataset for each AFS type and one for the remaining land use/cover classes. Afterward, four maps were generated using a random forest (RF) classifier. Finally, we combined the four maps into a unique land-cover map through a maximum likelihood algorithm. Using a validation sample of 932 sites derived from Planet images (4.5 m pixel size), we estimated a 95% map overall accuracy. Two AFS types were classified as having low error; the third, with the highest tree density, had the lowest accuracy. The results obtained show that the infrared and near-infrared bands from the Sentinel-2 scenes are particularly useful for coffee AFS discrimination. However, supplementary data are required to improve the performance of the classifier. Our findings also highlight the importance of the multi-temporal and multi-dataset approach for identifying complex production systems in areas of high topographic heterogeneity.

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 (2) of 0.28 and 0.31 and a relative mean square error of 38.5% and 33.0% for aboveground biomass and species richness, respectively, at pixel level. Estimates of carbon density were influenced mostly by radar backscatter and climatic data, while those of species richness were influenced mostly by radar texture and climatic variables. Correlation between carbon density and species richness was positive in 79.3% of the peninsula, while bivariate maps showed that 39.6% of the area in the peninsula had high carbon stocks and species richness. Our results highlight the importance of combining carbon and diversity maps to identify areas that are critical—both for maintaining carbon stocks and for conserving biodiversity.

[This corrects the article DOI: 10.1371/journal.pone.0222908.].

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.

Analizamos las relacionesdela benignidad ambiental, la estacionalidad y la calidad/fertilidad del suelo con la distribución de nueve especies de Pinus de Los Altos de Chiapas, México. Usamos información georreferenciada de 241 ejemplares herborizados colectados en 180 sitios entre 1939 y 1999. Tres componentes principales explicaron 76.5% de la variación en la distribución de las especies. El primer componente se relacionón con las condiciones de humedad, el segundo con la temperatura y el trcerocon la calidad/fertilidad del suelo. Se intentó exolicar la distribución de las especies con una regresión logística. La presencia de P. maximinoi, P. oocarpa, P. pseudostrobus y P. tecumumanii se explicó con la altitud (P