Geography Commons^™

A Climatological Analysis Of Drought And Flood In California, Kristen Faith Coston

Masters Theses

This research is investigating the climate states that produce droughts and floods and seeks to explain how a sudden shift from persistent drought to drought-alleviating flood is possible. Objectives include investigating how drought and flood in California are influenced by temperature and climate patterns, whether there are any correlation between climate/ocean indices and the Standardized Precipitation Index (SPI), and whether Atmospheric Rivers (ARS) are influenced by certain atmospheric/oceanic states.

All data is collected for 41 years, between 1983 and 2024. The SPI dataset is obtained by coding to filter for the mean value of all pixels in Sacramento County. Monthly …

Geospatial Intelligence And Multi-Criteria Analysis For Mapping Groundwater Potential Zones And Sustainable Resource Management In Wadi Qena Basin, Eastern Desert, Egypt, El-Taher M. M. Shams, Rashad Sawires, Sahar N. E. Tawfiq, Hanaa R. Youssef, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Groundwater is a rare and valuable resource in arid and hyperarid areas. Over the past few decades, population growth, urbanization, and agricultural activities—particularly in developing countries like Egypt—have greatly increased the demand for water supplies. The purpose of this study is to apply a multi-criteria analytical hierarchy process (AHP) in conjunction with remote sensing and geographic information systems methodologies to identify potential zones for groundwater recharge in Wadi Qena, Eastern Desert of Egypt. This valley is considered as one of the most potential valleys for government-led land reclamation and development initiatives. Using several data sources (e.g., Landsat-8 Enhanced Thematic Mapper …

Assessment Of Vegetation Dynamics After South Sugar Loaf And Snowstorm Wildfires Using Remote Sensing Spectral Indices, Ibtihaj Ahmad

UNLV Theses, Dissertations, Professional Papers, and Capstones

Wildfires are increasingly common in sagebrush ecosystems across the western United States, leading to vegetation loss and ecosystem restructuring. This study investigated vegetation recovery within two large Nevada burn scars, the Snowstorm Fire of 2017 and the South Sugar Loaf Fire of 2018. Landsat 8 surface reflectance imagery supplied multi temporal spectral data, and vegetation burn severity was mapped with the difference Normalized Burn Ratio. The research aimed to quantify how vegetation health spectral indicators respond over time across severity gradients and to detect shifts in land cover composition from pre fire to post fire conditions.Four spectral indices—NDVI, MSI, MCARI2, …

Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). …

Monitoring The Qosh Tepa Canal Project: A Geospatial Timeline Of Taliban Water Diversion, Enerel L. Crosslin

Honors Thesis

The World Food Program states that “acute malnutrition in Afghanistan is above emergency thresholds in 25 out of 34 provinces and is expected to worsen”. Aiming to support agriculture, the Taliban began to build the 285 km “Qosh Tepa Canal” to divert 17% of the Amu Darya, which supports the livelihoods of millions of people in downstream Uzbekistan and Turkmenistan. The World Bank estimated that roughly 2.4 million Central Asians could become climate refugees by 2050. Our objectives are to create a timeline of satellite imagery of the canal’s construction. Our research questions are (1) Can we characterize water diversion …

Measuring Agricultural Adaptation Using Remote Sensing: A Study Of Pigeonpea In Malawi, Maria Gorret Nabuwembo

Graduate Theses and Dissertations

Drought, heatwaves, and flooding cause tremendous damage to agricultural production in Malawi, and the continuous production of maize has led to widespread soil degradation. Additionally, climate change is transforming the environment at a scale beyond historical records, posing significant challenges to social, political, and economic systems. To combat these issues, agricultural innovations have been implemented, such as the installation of irrigation systems and development of heat/drought resilient crop varieties. Pigeonpea is one of the prominent drought-tolerant and temperature resilient crops grown as a diversification strategy since it allows farmers to adapt to changes while maintaining subsistence needs. This research investigates …

Advancing Snow Cover Observations In Glacierized And Alpine Environments Using Optical Remote Sensing, Rainey Aberle

Boise State University Theses and Dissertations

Warming air temperatures in recent years have led to declines in seasonal snow pack across western North America. These changes have profoundly impacted communities that depend on seasonal snow melt for water resources, as well as glaciers that act as critical long-term reservoirs in the region. While global snow cover extent products are available, they often fail to reliably distinguish between snow, glacier ice, and clouds, which are prevalent in western North America. Additionally, water managers in the region typically rely on point-based observations of snow mass for decision-making, despite the pressing need for comprehensive, watershed-scale estimates. To address these …

