Intelligent drone-based framework for autonomous inventory data inspection

Urban trees provide important ecosystem services (ESS), but their contributions are often undervalued and less acknowledged due to the complexity of quantifying them. Therefore, ESS assessment for urban trees at the individual tree level using ESS models is crucial for a more knowledge-based management of urban green spaces. In this study, we used very high-resolution aerial and satellite-based remote sensing imagery to derive the geospatial input for the CityTree model to estimate regulating ESS from over 160,000 individual trees in Munich, Germany. Our assessment includes both, trees on public and private land and enables fine-scale spatial modeling of eight ESS (carbon storage, carbon sequestration, CO 2 sequestration, evapotranspiration of trees, runoff under the tree, transpiration, cooling by transpiration and shading). We found that public trees, especially those in recreational areas such as parks and woodlands, contribute largely to ESS provision. Private trees also play a meaningful role by contributing around one third of the total ESS. A statistical comparison with the tree inventory data revealed good agreement between the two datasets. However, we also found systematic measurement differences, possibly due to rounding in field measurements and limitations in remote sensing datasets. However, the size effect of these differences is small in practical terms, indicating that both data sources are comparable and complementary. Our findings support the use of remote sensing as a scalable, area-wide, consistent, and resource-efficient approach for urban ESS estimations.

Life cycle assessment as a tool for eco-design of modified materials for water treatment

Accurate estimation of forest carbon stocks is essential for climate change mitigation, particularly in mountainous regions where strong spatial heterogeneity constrains the performance of conventional remote sensing models. This study investigates whether incorporating local sample weighting and robust parameter optimization can enhance the reliability of forest carbon stock estimation under spatial heterogeneity. A locally weighted regression framework based on Support Vector Regression was developed by integrating neighborhood-based sample weighting with a global hyperparameter optimization strategy and applied to Huoshan County, Anhui Province, China. The analysis was based on Landsat optical data and DEM-derived topographic variables, combined with field-based forest inventory data. Results indicate that the proposed approach achieved higher predictive performance than five commonly used regression models, with a test-set coefficient of determination (R2) of 0.763 and a root mean square error (RMSE) of 6.72 t·ha-1, and demonstrated improved robustness in spatially heterogeneous forest areas. The findings suggest that locally weighted machine learning methods provide an effective and reliable tool for regional forest carbon stock mapping in complex mountainous environments.

Abstract Background Weather and fuels are among the critical, interacting factors that drive wildland fire behavior, and thus are primary factors in fire operations planning and decision support tools. In mesic forests, variation in stand structure may lead to heterogeneous microclimate and fuel moisture conditions in the understory where fires often ignite and spread. However, such variation in fuel availability is often overlooked by fire behavior models that assume spatially uniform weather and fuel conditions. In this study, we analyze a combination of field-based understory meteorology and fuel moisture data with terrestrial laser scanning (TLS) and traditional forest inventory data to develop new knowledge about relationships between forest structure, microclimate, and dead fuel moisture in USA northern conifer forests that can inform fire operations planning and decision support. Results We found that open canopy plots were significantly warmer (+ 7.82 °C average daily maximum air temperature) and drier (-24.1% average daily relative humidity) and with drier fuels (+ 8.03% fuel moisture) at midday in summer compared to closed canopy plots. Directly using microclimate variables (i.e., air temperature and relative humidity) resulted in better predictions of dead fuel moisture content (mean R 2 = 0.88) than using forest structure variables such as canopy openness (R 2 = 0.60). Furthermore, forest structure variables derived from TLS were better predictors of dead fuel moisture content (R 2 = 0.74) than traditional forest inventory metrics. Conclusions Our study used multi-modal measurements to demonstrate that dense forest cover linearly reduces fuel availability by buffering microclimate and maintaining fuel moisture. This research can be used to develop thinning prescriptions to achieve certain thresholds of understory temperature, relative humidity, and dead fuel moisture. Moreover, our results highlight the microclimate buffering effect of shaded fuel breaks used in fire suppression and containment tactics. Finally, our work suggests that tools like TLS can be used to fine tune fuel-weather relationships in fire behavior models that use spatially explicit fuels data to inform planning and predict fuels treatment effectiveness. This research enhances fire managers’ ability to plan and implement fuel treatments by highlighting how changes in forest stand structure drive fine scale heterogeneity in fuel availability.

