Scholarly Works, Geography

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Post-Hurricane Debris and Community Flood Damage Assessment Using Aerial Imagery
Aggarwal, Diksha; Gautam, Suyog; Whitehurst, Daniel; Kochersberger, Kevin (MDPI, 2025-09-12)
Natural disasters often result in significant damage to infrastructure, generating vast amounts of debris in towns and water bodies. Timely post-disaster damage assessment is critical for enabling swift cleanup and recovery efforts. This study presents a combination of methods to efficiently estimate and analyze debris on land and on water. Specifically, analyses were conducted at Claytor Lake and Damascus, Virginia where flooding occurred as a result of Hurricane Helene on 27 September 2024. We use the Phoenix U15 motor glider equipped with the GoPro Hero 9 camera to collect aerial imagery. Orthomosaic images and 3D maps are generated using OpenDroneMap (ODM) software, version 3.5.6, providing a detailed view of the affected areas. For lake debris estimation, we employ a hybrid approach integrating machine learning-based tools and traditional techniques. Lake regions are isolated using segmentation methods, and the debris area is estimated through a combination of color thresholding and edge detection. The debris is classified based on the thickness and a volume range of debris is presented based on the data provided by the Virginia Department of Environmental Quality (VDEQ). In Damascus, debris estimation is achieved by comparing pre-disaster LiDAR data (2016) with post-disaster 3D ODM data. Furthermore, we conduct flood modeling using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) to simulate disaster impacts, estimate the flood water depth, and support urban planning efforts. The proposed methodology demonstrates the ability to deliver accurate debris estimates in a time-sensitive manner, providing valuable insights for disaster management and environmental recovery initiatives.
Lidar-Based Detection and Analysis of Serendipitous Collisions in Shared Indoor Spaces
Flack, Addison H.; Pingel, Thomas J.; Baird, Timothy D.; Karki, Shashank; Abaid, Nicole (MDPI, 2025-09-18)
Indoor environments significantly influence human interaction, collaboration, and well-being, yet evaluating how architectural designs actually perform in fostering social connections remains challenging. This study demonstrates the use of 11 static-mounted lidar sensors to detect serendipitous encounters—collisions—between people in a shared common space of a mixed academic–residential university building. A novel collision detection algorithm achieved 86.1% precision and detected 14,022 interactions over 115 days (67 million person-seconds) of an academic semester. While occupancy strongly predicted collision frequency overall (R² ≥ 0.74), significant spatiotemporal variations revealed the complex relationship between co-presence and social interaction. Key findings include the following: (1) collision frequency peaked early in the semester then declined by ~25% by mid-semester; (2) temporal lags between occupancy and collision peaks of 2–3 h in the afternoon indicate that social interaction differs from physical presence; (3) collisions per occupancy peaked on the weekend, with Saturday showing 52% higher rates than the weekly average; and (4) collisions clustered at key transition zones (elevator areas, stair bases), with an additional “friction effect”, where proximity to seating increased interaction rates (>30%) compared to open corridors. This methodology establishes a scalable framework for post-occupancy evaluation, enabling evidence-based assessment of design effectiveness in fostering the spontaneous interactions essential for creativity, innovation, and place-making in built environments.
Human-driven fire and vegetation dynamics on the Caribbean island of Barbuda from early indigenous to modern times
LeBlanc, Allison R.; Kennedy, Lisa M.; Burn, Michael J.; Bain, Allison; Perdikaris, Sophia (SAGE Publications, 2024-08)
We present a multiproxy analysis of a sediment core from Freshwater Pond, Barbuda, one of just a few inland paleoenvironmental records from the Lesser Antilles. Our results shed light on the relative contributions of climate variability and Pre- and Post-Columbian human activities to vegetation and fire dynamics on Barbuda. The presence of macroscopic charcoal and pollen of ethnobotanically-useful and disturbance-indicator plant taxa in the sediment record suggests that Pre-Columbian subsistence activities occurred within a few kilometers of the pond between ~150 BCE and ~1250 CE. Our record extends anthropogenic fires back into the early Ceramic (500 BCE–1500 CE) and possibly late Archaic Ages (3000–500 BCE) adding evidence to the timing of arrival of the island’s earliest inhabitants. The history of island-wide biomass burning inferred from microscopic charcoal fragments showed heightened fire activity between ~540 and ~1610 CE followed by a period of quiescence that reflected the transition from Pre- to Post-Columbian land-use practices associated with European colonization of the region. The British established a permanent settlement on Barbuda in the 1660s, but given Barbuda’s unsuitability for large-scale agriculture, timber harvesting, small-scale farming, and livestock rearing, activities that left no detectable charcoal footprints likely dominated post-colonial land use. The lack of any clear correspondence between the reconstructed histories of fire and effective moisture at Freshwater Pond supports the idea that Late-Holocene fire activity on Barbuda was driven primarily by human activity.
