Remote Sensing Commons^™

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 …

Performance Mapping And Weighting For The Evapotranspiration Models Of The Openet Ensemble, M. Reitz, J. M. Volk, T. Ott, M. Anderson, G. B. Senay, F. Melton, A. Kilic, R. Allen, Joshua B. Fisher, A. Ruhoff, A. J. Purdy, J. Huntington

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Evapotranspiration (ET) accounts for the majority of water available from precipitation in the terrestrial water cycle, and improvements to the accuracy, resolution, and coverage of ET data can enhance hydrologic models and assessments. The OpenET collaboration of six remotely sensed ET modeling teams has demonstrated that an ensemble approach to ET estimation generally provides improved accuracy relative to individual ensemble members. The performance of individual models has been shown to vary by land cover type and climate zone, but a thorough study of the variables that influence model performance differences has not yet been conducted. In this paper, we model …

First Report Of Fibropapillomatosis And Critical Habitat Use In Green Sea Turtles In Curaçao, M. Tripepi, I. J. R. Van Veghel, A. D. Vreugdenhil, E. Brunelli

College of Life Sciences Faculty Papers

Fibropapillomatosis, a disease affecting green sea turtles (Chelonia mydas), has been documented in many regions of the world, including the Caribbean, but has not been scientifically reported in Curaçao until now. The present study utilized VHF telemetry in Caracas Bay, Curaçao, to track green sea turtles, both healthy and affected by fibropapillomatosis, with the objective of ascertaining their foraging locations. VHF telemetry proved to be a pivotal method for assessing habitat use in areas characterized by limited visibility and high boat traffic, conditions that present significant challenges for divers and snorkellers attempting to access the area. The results …

Identifying Topographic Influences Of Landcover Change In A Subarctic Watershed, Mason Bull

Boise State University Theses and Dissertations

Increased temperatures over the last 40 years have globally promoted a loss of ice and increase of plant cover and density, known as greening. Greening is seen more in arctic and alpine environments than elsewhere, and current literature states that new growth consists of low-lying tundra plants, as well as encroaching shrub communities into previously uninhabited rocky slopes. However, our understanding of greening processes and extent is potentially biased by study strategies. Many vegetation studies take place at either the small plot or arctic regional scale, and do not encapsulate the intricacies of watershed scale processes on greening. This study …

Assessing Coastal Vulnerability And Climate-Driven Migration Risk In West Africa, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study presents a GIS-based multi-criteria decision-making framework to assess climate-induced migration risk along the West African coast. We developed a comprehensive risk index that integrates environmental hazards such as flood frequency and socio-economic vulnerability indicators, including poverty levels, population density, and adaptive capacity. By utilizing datasets such as the Geocoded Disasters (GDIS) dataset, Social Vulnerability Index (SVI), Poverty and Adaptive Capacity Index (PACI), and the Population Exposure Index (PEI), the study identifies regions most susceptible to displacement. Results reveal that areas like Benin’s Abomey-Calavi, Cotonou, and Akpo-Misserete are especially vulnerable due to high disaster frequency, substantial population exposure, and …

Remotely-Sensed Urbanization And Local Perceptions Of Change From 1985-2024 In Southern Indiana., Marlea Ferber

Electronic Theses and Dissertations

This study is focused on the land cover change of non-built land cover being transformed into built land cover in two counties in Southern Indiana using mixed methods. Remote sensing was used to identify and quantify land cover change, and interviews were used to understand local perceptions of the physical land cover changes. The study area is situated along the rural-urban continuum with Louisville, Kentucky across the Ohio River. It is important to quantify the amount of land change conversion to built settlement as patterns and rate of urbanization help us to better manage transitions along the rural-urban continuum, but …

Insights From Swot Data On Transboundary Upstream-Downstream Impacts In The Nile Basin, Hesham Morgan, Wenzhao Li, Ali Elgendy, Surendra Maharjan, Rejoice Thomas, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study utilizes high-resolution data from NASA's Surface Water and Ocean Topography (SWOT) mission to investigate water dynamics and upstream-downstream impacts across key reservoirs in the Nile Basin. Focusing on the Grand Ethiopian Renaissance Dam (GERD), Rosaries Dam, Merowe Dam, and the Aswan High Dam, the analysis spans 15 months (August 2023 to October 2024). By systematically selecting 30 points across each reservoir, monthly boxplots of surface water elevation were generated, revealing significant temporal and spatial variability. The results show that GERD’s filling phase led to a steady increase in water levels (peaking at ~615 meters from June to October …