Abstract Management of western United States dry-conifer forests has experienced a design shift from spacing-based treatments to maximize forest growth and minimize fire hazard towards balancing fuels reduction while restoring historical forest spatial patterns. However, successfully implementing spatially heterogeneous silvicultural treatments capable of mimicking a landscape’s historical range of variability (HRV) has proven challenging. Early individual tree marking (ITM) strategies aiming to create heterogeneous forest structure often had unclear or confusing instructions. This study evaluates the ability of five ITM strategies to recreate HRV forest structure in Black Hills, South Dakota ponderosa pine forests. We started by first comparing uncrewed aerial system (UAS) derived forest inventory data against field plots and then assessing the UAS-derived tree spatial patterns of each ITM strategy against HRV. The UAS-detected trees were comparable to field plots providing an F-score averaging 0.943. The five ITM strategies created tree group size distributions that captured the range of individual trees and tree-clump-sizes but varied in their approximation of the HRV distribution. The individuals, clumps, and openings strategy and the two lowest cut-to-leave tree frequency strategies consistently matched four of five HRV group size metrics, while the other strategies typically only matched two or three metrics. Additionally, the frequency cut-to-leave tree strategy with 1:2 small-tree and 1:1 large-tree more often achieved all five HRV group size metrics compared to all other ITM strategies. Given the relatively even-aged starting forest structure from the stand’s past management history, the lowest frequency cut-to-leave tree strategy provided the easiest to implement and most effective means of reproducing the HRV forest structure.

ABSTRACT Trees are an essential component of urban greenscapes that provide shade, habitat for wildlife, and aesthetic benefits. Given their extensive landscapes, many college campuses function as green spaces within the urban matrix. An exploratory investigation of college campus tree inventories was conducted to identify the availability and accessibility of campus tree inventories in the northeastern United States, compare species diversity and tree measurements across campuses, and identify and promote best management practices. Using directed searches and non-probabilistic sampling, campus tree inventories were obtained from twenty-eight colleges spanning Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. It was observed that trees are more often identified with common name than scientific name, that the quality of data varied considerably, and that campus tree populations lack diversity among genera (Acer, Quercus, and Pinus were often abundantly represented). Priority should be given to ensuring that 1) inventories are maintained, 2) tree species are identified using scientific names, and 3) key measurements are obtained. Standardising and recording campus inventory data in a systematic, consistent method would more readily promote direct comparison, robust analysis, and foster a broader understanding of the state of campus trees beyond a single institution. Tree programmes and student volunteers may incentivise more comprehensive campus tree inventory management.

Purpose: While there is an increasing body of evidence demonstrating the impact of intercultural learning (ICL) activities in preprofessional programs through varied pedagogical approaches, there is limited information about long-term outcomes of these approaches. The purpose of this study is to evaluate longitudinal outcomes of undergraduate students enrolled in speech-language pathology and audiology courses that embed intentional and structured ICL activities in their syllabi. Method: Participants ( n = 50) were undergraduate students enrolled in at least two of four possible speech, language, and hearing sciences courses that embedded ICL activities. All courses had multiple ICL activities throughout the semester and required critical reflection papers after each ICL activity and a final reflection paper at the end of the semester. Using a mixed-methods approach, quantitative data using the Intercultural Development Inventory (IDI) and qualitative data from reflection papers were obtained from each participant. Results: Quantitative findings indicated that group IDI developmental orientation scores that increased after the first course with embedded ICL activities were maintained at the start of the final course incorporating ICL. Additionally, the group had another significant increase in developmental orientation scores after the final course. These quantitative findings were supported by themes that resulted from analysis of the qualitative reflections from students. Conclusions: The results of this study showed that structured ICL activities embedded throughout multiple courses facilitated lasting intercultural growth among undergraduate students. This study affirmed the value of intentional, longitudinal curricular design in order to prepare future clinicians and professionals to provide patient-centered care and to work effectively in diverse contexts.

Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA's completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.

Pavement engineers frequently need a rapid and accurate evaluation of layer thicknesses and conditions. Such an assessment is critical for evaluating current conditions and identifying optimal maintenance and rehabilitation needs. The objective of this study was to use remote sensing for assessing pavement thickness uniformity. For this purpose, the potential use of Ground-Penetrating Radar (GPR) data was considered. Traditional GPR data interpretation methods are generally not intended to quantify the spatial variability information required for pavement management-related analyses. Thus, the method presented herein is based on several layers of statistical assessment of pavement thickness changes for identifying homogeneous sections. The suggested approach provides consistent thickness assessment over consecutive pavement segment lengths. Such evaluation is particularly useful for integration into Pavement Management System (PMS) analyses at both the project and network levels. The approach was used in concrete pavements, and data from an in-service roadway are provided as an example to demonstrate how this analysis is applied. This analysis approach provides several benefits to highway agencies: a quick and accurate condition assessment regarding existing pavement thickness; better decision-making in identifying alternative maintenance and rehabilitation techniques for uniform sections with respect to thickness, which clearly need to be combined with condition assessment of pavement layer materials; and efficient use of remote sensing data for pavement sections where construction inventory data may not be available.

