The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha⁻¹ and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

Armas, D., Guevara, M., Alcaraz-Segura, D., Vargas, R., Soriano-Luna, M.A., Durante, P., Oyonarte, C. 2017. Mapa digital del perfil del carbono orgánico en los suelos de Andalucía, España. Ecosistemas 26(3):80-88. Doi.: 10.7818/ECOS.2017.26-3.10 Conocer y comprender la distribución espacial del carbono orgánico del suelo (COS) es necesario para el manejo de este reservorio del ciclo global del carbono, y la planificación de acciones para mitigar el cambio climático. El objetivo fue generar mapas de porcentaje de COS en Andalucía mediante modelos consistentes, cuantificando la incertidumbre asociada, e identificando los factores que controlan su variabilidad. Se empleó una base de datos patrimonial con 1500 perfiles de suelo, y 20 covariables ambientales como factores predictivos descriptores del clima, topografía, y atributos funcionales de los ecosistemas. Se utilizó una combinación de modelos lineales y un ensamble de árboles de regresión combinado con geoestadística para estimar la distribución espacial (horizontal y vertical) del COS. Se generaron mapas de la distribución del COS a seis intervalos de profundidad (0-5, 5-15, 15-30, 30-60, 60-100 y 100-200 cm). La varianza explicada por los modelos osciló entre el 57 y 63.0 %, pero con alta incertidumbre en los sitios con mayor concentración de COS (hasta el 8 %). La variabilidad del COS respondió a una compleja combinación de factores, siendo la precipitación el predictor más importante en todas las profundidades, el EVI (Indicador de la productividad) en los horizontes superficiales, y la topografía en los profundos. Los mapas y modelos producidos resultan herramientas útiles para la gestión ambiental, al facilitar la actualización periódica de los contenidos del COS y aportar información para el manejo de este reservorio. Digital map of the organic carbon profile in the soils of Andalusia, Spain. Armas, D., Guevara, M., Alcaraz-Segura, D., Vargas, R., Soriano-Luna, M.A., Durante, P., Oyonarte, C. 2017. Digital map of the organic carbon profile in the soils of Andalusia, Spain. Ecosistemas 26(3):80-88. Doi.: 10.7818/ECOS.2017.26-3.10 To know and understand the spatial distribution of soil organic carbon (SOC) is the first step for management of this important pool in the global carbon cycle, and to develop actions to address climate change. The objective of this work was to generate maps of percent SOC across Andalucía through the use of consistent models, quantifying the associated uncertainty and identify controls of the spatial variability. We used a legacy soil profile collection with 1500 soil profiles and 20 environmental covariates as prediction factors for climate, topography, and ecosystem functional attributes related to the dynamic of primary production. A combination of linear models and an ensemble of regression trees coupled with geostatistics was used to estimate the spatial distribution (horizontal and vertical) of SOC, maps of SOC distribution across six soil depths (0-5, 5-15, 15-30, 30-60, 60-100 y 100-200 cm). Explained variance of our models varied between 63 to 57 %, with high uncertainty at sites with the highest values of SOC (up to 8%). The variability of SOC corresponded to a complex interaction of factors, whereas precipitation is an outstanding predictive factor across all depths, annual primary production (EVI) at the superficial horizons, and topography across deep horizons. Generated maps result in a useful tool for environmental policy, because they facilitate the periodical update of SOC and provide information for the management of this pool.

Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km² to 675 239 km². These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range <10%–80%) and subtropical steppe ecological zones (range <10%–60%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km² with high forest extent agreement, and 340 661 km² with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km² is comparable to the official national estimate of 656 920 km². We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.

The National Forestry Commission of Mexico continuously monitors forest structure within the country’s continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., vegetation indexes, surface temperature). Training data is from the 2009-2014 cycle (n>26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r2=0.4 [0.15,0.55] (mean[min, max]) than for tree density r2=0.2[0.10,0.31]. Maximum values of tree height were for coniferous forests, coniferous-broadleaf forests and cloud mountain forest (~36 m, 30 m and 21 m, respectively). Tropical forests had maximum values of tree density (~1370 trees/ha), followed by tropical dry forest (1006 trees/ha) and coniferous forest (988 trees/ha). Although most forests had relatively low values of uncertainty, e.g., values 60%. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for technical capabilities aimed to use and resignify all the effort done by the Mexican Forestry Commission in implementing the INFyS.

Background: Quantifying aboveground biomass (AGB) is crucial for studying the carbon cycle and estimating mitigation potential of climate change. Combining field inventory data and remote sensing such as Landsat imagery, is a common approach for landscape-Level AGB analysis. However, uncertainties in biomass estimations persist, highlighting the need for improved statistical methods. The objectives of this study were (i) model the AGB of temperate forests managed for timber production using Landsat 8 data and three regression algorithms (linear regression, generalized additive models [GAM], and random forests), and (ii) quantify interannual AGB variations (2013-2022) across a forest landscape. Predictor variables included spectral bands, vegetation indices (VI), textural metrics, and stand age.