Stephanie P. George-Chacon

Accurate assessment of forest aboveground biomass density (AGBD) is essential for understanding the role of vegetation in climate change mitigation and developing forest management and environmental policies at national and regional levels. The Global Ecosystem Dynamics Investigation (GEDI) uses full‐waveform LiDAR and provides a valuable tool for estimating AGBD. Calibrating GEDI biomass products with local field data is vital for improving model accuracy, as current estimates rely on global datasets. Additionally, evaluating key factors that influence biomass estimation is essential to refine GEDI‐based models. In this research, we calibrated linear models with field AGBD as the dependent variable and GEDI metrics as independent variables, and compared the performance against the GEDI L4A product across forest types. Additionally, we evaluated the effects of terrain slope, forest structural complexity, and forest type on the accuracy of the models. Finally, we mapped AGBD in Mexico by aggregating footprint‐level estimates with local models and compared it with the GEDI AGBD map (L4B product). Model validation showed R ² values from 0.35 to 0.46 across forest types, with most models having %RMSE below 52.0. Errors were 32.7 to 34.2% lower than GEDI L4A, highlighting a notable accuracy improvement. The total carbon stocks in Mexico estimated here are approximately 1.78 Gt, aligning closely with official FAO estimates, whereas GEDI estimates are 33.5% higher than the official estimate. Biomass estimation with GEDI is most accurate in areas with moderate slopes and low forest structural complexity. Coniferous and tropical forests showed the lowest errors in estimating AGBD with GEDI (46.7 and 47.3 of %RMSE, respectively) likely due to the widespread presence of uniformly structured coniferous trees and the moderate terrain slopes found in tropical forests. Our findings highlight the importance of calibrating local AGBD data with GEDI forest structure metrics to improve biomass estimations at the footprint and national levels.

Gabon is one of 11 high-forest, low-deforestation (HFLD) countries in the world. It has the highest proportion of preserved forests in the Congo Basin and is the first country to create large forest carbon offset credits in the market. However, about 60% of forests in Gabon is allocated to logging concessions, causing concerns for forest degradation and the sustainability of carbon credits. Here, we use a combination of air- and space-borne remote sensing data and the-state-of-the-art gradient boosted regression trees to estimate forest structure and aboveground biomass carbon density (ACD) of trees at 100 m resolution for the year circa 2020. Mapping spatial variations of ACD across floristically diverse landscapes, we estimate average density and total living carbon storage of trees at the national and sub-national levels. The estimated ACD of trees in forestlands within the country was 142.12 ± 7.3 Mg C ha⁻¹ with the highest values found in central Gabon (150.08 ± 5.8 Mg C ha⁻¹) and on highlands (161.18 ± 6.7 Mg C ha⁻¹). On average, in every region, ACD of forests found within logging concessions (149.89 ± 6.1 Mg C ha⁻¹) was higher than unmanaged forests of unprotected areas (122.81 ± 4.4 Mg C ha⁻¹), indicating the combined effects of logging in carbon-rich forests and increased productivity due to management. The country’s total estimated biomass carbon for trees (above and belowground) stored within the forests was 4.14 ± 0.3 Pg C with 68% found within logging concessions and 14% within protected areas. The map provides high precision and comprehensive assessment of carbon stocks of trees in Gabon’s forests, significantly improving the country’s prospects to implement climate mitigation policies and to participate in carbon markets.

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.

Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarcity of field data and robust protocols to propagate uncertainty prevent a robust estimation through remote sensing. We upscaled AGB from field data to LiDAR, and to landscape scale using Sentinel-2 and ALOS-PALSAR through machine learning, propagated uncertainty using a Monte Carlo framework and explored the relative contributions of each sensor. Sentinel-2 outperformed ALOS-PALSAR (R² = 0.66, vs 0.50), however, the combination provided the best fit (R² = 0.70). The combined model explained 49% of the variation comparing against plots within the calibration area, and 17% outside, however, 94% of observations outside calibration area fell within the 95% confidence intervals. Finally, we partitioned the distribution of AGB in different management and conservation categories for evaluating the potential of different strategies for conserving carbon stock.

El índice de área foliar (IAF) proporciona información acerca de la cantidad de superficie fotosintética que existe en relación con la superficie total del ecosistema y se relaciona con procesos vitales como la fotosíntesis, la respiración y la productividad. Por lo tanto, es importante contar con información sobre la distribución espacial del IAF a escala de paisaje. El método indirecto más utilizado para la estimación del IAF se basa en imágenes de satélite y consiste en asociarlo con características espectrales e índices de vegetación. Sin embargo, estos índices tienen una fuerte limitación debido a problemas de saturación, lo cual restringe la posibilidad de generar mapas precisos de IAF, particularmente en bosques con altos niveles de biomasa. En el presente trabajo se obtuvieron modelos para mapear el IAF en un bosque tropical seco de Yucatán durante las estaciones de lluvia y estiaje a partir de imágenes de alta resolución, utilizando un procedimiento de regresión combinado con kriging. Este procedimiento integra la relación del IAF, tanto con datos espectrales y de textura de las imágenes, como con la dependencia espacial de los residuales. Se obtuvieron valores de IAF por medio de fotografías hemisféricas con una precisión aceptable y valores medios significativamente diferentes entre la temporada de lluvias (3.37) y la de estiaje (2.49). Los valores de R2aj de los modelos de regresión múltiple fueron de 0.58 y 0.63 para la temporada de lluvias y estiaje, respectivamente. En general, los resultados demuestran que, al utilizar el análisis de textura, se pueden generar modelos aceptables para la estimación del IAF en bosques tropicales secos con altos niveles de biomasa.

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.