Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
438
result(s) for
"Fraser, Benjamin"
Sort by:
Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models
Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services and management costs. To understand the patterns and severity of these stressors across complex landscapes, there must be an increase in reliable data at scales compatible with management actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate the capability of UAS multispectral imagery and freely available National Agricultural Imagery Program (NAIP) imagery for differentiating coniferous healthy, coniferous stressed, deciduous healthy, deciduous stressed, and degraded individual trees throughout a complex, mixed-species forests. These methods are first compared to assessments of crown vigor in the field, to evaluate the potential in supplementing this resource intensive practice. This investigation uses the random forest and support vector machine (SVM) machine learning algorithms to classify the imagery into the five forest health classes. Using the random forest classifier, the UAS imagery correctly classified five forest Health classes with an overall accuracy of 65.43%. Using similar methods, the high-resolution airborne NAIP imagery achieved an overall accuracy of 50.50% for the five health classes, a reduction of 14.93%. When these classes were generalized to healthy, stressed, and degraded trees, the accuracy improved to 71.19%, using UAS imagery, and 70.62%, using airborne imagery. Further analysis into the precise calibration of UAS multispectral imagery, a refinement of image segmentation methods, and the fusion of these data with more widely distributed remotely sensed imagery would further enhance the potential of these methods to more effectively and efficiently collect forest health information from the UAS instead of using field methods.
Journal Article
Visible cities, global comics : urban images and spatial form
\"More and more people are noticing links between urban geography and the spaces within the layout of panels on the comics page. Benjamin Fraser explores the representation of the city in a range of comics from across the globe. Comics address the city as an idea, a historical fact, a social construction, a material-built environment, a shared space forged from the collective imagination, or as a social arena navigated according to personal desire. Accordingly, Fraser brings insights from urban theory to bear on specific comics. The works selected comprise a variety of international, alternative, and independent small-press comics artists, from engravings and early comics to single-panel work, graphic novels, manga, and trading cards, by artists such as Will Eisner, Tsutomu Nihei, Hariton Pushwagner, Julie Doucet, Frans Masereel, and Chris Ware. In the first monograph on this subject, Fraser touches on many themes of modern urban life: activism, alienation, consumerism, flânerie, gentrification, the mystery story, science fiction, sexual orientation, and working-class labor. He leads readers to images of cities such as Barcelona, Buenos Aires, London, Lyon, Madrid, Montevideo, Montreal, New York, Oslo, Paris, São Paolo, and Tokyo. Through close readings, each chapter introduces readers to specific comics artists and works and investigates a range of topics related to the medium's spatial form, stylistic variation, and cultural prominence. Mainly, Fraser mixes interest in urbanism and architecture with the creative strategies that comics artists employ to bring their urban images to life.\" -- Provided by publisher.
Book
Quantifying the Accuracy of UAS-Lidar Individual Tree Detection Methods Across Height and Diameter at Breast Height Sizes in Complex Temperate Forests
Unpiloted aerial systems (UAS) and light detection and ranging (lidar) sensors provide users with an increasingly accessible mechanism for precision forestry. As these technologies are further adopted, questions arise as to how select processing methods are influencing subsequent high-resolution modelling and analysis. This study addresses how specific individual tree detection (ITD) methods impact the successful detection of trees of varying sizes within complex forests. First, while many studies have compared ITD methods over several sites, algorithms, or sets of parameters based on a singular validation metric, this study quantifies how 10 processing methods perform across varying tree-height size quartiles and varying tree diameter at breast height (dbh) size quartiles. In total, over 1000 reference trees from 20 species within three complex temperate forest sites were analyzed at an average point density of 826.8 pts/m2. The results indicate that across four tree height size classes, the highest overall F-score (0.7344) was achieved with F-scores ranging from 0.857 for the largest and 0.633 for the smallest height size class. To further expand on this analysis, generalized linear models were used to compare the top performing and worst performing ITD method for each tree size variable and study site along a continuous gradient. This analysis suggests clear distinctions in the performance (true positive and false positive rates) based on tree sizes and ITD method. UAS-lidar users must ensure that demonstrated ITD processing methods are validated in ways that communicate their relative effectiveness for trees of all sizes. Without such consideration, the results of this study show that forest surveys and management conducted using these technologies may not accurately characterize trees present within complex forests.
