Remote Sensing Commons^™

A Distributed Model For Undergraduate Education In Environmental Remote Sensing: Increased Student Interest In Science And Sense Of Science Identity And Belonging, Gregory R. Goldsmith, Monae Verbeke, Jeremy Forsythe, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

"We present results from a new open-access course in satellite remote sensing of the environment that uses evidence-based, active learning pedagogy to train the next generation of interdisciplinary scientists The course, called Observing Earth from Above, teaches students how to access, visualize, and communicate satellite remote sensing data from NASA’s ECOSTRESS instrument to address a variety of environmental challenges. The resources focus on follow-along tutorials for students and also include recorded lectures and interviews with remote sensing scientists."

Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Punjab, India's primary rice and wheat production hub, has witnessed rapid expansion of paddy cultivation over the past two decades, driven by minimum support price incentives, changes in government policies, alignment of sowing with the monsoon season and the adoption of high-yielding varieties. This transition has intensified groundwater extraction and shortened the fallow period between rabi and kharif crop seasons, reducing the window between rice harvesting and wheat sowing, leading to widespread open-field burning of rice residue and recurrent post-monsoon air-quality deterioration across the Indo-Gangetic Plain. Despite numerous short-term or single-pollutant assessments, a spatially resolved, multi-pollutant and multi-decadal evaluation linking …

Super-Resolution Remote Sensing Datasets For Application To Caral–Supe Archeological Sites Employing Sar And Dems, Jungrack Kim, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Publicly accessible spaceborne remote sensing datasets often lack the spatial resolution required to reliably distinguish archeological features from their surrounding geomorphological contexts. In this study, we assess the potential of super-resolution (SR) products derived from multiple public-domain remote sensing datasets for a systematic archeological survey in the Caral–Supe region. We focus on Synthetic Aperture Radar (SAR) and topographic datasets—including Sentinel-1, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), and Digital Elevation Models (DEMs)—because of their capacity to detect subtle surface expressions and shallow subsurface structures obscured by vegetation or sediment cover. Using state-of-the-art deep learning algorithms, …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify …

Relationship Between Vegetation Greenness (Ndvi) And Land Surface Temperature Across Land Cover Types In Ciwidey Sub-Watershed (1990–2020), Syal Syabila, Kuswantoro Marko, Revi Hernina

Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)

Land cover change significantly influences vegetation greenness and land surface temperature (LST), particularly in upstream watershed regions experiencing rapid development. This study aims to analyze changes in vegetation greenness (NDVI), land surface temperature, and their relationship across different land cover types in the Ciwidey Sub-Watershed, Bandung Regency, during 1990–2020. Landsat 5 TM and Landsat 8 OLI images (Path/Row 122/65) acquired in July 1990, 2005, and 2020 were processed using radiometric correction, supervised classification (Maximum Likelihood), NDVI extraction, and mono-window LST algorithm. Land cover classification accuracy was assessed using confusion matrix analysis. Linear regression was applied to evaluate the relationship between …

Validating Uas-Based Ndvi Data With Satellite Landsat Imagery For Bald Eagle Habitat Prediction In The Del Rio Springs Ecosystem, Noah Morales, Ronny Schroeder, Elise Anderson

Student Works

Vegetation health is commonly assessed using the Normalized Difference Vegetation Index (NDVI), which can be derived from multispectral sensors operating at different spatial resolutions. Validating NDVI products across sensor platforms is essential to determine their reliability for environmental monitoring and habitat assessment. This research compares NDVI derived from moderate-resolution satellite imagery and high-resolution unmanned aircraft system (UAS) imagery collected over the same study area. Landsat imagery, provided through the joint USGS–NASA mission, was used to represent satellite-based vegetation patterns, while high-resolution multispectral data were acquired using a MicaSense sensor mounted on a UAS to capture fine-scale vegetation detail.

NDVI values …

Satellites, Urban Heat, And Environmental Justice: Community As The Bridge Between Analysis And Action, Joshua B. Fisher, Ambar Rivera, Ava Cison, Ashley Agatep, Kainani Tacazon, Sophia Spiegleman, Alison Mckenery, Rio E. Fisher, Reginald Archer, Jason A. Douglas

