Remote Sensing Commons^™

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 …

Geospatial Investigations Of Big Buckhead Cemetery, Millen, Ga, Audrey E. Popard

College of Graduate Studies: Theses & Dissertations

Geospatial investigations of burials are increasingly recognized as the most efficient and ethical means of determining grave locations in forensic and bioarchaeological research. A methodology of multi-tiered geospatial investigation has been applied to the Big Buckhead Baptist Church cemetery in Millen, GA. Using the systematic layering of geospatial technologies, the present study seeks to identify ground surface anomalies, with the objective of delineating known and potential unknown burial locations. It is hypothesized that the layered use of Light Detection and Ranging (LiDAR), Geographic Information Systems (GIS), and Ground Penetrating Radar (GPR), will allow for the most efficient and accurate demarcation …

Mapping Robusta Coffee (Coffea Canephora) Cropping Systems In Uganda: A Two-Step Pixel And Sub-Pixel Based Approach With Sentinel-2 Data, Getachew Kebede, Bester Tawona Mudereri, Onisimo Mutanga, Tobias Landmann, John Odindi, Natacha Motisi, Fabrice Pinard, Henri E.Z. Tonnang, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

Coffee is a highly valued commodity and a widely consumed beverage, playing an important role in global trade. However, coffee farming landscapes are increasing transitioning into smaller-scale agricultural setups. This transformation highlights the critical need for accurate classification and mapping of coffee cropping systems (CS), especially in countries like Uganda, where dense vegetation and complex terrain present substantial challenges to traditional land survey methods. Moreover, understanding the spatial distribution of Robusta coffee (Coffea canephora) CS is essential for developing site-specific management strategies, guiding extension services, and informing evidence-based policy decisions. To address this gap, the present study aimed to enhance …

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 …

Marooned: A Western Frontier Dismal Swamp Narrative, Professor Meya E. Hargett

The Scholarship Without Borders Journal

Marooned: A Western Frontier Dismal Swamp Narrative centers the erased figure of Samuel Mars, a lost Black station master whose movements through the Great Dismal Swamp have been excluded from dominant Underground Railroad cartographies. Here, the term “Marooned” refers not to abandonment, but to the maroon geographies of Black and Indigenous resistance encoded in land, lineage, and fugitive infrastructure.

Guided by Diasporic Maroon Memory Theory, Lineage as Method, and the Maroon Geographies Framework, this research reconstructs Mars’s trajectory through oral testimony, spatial patterning, and ancestral land memory. Rather than casting the Dismal Swamp merely as a site of refuge, this …

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 …

Unmixing In Very High Spatial Resolution Hyperspectral Images, Ana C. Chavez Lopez

Open Access Theses & Dissertations

Hyperspectral Imaging (HSI) captures hundreds of contiguous narrow wavelength bands across the optical region of the electromagnetic spectrum collecting the spectral signature of materials in the field of view of the sensor enabling detailed analysis of each pixel's spectral signature. Satellite or airborne remote sensing systems often capture imagery with low to moderate spatial resolution (LMSR). At these resolutions, the measured spectral signature is a mixture of the signatures of the materials within a single pixel. This mixing of spectral information makes analysis and material identification difficult. Hyperspectral unmixing is an analysis technique that decomposes a pixel's spectrum into constituent …

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 …

Advancing Precision Agriculture Through The Application Of Remote Sensing Technologies In Plant Health Assessment, Thomas Wilbur Davis

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

Monitoring plant health, including nutritional status assessment, is an important component of crop management decisions. This doctoral document addresses the use of agricultural remote sensing through focused investigations of plant disease detection and nutrient status assessment technologies that advance precision agriculture. The first chapter provides a comprehensive overview of remote sensing technologies in agriculture and examines current capabilities, limitations, and future directions. The final two chapters address studies that evaluated the virus-nitrogen interaction in wheat and compared methods to determine the nitrogen status of midseason field corn.

The first study evaluated the wheat streak mosaic virus (WSMV) × nitrogen interaction …

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 …

Physics-Guided Strategies For Enhancing Neural Networks Trained With Limited Data, Jose Guadalupe Perez Zamora

Open Access Theses & Dissertations

Deep neural networks excel at a wide range of processing tasks across various disciplines. However, the quantity and quality of data significantly impact network performance. In specialized domains, high-quality datasets are often difficult to gather, interpret, and curate for effective learning. Few-shot learning techniques, including transfer learning, data augmentation, and meta-learning, have emerged to address these constraints.

