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The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents.

El Soconusco se caracteriza por ser una de las regiones agrícolas más productivas del estado de Chiapas, en la que aún se conservan zonas de alta biodiversidad amenazadas por el cambio de uso del suelo. El objetivo de este trabajo fue analizar las trayectorias del cambio en la cobertura y usos del suelo en tres cuencas de dicha región durante un período de 25 años. Se clasificaron imágenes de satélite de 1990, 2000 y 2015. Los resultados mostraron una significativa pérdida de la vegetación natural (bosques, manglares y vegetación secundaria), además de la expansión de los asentamientos humanos. Se identificaron procesos diferentes de cambio en cada uno de los paisajes evaluados: Costa, Planicie y Sierra. En la Planicie, el cambio de uso de suelo predominante fue la sustitución de cultivos básicos por plantaciones agrícolas. En los paisajes de Sierra y Costa, donde hay remanentes importantes de bosques, el cambio más evidente fue la deforestación. A pesar de que en la región se registraron fuertes incrementos en la densidad poblacional y en las áreas urbanas, las zonas dedicadas a la producción de cultivos básicos se han mantenido sin modificaciones. El patrón de cambios en la cobertura de suelo indica que la región de estudio está en un proceso de intensificación de la agricultura y de la urbanización.

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.

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.

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

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 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.

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).