Inventory and Assessment of Wisconsin’s Baraboo Hills Country (USA) as an Aspiring UNESCO Global Geopark
Swift, Troy P.; Kennedy, Lisa M. (Springer, 2025-09-17)
We analyzed the Baraboo Hills in south-central Wisconsin (USA) as a first step in consideration of its potential for UNESCO Global Geopark designation. Well over 200 Geoparks exist around the globe; presently none are in the USA. The basis for designation is a geographical area that contains geological heritage of international significance, but a Geopark’s fuller mission according to UNESCO is to “explore, develop and celebrate the links between that geological heritage and all other aspects of the area’s natural, cultural, and intangible heritages.” The Baraboo Hills, bisected by the boundary between glaciated and unglaciated landscapes, offer a surprising level of geodiversity with dramatic peaks, canyons, cliffs, waterfalls, and massive exposures of the somewhat rare and certainly ancient Baraboo quartzite. The Hills, with a broad array of land managers on public and privately owned land, have already garnered national designations. We followed a published approach that combined qualitative and quantitative methods to inventory and assess 62 sites within the region for their scientific, educational, and touristic merit, along with degradation risk. We expanded on that method in two significant ways. First, we combined those four established metrics into a meaningful summary metric (Importance) to improve intra-site comparisons. We also applied geospatial modeling (Kernel Density Surface) across the study area to examine spatial relationships in our data and to determine a perimeter to encompass the area that would benefit from unified protection—a strategy that could enhance future Geopark proposals. This research highlights the significant geological heritage of the Baraboo Hills and documents the region’s potential for Geopark designation.
Woody plant dynamics in a foundation conifer woodland of the Appalachian foothills, Alabama
Bhuta, Arvind A. R.; Kennedy, Lisa M. (Eagle Hill Publications, 2021-09-23)
We documented the structure and composition of a Pinus palustris (Longleaf Pine) woodland community in the Appalachian foothills of Alabama using field measurements and investigated the drivers of forest dynamics using dendroecology paired with historical records of disturbance. Longleaf Pine dominated the canopy, exhibiting a reverse-J–shaped diameter distribution not related with age distribution. Longleaf Pines dated as far back as 1669 to as recently as the early 2000s. In contrast to many other forests, the spatial distribution of Longleaf Pine stems in our site trended toward a random distribution when trees were weighted by DBH or age. Based on ring patterns from 322 Longleaf Pine individuals, growth releases from disturbances occurred continuously from the early 1900s through the 1940s and between 1985 and 1995, with Longleaf Pine establishment peaking 3 times: in the 1880s, 1940s, and 1990s. A superposed epoch analysis revealed that release events were not related with recorded large-scale meteorological (e.g., hurricanes) or local human-induced disturbances, suggesting that other factors have influenced the dynamics of this community. This Longleaf Pine community in the Piedmont shared similarities in composition and structure to other Longleaf Pine communities of the southeastern United States. A combination of fire suppression over the last 80 years and high-intensity arson fires over the last decade has caused an increase in density of both live and dead Longleaf Pine and recruitment of fire-sensitive pines and hardwoods into the seedling/sapling classes and canopy. Restoration of the historical fire regime may be needed for Longleaf Pine to maintain its dominance in this community, as fire may have appeared to exert strong control over the dynamics of this community.
Generative AI for Geospatial Analysis: Fine-Tuning ChatGPT to Convert Natural Language into Python-Based Geospatial Computations
Sherman, Zachary; Sharma Dulal, Sandesh; Cho, Jin-Hee; Zhang, Mengxi; Kim, Junghwan (MDPI, 2025-08-18)
This study investigates the potential of fine-tuned large language models (LLMs) to enhance geospatial intelligence by translating natural language queries into executable Python code. Traditional GIS workflows, while effective, often lack usability and scalability for non-technical users. LLMs offer a new approach by enabling conversational interaction with spatial data. We evaluate OpenAI’s GPT-4o-mini model in two forms: an “As-Is” baseline and a fine-tuned version trained on 600+ prompt–response pairs related to geospatial Python scripting in Virginia. Using U.S. Census shapefiles and hospital data, we tested both models across six types of spatial queries. The fine-tuned model achieved 89.7%, a 49.2 percentage point improvement over the baseline’s 40.5%. It also demonstrated substantial reductions in execution errors and token usage. Key innovations include the integration of spatial reasoning, modular external function calls, and fuzzy geographic input correction. These findings suggest that fine-tuned LLMs can improve the accuracy, efficiency, and usability of geospatial dashboards when they are powered by LLMs. Our results further imply a scalable and replicable approach for future domain-specific AI applications in geospatial science and smart cities studies.
Evaluating the Evaluation Matrices: Integrating Spatial Assessment in Geospatial AI Model Training and Evaluation
Lyu, Fangzheng (Purdue University, 2025-07-21)
This paper examines the limitations of current evaluation metrics in GeoAI. Through two case studies on deep learning models—a building detection classification problem and a remote sensing image fusion regression problem—this paper demonstrates how traditional statistical evaluation matrices alone can be misleading in geospatial problems. The findings indicate that traditional metrics (e.g., RMSE, MAE) used in current GeoAI models can have difficulty capturing the spatial dimensions inherent to geospatial problems. This paper suggests that the model evaluation process in GeoAI should move beyond traditional evaluation matrices by integrating spatial thinking throughout the modeling pipeline—not only incorporating spatial accuracy in model evaluation but also embedding it within optimization functions in model structure and model training.
From Roots to Resilience: Exploring the Drivers of Indigenous Entrepreneurship for Climate Adaptation
Dharmasiri, Indunil P.; Galappaththi, Eranga K.; Baird, Timothy D.; Bukvic, Anamaria; Rijal, Santosh (MDPI, 2025-05-14)
Our study investigates the drivers that foster the emergence of entrepreneurial responses to climate change among Indigenous communities. Indigenous peoples possess distinct worldviews and approaches to enterprise that prioritize community well-being and environmental stewardship over individual profit. Conventional entrepreneurship theories do not adequately capture Indigenous business approaches, leaving a limited understanding of how Indigenous communities merge traditional ecological knowledge with entrepreneurial activities to adapt to climate challenges. Through a systematic literature review (65 articles) and a case study of six Sri Lankan Vedda communities, we identified 15 key drivers that shape Indigenous climate-adaptive ventures and categorized them under five themes: (1) place-based relationships (resource stewardship, territorial connections, environmental risk factors); (2) intergenerational learning (traditional knowledge transfer, adaptation learning, collective experience); (3) community institutions (social networks, institutional support, overcoming the agency–structure paradox); (4) collective capacity (access to information, access to capital, community-oriented entrepreneurial traits); and (5) culturally aligned venture strategies (Indigenous business models, traditional products, local market relationships). Our study demonstrates how Vedda communities integrate entrepreneurship with cultural values to enhance climate resilience. Our research advances the field of Indigenous entrepreneurship while providing insights for policymakers and practitioners to support culturally appropriate climate adaptation strategies that enhance both community well-being and environmental sustainability.
Comparing Reflectivity from Space-Based and Ground-Based Radars During Detection of Rainbands in Two Tropical Cyclones
Matyas, Corene J.; Zick, Stephanie E.; Wood, Kimberly M. (MDPI, 2025-03-06)
With varying tangential winds and combinations of stratiform and convective clouds, tropical cyclones (TCs) can be difficult to accurately portray when mosaicking data from ground-based radars. This study utilizes the Dual-frequency Precipitation Radar (DPR) from the Global Precipitation Measurement Mission (GPM) satellite to evaluate reflectivity obtained using four sampling methods of Weather Surveillance Radar 1988-Doppler data, including ground radars (GRs) in the GPM ground validation network and three mosaics, specifically the Multi-Radar/Multi-Sensor System plus two we created by retaining the maximum value in each grid cell (MAX) and using a distance-weighted function (DW). We analyzed Hurricane Laura (2020), with a strong gradient in tangential winds, and Tropical Storm Isaias (2020), where more stratiform precipitation was present. Differences between DPR and GR reflectivity were larger compared to previous studies that did not focus on TCs. Retaining the maximum value produced higher values than other sampling methods, and these values were closest to DPR. However, some MAX values were too high when DPR time offsets were greater than 120 s. The MAX method produces a more consistent match to DPR than the other mosaics when reflectivity is <35 dBZ. However, even MAX values are 3–4 dBZ lower than DPR in higher-reflectivity regions where gradients are stronger and features change quickly. The DW and MRMS mosaics produced values that were similar to one another but lower than DPR and MAX values.
