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Forest fuels play an important role in the field of global warming, therefore a proper estimation will allow a decrease of the uncertainty levels of carbon (C) contents. This paper describes the evaluation process for loads of duff (litter and fermentation layer, M) and downed woody material (MLC). A summary of the C content reported in Mexico is also presented. The evaluation of these fuels has been primarily based on the strategy of planar intersections. The contents of C were determined using different percentages of C content with respect to mass (dry weight), ranging from 47.5% to 66.9%, although a percentage of 50% was used, in general. Most works has been focused on temperate ecosystems, where 9.43 Mg C ha-1 of MLC was reported. While for tropical forests there was 8 Mg C ha-1. For M was reported 6.91 Mg C ha-1 and 4.25 Mg C ha-1 in duff, and 5.88 Mg C ha-1 and 1.39 Mg C ha-1 in the fermentation layer for temperate regions and tropical forests, respectively. It is concluded that there is little information on C evaluations regarding M and MLC. In addition, other regions should be considered and standardized evaluation procedures should be used. In this way, the information generated will be comparable and compatible. This will not only reduce levels of uncertainty, but also present information at different scales., Resumen Los combustibles forestales juegan un papel importante en el ámbito del calentamiento global, por lo que la adecuada estimación de las camas de combustible permitirá disminuir los niveles de incertidumbre de los contenidos de carbono (C) de los ecosistemas forestales. Este trabajo describe los procesos de evaluación de las cargas de mantillo (hojarasca y capa de fermentación, M) y de material leñoso caído (MLC); además, presenta una síntesis de los contenidos de C registrados en México. La evaluación de M y MLC se basó principalmente en la estrategia de intersecciones planares. Los contenidos de C se determinaron usando diferentes porcentajes de contenido de C respecto a la masa (peso seco), que variaron desde 47.5% hasta 66.9% aunque, en general, se consideró 50%. La mayoría de trabajos revisados se enfocaron en ecosistemas de regiones templadas, con registros de 9.43 Mg C ha-1 de MLC, mientras que para selvas se registraron 8 Mg C ha-1. En cuanto a M, se observó 6.91 Mg C ha-1 y 4.25 Mg C ha-1 en la capa de hojarasca y 5.88 Mg C ha-1 y 1.39 Mg C ha-1 en la capa de fermentación para regiones templadas y selvas, respectivamente. Se encontró muy poca información sobre las evaluaciones de C correspondientes a M y MLC, la mayoría enfocada a ecosistemas templados y tropicales, por lo que se resalta la necesidad de considerar otras regiones climáticas. Las evaluaciones de las cargas de los combustibles forestales deben hacerse en forma estandarizada y con protocolos comunes, de tal forma que la información generada pueda ser tanto comparable como compatible. Esto permitirá reducir los niveles de incertidumbre y presentar la información en diferentes escalas. De esta manera, será posible no solo reducir los niveles de incertidumbre, sino también escalar la información.

The negotiation of carbon capture projects under REDD+ and CDM mechanisms carried out in the different land and vegetation uses requires knowing the storage capacity (capture) that ecosystems have under current conditions and the approximate maximum level they can reach in the future. Because of this, the temporal variations of the different compartments (aerial biomass, standing woody material, litter-mulch-fallen woody material, underground biomass and soil) as they can contribute as CO2 sinks must be studied. The purpose of this work is to collect, review and synthesize the information available in Mexico about the carbon contents studied in a chronosequences approach. Results of the carbon contents of aerial biomass were found only for 21 types of vegetation of 50 represented in Mexico. The information on the temporal variations in the five carbon stores, can be used as reference to establish buying-selling commitments of carbon bonds in terms of amount and time in which mitigation and adaptation to climate change goals are met., Resumen La negociación de proyectos de captura de carbono bajo mecanismos REDD+ y MDL requiere estimar la capacidad y nivel máximo de almacenamiento de CO2 de los ecosistemas bajo las condiciones actuales y futuras. Para lo anterior debe considerarse la variación temporal, como sumideros de carbono de los compartimientos de biomasa aérea, material leñoso en pie, hojarasca-mantillo, material leñoso caído, biomasa subterránea y suelo. Se recopiló, revisó y sintetizó la información disponible de los contenidos de carbono de los ecosistemas terrestres de México con un enfoque de cronosecuencias. Los compartimentos más investigados en 90 estudios fueron la biomasa viva sobre el suelo y la biomasa muerta en piso y mantillo. Se presentan resultados del contenido de carbono de la biomasa aérea para 21 tipos de vegetación, de los 50 reconocidos para México. La información de la variación temporal del carbono en México, en sus cinco almacenes, será referencia para establecer los compromisos de compra-venta de bonos de carbono en términos de cantidad y tiempo en que se conseguirían las metas de mitigación y adaptación al cambio climático.