We propose three physics-guided strategies for enhancing neural networks trained with limited data: (1) combining existing models like LSTMs with Physics-Informed Neural Networks through two-branch architectures that merge their outputs, (2) deriving custom physics-informed data augmentation algorithms to expand limited datasets, and (3) …

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 …

A Comparative Analysis Of Yolo, Ssd, And R-Cnn Models For Ship Detection In Satellite Imagery, Abdulla Mohamed Alhemeiri

Thesis/ Dissertation Defenses

The importance of rapid, reliable ship detection in satellite imagery is underscored by needs in maritime safety, environmental protection, and sustainable fisheries. In this study, a comparative assessment of three widely used object detectors—Faster R-CNN, YOLOv3, and SSD (300/512)—is presented to clarify how accuracy and speed are balanced for ship and dock detection. A unified pipeline (MMDetection) was employed so that model training, validation, and evaluation were standardized. ShipRSImageNet, a high-resolution dataset with COCO-style annotations, was used as the primary benchmark, while Airbus Ship Detection data were reformatted from masks to bounding boxes and standardized to COCO to ensure consistency. …

Analyzing Sea Level Rise Scenarios Impact On The Mobility, Infrastructures, Environment Of Abu Dhabi And Defining Solutions By Creating A Digital Twin Using Gis And Game Engine, Justine Sylviane Lucie Sarrau

Thesis/ Dissertation Defenses

This dissertation is concerned with the potential impact of future sea level rise scenarios on the city of Abu Dhabi. With climate change, many coastal towns are at risk of experiencing this type of natural hazard. Currently, no precise scenario simulations have been developed, mainly related to the use of low spatial resolution elevation data. Additionally, the difficulty in understanding the real impacts persists when relying solely on traditional cartography and GIS methods. This dissertation mainly aims to take a further step by offering a new approach, which involves creating a real-time 3D simulation and dynamic flowing water. This is …

Studying The Difference Between Mapping Accuracy Of Non-Rtk Ultra-Lightweight And Rtk-Enabled Survey-Grade Drones, Mostafa Arastounia

Faculty Articles

This study compares the mapping accuracy of a non-RTK ultra-lightweight drone (DJI Mini2) with two survey-grade RTK-enabled drones (DJI Mavic3E and Phantom4) in three different sites. Flight parameters and weather conditions were the same on each site. The outputs were orthomosaics and digital surface models, whose accuracies were inspected by descriptive statistics and variance analysis tools. The data of the ultralight drone on the first site could not be processed due to strong wind, but its results for the second site (11 hectares) were comparable to those of survey-grade drones, i.e., the range and average of checkpoint errors for Mini2 …

Validation And Trend Analysis Of Satellite-Derived Surface Water Temperature Observations Over Adirondack Lakes, Marzi Azarderakhsh, Carolien Mossel, Abdou Rachid Bah, Aisha Malik, Fahmeda Khanom, Jonathan Borrelli, Pete Mcintyre, Hamidreza Norouzi, Kevin Rose

Publications and Research

This study aims to validate and evaluate satellite remote sensing observations from the Landsat series over 135 lakes in the Adirondack State Park, located in upstate New York, and to examine their surface temperature trends over the past 40 years. It utilizes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), along with Landsat 5 and 7. Park-scale results were derived by extracting MODIS surface temperatures within the park boundary, while lake-scale results were estimated using Landsat 5 (1984-2012) and Landsat 7 (1999-2023) observations. In addition, field observations were utilized to perform a comprehensive validation and evaluation of satellite-based surface temperature …

Near Real-Time Monitoring Reveals Extensive Recent Forest Disturbance In Ghana’S Protected Areas, Luofan Dong, Xiaojing Tang, Foster Mensah, Bashara Ahmed Abubakari, Kelsee H. Bratley, Pontus Olofsson, Curtis E. Woodcock

Faculty Scholarship

The Protected Areas (PAs) in Ghana play a critical role in preserving the abundant biodiversity of the West Africa Green Belt. But recent changes in policies and regulations have facilitated logging and mining activities, which have accelerated forest disturbances. While there is a consensus that PAs are undergoing destructive change, the extent, rate, and locations of forest disturbances are undocumented. In this study, we applied the fusion near real-time (FNRT) algorithm that utilizes Landsat, Sentinel-1, and Sentinel-2 data and sampling to monitor forests in the PAs of Ghana. The results reveal that 704.74 (±177.24) km2 of forest in the PAs …

Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R² = 0.82), rice (R² = 0.71), and walnut (R² = 0.68), …

Spectral–Spatial Transformer With Multiscale Convolutional Attention For Hyperspectral Image Classification, Junde Chen, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hyperspectral image (HSI) classification plays a vital role in remote sensing by leveraging rich spectral and spatial information for accurate material recognition. However, existing methods, particularly Transformer-based approaches, still face challenges in effectively modeling multiscale spatial–spectral features, preserving local details, and maintaining robustness to noise. To mitigate these limitations, we propose TMCANet, a spectral–spatial Transformer with multiscale convolutional attention, designed to effectively leverage both local and global contextual dependencies for HSI classification. Our design is guided by three core strategies: first, a convolutional feature extraction module, consisting of four convolutional layers, to learn hierarchical spectral multiscale representations and enhance local …