Participatory GIS for growth management in the Cheat Lake planning district of Monongalia County, West Virginia

Participatory Geographic Information Systems (PGIS) can be an effective methodology for collecting public input in local land use planning and smart growth management. PGIS-based approaches provide residents with an opportunity to discuss and map their priority land use issues and to identify land use hotspots in a way that is not typically possible in a general public meeting. This thesis research explores how community qualitative information about land use can be incorporated into a PGIS for the Cheat Lake Planning District of Monongalia County, West Virginia. The research presented in this thesis demonstrates how qualitative information strengthens land use planning and improves communication about smart growth management options at the local level. Multimedia information, such as community narratives, mental maps, and geo-referenced photographs are collected using qualitative research methods and combined with existing geo-spatial information in order to shape future political discussions about land use planning in the case study area

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data

Traffic restrictions during the 2008 Olympic Games reduced urban heat intensity and extent in Beijing

Satellite thermal remote sensing has been utilized to examine the urban heat dynamics in relation to the urban traffic restriction policy. During the 2008 Olympic Games in Beijing, the traffic volume was approximately cut off by half through the road space rationing. Based on daily MODIS satellite thermal observations on the surface temperature, statistical models were developed to analyze the contribution of traffic volume reduction to the urban heat intensity and spatial extent. Our analyses show that cutting off half of the traffic volume has led to a marked decrease in the mean surface temperature by 1.5–2.4 °C and shrinkage of the heat extent by 820 km2 in Beijing. This research suggests that the impact of urban traffic on heat intensity is considerably larger than previously thought, and the management of urban traffic and vehicle fossil fuel use should be included in the future urban heat mitigation plan

Developing an Introductory UAV/Drone Mapping Training Program for Seagrass Monitoring and Research

Unoccupied Aerial Vehicles (UAVs), or drone technologies, with their high spatial resolution, temporal flexibility, and ability to repeat photogrammetry, afford a significant advancement in other remote sensing approaches for coastal mapping, habitat monitoring, and environmental management. However, geographical drone mapping and in situ fieldwork often come with a steep learning curve requiring a background in drone operations, Geographic Information Systems (GIS), remote sensing and related analytical techniques. Such a learning curve can be an obstacle for field implementation for researchers, community organizations and citizen scientists wishing to include introductory drone operations into their work. In this study, we develop a comprehensive drone training program for research partners and community members to use cost-effective, consumer-quality drones to engage in introductory drone mapping of coastal seagrass monitoring sites along the west coast of North America. As a first step toward a longer-term Public Participation GIS process in the study area, the training program includes lessons for beginner drone users related to flying drones, autonomous route planning and mapping, field safety, GIS analysis, image correction and processing, and Federal Aviation Administration (FAA) certification and regulations. Training our research partners and students, who are in most cases novice users, is the first step in a larger process to increase participation in a broader project for seagrass monitoring in our case study. While our training program originated in the United States, we discuss our experiences for research partners and communities around the globe to become more confident in introductory drone operations for basic science. In particular, our work targets novice users without a strong background in geographic research or remote sensing. Such training provides technical guidance on the implementation of a drone mapping program for coastal research, and synthesizes our approaches to provide broad guidance for using drones in support of a developing Public Participation GIS process

Mapping Emotional Attachment as a Measure of Sense of Place to Identify Coastal Restoration Priority Areas

Our applied case study demonstrates how knowledge from community stakeholders about emotional attachment (as a key component of sense of place) can inform and influence future coastal restoration priorities at various scales in the Indian River Lagoon, Florida (USA). We map aggregate measures of emotional attachment from community stakeholders using Geographic Information Systems. We then analyze this human systems level data with kernel density estimation measures at the broader lagoon scale and with inverse distance weighted measures at more localized scales. By connecting these mapped results back to the primary reasons that participants provided for having high or low emotional attachment in a location, we show how varying spatial patterns of emotional attachment as a primary component of sense of place within and across broader geographic regions can be represented, mapped, and visualized to enhance future restoration priorities. We demonstrate how aggregate results gained from community stakeholders can help restoration teams prioritize their science communication and education strategies to align human systems level data with natural systems level data

Mycobacterium tuberculosis Transmission from Human to Canine

A 71-year-old woman from Tennessee, USA with a 3-week history of a productive, nonbloody cough was evaluated. Chest radiograph showed infiltrates and atelectasis in the upper lobe of the right lung. A tuberculosis (TB) skin test resulted in a 14-mm area of induration. Sputum stained positive for acid-fast bacilli (AFB) and was positive for Mycobacterium tuberculosis by DNA probe and culture. Treatment was initiated with isoniazid, rifampicin, and pyrazinamide. After 14 days of daily, directly observed therapy, the patient complained of nausea, vomiting and diarrhoea. Treatment adjustments were made, and therapy was completed 11 months later with complete recovery. Six months after the patient\u27s TB diagnosis, she took her three and a half-year-old male Yorkshire Terrier to a veterinary clinic with cough, weight loss, and vomiting of several months\u27 duration. Initial sputum sample was negative on AFB staining. Eight days after discharge from a referral veterinary teaching hospital with a presumptive diagnosis of TB, the dog was euthanized due to urethral obstruction. Liver and tracheobronchial lymph node specimens collected at necropsy were positive for M. tuberculosis complex by polymerase chain reaction. The M. tuberculosis isolates from the dog and its owner had an indistinguishable 10-band pattern by IS6110-based restriction fragment length polymorphism genotyping

Low-Altitude UAV Imaging Accurately Quantifies Eelgrass Wasting Disease From Alaska to California

Declines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low-altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low-altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions

Disease surveillance by artificial intelligence links eelgrass wasting disease to ocean warming across latitudes

Ocean warming endangers coastal ecosystems through increased risk of infectious disease, yet detection, surveillance, and forecasting of marine diseases remain limited. Eelgrass (Zostera marina) meadows provide essential coastal habitat and are vulnerable to a temperature-sensitive wasting disease caused by the protist Labyrinthula zosterae. We assessed wasting disease sensitivity to warming temperatures across a 3500 km study range by combining long-term satellite remote sensing of ocean temperature with field surveys from 32 meadows along the Pacific coast of North America in 2019. Between 11% and 99% of plants were infected in individual meadows, with up to 35% of plant tissue damaged. Disease prevalence was 3× higher in locations with warm temperature anomalies in summer, indicating that the risk of wasting disease will increase with climate warming throughout the geographic range for eelgrass. Large-scale surveys were made possible for the first time by the Eelgrass Lesion Image Segmentation Application, an artificial intelligence (AI) system that quantifies eelgrass wasting disease 5000× faster and with comparable accuracy to a human expert. This study highlights the value of AI in marine biological observing specifically for detecting widespread climate-driven disease outbreaks.This work was supported by the National Science Foundation (awards OCE-1829921, OCE-1829922, OCE-1829992, OCE-1829890). This is contribution 104 from the Smithsonian's MarineGEO and Tennenbaum Marine Observatories Network.Peer reviewe