In the State of Chiapas, where emissions from the LULUCF (Land Use, Land Use Change and Forestry) and Agricultural (including livestock) sectors cover 78% of total emissions, it is necessary to propose mitigation activities in the rural sector (in particular the reduction of emissions from deforestation and forest degradation). In this work, abatement cost curves and mitigation scenarios for Chiapas were generated using state and transition models prepared for eight regions of the state. These models allow us to know the impacts associated with the transition between an initial state (land use or vegetation type) and a final one in terms of carbon and socio-economic factors. The restrictions considered were: positive and negative opportunity costs, subsidy, credit and interest rate. The state has great potential to mitigate emissions in the rural sector, particularly the Selva Maya and Altos regions. The analysis of different scenarios shows that an opportunity cost for USD 4/t CO2 - USD 6/t CO2 is enough for a good portion of the GHG mitigation potential of Chiapas. The subsidy and credit for the implementation activities affects the mitigation potential for certain ranges of opportunity costs, also, the activities with negative opportunity costs are an opportunity area to mitigate emissions reorienting public policies. To improve the analysis capacity of the models it is necessary to encourage research on activities related to forest degradation, sustainable forest management and increase of forest and agricultural carbon stores., Resumen En el estado de Chiapas, donde las emisiones de los sectores Uso del Suelo, Cambio de Uso del Suelo y Silvicultura [Uscusys] y Agrícola (incluida la ganadería) abarcan 78% de las emisiones totales, es necesario plantear actividades de mitigación en el sector rural (en particular, la reducción de emisiones por deforestación y degradación forestal). En este trabajo se generaron curvas de costos de abatimiento y escenarios de mitigación para Chiapas utilizando los modelos de estados y transiciones (METs) elaborados para ocho regiones del estado. Estos modelos permiten conocer los impactos asociados con la transición entre un estado (uso del suelo o tipo de vegetación) inicial y otro final en términos de carbono y factores socio-económicos. Las restricciones consideradas fueron: costos de oportunidad positivos y negativos, subsidio, crédito y tasa de interés. El estado presenta un gran potencial de mitigación de emisiones en el sector rural, en particular las regiones Selva Maya y Altos. El análisis de distintos escenarios muestra que un costo de oportunidad de USD 4/t CO2 a USD 6/t CO2 es suficiente para una buena porción del potencial de mitigación de gases de efecto invernadero de Chiapas. El contar con subsidio y crédito para las actividades de implementación afecta el potencial de mitigación para determinados intervalos de costos de oportunidad; asimismo, las actividades con costos de oportunidad negativos son un área de oportunidad para mitigar emisiones reorientando las políticas públicas. Para mejorar la capacidad de análisis de los modelos es necesario incentivar la investigación sobre actividades relacionadas con la degradación forestal, manejo forestal sostenible e incremento de almacenes de carbono forestales y agropecuarios.

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 +/- 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 +/- 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 +/- 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log -linear model result (63.29 +/- 1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log -linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 g

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 +/- 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 +/- 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 +/- 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log -linear model result (63.29 +/- 1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log -linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 g