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Heat waves are increasing in frequency, intensity, magnitude, and duration, causing a disproportionate impact on marginalized communities exposed to urban heat islands. Newly emerging spaceborne thermal sensing instruments, such as ECOSTRESS and Hydrosat, now have the capabilities to measure urban surface temperatures accurately at the block level (< 100 m) and with enough frequency to capture transient heat waves (daily to subweekly). Such data are critical for monitoring and informing policy and mitigation efforts, such as resurfacing, green space, cooling stations, and medical mobilization. These serve to advance environmental justice and reduce health risks—and deaths—among the most vulnerable: minority, low-income, elderly, those with physical- and mental-health preconditions, unhoused, children, and outdoor workers. While scientists have increasingly used satellite data to quantify urban heat islands and risks to communities, there remains a significant gap in action resulting from such analyses—a figurative and literal “valley of death.” Reviewing over 500 scientific publications, we identify a critical lack of engagement with the communities being analyzed (10.9%; n = 58); yet, community engagement is key to bridging such analysis with subsequent action. Here, we demonstrate how participatory community engagement directly with data and analysis leads to increased policy changes and mitigation efforts. Our framework has immediate implications for how scientists may augment their work and thought processes to achieve …

Prediction Of Methane Gas By Using Hyperspectral Imageries And Remote Sensing Techniques, Aysha Ali Alharmasi Alhajeri

Thesis/ Dissertation Defenses

Greenhouse gases is important for sustaining life on earth as well as in mitigating climate change. Methane (CH4) is considered as one of the most important critical gases for the global climate change and have significant influences on our life. Accordingly, the prediction of this greenhouse gas emissions is very important for avoiding the climate change effects and to maintain environmental sustainability. The objective of this study is to explore the potential applications for remote sensing to predict methane levels in the Earth’s atmosphere with a combination of local ground data and data from hyperspectral satellite imagery. By using hyperspectral …

The World's Largest Saddle Dam At Risk: Multisensor Geohazard Analysis And Downstream Impacts, Hesham El-Askary, Hesham Morgan, Surendra Maharjan, Ali Elgendy, Wenzhao Li, Rejoice Thomas, Austin Madson, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Grand Ethiopian Renaissance Dam (GERD) Saddle Dam, which holds approximately 89% of the main reservoir's live storage, is one of the largest and most critical auxiliary dams globally; its construction on Ethiopia's Blue Nile has consequently raised significant regional and international concerns regarding potential environmental impacts and geohazard risks. This study presents a comprehensive risk assessment of the GERD Saddle Dam by integrating high-resolution satellite data (GRACE, Sentinel-1, Sentinel-2, WorldView-3), hydrological modeling (SWAT), Persistent Scatterer Interferometry (PSI), geospatial analysis, and advanced statistical techniques. The results highlight critical structural vulnerabilities, including groundwater infiltration estimated at approximately 41 ± 6.2 billion …

Evaluating Climatic Niche Suitability For Bos Javanicus Reintroduction In Cagar Alam Pananjung Pangandaran Using Maxent And Native-Habitat Benchmarks From Ujung Kulon And Alas Purwo, Azhari Al Kautsar, Masita Dwi Mandini Manessa

Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)

The Javan banteng (Bos javanicus) persists on Java mainly in a small number of protected-area strongholds, making robust climatic niche characterization important for conservation planning and for evaluating potential management or restoration targets. Here, we modeled banteng climatic suitability in southwestern Java using a MaxEnt (maxnet) framework calibrated with bioclimatic predictors from CHELSA and benchmark occurrence records from extant populations in Ujung Kulon National Park (UKNP) and Alas Purwo National Park (APNP). To contextualize transferability to non-occupied protected habitat, we also projected suitability to Cagar Alam Pananjung Pangandaran (CAPP) and quantified environmental novelty using the Multivariate Environmental Similarity Surface (MESS). …

A Student-Centered Gis Classroom: Second Chances And Real-World Practice Teaching Portfolio For Nres218 Introduction To Geospatial Technologies, Ran Wang

UNL Faculty Course Portfolios

This course portfolio documents the design, implementation, and reflection of NRES218 Introduction to Geospatial Technologies, an introductory undergraduate geographic information system (GIS) course. This course aims to integrate basic and applied sciences to help students develop spatial thinking and spatial analysis skills for proposing spatial science–oriented solutions. Instructional strategies emphasize experiential learning through structured laboratory exercises and outdoor field activities that connects real-world observation with GIS analysis. To support student learning and persistence, the course also incorporated flexible assessment practices, including second-chance exams. Reflection on student engagement and performance revealed that while these strategies were highly beneficial for some students, …

Prediction Of Methane Gas By Using Hyperspectral Imageries And Remote Sensing Techniques, Aysha Ali Alhajeri

Theses

Greenhouse gases is important for sustaining life on earth as well as in mitigating climate change. Methane (CH4) is considered as one of the most important critical gases for the global climate change and have significant influences on our life. Accordingly, the prediction of this greenhouse gas emissions is very important for avoiding the climate change effects and to maintain environmental sustainability. The objective of this study is to explore the potential applications for remote sensing to predict methane levels in the Earth’s atmosphere with a combination of local ground data and data from hyperspectral satellite imagery. By using hyperspectral …