This paper selects 78 companies having incorporated data inventories into their financial statements from 2023 to 2024 as the research samples to empirically examine how data inventory incorporation influences and varies in enterprise cost rigidity. According to the research, when companies include data inventories on their balance sheets, it tends to beef up their cost stickiness index, which in turn helps them rein in this particular financial tendency. This conclusion still holds true even after replacing the explanatory variables with the proportion of data inventories in the company's revenue. And the control variable test shows that state-owned enterprises and enterprises with high debt-to-asset ratios have lower cost stickiness, while large-scale enterprises have higher cost stickiness. In addition, the heterogeneity evaluation shows that integrating the data list into the balance sheet can obviously reduce the cost stickiness, and this effect is obvious in public, private, large companies and small enterprises. However, private companies are more sensitive to cost stickiness, which is related to their operation scale and efficiency. Because of the problems of internal management quality and operation ability, the cost of small companies fluctuates more, and the inventory level in financial statements has a slightly stronger influence on them than that of large companies. This study discusses how to integrate inventory data into the balance sheet, which can better reflect the economic significance of accounting policy adjustment, and also provide practical methods for enterprises to help them reduce cost stickiness by this method.

ABSTRACT Hurricanes are increasingly frequent and intense disturbances that cause substantial economic and ecological damage to forested landscapes worldwide. Following such events, multiple stakeholders – including timberland owners, forest management organizations, logging and transportation providers, and wood-processing mills – face urgent salvage decisions under uncertain and spatially heterogeneous timber supply conditions. This study presents a scenario-based stochastic optimization framework for post-hurricane salvage logging that explicitly accounts for uncertainty in timber volume loss and quality degradation. Using Hurricane Michael (2018) as a case study in the southeastern United States, forest inventory data, remotely sensed damage indices, species-specific yield curves, and market information are integrated into a spatial optimization model that jointly determines salvage harvest locations and wood transportation decisions. Uncertainty in salvageable supply is represented through multiple simulated damage scenarios with varying levels of sawtimber downgrade to pulpwood. The framework compares risk-neutral and risk-averse strategies, including a mean – risk formulation based on Conditional Value-at-Risk (CVaR), and evaluates sensitivity to key economic and logistical parameters. Results indicate that risk-averse and performance-stable salvage strategies reduce transportation costs and mitigate losses in timber value relative to deterministic approaches, particularly under high uncertainty in damage severity and downgrade rates. Overall, the findings demonstrate that representing post-hurricane timber supply uncertainty through scenario-based simulations and evaluating alternative downgrade and risk preferences materially influence salvage location choices, transportation costs, and economic outcomes. The proposed framework provides a decision-support tool for assessing trade-offs between expected returns and downside risk across a wide range of plausible post-disturbance conditions.

This study assessed mountain tugai poplar forests in southern and southeastern Kazakhstan by conducting forest inventory analysis and field surveys in Zhongar Alatau, Northern Tien Shan, and Western Tien Shan. The forest inventory identified 1,622.5 hectares of poplar forests, mainly consisting of mature and overmature stands with low density and productivity. Only 47% of these forests were located within protected areas. The primary species, Populus macrocarpa (Schrenk) Pavlov (also known as P. talassica Kom.), was confirmed through molecular genetic analysis using ITS, matK, and rbcL markers. Natural regeneration mainly occured vegetatively, with limited seed reproduction. Recreational activities had a significant impact on stand condition, reducing the proportion of viable trees from 70–85% in protected areas to 50–55% in heavily visited sites. B-INTACT modeling predicted substantial ecosystem degradation with increased recreational infrastructure. Conservation efforts should focus on expanding protected area coverage, controlling invasive species, and implementing systematic long-term monitoring to address these challenges.

