Accurate prediction of forest fires is crucial for enhancing regional fire prevention and control. Existing models frequently rely on static factors such as weather and terrain, while insufficiently taking into account the Fuel Moisture Content (FMC), a critical internal factor that directly determines fire behavior. Instead, proxies like the Normalized Difference Vegetation Index (NDVI) are commonly employed, which weakens the physical foundation of predictions. This study assesses the marginal contribution of integrating dynamic FMC into fire prediction models. Concentrating on California, we developed a random-forest-based model that incorporates high-resolution FMC products retrieved by our team, along with meteorological, topographic, vegetation, and anthropogenic data. Through comparative experiments and SHapley Additive exPlanations (SHAP) analysis, we evaluated model improvements and the contribution mechanisms of key drivers. The results indicated that: (1) Incorporating FMC significantly enhanced model performance, with precision and specificity increasing by 3.93% and 3.60%, respectively, and the Area Under the Curve (AUC) showing improvements, suggesting heightened sensitivity in detecting actual fire occurrences. (2) SHAP analysis disclosed nonlinear effects and threshold dynamics: temperature was the dominant positive driver (the fire risk soared above 20 degrees C); FMC demonstrated a negative correlation with fire risk, with 100% serving as a potential threshold; elevation presented an inverted U-shaped pattern (the peak risk occurred at 1000-1500 m); and population density exhibited a shifting influence from positive to negative. (3) The monthly risk maps for California in 2023 captured the seasonal progression of fire risk and spatial patterns consistent with historical fire points. The fire risk map for 9 September 2020 also demonstrated consistency with the spatial distribution of the actual fire points on that day. This study validates that the integration of dynamic FMC strengthens the mechanistic foundation and early-warning capacity of fire prediction models, providing scientific backing for targeted fire management.
Accurate estimation of forest aboveground biomass (AGB) is pivotal for assessing forest carbon sequestration and informing global change studies. Conventional LiDAR-based AGB estimation approaches primarily rely on height and density metrics, which inadequately characterize the complex three-dimensional (3D) structure of forest canopies. This study developed and evaluated a novel method utilizing voxel-based 3D canopy structural metrics derived from airborne LiDAR (ALS) to improve AGB estimation accuracy across diverse forest types. First, voxel-based metrics (Voxel Canopy Height Model (VCHM), canopy volume, and canopy surface area) were extracted from voxelized point clouds. Their distribution patterns across five forest types (Pinus massoniana, Cunninghamia lanceolata, coniferous, broadleaf, and mixed conifer-broadleaf forests) and their correlations with AGB were systematically examined. The results revealed distinct 3D canopy architectures among forest types, with all three voxel metrics showing highly significant positive correlations with AGB; VCHM demonstrated the strongest association. We then constructed two Random Forest models: a baseline model using traditional metrics only, and an enhanced model integrating both traditional and voxel-based metrics. The 10-fold cross-validation indicated that the model incorporating voxel metrics achieved markedly higher accuracy (R2 in 0.490-0.684) than the traditional model (R2 in 0.480-0.607), representing a relative improvement of 2.1% to 32.7%. The most substantial gain occurred in structurally complex broadleaf forests. The enhanced model was subsequently applied to generate a wall-to-wall AGB map of the study region, yielding a total estimated AGB stock of 8.36 & times; 106 t, which exhibited a patchy spatial distribution. Pinus massoniana forests accounted for the largest proportion (57.8%) of the total stock. This study demonstrates that voxel-based 3D canopy metrics can more effectively capture forest structural heterogeneity and substantially improve the accuracy of AGB estimation models, particularly for complex forest stands. The findings provide a significant advancement toward precise, stand-scale forest biomass monitoring founded on detailed 3D structural information.