Deep Learning Style Transfer For Enhanced Smoke Plume Visibility: A Standardized False Color Composite (Sfcc) In Gems Satellite Imagery, Yemin Jeong, Seung Hee Kim, Menas Kafatos, Jeong-Ah Yu, Kyoung-Hee Sung, Seung-Yeon Kim, Goo Kim, Jae-Jin Kim, Yangwon Lee

Institute for ECHO Articles and Research

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr2, and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, …

Modelling Land Use Land Cover Change In Banyumas Regency Using Remote Sensing Data For Tourism Policy Evaluation, Revi Hernina, Arif Wicaksono, Adi Wibowo, Astrid Damayanti

Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)

Tourism is essentially a geographical phenomenon, encompassing the movement and flow of people and spatial distribution patterns relating to land use consumption. The impacts of tourism on LUCC must track and monitor regrading effected to environment and human side. For decades, Banyumas Regency has been known for its famous tourist destinations such Baturaden District and numerous waterfalls. However, the development of tourism infrastructures within its vicinity has sparked complaints from communities, particularly damaged roads and decreasing tourist visits. From environmental perspective, excessive tourism development might cause decreasing natural carrying capacity. Therefore, to provide deeper analysis regarding the current tourism development, …

A Robust Deep Learning Ensemble Framework For Waterbody Detection Using High-Resolution X-Band Sar Under Data-Constrained Conditions, Soyeon Choi, Seung Hee Kim, Son V. Nghiem, Menas Kafatos, Minha Choi, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, …

High Spatiotemporal Resolution Monitoring Of Crop Water Stress Across The Contiguous United States Using Harmonized Landsat And Sentinel-2 Data, Na Chen, Yanlei Feng, Na Wang, Jevan Yu, Mohammad Reza Alizadeh, Yifeng Cui, Ning Ye, Wenzhe Jiao, Joshua B. Fisher, César Terrer

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and timely monitoring of crop water stress is essential for efficient agricultural water management, ultimately maintaining and improving crop productivity. While Landsat has been used for this purpose, its temporal resolution hampers timely detection of crop water stress. The recently released Harmonized Landsat and Sentinel-2 Version 2.0 dataset, which enables a higher-frequency time series of satellite observations (2–3 days, 30 m), offers a promising solution to this challenge. However, its potential for crop stress monitoring remained unexplored. In this study, we utilized 923 HLS satellite tiles to assess crop water stress across the contiguous United States (CONUS). Crop water …

Submesoscale Dynamics Of Phytoplankton And Carbon Export Revealed By High-Resolution Airborne And Satellite Remote Sensing Of Currents And Ocean Color, Sarah E. Lang

Open Access Dissertations

Satellites and airborne sensors reveal submesoscale (1 - 10 km) variability in ocean color in the form of filaments, eddies, and patches. The variability in ocean color is closely tied to the physical dynamics that restructure phytoplankton distributions and drive active biological responses like changes in primary productivity and community structure. As the base of the marine food web and a key component of the biological carbon pump, phytoplankton are crucial to the overall health of marine ecosystems and to the ocean's role in climate. This dissertation focuses on the use of airborne and satellite remote sensing to study the …

Datums And Benchmarks From Sylvester Manor Umass Boston Archaeological Work, Shelter Island, Ny, John M. Steinberg, John Schoenfelder, Chiara M. Torrini, Joseph E. Kinney, Stephen A. Mrozowski, David B. Landon

Data and Datasets

Datums from Sylvester Manor Archaeological work (2019-2026).  Includes zipped shapefile of points and complementary csv, that includes the site areas for each datum.

Validating Uas-Based Ndvi Data With Satellite Landsat Imagery For Bald Eagle Habitat Prediction In The Del Rio Springs Ecosystem, Noah Morales, Colton Weeks, Hank Vincent, Ronny Schroeder

Student Works

Vegetation health is commonly assessed using the Normalized Difference Vegetation Index (NDVI), which can be derived from multispectral sensors operating at different spatial resolutions. Validating NDVI products across sensor platforms is essential to determine their reliability for environmental monitoring and habitat assessment. This research compares NDVI derived from moderate-resolution satellite imagery and high-resolution unmanned aircraft system (UAS) imagery collected over the same study area. Landsat imagery, provided through the joint USGS–NASA mission, was used to represent satellite-based vegetation patterns, while high-resolution multispectral data were acquired using a MicaSense sensor mounted on a UAS to capture fine-scale vegetation detail.