Integrated data imputation of national inventories for bridging information gaps and outcome risk uncertainty in community resilience modeling

Substituting traditionally used harmful solvents to extract valuable bioactive components is crucial for reducing environmental harm, increasing user safety, and advancing process sustainability. This study evaluates four green and bio-based biphasic solvent systems 2-methyltetrahydrofuran (2-MeTHF)/(ethanol or 1-butanol)/water and cyclopentyl methyl ether (CPME)/(ethanol or 1-butanol)/water, against two conventional biphasic solvent systems, chloroform/methanol/water and hexane/ethanol/water, for β-carotene and cellular lipids extraction from the oleaginous yeast Rhodosporidium toruloides . Laboratory-scale extraction experiments determined yields, solvent consumption, and evaporation energy requirements for each system. Afterwards, the selected systems were simulated with Aspen Plus to obtain mass and energy balances for industrial-scale operation. These results formed the basis of a cradle-to-gate life cycle assessment (LCA) using the EF 3.1 method, applying functional units of 1 kg extract and 1 g β-carotene. Experimentally, 2-MeTHF-based systems achieved the highest total extraction yields (0.25 g extract /g DCW ) and β-carotene yields (0.046 mg/g DCW ), outperforming CPME and matching or exceeding conventional systems. Aspen Plus simulations indicated the lowest solvent consumption for 2-MeTHF/ethanol/water and the lowest thermal energy demand for chloroform/methanol/water. The LCA results showed that 2-MeTHF/ethanol/water exhibited the lowest climate change impact per kg extract, while 2-MeTHF/1-butanol/water performed best per g β-carotene. CPME-based systems had higher impacts, largely due to greater solvent and energy requirements, though results may be influenced by limited industrial-scale inventory data. • Extraction of β-carotene and lipids from wet biomass of the oleaginous yeast R. toruloides. • Evaluation of green and bio-based solvents as an alternative to fossil-based ones. • Analysis of extraction yield, solvent consumption, and thermal energy demand. • Industrial-scale extraction process simulation and life cycle assessment. • 2-MeTHF-based solvent systems are promising, sustainable alternatives to harmful ones.

Abstract Above‐ground and below‐ground interactions are essential for the assembly of forest communities and the maintenance of multiple ecosystem functions. However, there is limited understanding of how above‐ground plant and below‐ground soil microbial communities are associated across different climatic zones and along environmental gradients. Using comprehensive inventory data from 186 permanent plots along elevation gradients across three climate zones (subtropical, temperate‐subtropical and temperate mountain forests), we examined the diversity associations between different groups of plants (woody and herbaceous) and soil microbes (fungi and bacteria). Associations between soil fungi and plants were stronger than those between soil bacteria and plants, particularly at the beta diversity level. Moreover, we observed that associations between soil microbes and plants were more pronounced in forests at low latitudes and intermediate elevations, and were sensitive to local abiotic conditions, including climatic and edaphic variables. Synthesis : These findings suggest that variation in climatic and edaphic conditions along environmental gradients is associated with differences in the strength of above‐ground–below‐ground associations. In addition to direct effects of climate change, plant and soil microbial communities could thus be indirectly affected via their biotic interactions under changing environmental conditions. This could have far‐reaching consequences for community assembly and ecosystem functioning.

Life cycle assessment of microwave torrefaction and pellet production from agri-forest residues.

Integration of Google’s Alpha Earth Foundations into biomass estimation combined with GEDI spaceborne lidar and field inventory data

Biodiversity is highly heterogeneous across space, shaped by factors such as climate, geographical distance, and human activities. However, as we enter the Anthropocene, the impacts of habitat loss and fragmentation on biodiversity are becoming increasingly severe and insufficiently disentangled. Here we assess how habitat amount, habitat fragmentation, regional climate, and geographical distance influence beta diversity and endemism levels in tree communities across 95 landscapes in the Brazilian Atlantic Forest. We generated landscapes of 100 km2 around forest inventory data extracted from the Neotropical Tree Communities database (TreeCo). Each landscape was characterized in terms of habitat amount, fragmentation (number of patches, mean patch size, and mean distance to the nearest neighbor), mean spatial distance (geographical distance among forest inventories), and climate across the region. The landscape variables (habitat amount and fragmentation) and the mean spatial distance were stronger predictors of beta diversity and endemism level when compared to regional climate. Beta diversity declined with increasing fragmentation, suggesting biotic homogenization in highly fragmented landscapes. On the other hand, spatial distance promoted increases in beta diversity, with more dissimilar communities as the distance increased. Moreover, endemism increased with habitat amount, with higher endemism levels in landscapes with more habitat. We highlight that our study contributes to the ongoing debate on the effects of fragmentation on biodiversity, reporting negative effects of habitat fragmentation on beta diversity within landscapes across the entire Atlantic Forest. Our findings successfully separate the effects of habitat fragmentation, habitat amount, and spatial distance on biodiversity, providing a better understanding of the processes that determine biodiversity change at the landscape scale. We advocate for conservation strategies that simultaneously protect both large and smaller habitat patches to sustain tree beta diversity and endemism across human-modified landscapes.

Knowledge graph embeddings for extrapolation life cycle inventory data: A novel approach to bridge data gaps in environmental impact assessment