Bogdan M. Strimbu

A networked swarm model for UAV deployment in the assessment of forest environments

Autonomous Unmanned Aerial Vehicles (UAVs) have gained popularity due to their many potential application fields. Alongside sophisticated sensors, UAVs can be equipped with communication adaptors aimed for inter-UAV communication. Inter-communication of UAVs to form a UAV swarm raises questions on how to manage its communication structure and mobility. In this paper, we consider therefore the problem of establishing an efficient swarm movement model and a network topology between a collection of UAVs, which are specifically deployed for the scenario of high-quality forest-mapping. The forest environment with its highly heterogeneous distribution of trees and obstacles represents an extreme challenge for a UAV swarm. It requires the swarm to constantly avoid possible collisions with trees, to change autonomously the trajectory, which can lead to disconnection to the swarm, and to reconnect to the swarm after passing the obstacle, while continue collecting environmental data that needs to be fused and assessed efficiently. In this paper, we propose a novel solution to the formation flight problem for UAV swarms. The proposed method provides an adaptive and reliable network structure, which maintains swarm connectivity and communicability. These characteristics are needed to achieve a detailed and accurate description of the environment from the data acquired by the UAV swarm. The main characteristics of our approach are high scalability regarding the number of UAVs in the swarm and the adaptive network topology within the swarm.

An analytical platform for cumulative impact assessment based on multiple futures: The impact of petroleum drilling and forest harvesting on moose (Alces alces) and marten (Martes americana) habitats in northeastern British Columbia

The combined influence on the environment of all projects occurring in a single area is evaluated through cumulative impact assessments (CIA), which consider the consequences of multiple projects, each insignificant on its own, yet important when evaluated collectively. Traditionally, future human activities are included in CIA using an analytical platform, commonly based on complex models that supply precise predictions but with reduced accuracy. To compensate for the lack of accuracy in current CIA approaches, we propose a shift in the paradigm governing CIA. The paradigm shift involves a change in the focus of CIA investigations from the detailed analysis of one unlikely future to the identification of the patterns describing multiple potential future changes in the environment. To illustrate the approach, a set of 144 possible and equally likely futures were developed that aimed to identify the potential impacts of forest harvesting and petroleum drilling on the habitat suitability of moose and marten in northeast British Columbia, Canada. The evolution of two measures of habitat suitability (average habitat suitability index and surface of the stands with habitat suitability index >0.5) revealed that the human activities could induce cycles in the habitat dynamics of moose and marten. The planning period of 100 years was separated into three distinct periods following a sinusoidal pattern (i.e., increase - constant - decrease in the habitat suitability measures). The attributes that could induce significant changes in the assessment of environment are the choice of harvesting age and species.

Multiple Point-of-Testing Nutrition Counseling Sessions Reduce Risk Factors for Chronic Disease Among Older Adults

The objectives of this study were to explore the relationships of baseline dietary intakes and frequency of attendance at point-of-testing nutrition counseling sessions to selected risk factors for chronic diseases during a 3-year intervention. This study was part of a large multidisciplinary, community-based health outreach project conducted in a rural community of northern Louisiana. Screenings, point-of-testing counseling, weekly group exercise sessions, and group nutrition education sessions were provided over a period of 3 years. Outcome variables assessed at 6-month intervals over 3 years were body mass index (BMI), systolic and diastolic blood pressure, fasting blood glucose, and total and LDL cholesterol and dietary intake. Repeated measure analysis of variance was used to investigate the impact of the frequency of counseling sessions on outcome variables. Paired t-tests were used to identify points at which significant changes occurred. A total of 159 subjects ages 65 years and older participated in this study. The majority of the participants were female (62%) and White (82%). Attending the point of testing counseling for more than two sessions was important for a significant improvement in BMI (p ≤ 0.001), LDL cholesterol (p ≤ 0.03), blood glucose (p ≤ 0.03), and diastolic blood pressure (p ≤ 0.045). Participants who attended at least three sessions had significant reductions in risk factors for obesity and related chronic diseases, underscoring the importance of follow-up sessions after health screening.

Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

The estimation of tree biomass and the products that can be obtained from a tree stem have focused forest research for more than two centuries. Traditionally, measurements of the entire tree bole were expensive or inaccurate, even when sophisticated remote sensing techniques were used. We propose a fast and accurate procedure for measuring diameters along the merchantable portion of the stem at any given height. The procedure uses unreferenced photos captured with a consumer grade camera. A photogrammetric point cloud (PPC) is produced from the acquired images using structure from motion, which is a computer vision range imaging technique. A set of 18 loblolly pines (Pinus taeda Lindl.) from east Louisiana, USA, were photographed, subsequently cut, and the diameter measured every meter. The same diameters were measured on the point cloud with AutoCAD Civil3D. The ground point cloud reconstruction provided useful information for at most 13 m along the stem. The PPC measurements are biased, overestimating real diameters by 17.2 mm, but with a reduced standard deviation (8.2%). A linear equation with parameters of the error at a diameter at breast height (d1.3) and the error of photogrammetric rendering reduced the bias to 1.4 mm. The usability of the PPC measurements in taper modeling was assessed with four models: Max and Burkhart [1], Baldwin and Feduccia [2], Lenhart et al. [3], and Kozak [4]. The evaluation revealed that the data fit well with all the models (R2 ≥ 0.97), with the Kozak and the Baldwin and Feduccia performing the best. The results support the replacement of taper with PPC, as faster, and more accurate and precise product estimations are expected.

Twenty years of research on water management issues in the Danube Macro-region - past developments and future directions.

The Danube River-Danube Delta-Black Sea (DBS) region has witnessed major political, social and economic changes during the past three decades, which have profoundly affected the riverine, coastal and marine systems, their water management situation and the development of related research programmes. We reviewed the research activities in the DBS system of the past twenty years to determine the main funding bodies and to assess key research areas and how they varied over time and geographic region. As data basis we used a metadatabase filled with 478 projects addressing environmental and water management issues in the Danube River Basin, covering also the Danube Delta and the north-western Black Sea. As overall outcome extensive research efforts in the field of water management could be proven for the past two decades, despite the tumultuous times of political and economic transformations. One of the main findings was that EU funded projects played a key role for the development of transboundary research collaboration and were also the scientifically most productive ones. Historically, nutrient pollution was the main problem addressed, shifting to pollution in a broader sense and hydromorphological alterations in recent years. The newly arising challenges of climate change impacts and sediment management became important research questions in the last years, too. Most research was performed in the thematic field of navigation, followed by restoration and biodiversity issues. To meet all of the already identified and newly emerging challenges in the DBS System, cross-border and integrated (river-delta-sea) research activities are of major importance and have to be further promoted. We thus suggest drawing up a regional DBS Research Agenda linked to key challenges in water management to strengthen research collaboration and advance targeted scientific projects, an approach fostering also the scientific capacity in the region.

Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic tree segmentation based on the waveform-based canopy height model (CHM) using three approaches including TreeVaW, watershed algorithms and the combination of TreeVaW and watershed (TW) algorithms. Subsequently, the Random forests (RF) and Conditional inference forests (CF) models were employed to identify important tree-level waveform metrics derived from three distinct sources, such as raw waveforms, composite waveforms, the waveform-based point cloud and the combined variables from these three sources. Further, we discriminated tree (gray pine, blue oak, interior live oak) and shrub species through the RF, CF and Bayesian multinomial logistic regression (BMLR) using important waveform metrics identified in this study. Results of the tree segmentation demonstrated that the TW algorithms outperformed other algorithms for delineating individual tree crowns. The CF model overcomes waveform metrics selection bias caused by the RF model which favors correlated metrics and enhances the accuracy of subsequent classification. We also found that composite waveforms are more informative than raw waveforms and waveform-based point cloud for characterizing tree species in our study area. Both classical machine learning methods (the RF and CF) and the BMLR generated satisfactory average overall accuracy (74% for the RF, 77% for the CF and 81% for the BMLR) and the BMLR slightly outperformed the other two methods. However, these three methods suffered from low individual classification accuracy for the blue oak which is prone to being misclassified as the interior live oak due to the similar characteristics of blue oak and interior live oak. Uncertainty estimates from the BMLR method compensate for this downside by providing classification results in a probabilistic sense and rendering users with more confidence in interpreting and applying classification results to real-world tasks such as forest inventory. Overall, this study recommends the CF method for feature selection and suggests that BMLR could be a superior alternative to classical machining learning methods.