NDVI values …

Validating Uas Lidar With Airborne Lidar For Precision Streamline Generation In Del Rio Springs, Arizona, Brad Rudy, Colton Weeks, Hank Vincent, Ronny Schroeder

Student Works

Accurate streamline delineation and high-resolution topographic products are essential across numerous disciplines, including hydrological analysis, environmental monitoring, construction, and erosion modeling. Products derived from high-accuracy elevation data provide greater reliability and improved decision-making outcomes for all fields that depend on them. A 2018 USGS airborne LiDAR dataset covering the Del Rio Springs riparian area north of Chino Valley, Arizona, offers a valuable opportunity to evaluate the relative accuracy of the DJI L1 LiDAR sensor when mounted on a Matrice 300 RTK UAV platform. Compared to traditional manned airborne systems, the UAV-mounted L1 provides high-accuracy, high-density point cloud data over small …

Integrated Geospatial Analysis Of Burn Severity And Vegetation Recovery Of The California August Complex Fire In 2020, Dharm Barot, Ronny Schroeder

Student Works

Large wildfires increasingly alter vegetation structure and ecosystem recovery trajectories at landscape scales, requiring reliable geospatial methods for post-fire assessment. This study evaluates burn severity and vegetation recovery following the 2020 California August Complex Fire using an integrated framework combining multispectral satellite imagery, spatial statistics, and airborne LiDAR data.

Burn severity was quantified using differenced Normalized Burn Ratio (dNBR), and vegetation recovery was assessed through a multi-temporal NBR time series spanning pre-fire (2015), fire-year (2020), and post-fire (2025) conditions. Optimized Hotspot Analysis (Gi*) was applied to isolate statistically significant clusters of high burn severity and reduce bias in recovery estimates. …

Integrated Geospatial Analysis Of Burn Severity And Vegetation Recovery Of The California August Complex Fire In 2020, Dharm Barot, Ronny Schroeder, Elise Anderson

Student Works

Large wildfires increasingly alter vegetation structure and ecosystem recovery trajectories at landscape scales, requiring reliable geospatial methods for post-fire assessment. This study evaluates burn severity and vegetation recovery following the 2020 California August Complex Fire using an integrated framework combining multispectral satellite imagery, spatial statistics, and airborne LiDAR data.

Burn severity was quantified using differenced Normalized Burn Ratio (dNBR), and vegetation recovery was assessed through a multi-temporal NBR time series spanning pre-fire (2015), fire-year (2020), and post-fire (2025) conditions. Optimized Hotspot Analysis (Gi*) was applied to isolate statistically significant clusters of high burn severity and reduce bias in recovery estimates. …

Geospatial Analysis Of Wildfire Ignitions And Proximity To Electric Transmission Lines In Arizona’S National Forests, Shane Ishmael, Ronny Schroeder

Student Works

The number of wildfires in Arizona rose by 18% from 2023 to 2024. Wildfires hit the Western United States hard, especially in states like Arizona and California, where vast national forests often fall victim to the biggest blazes. According to the Western Fire Chiefs Association, 19% of wildfires from 2016 to 2020 were sparked by electrical transmission lines.

This study explores whether wildfire start-location hotspots line up with power transmission routes running through Arizona’s Coconino and Tonto National Forests. The main hypothesis is that areas near power lines are more likely to become wildfire hotspots than other regions.

We used …

Precision Rockslide Hazard Mapping With Multispectral Imaging And Lidar Along Arizona Highway 89a, Hank Warner, Ronny Schroeder

Student Works

Along mountainous roads, rockslides, mud slides and avalanches pose a significant risk for continued access to a region and can cause large amounts of damage to infrastructure, taking time to clear and repair. The prediction of where these events will occur can allow preventative measures to be taken, allowing sustained access and preventing costly repairs.

This study develops a method to analyze and predict rockslide risk using satellite-sourced multispectral imagery and airborne LiDAR data.

The developed method started with multispectral LANDSAT 8 imagery and airborne LiDAR captures over Arizona Highway 89A, with all data taken between late August and early …

Regional Oceanographic Controls On Water Column Nitrogen Fixation In Northern Australian Waters, Douglas G. Capone, Ajit Subramaniam, Yubin Raut, Joseph P. Montoya, Margaret R. Mulholland, Rachel Ann Foster, Miles Furnas, Edward J. Carpenter