Unregulated drinking water contaminants and adverse birth outcomes in Virginia
Young, Holly A.; Kolivras, Korine N.; Krometis, Leigh-Anne H.; Marcillo, Cristina E.; Gohlke, Julia M. (PLOS, 2024-05-01)
Through the Unregulated Contaminant Monitoring Rule (UCMR), the Environmental Protection Agency monitors selected unregulated drinking water contaminants of potential concern. While contaminants listed in the UCMR are monitored, they do not have associated health-based standards, so no action is required following detection. Given evolving understanding of incidence and the lack of numeric standards, previous examinations of health implications of drinking water generally only assess impacts of regulated contaminants. Little research has examined associations between unregulated contaminants and fetal health. This study individually assesses whether drinking water contaminants monitored under UCMR 2 and, with a separate analysis, UCMR 3, which occurred during the monitoring years 2008–2010 and 2013–2015 respectively, are associated with fetal health outcomes, including low birth weight (LBW), term-low birth weight (tLBW), and preterm birth (PTB) in Virginia. Singleton births (n = 435,449) that occurred in Virginia during UCMR 2 and UCMR 3 were assigned to corresponding estimated water service areas (n = 435,449). Contaminant occurrence data were acquired from the National Contaminant Occurrence Database, with exposure defined at the estimated service area level to limit exposure misclassification. Logistic regression models for each birth outcome assessed potential associations with unregulated drinking water contaminants. Within UCMR 2, N-Nitrosodimethylamine was positively associated with PTB (OR = 1.08; 95% CI: 1.02, 1.14, P = 0.01). Molybdenum (OR = 0.92; 95% CI: 0.87, 0.97, P = 0.0) and vanadium (OR = 0.96; 95% CI: 0.92, 1.00, P = 0.04), monitored under UCMR 3, were negatively associated with LBW. Molybdenum was also negatively associated (OR = 0.90; 95% CI: 0.82, 0.99, P = 0.03) with tLBW, though chlorodifluoromethane (HCFC-22) was positively associated (OR 1.18; 95% CI: 1.01, 1.37, P = 0.03) with tLBW. These findings indicate that unregulated drinking water contaminants may pose risks to fetal health and demonstrate the potential to link existing health data with monitoring data when considering drinking water regulatory determinations at the national scale.
Addressing Food Insecurity Through Community Kitchens During the COVID-19 Pandemic: A Case Study from the Eastern Cape, South Africa
Carlos Bezerra, Joana; Nqowana, Thandiswa; Oosthuizen, Rene; Canca, Monica; Nkwinti, Nosipho; Mantel, Sukhmani Kaur; New, Mark; Ford, James; Zavaleta-Cortijo, Carol Claudia; Galappaththi, Eranga K.; Perera, Chrishma D.; Jayasekara, Sithuni M.; Amukugo, Hans Justus; Namanya, Didacus B.; Togarepi, Cecil; Hangula, Martha M.; Nkalubo, Jonathan; Akugre, Francis A.; Pickering, Kerrie; Mensah, Adelina M.; Chi, Guangqing; Reckford, Lenworth; Chicmana-Zapata, Victoria; Dharmasiri, Indunil P.; Arotoma-Rojas, Ingrid (MDPI, 2025-02-07)
One of the most critical impacts of the COVID-19 pandemic was on food security. Food insecurity increased in many communities, with some showing signs of resilience through autonomously creating community kitchens that enhanced food security and built support networks. These initiatives filled gaps left by government programmes and provided a critical lifeline for vulnerable communities during the pandemic, fostering community solidarity. This paper aims to investigate the experiences and perceptions of community kitchen managers in addressing food insecurity during the COVID-19 pandemic by using a town in South Africa in 2020–2022 as a case study. Using arts-based participatory approaches, researchers interviewed 11 community kitchen managers representing 10 community kitchens in four sessions between June and November 2021. The results showed that a lack of jobs and food insecurity were identified as the main threats, whereas COVID-19 was not even identified as a threat by all of the community kitchen managers. Lacking support from the local government, these initiatives depended on individuals and community-based organisations for backing. However, this support decreased in 2021 and 2022, raising concerns about the sustainability of these efforts.
