Wildfire response of forest species from multispectral LiDAR data. A deep learning approach with synthetic data

Forests play a crucial role as the lungs and life-support system of our planet, harbouring 80% of the Earth's biodiversity. However, we are witnessing an average loss of 480 ha of forest every hour because of destructive wildfires spreading across the globe. To effectively mitigate the threat of wildfires, it is crucial to devise precise and dependable approaches for forecasting fire dynamics and formulating efficient fire management strategies, such as the utilisation of fuel models The objective of this study was to enhance forest fuel classification that considers only structural information, such as the Prometheus model, by integrating data on the fire responses of various tree species and other vegetation elements, such as ground litter and shrubs. This distinction can be achieved using multispectral (MS) Light Detection and Ranging (LiDAR) data in mixed forests. The methodology involves a novel approach in semantic classifications of forests by generating synthetic data with semantic labels regarding fire responses and reflectance information at different spectral bands, as a real MS scanner device would detect. Forests, which are highly intricate environments, present challenges in accurately classifying point clouds. To address this complexity, a deep learning (DL) model for semantic classification was trained on synthetic point clouds in different formats to achieve the best performance when leveraging MS data Forest plots in the study region were scanned using different Terrestrial Laser Scanning sensors at wavelengths of 905 and 1550 nm. Subsequently, an interpolation process was applied to generate the MS point clouds of each plot, and the trained DL model was applied to classify them. These classifications surpassed the average thresholds of 90% and 75% for accuracy and intersection over union, respectively, resulting in a more precise categorisation of fuel models based on the distinct responses of forest elements to fire. The results of this study reveal the potential of MS LiDAR data and DL classification models for improving fuel model retrieval in forest ecosystems and enhancing wildfire management effortsMinisterio de Universidades | Ref. FPU16/00855Agencia Estatal de Investigación | Ref. PCI2020-120705-2Universidade de Vigo/CISU

Developing Ecological Citizenship: The Role of Political Agents Using Bronfenbrenner\u27s Bioecological Model

Despite decades of research on environmental behavior, it is unknown how various political actors aid in the development of ecological citizenship (EC). The purpose of this correlational study was to determine the relationship between environmental worldview (NEP) and willingness to take action (WTTA) among political actors within 5 states: Iowa, Kansas, Nebraska, South Dakota, and North Dakota. The overarching research question examined how EC can be increased within the 5-state region by identifying the similarities and differences in NEP and WTTA between state legislators, state partners, and nongovernmental organizations (NGOs). Bronfenbrenner\u27s bioecological model provided the theoretical framework for the study. Out of 1,800 invited participants, 117 state legislators, 328 formal partnership directors, and 237 NGO administrators from the 5-state region participated in an online survey that measured their NEP, WTTA, and endorsement of EC principles. Nearly 20% of all respondents endorsed EC indicated by a high NEP and a high WTTA. Results of correlational analyses found a significant positive relationship between NEP and WTTA for each group. Further regression analysis found variation in group WTTA attributable to NEP varied from 32% for partnership directors and 36% for NGO administrators to 61% for state legislators. These findings indicated that EC can be affected by both private and public stakeholders. The implications for positive social change include demonstrating how state governments, in partnership with NGOs and other agencies, can increase EC within their states, and how improved partnerships can increase local opportunities to foster EC

Uncertainty modeling : fundamental concepts and models

This book series represents a commendable effort in compiling the latest developments on three important Engineering subjects: discrete modeling, inverse methods, and uncertainty structural integrity. Although academic publications on these subjects are plenty, this book series may be the first time that these modern topics are compiled together, grouped in volumes, and made available for the community. The application of numerical or analytical techniques to model complex Engineering problems, fed by experimental data, usually translated in the form of stochastic information collected from the problem in hand, is much closer to real-world situations than the conventional solution of PDEs. Moreover, inverse problems are becoming almost as common as direct problems, given the need in the industry to maintain current processes working efficiently, as well as to create new solutions based on the immense amount of information available digitally these days. On top of all this, deterministic analysis is slowly giving space to statistically driven structural analysis, delivering upper and lower bound solutions which help immensely the analyst in the decisionmaking process. All these trends have been topics of investigation for decades, and in recent years the application of these methods in the industry proves that they have achieved the necessary maturity to be definitely incorporated into the roster of modern Engineering tools. The present book series fulfills its role by collecting and organizing these topics, found otherwise scattered in the literature and not always accessible to industry. Moreover, many of the chapters compiled in these books present ongoing research topics conducted by capable fellows from academia and research institutes. They contain novel contributions to several investigation fields and constitute therefore a useful source of bibliographical reference and results repository. The Latin American Journal of Solids and Structures (LAJSS) is honored in supporting the publication of this book series, for it contributes academically and carries technologically significant content in the field of structural mechanics