OES Faculty Publications

Large blooms of the diazotrophic cyanobacteria, Trichodesmium, have been re-ported along the north coast of Australia and are readily evident in remote sensing images. During a research cruise in November 1999, we sampled from Townsville to Broome, examined Trichodesmium population densities and their rates of carbon and N₂ fixation alongside microscopy-based cell counts of them and other cyanobacte-rial diazotrophs. Additionally, we also enumerated the picophytoplankton community using flow-cytometry, measured bulk chlorophyll concentrations, carbon and N₂ fixation rates, and water column hydrography, enabling comparison of diazotrophic and picophytoplankton functional groups across the system. Agglomerative hierarchical clustering analysis of physicochemical oceanographic …

Remote Sensing Of Particulate Organic Carbon And Phytoplankton Carbon In The Northern South China Sea, Xiaoju Pan, Tung-Yuan Ho, George T. F. Wong, Jen-Hua Tai, Lijuan Nie

OES Faculty Publications

Surface concentrations of particulate organic carbon (POC) and phytoplankton carbon (C_phy) in the northern South China Sea (NSCS) have been assessed successfully by remote sensing with appropriate regional tuning. Match-up comparisons between satellite derivations and field observations showed uncertainties of ± 28.5% for POC and ± 37.0% for C_phy. The satellite-derived climatological distributions are consistent with previous reports. In the open NSCS where phytoplankton growth is controlled primarily by the convective overturn, both POC and C_phy are higher in winter, associating with the effect of stronger northeast monsoons and surface cooling, than in summer, when …

Climate Change In Gilgit-Baltistan: Satellite-Based Land Use/Land Cover Change Detection, Socio-Economic Dimensions, And Adaptation Strategies, Ali Muhammad

Graduate Theses/Dissertations

Climate change is increasingly transforming the cryosphere, hydrology, and human landscape of Gilgit-Baltistan, a highly climate-sensitive mountain region in northern Pakistan. This thesis investigates these transformations through satellite-based land use/land cover (LULC) change detection in four representative tehsils of Gilgit-Baltistan—Ali Abad, Gilgit, Nagar, and Sikander Abad—selected to span a gradient of human pressure and cryospheric exposure within the region. Using summer, cloud-free (< 10%) imagery from USGS Landsat 7 (2000) and Landsat 8 (2025), it conducts a multi-temporal comparison of environmental and socio-spatial change. After atmospheric correction and band compositing, the imagery is classified in ArcGIS Pro into six classes—Water, Barren land, Vegetation, Snow, Glacier, and Built-up—using a Support Vector Machine (SVM) classifier, with Maximum Likelihood Classification and Random Forest also tested but found less suitable for the final workflow. The analysis detects a pronounced reduction in mapped glacier-class area alongside a comparatively stable snow class, together with built-up expansion, while examining how temperature, precipitation, tourism, and population dynamics relate to observed LULC transitions. The results reveal a pattern of cryospheric decline and urban growth, with implications for water availability, ecological stability, hazard exposure, and settlement pressure. By integrating geospatial change detection with climatic and socio-economic interpretation, the thesis moves beyond mapping to explain interacting environmental and human drivers of landscape transformation and provides a reproducible remote-sensing baseline for monitoring land-surface change in heterogeneous mountainous terrain. It recommends integrated water-resource management, climate-resilient land-use planning, watershed and glacier monitoring, and sustainable tourism governance, supporting evidence-based decision-making by the Government of Gilgit-Baltistan and organizations working on climate adaptation, disaster risk reduction, and sustainable regional development.

Using Remote Sensing Technology To Develop A Framework For Improving Hydrologic Models, Marissa Cook

Theses, Dissertations and Capstones

With increased storm intensity due to climate change and urbanization, flash flooding has become an increasingly significant issue globally and regionally. Although the factors influencing urban flash flooding are well-known, there is a growing need for technology to accurately and remotely predict the chance of a flash flood occurring from any given rain event to give people time to prepare. This study aims to use multispectral satellite imagery to provide a framework for improving near real-time flood predictions in an urban area of a high gradient, fourth order stream impacted by flooding. Specifically, we utilize satellite imagery to create the …

Data-Driven Methodologies For Mapping Cultural Heritage: The Case Of The National Coal Heritage Area, West Virginia, Usa, Hossain Mohammad Nahyan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The objective of this dissertation was to develop a comprehensive, data-driven spatial framework for characterizing the complex cultural landscape of the National Coal Heritage Area (NCHA) in West Virginia. By transitioning away from traditional, heuristic spatial mapping, this research integrates advanced spatial statistics, machine learning, and GIS-based methodologies to objectively quantify the physical, visual, and cultural dimensions of the post-mining environment. The research is structured around three interconnected empirical studies, each addressing a specific scale of the Landscape Character Assessment (LCA) framework to support heritage conservation and sustainable spatial planning. The first paper focused on landform classification, developing an automated …