Tackling Data Quality Challenges In Remote Sensing: Solutions For Reliable Urban Heat Island Analysis, Wei Xia, Aqil Tariq, Hesham El-Askary, Rana Waqar Aslam, Elgar Barboza, Dmitry E. Kucher, Youssef M. Youssef, Habib Kraiem

Mathematics, Physics, and Computer Science Faculty Articles and Research

Urban heat islands (UHIs) pose critical challenges to public health, energy demand, and environmental sustainability, particularly in rapidly expanding urban regions. This study examines the complex relationship between building configurations and integrated green spaces, as well as their combined impact on thermal regulation. It focuses on addressing data quality issues commonly encountered in remote sensing applications. Using high-resolution multispectral and thermal imagery, we developed an integrated modeling approach that captures the collective influence of built form and green infrastructure on urban microclimates. A key finding is the significant linear inverse relationship between green space coverage and land surface temperature, underscoring …

The Dynamics Of Urbanization And Changes In Ekistics Elements: A Case Study Of Surakarta City, Sarah Astita

Cities and Urban Development Journal

Urbanization has become a driving force behind significant spatial, environmental, and socio-economic transformations in urban areas, including Surakarta, the most densely populated city in Central Java. The rapid increase in population and urban expansion has led to drastic changes in land use and the structure of human settlements. This study aims to analyze the impact of urbanization on the ekistic elements of Surakarta (nature, humans, society, space/shells, and networks) and how these changes manifest spatially over time. A descriptive qualitative approach is employed using literature analysis and the interpretation of Landsat satellite imagery from 2011 and 2022. The findings show …

High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le

Mathematics, Physics, and Computer Science Faculty Articles and Research

Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3–5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.

Hyperspectral Band Selection Via Heterogeneous Graph Convolutional Self-Representation Network, Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain, often overlooking the structural characteristics of pixels and spectral bands, such as spatial continuity and spectral dependencies. In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI …

Remote Sensing-Based Assessment Of Evapotranspiration Patterns In A Unesco World Heritage Site Under Increasing Water Competition, Maria C. Moyano, Monica Garcia, Luis Juana, Laura Recuero, Lucia Tornos, Joshua B. Fisher, Néstor Fernández, Alicia Palacias-Orueta

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Kabe Aberle, Nadia Kako, Kateri Mcrae, Brooke Agulnek, Sky Palmon, Yasmine Ramirez, Francisca Aguirre Beltran, Ashley Juarez, Bridget Kim, Tessa Appel, Sterling Kerr, Spencer Ingley, Gabe Meyer, Robin Tinghitella, Dale Broder, Lily Baeza, Chloe Beers, Julia Coakley, Whitney Kelsey, Sydney Gainforth, Gabi Wing, Audrey Martin, Aaliyah Amore Berry, Brooke Watley, Kiruthika Venkatesan, Rachel Bienstock, Annabella Brotherston, Madison Bryant, Mia Burgener, Emma P. Lieb, Rachel A. Johnson, Jennifer L. Hoffman, Kania Campbell, Kiena Campbell, Courtney Cassidy, Sage Krzyzkowski, Maddox Jones, Skylar Abookire, Luke Hawkins, Sunnah Yoon, Andrea Chu, Yan Qin, Nyah Cubbison, Brian Gearity, Daniel Mcintosh, Mariely Cruz, Edward Garrido, Grady Dionne, Nicole Doris, Lyndsie Salvagio, Ann-Charlotte Granholm-Bentley, Anna Dymov, Hannah Eckert, Gabrielle Welsh, Erica Larson, Charlie Ernst, Anna Zhou, Sarah Watamura, Larissa Fedorovich-Klein, Georgie Fields, Kimberly A. Guevara, Aven Mccall, Ben Peltier, Feruz Yahia, Patrick Flores, Jadyn Floyd, Sophia Forcier, J. Von R. Monteza, Peter Sokol-Hessner, Gwendolyn Geiger, Scott Nichols, Camryn Gunter, Kendal Hengst, Charlie Bednarz, Issy Garside, Addison Baker, Rachel Mina, Brooke Hermanson, Amanda Klingler, William Highfill, Sydney Jaques, Kerstin Lewey, Allison Grossery, Daniel Linseman, Ethan Lim, Jagger Livengood, Owen Mantelli, Gabby Pappas, Abby Mcdonald, Madeleine Dierking, Eve Miller, Emma Loeber, Anna Marlow, Michael Kerwin, Ella Mathews, Hillary Hamann, Khadija Mohamed, Vivian Nguyen, Gabri Notov, Ifunayachi Ogbonna-Ukuku, Sunil Kumar, Charles Baysah, Sarah Olson, Don Sullivan, Anna Paradiso, Jay Parrish, Mira Pronobis, Alisha Pravasi, Kerstin Haring, Diego Ramirez, Christopher Reardon, Juliana Ramirez, Casey Doherty, Ella Kestner, Teagan Weindel, Cate Billings, Pablo Torre-Walter, Lucy Rand, Samantha Reynolds, Mark Siemens, Khadeeja Rashid, Laine Satterlee, Piper Heilbronner, Lily Pound, Ben Whitehurst, Anna Respet, Lizzie Lesoing, Sydney Hertel, Aya Saad-Masri, Brooke Ballenger, Max Proske, Hannah Rosenberg, Ellia Nakahara, Sophia Espinoza, Ivan Woolhouse, Simon Ruland, Gorkem Er, Timothy Sweeny, Melaku Saketa, Michela Schenk, Maren Lynch, Madi Hamm, Grace Schroeder, Michelle Rozenman, Rana Seif, Jackson Hall, Marisela Simental, Daniel Paredes, Aaron Mena, Preston Spaan, Evelyn Stovin, David Andrew Swartz, Anh Tran, Daniel Pittman, Luke Farchione, Emily Boyer, Ukari Verner, Lacey Conrad, Jonathan Velotta, James Weiner, Jagger Gossett, Noah Sherry, Sam Proud, Ben Block, Avi Narayana, Zoey Weiss, Alyssa Wilson, Gabrielle Walsh, David Zonana, Keely Wright, Kena Riveria, Lillybelle Deer, Jena Doom, Elysia Davis, Isabelle Yaremenko, Caitlyn Young