Assessing Spatial And Temporal Variation In Photoprotective Responses Of Deciduous And Evergreen Tree Canopies With Leaf Spectroscopy, Alexander Piper

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

Environmental conditions frequently prevent carbon fixation by plants, leading to the absorption of excess light that can damage photosynthetic machinery if not dissipated. To do so, plants utilize several photoprotective mechanisms, some detectable remotely using Photochemical Reflectance Index (PRI). Two components of PRI correspond to the facultative engagement of the xanthophyll cycle (ΔPRI) and constitutive changes in xanthophyll pigment pool sizes (PRI0), representing distinct mechanisms regulating shorter and longer-term photoprotection, respectively. Our understanding of interspecific and intraspecific variation in these mechanisms is limited, primarily because PRI components are often not clearly distinguished. This study aimed to assess the variation in …

Radar Precursors To Severe Weather Reports In Left-Moving Supercells, Eric A. Carothers

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

While much research has examined dual-polarimetric signatures of right-moving supercells, very little has been done with left-moving supercells. Given that left-moving supercells are thought to be disproportionate producers of large hail, understanding their internal dynamics is vitally important. This study examines differences and trends in the dual-polarimetric signatures of left-moving supercells to identify precursors to severe weather reports. A dataset of left-moving supercells associated with severe weather reports was created. These storms are processed with an automated analysis algorithm that identifies and quantifies the polarimetric signatures in each storm. A method for analysis of differences and trends in their dual-polarization …

Comparison Of Airborne Lidar-Derived Elevation Data In Fayetteville, Arkansas, Usa, Angelica M. Otting

Graduate Theses and Dissertations

Light detection and ranging (lidar) laser scanners are prominent remote sensing tools to produce high resolution three-dimensional (3D) imagery of the Earth’s surface. These laser scanners combined with global navigation satellite systems (GNSS) and real-time kinematic (RTK) reference stations can generate some of the most accurate ground surface imagery and elevation data for terrain mapping and related applications. Lidar aerial survey is an important tool in industries such as architecture, civil engineering, forestry, geology, geography, and agriculture where digital terrain models (DTMs) can be used to examine the geographical landscape and urban industry. Currently, there are three different common laser …

Considerations And Techniques For Producing Urban Tree Canopy Maps Using Freely Available And Accessible Methods, Hugh Reed Ellerman

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

Urban forests are valued as green infrastructure for the variety of benefits they provide across ecological, social, public health, and economic domains. To understand the distribution of trees, maps are required. Many methods of mapping tree cover use expensive and data-intensive datasets such as hyperspectral and LiDAR imagery, making these methods inaccessible to municipal forest managers. Variations in the modelling approaches of existing, freely available methods are not addressed in sufficient enough detail to understand the trade-offs implicit in these variations. Further, characteristics of urban tree canopy mapping (high spatial resolution imagery, heterogeneous urban environments, the importance of the tree …

Mapping And Analyzing Urban Growth In The Abu Dhabi Metropolitan Using Geospatial Technologies Integrated With Machine Learning, Hamsa Mohamed Yusuf

Thesis/ Dissertation Defenses

Urbanization is happening at a rate twice the increase in population on a worldwide scale. This has great environmental and social impacts, along with large impacts on regional climate and one of the reasons for this is the ever-continuous change of land use and land cover. The availability of high-resolution satellite imagery in addition to the advancements in geospatial technology allow mapping of LULC changes to be done accurately, efficiently and covering wide areas.
This thesis studies the urban Land Use and Land Cover Change (LULCC) that happened in Abu Dhabi city for the last 3 decades by utilizing geospatial …

A Survey Of Sampling Methods For Hyperspectral Remote Sensing: Addressing Bias Induced By Random Sampling, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

Identified as early as 2000, the challenges involved in developing and assessing remote sensing models with small datasets remain, with one key issue persisting: the misuse of random sampling to generate training and testing data. This practice often introduces a high degree of correlation between the sets, leading to an overestimation of model generalizability. Despite the early recognition of this problem, few researchers have investigated its nuances or developed effective sampling techniques to address it. Our survey highlights that mitigation strategies to reduce this bias remain underutilized in practice, distorting the interpretation and comparison of results across the field. In …

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

Flood Risk Assessment In Humanitarian Contexts: A Remote Sensing And Gis Methodology Applied To Nyarugusu Refugee Camp, Tanzania, Carolyne Vincent Mbirika

Theses

Flooding is a global challenge, with effects mostly experienced in developing countries due to insufficient data for effective flood risk assessment and management. Refugee settlements are particularly vulnerable to flooding due to their remote locations, high population density, and temporary shelters, necessitating flood susceptibility mapping to effectively mitigate risks and minimize damage prior to flooding events. This study assessed flood susceptibility in the Nyarugusu refugee camp, Tanzania, through Multi-criteria Decision Analysis (MCDA) and Analytical Hierarchy Process using remote sensing and Geographic Information Systems (GIS) approaches. The flood susceptibility map that resulted from this process categorizes flood-prone areas into three classes: …

Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat

School of Computing: Dissertations, Theses, and Student Research

High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …

Geo-Ai For Wetland Classification And Evolutionary Analysis, Lirong Yin

LSU Doctoral Dissertations

Wetlands, as a crucial component of the Earth's ecosystem, play a vital role in maintaining ecological balance and preserving biodiversity. However, wetlands are currently facing severe challenges, as both natural and human-induced factors are contributing to their global degradation. Gaining a deep understanding of the species composition and biodiversity within wetland-covered areas and accurately analyzing their changing trends not only helps assess the current and future state of wetlands but also provides strong support for the formulation of scientifically sound wetland conservation strategies. In recent years, the rapid development of geospatial intelligence and remote sensing technologies has brought new opportunities …

Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

It is significant to simulate grassland gross primary production (GPP) to understand the terrestrial carbon budget over Inner Mongolia (IMG), China. Nevertheless, there is not sufficient in situ GPP data over this region. In this study, we proposed a novel model-based transfer learning (MTL) approach with generative adversarial networks-long short-term memory (GAN-LSTM) and light use efficiency (LUE) models to derive grassland GPP over IMG, China. We first used 25 grassland eddy covariance sites over the conterminous United States to establish the GAN-LSTM model and then fine-tuned it with six sites over IMG to estimate water constraints that were embedded into …

Simulating The Impact Of Self-Sensing Materials On Aircraft Sortie Generation, Harmoni J. Blackstock

Theses and Dissertations

In conjunction with the Air Force Research Laboratory Materials Lab(AFRL-RX), this study evaluates the potential military value of the prototype material sensing composites on Unmanned Aerial Vehicle (UAV) operations in intelligence, surveillance, reconnaissance (ISR), and close air support (CAS) missions within a contested Indo-Pacific theater. Using a Simio based simulation,UAV performance was assessed under varying combat conditions, focusing on Remote Sensing, deployment strategies, initial lay-downs, and varying loss rates. Re-sults show that UAVs equipped with Remote Sensing technology significantly improved sortie generation and logistical efficiency. Scenario 17 achieved the highest sortie rate(965.5 sorties), outperforming the next-best scenario by 25 sorties. …

Spatiotemporal Trends In Anopheles Funestus Breeding Habitats, Grace R. Aduvukha, Elfatih M. Abdel-Rahman, Bester Tawona Mudereri, Onisimo Mutanga, John Odindi, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Effective identification and control of malaria vector larval breeding habitats are crucial for the management and eradication of malaria. Despite its importance, the last decade has seen a decline in data availability and intervention efforts due to reduced attention and prioritization. This study addresses the geographic data scarcity concerning Anopheles funestus larval breeding habitats in a malaria-prone region of western Kenya. Employing a two-step methodological approach, we integrated multi-criteria decision analysis (MCDA) and rule-based fuzzy logic analysis to evaluate the spatiotemporal similarity or divergence of these habitats. The analysis spanned a five-year interval, 2008, 2013, and 2018 with 2013 serving …

Scaling Arctic Landscape And Permafrost Features Improves Active Layer Depth Modeling, Wouter Hantson, Daryl Yang, Shawn P. Serbin, Joshua B. Fisher, Daniel J. Hayes

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD …

Global Estimates Of The Storage And Transit Time Of Water Through Vegetation, Andrew Felton, Joshua B. Fisher, Koen Hufkens, Adam J. Purdy, Seth A. Spawn-Lee, Lou F. Duloisy, Gregory R. Goldsmith

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The time it takes for water to transit from the ground back to the atmosphere affects weather, climate, biogeochemistry and ecosystem function. The transit time of water through vegetation, defined as the age of water transpiring from vegetation since time of entry, is a particularly understudied aspect of the terrestrial hydrologic cycle. Here we use a synergy of satellite remote sensing measurements over a five-year period to estimate global aboveground vegetation water storage to be on average 484 km³, roughly half of which is stored in Earth’s water-limited savannah, grassland and shrubland ecosystems. We then combine these storage …

Geospatial Machine Learning Approaches For Studying Urbanization Impact, Surface Reflectance Patterns, And Groundwater Health Risks, Bibhash Nath

Theses and Dissertations

Geospatial machine learning techniques have been used to study: the impact of urbanization on land use and land cover change, surface reflectance patterns in boreal regions, and groundwater health risks from arsenic in India. These studies combined spatial data, remote sensing, and predictive models to gain valuable insights for sustainable living and protecting human health.

Forest Above-Ground Biomass Estimation Using Nasa Gedi Lidar Waveforms And Global Tree Allometry, Ian Grant

Theses and Dissertations

Above-ground forest biomass plays a crucial role in global carbon cycles, yet accurately estimating biomass at global scales remains challenging. This thesis addresses two key challenges in processing NASA’s Global Ecosystem Dynamics Investigation (GEDI) space-borne lidar data to estimate above-ground biomass density (AGBD): accounting for global variation in forest structure and developing robust physical interpretations of lidar returns. The first component of the thesis analyzes global patterns of tree allometry using the Tallo tree allometry dataset, examining relationships between tree dimensions across biomes, continents, and plant functional types. This analysis reveals consistent allometry across continents for some biomes (e.g., tropical …

Devising Optimized Maize Nitrogen Stress Indices In Complex Field Conditions From Uav Hyperspectral Imagery, Jiating Li, Yufeng Ge, Laila A. Puntel, Derek M. Heeren, Geng Bai, Guillermo R. Balboa, John A. Gamon, Timothy J. Arkebauer, Yeyin Shi

School of Natural Resources: Faculty Publications

Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSI_uni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared …

Restored Wetlands Show Rapid Vegetation Recovery And Substantial Surface-Water Expansion, Thilina D. Surasinghe, Yin-Hsuen Chen, Kunwar K. Singh

Center for Geospatial Science, Education & Analytics Faculty Publications

Ecological restoration is essential for improving the ecological integrity of degraded ecosystems to enhance ecosystem services and biodiversity. In this study, we assessed the effectiveness of wetland restoration on retired cranberry farms by analyzing vegetation recovery and surface-water dynamics using the enhanced vegetation index (EVI) and normalized difference water index (NDWI) derived from Sentinel-2 satellite imagery. To quantify temporal dynamics of both vegetation recovery and surface-water cover, we identified the spectral distinctions among restored wetland plant communities. Our results indicated the emergence of distinct plant communities upon restoration. Restored wetlands in general showed significant and progressive vegetation recovery and expanding …

Dataset Supporting The Manuscript Fire Spread, Intensity, And Emissions Observations By Multiple Satellites: The Southern California Wildfires Of January 2025, Fangjun Li, Xiaoyang Zhang, Mark Cochrane, Shobha Kondragunta, Shuai An

Global Land Surface Season Data Sets

The data is for the paper "Fire Spread, Intensity, and Emissions Observations by Multiple Satellites: the Southern California Wildfires of January 2025" which has been submitted to the journal AGU Advances.

The downloadable file contains:

• README file that provides an overview of all all supporting files and scripts.
• 12 files folders containing the data and scripts

File type : zip/application
File size :  1.2GB

Evaluating Spatiotemporal Vegetation Index Variation To Detect Salt Marsh Dieback On The Georgia Coast, Emmanuella Bosompemaa Obeng

College of Graduate Studies: Theses & Dissertations

Salt marshes, essential for coastal protection and carbon sequestration, are increasingly vulnerable to dieback events, threatening ecosystem resilience and vital services. Detecting these shifts early is essential for timely intervention. However, few studies have applied Early Warning Signals (EWS) to coastal marsh systems. Most EWS research has focused on lakes, forests, or climate tipping points, with limited application to salt marsh dieback in the southeastern U.S. Site-specific, long-term spatial analyses are also lacking, as prior work often examines short time frames or single disturbance events. This study investigates the spatiotemporal patterns of salt marsh dieback on the Georgia coast from …

Wildlife Distribution Mapping And Analysis In Gorongosa National Park, Mozambique, Using Site And Satellite Imagery Data, Pinho Joaquim Munhequeira Mr

Murray State Theses and Dissertations

One of Mozambique's most important conservation sites, Gorongosa National Park has seen critical ecological changes over the past several decades resulting from climate variability, habitat loss, and political instability. This research integrates Geographic Information Systems (GIS) and remote sensing to examine the relationship between vegetation dynamics and wildlife population, and distribution. The main goals of this study are to evaluate the spatial distribution of particular wildlife species: buffalo (Syncerus caffer), elephant (Loxodonta africana), hippopotamus (Hippopotamus amphibius), and zebra (Equus quagga), and determine the impact of vegetation density and land cover on their …

Ensemble Machine Learning Approaches For Bathymetry Estimation In Multi-Spectral Images, Kazi A. Islam, Omar Abdul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li

OES Faculty Publications

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs …