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International audienceKey messageWe present a comprehensive database of 478 allometric equations to estimate biomass of trees and other life forms in Mexican forest and scrubland ecosystems.ContextAccurate estimation of standing biomass in forests is a prerequisite for any approach to carbon storage and a number of additional applications.AimsTo provide a comprehensive database with allometric equations applicable to a large number of tree and shrub species of Mexico.MethodsAn intensive literature search was carried out to pull together all publications related to allometric equations in the libraries of the most important forest research institutes across Mexico and the neighboring countries.ResultsA total of 478 equations were compiled. Four hundred fourteen equations included a detailed analysis of all compartments of the trees; 7 equations applied to shrubs, 15 to bamboos, and 2 to palms. The collected equations are applicable to a wide variety of forest ecosystems in Mexico ranging from desert scrublands in the North to lowland evergreen rainforests in the South. The attached database of allometric equations is possibly the most extensive compilation of equations currently available for Mexico.ConclusionThe database covers almost 100 % of the individuals recorded in the National Forest Inventory

El método más común para estimar variables dasométricas a gran o pequeña escala es el inventario forestal basado en un muestreo en campo. En la actualidad la teledetección ofrece un abanico de posibilidades para incorporarse en las estimaciones forestales, tal es el caso de LiDAR (Light Detection And Ranging) que permite caracterizar de forma tridimensional el bosque. En este trabajo se estudió la relación entre datos derivados de LiDAR con los datos medidos en campo para estimar variables dasométricas como: área basal (AB), biomasa total (BT), cobertura arbórea (COB) y volumen de madera (VOL), mediante cuatro métodos: 1) regresión lineal múltiple, 2) regresión no lineal, 3) estimador de razón y 4) inventario forestal tradicional (muestreo estratificado). Las estimaciones totales derivadas del estimador de razón se encuentran dentro del intervalo de confianza al 95% calculado mediante inventario tradicional para AB, BT y VOL, siendo este el estimador que arroja los valores más cercanos y precisos a la estimación mediante inventario. En general, las estimaciones de los modelos no lineales fueron los más optimistas con respecto al inventario tradicional. Los resultados indican una buena relación (R2 > 0.50) entre las métricas de LiDAR y datos de campo, principalmente los percentiles de altura y las tasas de retorno sobre una altura definida. A partir de los modelos lineales, se generó la cartografía de cada una de las variables analizadas.

The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.