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

A Novel Approach To Increase Accuracy In Remotely Sensed Evapotranspiration Through Basin Water Balance And Flux Tower Constraints, Kul Khand, Gabriel B. Senay, Mackenzie Friedrichs, Koong Yi, Joshua B. Fisher, Lixin Wang, Kosana Suvočarev, Arman Ahmadi, Housen Chu, Stephen Good, Kanishka Mallick, Justine Missik, Jacob A. Nelson, David E. Reed, Tianxin Wang, Xiangming Xiao

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address …

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km² from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R² = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

Use Of High-Throughput Phenomics With And Without Fungicide As A Wheat Breeding Tool, Gerardo Ivan Rivera Collazo

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

Wheat (Triticum aestivum) is an important food staple for many countries around the world and current consumption and production data demonstrate a production deficit. Breeders are challenged to help close this gap in production by selecting better cultivars with improved yields. Several methods aim to accelerate the breeding process to reduce the time required for releasing an elite variety. One of the main breeding bottlenecks is the lack of fast and reliable phenotypic data acquisition that could dissect physiological and morphological plant data. High-throughput remote sensing could have the potential to reduce this bottleneck by streamlining data acquisition, …

The Surface Water And Ocean Topography (Swot) Mission For River And Lake Ice Application, Sunwoo Yoon

Earth Sciences Theses and Dissertations

The Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, is designed for global survey of Earth’s surface water. However, the seasonal freezing of lakes and rivers combined with SWOT’s unique interferometric radar characteristics presents a valuable opportunity to assess its potential for river and lake ice applications. In this dissertation, I first compare backscatter characteristics over open water and frozen lakes and rivers. I demonstrate strong contrast in backscatter between water and ice while accounting for incidence angle, highlighting SWOT’s capability to discriminate between surface cover types. However, overlapping backscatter signatures suggest further investigation of drivers of …

Identifying The Drivers Behind The Decline Of Douglas-Fir And True Fir Species In Sw Oregon, Alexander Bryant Bentley

Dissertations and Theses

Increased levels of Pseudotsuga menziesii (DF) and Abies species (TF) decline throughout dry forests in the Klamath Mountains and Western Cascades ecoregions have been recorded in recent years, with preliminary evidence suggesting that factors influenced by changing climatic conditions and encroachment from fire suppression policies are primarily to blame. Yet, the physiological and environmental mechanisms of drought-driven tree decline are poorly understood across biophysical and spatial gradients. Given the complex relations that drive decline, determining the degree of influence of specific drivers is difficult, creating unique challenges for modern forest management. To elucidate the most important factors driving DF and …

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 …

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 …

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 …