Vegetation Succession Patterns at Sperry Glacier’s Foreland, Glacier National Park, MT, USA
Bryant, Ami; Resler, Lynn M.; Gielstra, Dianna; Pingel, Thomas (MDPI, 2025-02-02)
Plant colonization patterns on deglaciated terrain give insight into the factors influencing alpine ecosystem development. Our objectives were to use a chronosequence, extending from the Little Ice Age (~1850) terminal moraine to the present glacier terminus, and biophysical predictors to characterize vegetation across Sperry Glacier’s foreland—a mid-latitude cirque glacier in Glacier National Park, Montana, USA. We measured diversity metrics (i.e., richness, evenness, and Shannon’s diversity index), percent cover, and community composition in 61 plots. Field observations characterized drainage, concavity, landform features, rock fragments, and geomorphic process domains in each plot. GIS-derived variables contextualized the plots’ aspect, terrain roughness, topographic position, solar radiation, and curvature. Overall, vegetation cover and species richness increased with terrain age, but with colonization gaps compared to other forelands, likely due to extensive bedrock and slow soil development, potentially putting this community at risk of being outpaced by climate change. Generalized linear models revealed the importance of local site factors (e.g., drainage, concavity, and process domain) in explaining species richness and Shannon’s diversity patterns. The relevance of field-measured variables over GIS-derived variables demonstrated the importance of fieldwork in understanding alpine successional patterns and the need for higher-resolution remote sensing analyses to expand these landscape-scale studies.
Examining Faculty and Student Perceptions of Generative AI in University Courses
Kim, Junghwan; Klopfer, Michelle; Grohs, Jacob R.; Eldardiry, Hoda; Weichert, James; Cox, Larry A., II; Pike, Dale (Springer, 2025-01-24)
As generative artificial intelligence (GenAI) tools such as ChatGPT become more capable and accessible, their use in educational settings is likely to grow. However, the academic community lacks a comprehensive understanding of the perceptions and attitudes of students and instructors toward these new tools. In the Fall 2023 semester, we surveyed 982 students and 76 faculty at a large public university in the United States, focusing on topics such as perceived ease of use, ethical concerns, the impact of GenAI on learning, and differences in responses by role, gender, and discipline. We found that students and faculty did not differ significantly in their attitudes toward GenAI in higher education, except regarding ease of use, hedonic motivation, habit, and interest in exploring new technologies. Students and instructors also used GenAI for coursework or teaching at similar rates, although regular use of these tools was still low across both groups. Among students, we found significant differences in attitudes between males in STEM majors and females in non-STEM majors. These findings underscore the importance of considering demographic and disciplinary diversity when developing policies and practices for integrating GenAI in educational contexts, as GenAI may influence learning outcomes differently across various groups of students. This study contributes to the broader understanding of how GenAI can be leveraged in higher education while highlighting potential areas of inequality that need to be addressed as these tools become more widely used.
Annual Cropping Intensity Dynamics in China from 2001 to 2023
Ren, Jie; Shao, Yang; Wang, Yufei (MDPI, 2024-12-23)
Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions.
Multimodal Large Language Models as Built Environment Auditing Tools
Jang, Kee Moon; Kim, Junghwan (Routledge, 2024-10-07)
This research showcases the transformative potential of large language models (LLMs) for built environment auditing from street-view images. By empirically testing the performances of two multimodal LLMs, ChatGPT and Gemini, we confirmed that LLM-based audits strongly agree with virtual audits processed by a conventional deep learning-based method (DeepLabv3+), which has been widely adopted by existing studies on urban visual analytics. Unlike conventional field or virtual audits that require labor-intensive manual inspection or technical expertise to run computer vision algorithms, our results show that LLMs can offer an intuitive tool despite the user’s level of technical proficiency. This would allow a broader range of policy and planning stakeholders to employ LLM-based built environment auditing instruments for smart urban infrastructure management.
