Remote Sensing Commons^™

Contrasting Coastal Dune Environments In Chile, Aurora Christianson

Discovery Day - Daytona Beach

Emerging coastalization and urbanization threats to the prehistoric Concón Dunes and Humedal de Mantagua coastal area of Chile is being investigated by researchers via uncrewed aircraft systems (UAS) in "Contrasting Coastal Dune Environments in Chile." Four UAS were utilized: Anzu Raptor T, DJI Mavic 3E, DJI Mavic 3M, and DJI Air 3 to collect various images of the coastal dunes. Multispectral and RGB cameras gather images by photogrammetry to create orthomosaics and monitor vegetation indexes in Pix4Dmapper. Thermal cameras provided images in rainbow, white hot infrared, and black hot infrared schemes to monitor wildlife and vegetation. The normalized difference vegetation …

Global Performance Of Remote Sensing-Based And Reanalysis-Driven Models To Estimate Open Water Evaporation, Júlia Brusso Rossi, Ayan Santos Fleischmann, Leonardo Laipelt, Bruno Comini De Andrade, Joshua B. Fisher, Justin L. Huntington, Christopher Pearson, R. Iestyn Woolway, Roseilson Vale, Júlio Tota, Gabriel B. Senay, Huilin Gao, Anderson Ruhoff

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, we analyze the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. We compare 3 remote sensing‐based …

Crustal Structure Of The Vøring Plateau And Vøring Spur From Joint Refraction And Reflection Travel-Time Tomography, Anika Nawar Mayeesha

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Crustal architecture of the Vøring Plateau and Vøring Spur in the mid-Norwegian continental margin remains debated because the geophysical data in the area do not uniquely constrain whether the crust is continental, oceanic, or transitional. This study reanalyzes wide-angle ocean-bottom seismometer data from Profile 11-03 across the Vøring passive continental margin using joint travel-time tomography of crustal refractions and Moho reflections. A total of 6,760 first-arrival picks and 542 PmP picks from sixteen instruments were inverted for P-wave velocity and reflector depth. Three starting models were inverted independently to test sensitivity to the initial crustal configuration. The first two used …

Evapotranspiration Everywhere, All The Time: Towards A Unified View From Earth Observation, Joshua B. Fisher, Martha C. Anderson, Diego G. Miralles, Kanishka Mallick, Paul C. Stoy, Youngryel Ryu, Wim G. M. Bastiaanssen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Scientists want to know everything, everywhere, and all the time. This is particularly true in Earth science, where we seek to understand processes that span from the molecular to the planetary scale in how the world works, how it affects us, and how we impact it—especially the water cycle. Evapotranspiration (ET) was the last component to be measured in closing the water cycle: for decades, closing the water budget meant adding up all the measurable components, then inferring ET as the residual. Early measurements relied on water loss from pans and weighing lysimeters, followed by sensors inserted into plants to …

Machine-Learning Landslide Susceptibility And Runout Modeling In The Nolichucky River Gorge After Hurricane Helene, Grace Braver

Electronic Theses and Dissertations

Extreme rainfall from Hurricane Helene (September 2024) triggered widespread landslides across the southern Appalachian region, highlighting the need for rapid landslide susceptibility assessments that capture both landslide initiation and downstream runout. Traditional susceptibility models often focus solely on initiation zones, limiting their ability to identify which slopes will generate destructive landslides or where material will travel. This study addresses that gap by (1) integrating Geographic Information System (GIS)-based machine learning susceptibility modeling using ArcGIS Pro: Maximum Entropy (MaxEnt) and Random Forest-Based and Boosted Classification and Regression (FBBC) and (2) the U.S. Geological Survey (USGS) Grfin (Growth, Flow, and Inundation) runout …

Passive Microwave Remote Sensing Of Flash Drought Impacts On Vegetation, Quinton R. Deppert

School of Natural Resources: Dissertations, Theses, and Student Research

In 2002, Dr. Mark Svoboda characterized a new form of drought known as flash drought. Flash drought was defined as a rapid decline in vegetation health caused by severe heat and drought. In recent years, attempts to quantify the impacts of flash drought via precipitation, soil moisture, evapotranspiration, and temperature indicators have proliferated. What has rarely been quantified is what the rapid decline in vegetation health amid flash drought looks like through remote sensing. This is because vegetation health indices like the Normalized Difference Vegetation Index (NDVI) are derived from the visible and infrared regions of the electromagnetic spectrum and …

Drainage Proximity And Sinkhole Occurrence In Sivrihisar (Central Turkey): A Comparative Analysis Of Linear, Poisson, And Negative Binomial Regression Models, Bilge Bingül, Emrah Pekkan, Resul Çömert

International Journal of Speleology

This study investigates the relationship between sinkhole occurrence and distance to drainage in the Sivrihisar region (Central Turkey) and evaluates the suitability of different regression approaches for modeling clustered count data in karst terrains. A comprehensive inventory of 104 sinkholes developed within the Neogene lacustrine limestones of the Akpınar Formation was compiled using official records, remote sensing analyses, and detailed field surveys. Sinkhole occurrences were analyzed relative to a drainage network derived from a high-resolution Digital Surface Model and grouped by proximity to drainage lines. Linear Regression (LM), Poisson Regression (PR), and Negative Binomial Regression (NBR) models were comparatively applied …

Monitoring Koyna Dam Displacements Using Persistent Scatterer Interferometry, Sara Zouriq, Gehan Hamdy, Amr Fawzy, Rejoice Thomas, Hesham El-Askary, Eehab Khalil, Mohamed Elsayad, Tarik El-Salawaky

Mathematics, Physics, and Computer Science Faculty Articles and Research

Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr−1, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven …

Changes In Land, Ocean, Atmospheric Parameters Associated With The 2025 Myanmar (Mw 7.7) Earthquake, Feng Jing, Akshansa Chauhan, Ashwani Raju, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Multiple parameters associated with the land, atmosphere, and ocean were analyzed to study short-term and immediate pre-earthquake changes associated with the 28 March 2025 Myanmar earthquake (Mw 7.7). Anomalous clear-sky outgoing longwave radiation (ClrOLR) and trace gases (CH₄, CO, and O₃) were detected within two months prior to the mainshock. Vertical changes at different pressure levels suggest a possible underground source. High-temporal-resolution observations of the infrared brightness temperature and surface air pressure revealed short-lived fluctuations shortly before the earthquake, which may reflect localized stress adjustments and surface latent heat flux release during the final stage of earthquake preparation. In the …

A Review Of Satellite-Derived Terrestrial Evapotranspiration: Theories, Methods And Products, Yunjun Yao, Jiquan Chen, Joshua B. Fisher, Changliang Shao, Yuanbo Liu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurately estimating terrestrial evapotranspiration (ET), the second-largest hydrologic flux in the terrestrial water cycle, is vital for understanding global water and carbon exchanges. It is difficult to measure and estimate terrestrial ET at regional and global scales. Satellites have provided us an effective tool to estimate regional and global terrestrial ET in recent decades. In this article, we provide a comprehensive review of the basic theoretical foundations, methods and products of satellite-derived terrestrial ET. The basic theoretical foundations for estimating terrestrial ET are the Monin–Obukhov similarity theory (MOST) and other budding theories (e.g., Maximum entropy production theory, and generalized Hamilton …

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 …

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 …

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 …

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, …

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 …

High-Resolution Mapping Of Soil Moisture Variation Using Uas Thermal And Multispectral Imagery, Jackline Amma Timah

Theses and Dissertations

In agricultural landscapes, soil moisture regulates hydrologic partitioning, nutrient transport and water quality, land-atmosphere energy exchange that shapes local climate, and ecosystem resilience. However, traditional monitoring approaches, such as in-situ sensors and satellite imagery, often lack the spatial resolution required to capture fine-scale soil moisture variability. This study evaluated whether unmanned aerial system (UAS)-derived thermal, multispectral, and terrain variables can capture fine-scale spatial variability in volumetric water content (VWC) within an SRB in central Illinois.

High-resolution imagery was collected and paired with 50 field-measured VWC observations. Land surface temperature (LST), vegetation indices (NDVI and NDRE), spectral bands, and slope were …

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 …

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 …

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 …

Multi-Satellite Image Matching And Deep Learning Segmentation For Detection Of Daytime Sea Fog Using Gk2a Ami And Gk2b Goci-Ii, Jonggu Kang, Hiroyuki Miyazaki, Seung Hee Kim, Menas Kafatos, Daesun Kim, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher …

Towards Spatial Inversion Of Aerial Gamma-Ray Survey Data For Measurement Of Naturally Occurring Radioactivity, Daniel A. Haber

UNLV Theses, Dissertations, Professional Papers, and Capstones

Aerial gamma-ray surveying (AGRS) is used in geologic and environmental contexts to provide data on the surface spatial distribution of naturally occurring radioactive material (NORM) or other gamma-ray-emitting isotopes. The standard data reduction workflow for AGRS data involves a process whereby pointwise gamma-ray spectral data are denoised, corrected for numerous sources of background radiation and aircraft height, and are finally converted to physical values by empirical conversion factors. The reduced pointwise data are then typically spatially interpolated to form a continuous surface map.

This dissertation introduces and explores a novel spatial inversion method for reduced AGRS data that considers aircraft …

Rainfall-Runoff Modelling In An Indonesian Humid Tropical Area Using Satellite-Based Precipitation Products, Noordiah Helda

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation explores three rainfall-runoff models in the humid tropical regions of Indonesia using satellite-based precipitation products (SBPPs) and develops integrated machine-learning modeling frameworks. Several ground-based observations from BMKG (Badan Meteorologi, Klimatologi, dan Geofisika (also known as the Indonesian Agency for Meteorology, Climatology, and Geophysics)) stations across Indonesia (133−165 stations) are compared and evaluated against satellite products, indicating that GPM performs well, with R-squared values ranging from 0.54 to 0.76 and correlation coefficients ranging from 0.45 to 0.69, respectively.

In the Martapura Watershed, South Kalimantan, due to a lack of observational discharge data, streamflow was generated using the FJ Mock …

Detecting Prescribed Fire, Haying And Grazing Events Via Remote Sensing To Create Grassland Disturbance Landcovers For The Ring-Necked Pheasant (Phasianus Colchicus), Megan Amy Baldissara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation developed disturbance detection models to fulfill the need for remote sensing landcover products describing grassland structure. The use of landcover products derived from remote sensing is increasing over time in pheasant (Phasianus colchicus) research. Such landcover, however, does not provide relevant pheasant structural habitat information (Chapter 1). Pheasants require tall, high-density grassland for nesting, tall grassland with medium density for brood rearing, and tall grassland for wintering. Time since disturbance can serve as a proxy for structure, as it shapes vegetation by removing biomass and resetting succession. Disturbance is easier to detect than structure with current …

Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …

Detecting Burned Vegetation Areas By Merging Spectral And Texture Features In A Resnet Deep Learning Architecture, Jiahui Fan, Yunjun Yao, Yajie Li, Xueyi Zhang, Jiquan Chen, Joshua B. Fisher, Xiaotong Zhang, Bo Jiang, Lu Liu, Zijing Xie, Luna Zhang, Fei Qiu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas …