Accurate estimation of forest biomass and carbon is essential for understanding ecosystem functions and supporting climate change mitigation efforts. This study presents regionally calibrated allometric equations to estimate aboveground biomass and carbon across different forest regions in Mexico, which is an important step toward improving national forest inventories and carbon accounting frameworks. We analyzed a dataset of 1919 trees from 13 Regional Forest Management Units (UMAFORs), collected through a targeted sampling strategy that encompasses all tree diameter classes. We evaluated two additive equation systems to predict aboveground biomass and carbon content by component (stem, branches, and total). Both systems were fitted simultaneously using the MODEL procedure and assessed through multiple goodness-of-fit statistics. The additive equations for tree components and total biomass and carbon showed acceptable fits. Although their overall performance was comparable, system 2 (S2) provided slightly higher predictive accuracy. The resulting region-specific equations offer practical tools for forest managers and policymakers to enhance sustainable forest management and carbon monitoring in the heterogeneous forest landscapes of Mexico.
Bog oak is a waterlogged archaeological wood characterized by distinctive physical characteristics that depend on the burial environment, conditions, and duration, making each sample nearly unique. The aim of this study was to investigate the relationship between chemical composition and the physical characteristics of two bog oak samples using CANOCO analysis. The samples differ in age and were recovered from two different locations in northern Italy. Results show that the CANOCO analysis is a suitable tool for analyzing complex materials, as it provides a graphical output that enables immediate and reliable interpretation.
Nature-based carbon credit markets are crucial for climate change mitigation in tropical regions. Accurate estimates of aboveground biomass (AGB) in these forests depend on wood density (WD), typically sourced from global databases that do not account for local variability, introducing substantial uncertainties in carbon stock assessments. This study evaluates the resistograph as a novel tool for improving WD predictions in dipterocarp forests of northern Borneo, addressing critical gaps in current carbon stock estimation methodologies. Using multiple linear regression, we developed a two-variable model linking WD to drilling resistance (DR) variables. The optimal model, incorporating the standard deviation of DR (DRStD) and the slope of DR (DRSlope), produced the best predictions (adjusted R2 = 0.604, RMSE = 0.083 g cm-3) and showed no significant difference from field WD. Log-transformed models were also tested as a robustness check and yielded lower prediction errors, but the final model was selected for its higher explanatory power and direct interpretability. By comparison, database WD values significantly overestimated field WD and resulted in inflated AGB estimates (RMSE = 32.260%) compared to those derived from the resistograph-based model (RMSE = 25.933%). Our findings demonstrate that the resistograph-based methodology accurately predicts field WD. As a non-destructive and portable tool, it has large potential to enhance the credibility of biomass quantification for nature-based carbon credit projects in Southeast Asia. Further work should expand geographical and species coverage to support broader application across tropical forest landscapes.
Over the past few decades, wildfire activity has increased globally. In the Central Zagros Mountains of Iran, widespread oak decline has significantly altered fuel structures and raised ecological concerns. However, the specific contribution of long-term vegetation degradation to wildfire burn severity remains under-quantified. This study addresses this gap by investigating whether decadal declines in the Normalized Difference Vegetation Index (NDVI) are associated with higher burn severity, while assessing their relative importance against short-term pre-fire environmental conditions, fuel types, topography, and human access gradients. Focusing on a major wildfire in June 2024 in the Kashkan Watershed, we mapped burn severity using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery. A comprehensive suite of predictors was evaluated, including the long-term NDVI trend (May- September 2014-2023), pre-fire Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), fuel categories based on ESA WorldCover, slope, aspect, spatial texture contrast, and distance to roads and settlements. A stratified sampling design supported statistical inference using ordinary least squares regression and predictive modeling via Random Forest and XGBoost algorithms with five-fold cross-validation. XGBoost demonstrated the highest predictive performance (cross-validated R2 = 0.70; RMSE = 0.11), outperforming Random Forest (R2 = 0.64) and linear regression (R2 = 0.34). Fuel moisture (NDMI) and thermal stress (LST) emerged as the primary drivers of severity, followed by land cover, topography, and spatial texture. While the long-term NDVI decline showed a small but consistent positive association with dNBR at the landscape scale, the results highlight a distinct multi-scale mechanism. In this dynamic, pre-fire moisture and temperature establish the broad watershed-scale environmental susceptibility, whereas specific fuel types and their spatial configurations drive the local-level amplification or damping of burn severity. These findings provide a framework for proactive vulnerability mapping in Zagros oak woodlands, suggesting that integrating dynamic fire-weather data and field-based fuel measurements could further refine future predictability.
Accurate volume measurement of stacked timber logs is essential for the timber industry. Photo-optical Automated Volume Measuring Systems (AVMS) offer efficient alternatives to manual methods but require verification to ensure reliability. This paper presents a simple, reliable, and easily implementable verification method for AVMS, based on measuring the wood stack front surface area. The proposed method utilizes reference models, calibrated using traceable working standards, to simulate timber log stacks with defined contours. This involves comparing the AVM’s measured area to the reference model’s area, enabling the determination of area measurement errors. The study investigates the selection of reference instruments, proposes a comprehensive AVMS verification procedure, and evaluates the method’s metrological characteristics. Laboratory tests involving 110 AVMS units revealed a repeatability of 0.46% and a total uncertainty of approximately 1.1%. The method successfully identified shortcomings in commercially available AVMS, including non-compliance with manufacturer-declared errors and design imperfections related to camera limitations. The developed method can be used in industry, metrological supervision, and scientific research to ensure accurate and reliable measurements of wood stacks and the long-term reliability of AVMS. This study also highlights the need to further improve photo-optical measurement systems to increase their reliability and accuracy.