Under climate change, extreme wind events are predicted to become both more common and more severe, increasing the vulnerability of plantation forests. In February 2023, ex-tropical Cyclone Gabrielle caused widespread wind damage to radiata pine (Pinus radiata D. Don) forests across the North Island of New Zealand, providing a rare opportunity to evaluate mechanistic wind-risk modelling under extreme storm conditions. This study assessed the performance of the ForestGALES model in predicting wind damage within the Rangipo genetic accelerator trial and examined the influence of stocking and cultivation on wind vulnerability. Using detailed pre-cyclone field measurements and high-resolution unmanned aerial vehicle light detection and ranging (ULS) data, ForestGALES was parameterised for the Rangipo trial and applied at both individual-tree and stand scales. Model predictions were compared with observed post-cyclone damage using balanced area under the receiver operating characteristic curve (AUC), accounting for strong class imbalance. Wind damage was observed in 16.7% of trees, of which 10.2% showed stem breakage and 6.5% overturning. Across both spatial scales, overturning was more accurately predicted than stem breakage. At the individual-tree scale, ForestGALES showed moderate predictive skill, with balanced AUC values of 0.650 +/- 0.014 for overturning, 0.595 +/- 0.011 for breakage, and 0.621 +/- 0.008 for total damage. Model performance was stronger at the stand scale, where discrimination was highest for overturning (AUC 0.811 +/- 0.122), followed by breakage (0.693 +/- 0.116) and total damage (0.623 +/- 0.083). Silvicultural treatments significantly influenced predicted critical wind speeds (CWS). High-stocking treatments exhibited consistently higher CWS values and therefore greater wind firmness than low-stocking treatments, while cultivation effects were smaller but significant. Simulated reductions in stocking further demonstrated increased wind vulnerability as stocking declined, highlighting thinning as a primary determinant of wind risk. These findings demonstrate that ForestGALES can reliably discriminate wind damage at operational stand scales under extreme cyclone conditions and highlight the importance of stand structure in improving plantation resilience under increasingly storm-prone climates.
Changing hydrothermal regime leads to pronounced changes in growth and ranges of Siberian tree species that are mostly negative at the southern part of the trees' habitat. Here we analyzed the response of Larix sibirica and Populus laurifolia to moisture changes in unique refugia that border the Mongolian desert in Southern Siberia. The great age of old-growth larch trees (>500 years) suggests that the refugia have existed throughout the Holocene. We aimed to (1) analyze larch and poplar growth and range response to the changing temperature and moisture regime, (2) explore the potential migration of trees into the desert, and (3) analyze Gross Primary Productivity (GPP) dynamics within the refugia and adjacent desert. We used on-ground surveys, remote sensing data, and dendroecological analysis. We found that since the warming onset (c. 1980), larch and poplar trees have increased their growth and population within and beyond the refugia (+300% for poplar and +45% for larch). Both species' growth has been controlled by atmospheric and soil droughts (measured by the Standardized Precipitation Evapotranspiration Index (SPEI) and Self-Calibrating Palmer Drought Severity Index (scPDSI)) and by microtopography-dependent moistening. Summer winds impair trees' growth via increased evapotranspiration. Both species were migrating to the southern sandy dunes. Although poplar is less drought-resistant than larch, it was shifting ahead of larch (5.6 m/year vs. 0.8 m/year). The mean and maximum treeline shifts were 260 and 450 m for poplar and 35 m and 70 m for larch. P. laurifolia occupied new climate-caused niches ahead of drought-resistant L. sibirica due to its higher prolificacy. We found a "desert greening" phenomenon, i.e., a significantly increasing GPP trend (R-2 = 0.31) in both refugia and sandy dunes. The GPP increase correlated with tree growth increase (r(2) = 0.36-0.39). The larch and poplar migration to the desert contradicts the predicted shrinkage of the tree ranges within their southern boundary. However, the projected increase in the moisture deficit by 2080-2100 may impair this phenomenon. Nevertheless, current changes in the hydrology regime are favorable for larch and poplar growth and expansion into the adjacent Mongolian desert.
We estimate Oregon's mass timber-related market value and economic contribution using two complementary valuation strategies and two IMPLAN implementations. Although mass timber includes CLT, glulam, nail-laminated timber, dowel-laminated timber, mass plywood panels, and structural composite lumber products, the empirical market-value estimates are centered primarily on CLT- and MPP-related evidence because these products have the most consistently available Oregon-specific data. Market value is inferred from production-based approaches, including facility capacity, Oregon's share of U.S. output, and tracer-product scaling, and from demand-based approaches, including harvest routing, construction floor area, and U.S. demand allocation. These direct values are then entered into industry contribution analysis (ICA) for Oregon's Engineered Wood Member and Truss Manufacturing sector and into analysis-by-parts (ABP) using a custom mass timber spending pattern. During 2018-2023, production-based estimates were larger and more variable than demand-based estimates, bracketing a plausible scenario range rather than providing a single point estimate. In 2022 price scenarios, all price-exposed cases scale proportionally with assumed panel prices. When identical direct values are used, ABP produces larger total employment and output effects than ICA because it routes more activity through upstream supplier industries. Output-per-worker sensitivity affects only direct employment in ABP. Forward scenarios for 2030 and 2035 indicate substantially larger total effects under ABP than ICA, but these estimates are conditional scenarios rather than forecasts. The framework provides a transparent basis for policy, investment, supplier-development, and workforce-planning discussions in an emerging industry with incomplete product-level data.
