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Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS's ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted. Novel LiDAR-based allometric models were tested using new LiDAR acquisitions; LiDAR predictions were based on tree crown size and immediate neighbourhood metrics; Model inputs were derived from drone and crewed aircraft LiDAR acquisitions; Model predictions were most accurate when applied to similar stand structures as the original model; Normalized root means square error for stem volume and diameter in pine stands with no thinning was <15%; Stem size estimates in thinned stands were consistently overestimated. [ABSTRACT FROM AUTHOR]

Over the last decades, Chile has experienced a long-term drought with significant consequences for water availability, forest productivity, and soil degradation, ultimately dramatically increasing the surface of burned area. Here, we quantify the Palmer Drought Severity Index (PDSI) to ascertain the extent of "moisture deficiency" across the central-southern region of Chile from 2000 to 2023 to assess the drought's relationship with the frequency of wildfires focusing on the impact of native forests. Our methodology quantifies the PDSI from the burned area data using MODIS MCD64A1 satellite imagery, validated by in situ wildfire occurrence records. The findings indicate that 85.2% of fires occurred under moderate to severe drought conditions. We identified 407,561 ha showing varying degrees of degradation due to wildfires, highlighting the critical areas for targeted conservation efforts. A significant increase in both the frequency of wildfires and the extent of the affected area in native forests was observed with the intensification of drought conditions in the 21st century within mesic to humid Mediterranean climatic zones where drought explains up to 41% of the variability in the burned area (r2 = 0.41; p < 0.05). This study highlights the relationship between drought conditions and wildfire frequency, showing the paramount need to adopt comprehensive wildfire mitigation management in native forests. [ABSTRACT FROM AUTHOR]

This study aimed to explore alternative substrates for growing forest species using eucalyptus bark. It evaluated the potential of extracted Eucalyptus globulus fiber bark as a substitute for commercial growing media such as coconut fiber, moss, peat, and compost pine. We determined the physicochemical parameters of the growing media, the germination rate, and the mean fresh and dry weights of seedlings. We used the Munoo-Liisa Vitality Index (MLVI) test to evaluate the phytotoxicity of the bark alone and when mixed with commercial substrates. Generally, the best mixture for seed growth was 75% extracted eucalyptus bark fiber and 25% commercial substrates. In particular, the 75E-25P (peat) mixture is a promising substitute for seedling growth of Pinus radiata, achieving up to 3-times higher MLVI than the control peat alone. For Quillaja saponaria, the best growth substrate was the 50E-50C (coconut fiber) mixture, which had the most significant MLVI values (127%). We added chitosan and alginate-encapsulated fulvic acid phytostimulants to improve the performance of the substrate mixtures. The fulvic acid, encapsulated or not, significantly improved MLVI values in Q. saponaria species and P. radiata in concentrations between 0.05 and 0.1% w/v. This study suggests that mixtures with higher levels of extracted fiber are suitable for growing forest species, thus promoting the application of circular economy principles in forestry. [ABSTRACT FROM AUTHOR]

Persistent drought events frequently intensify the aridity of ecosystems and cause catchment runoff depletion. Here, using large and long-term data sets of meteorological and hydrologic variables (precipitation, runoff, temperature, and potential evapotranspiration) investigated the major causes that modulated catchment runoff depletion between the years 1980 and 2020 in southern-central Chile. We identified the hydrological years where aridity index intensified, analyzed its relationship with annual runoff, and evaluated the effect of the annual evaporation index and annual aridity index on water balance of 44 catchments with different precipitation regimes located between 35° and 40°S. Our results showed that observed precipitation and runoff significantly decreased between 1980 and 2020 in 64% of the catchments in the study area. Potential evapotranspiration increased significantly in 39% of the catchments. Hydrological years in which precipitation decreased showed a decreased runoff trend. This result suggests that meteorological droughts tend to significantly decrease observed runoff. The runoff value decreased as the aridity index increased from 0.3 to 6.7, and the Budyko curve captured 98.5% of the annual variability of all catchments. [ABSTRACT FROM AUTHOR]