The Uneven Geography of Access to Live Performances of Western Classical Music in the United States
Jones, Will; Kim, Junghwan (Network Design Lab - Transport Findings, 2024-11-20)
This study evaluates accessibility to live performances of Western classical music across 3,109 U.S. counties. It analyzes 100 popular concertos and symphonies from this genre (e.g., Beethoven’s Symphony No. 9, Ode to Joy) to reveal socio-spatial disparities. Midwestern counties show poorer accessibility than West and East Coasts, with the highest mean accessibility scores in the fall and the lowest in summer. A hurdle model indicates that counties with higher population density are significantly associated with greater accessibility. An interactive online StoryMap embedded with recorded performances offers a synesthetic experience for exploring accessibility to live Western classical music performances.
Navigating Disparities in Dental Health—A Transit-Based Investigation of Access to Dental Care in Virginia
Kim, Junghwan; Karki, Shashank; Brickhouse, Tegwyn; Vujicic, Marko; Nasseh, Kamyar; Wang, Changzhen; Zhang, Mengxi (2024-10-30)
Objective: To identify vulnerable areas and populations with limited access to dental care in Virginia, the study aimed (1) to calculate travel time and accessibility scores to dental care in Virginia using a transit-based accessibility model for all dental clinics and dental clinics participating in the Medicaid dental program and (2) to estimate factors associated with accessibility to dental clinics participating in the Medicaid dental program in Virginia. Methods: The study used building footprints as origins of transit trips to dental care services (or destinations). The study then computed transit-based origin–destination travel time matrices based on the detailed trip information, including in-vehicle and out-of- vehicle travel time. Accessibility scores were calculated by counting the number of dental clinics that can be reached within 60 min. Regression analysis was used to measure factors associated with accessibility scores to dental clinics participating in Medicaid. Results: Residents in smaller regions spent longer travel time to dental clinics by public transit compared with those who resided in larger regions. Medicaid participants also faced longer travel time compared with the general population. Residents spent more than three-fourths of the time waiting for public transit and walking to clinics regardless of where they live and what type of insurance they have. Associations between sociodemographic factors and accessibility scores to dental clinics participating in the Medicaid dental program varied across regions. Conclusions: Disparities in dental care accessibility exist depending on the size of regions and Medicaid participation in Virginia. The disparities in transit-based access to dental clinics and a disproportionate amount of time spent waiting for public transit and walking to dental clinics could be improved through tailored interventions taking into account the sociodemographic and geographic characteristics of each region.
Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models
Ma, Bingxin; Shao, Yang; Yang, Hequn; Lu, Yiwen; Gao, Yanqing; Wang, Xinyao; Xie, Ying; Wang, Xiaofeng (MDPI, 2024-10-10)
This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.
Toward Collaborative Adaptation: Assessing Impacts of Coastal Flooding at the Watershed Scale
Mitchell, Allison; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L.; McLaughlin, Daniel L. (Springer Nature, 2022-12)
The U.S. Mid-Atlantic coastal region is experiencing higher rates of SLR than the global average, especially in Hampton Roads, Virginia, where this acceleration is primarily driven by land subsidence. The adaptation plans for coastal flooding are generally developed at the municipal level, ignoring the broader spatial implications of flooding outside the individual administrative boundaries. Flood impact assessments at the watershed scale would provide a more holistic perspective on what is needed to synchronize the adaptation efforts between the neighboring administrative units. This paper evaluates flooding impacts from sea level rise (SLR) and storm surge among watersheds in Hampton Roads to identify those most at risk of coastal flooding over different time horizons. It also explores the implications of flooding on the municipalities, the land uses, and land covers throughout this region within the case study watershed. The 2% Annual Exceedance Probability (AEP) storm surge flood hazard data and NOAA’s intermediate SLR projections were used to develop flooding scenarios for 2030, 2060, and 2090 and delineate land areas at risk of combined flooding. Findings show that five out of 98 watersheds will substantially increase in inundation, with two intersecting multiple municipalities. They also indicate significant inundation of military, commercial, and industrial land uses and wetland land covers. Flooding will also impact residential land use in urban areas along the Elizabeth River and Hampton city, supporting the need for collaborative adaptation planning on hydrologically influenced spatial scales.
Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods
Karki, Shashank; Pingel, Thomas J.; Baird, Timothy D.; Flack, Addison; Ogle, J. Todd (MDPI, 2024-09-18)
Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.

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