Journal Article
Cognitive disability aesthetics : visual culture, disability representations, and the (in)visibility of cognitive difference
\"Cognitive Disability Aesthetics explores the invisibility of cognitive disability in theoretical, historical, social, and cultural contexts. Benjamin Fraser's cutting edge research and analysis signals a second-wave in disability studies that prioritizes cognition. Fraser expands upon previous research into physical disability representations and focuses on those disabilities that tend to be least visible in society (autism, Down syndrome, Alzheimer's disease, schizophrenia). Moving beyond established literary approaches analyzing prose representations of disability, the book explores how iconic and indexical modes of signification operate in visual texts. Taking on cognitive disability representations in a range of visual media (painting, cinema, and graphic novels), Fraser showcases the value of returning to impairment discourse. Cognitive Disability Aesthetics successfully reconfigures disability studies in the humanities and exposes the chasm that exists between Anglophone disability studies and disability studies in the Hispanic world.\"-- Provided by publisher.
Book
Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories
The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts.
Journal Article
Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes
With the increasing occurrence of cyanobacteria blooms, it is crucial to improve our ability to monitor impacted lakes accurately, efficiently, and safely. Cyanobacteria are naturally occurring in many waters globally. Some species can release neurotoxins which cause skin irritations, gastrointestinal illness, pet/livestock fatalities, and possibly additional complications after long-term exposure. Using a DJI M300 RTK Unmanned Aerial Vehicle equipped with a MicaSense 10-band dual camera system, six New Hampshire lakes were monitored from May to September 2022. Using the image spectral data coupled with in situ water quality data, a random forest classification algorithm was used to predict water quality categories. The analysis yielded very high overall classification accuracies for cyanobacteria cell (93%), chlorophyll-a (87%), and phycocyanin concentrations (92%). The 475 nm wavelength, normalized green-blue difference index—version 4 (NGBDI_4), and normalized green-red difference index—version 4 (NGRDI_4) indices were the most important features for these classifications. Logarithmic regressions illuminated relationships between single bands/indices with water quality data but did not perform as well as the classification algorithm approach. Ultimately, the UAS multispectral data collected in this study successfully classified cyanobacteria cell, chlorophyll-a, and phycocyanin concentrations in the studied NH lakes.
Journal Article
Uncovering drone intentions using control physics informed machine learning
Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229.
Adolfo Perrusquia and colleagues propose a machine learning-based framework to unveil the hidden intentions of drones, without relying on explicit behavioral features. This framework can distinguish drone’s malicious intentions from naïve intentions, enhancing our ability to effectively identify potential threats posed by drones.
Journal Article
Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments
Unmanned Aerial Systems (UAS) offer users the ability to capture large amounts of imagery at unprecedented spatial resolutions due to their flexible designs, low costs, automated workflows, and minimal technical knowledge barriers. Their rapid extension into new disciplines promotes the necessity to question and understand the implications of data capture and processing parameter decisions on the respective output completeness. This research provides a culmination of quantitative insight using an eBee Plus, fixed-wing UAS for collecting robust data on complex forest environments. These analyses differentiate from measures of accuracy, which were derived from positional comparison to other data sources, to instead guide applications of comprehensive coverage. Our results demonstrated the impacts of flying height on Structure from Motion (SfM) processing completeness, discrepancies in outputs based on software package choice, and the effects caused by processing parameter settings. For flying heights of 50 m, 100 m, and 120 m above the forest canopy, key quality indicators within the software demonstrated the superior performance of the 100-m flying height. These indicators included, among others, image alignment success, the average number of tie points per image, and planimetric model ground sampling distance. We also compared the output results of two leading SfM software packages: Agisoft PhotoScan and Pix4D Mapper Pro. Agisoft PhotoScan maintained an 11.8% greater image alignment success and a 9.91% finer planimetric model resolution. Lastly, we compared the “high” and “medium” resolution processing workflows in Agisoft PhotoScan. The high-resolution processing setting achieved a 371% increase in point cloud density, with a 3.1% coarser planimetric model resolution, over a considerably longer processing time. As UAS continue to expand their sphere of influence and develop technologically, best-use practices based on aerial photogrammetry principles must remain apparent to achieve optimal results.
Journal Article