Physical Sciences and Mathematics Commons^™

Can Forest Thinning Activities Be Characterized With Public Data?: Evidence From California’S Million Acre Strategy (2020-2023), Selena Rowan

Cal Poly Humboldt theses and projects

Can forest thinning activities be quantitatively described using public data? This study evaluates the feasibility of doing so using geospatial datasets and project-level documentation associated with forest operations tracked under the California Wildfire & Forest Resilience Task Force’s Million Acre Strategy (2020–2023). As fuels reduction efforts expand to address increasing wildfire risk, there is growing demand for detailed information on thinning activity characteristics and associated biomass generation to support management evaluation, biomass utilization, and life-cycle emissions modeling. However, the extent to which existing public data provide sufficient detail to support such analyses remains unclear.

This thesis analyzes forest thinning activities …

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

OES Faculty Publications

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

Protomix: Unified Prototype Learning For Semantic Change Detection In Remote Sensing, Hengzhi Chen, Kun Hu, Patrick Filippi, Wei Xiang, Thomas Francis Bishop, Zhiyong Wang

Research outputs 2022 to 2026

As an emerging topic, semantic change detection (SCD) in remote sensing aims to both identify the locations of changes before and after events and determine the land cover (LC) classes of changed regions. While deep learning has demonstrated its great potential for SCD, the following two fundamental challenges remain: large variations among the same LC class as well as close similarity between different LC classes, and the severe underutilization of massive unchanged regions, which dominate remote sensing scenes but lack detailed semantic annotations. To address these issues, we propose a novel prototype learning framework for SCD, namely ProtoMix, by leveraging …

Study Of Fire Regimes In Southwestern Australia Using Geospatial Techniques, Ana Do Carmo Carvalho

Theses: Doctorates and Masters

The Northern Jarrah Forest in Southwestern Australia (SWA), part of a global biodiversity hotspot, is home to fire-sensitive tree species such as marri (Corymbia calophylla) and jarrah (Eucalyptus marginata). As climate change intensifies drought and extreme fire events, understanding the drivers of fire severity is increasingly critical. This thesis analyses fire regimes within the Mundaring drinking water catchment to better understand the spatiotemporal complexity of fire severity in relation to fire history and environmental factors.

The thesis commences with a review of three Fire History Databases (FHDs) used in SWA, highlighting significant inconsistencies and data gaps across time and space. …

Satellite Observations Reveal Ecosystem Resistance And Resilience To Short-Term Water Stress Driven By Dominant Vegetation Along A Rainfall Gradient In Australia, Huanhuan Wang, Qiaoyun Xie, Sally E. Thompson, Caitlin E. Moore, David L. Miller, Erik J. Veneklaas, Richard P. Silberstein, Xing Li, Jingfeng Xiao, Belinda E. Medlyn, William K. Smith

Research outputs 2022 to 2026

Climate change is projected to intensify water stress in many ecosystems and poses threats to their stability, which can be quantified through ecosystem resistance and resilience. Relevant studies mostly focused on multi-year or annual droughts, and in spatially homogeneous or species-specific ecosystems. However, resilience and resistance within complex ecosystems, where different plants exhibit different adaptations and recovery behaviours, are less understood. Using productivity data from satellite-derived GOSIF (Global Orbiting Carbon Observatory-2 Solar-Induced Fluorescence) and flux towers, we examined vegetation responses to short-term (<1 year) water stress events from 2000 to 2018 along the North Australia Tropical Transect, which spans a 1600 mm rainfall gradient and transitions from seasonal mesic to non-seasonal arid ecosystems. We define resistance as productivity maintained during stress relative to a multi-year average baseline, and resilience as the extent to which productivity recovered one year after stress relative to the same baseline. Our results show that ecosystem resistance to water stress was lowest in semi-arid regions but higher in both arid and mesic regions, while ecosystem resilience showed the opposite pattern. These spatial patterns occurred regardless of seasonality and were mainly associated with dominant vegetation type. Woody savanna-dominated mesic regions exhibited highest resistance (0.82 ± 0.13, p < 0.001) and lowest resilience (0.26 ± 0.19, p < 0.001), shrublands in arid areas had intermediate values of both resistance (0.81 ± 0.14, p < 0.001) and resilience (0.27 ± 0.22, p < 0.001), while the grasslands in semi-arid regions had low resistance (0.78 ± 0.15, p < 0.001) and high resilience (0.38 ± 0.24, p < 0.001). The highest likelihood (>75.0 %) of full recovery (i.e., exceeding baseline after one year) occurred during the wet season in …

Priority Questions For The Next Decade Of Blue Carbon Science, Peter I. Macreadie, George E. Biddulph, Pere Masque, Hilary Kennedy, Jimena Samper-Villarreal, J. Patrick Megonigal, Hannah K. Morrissette, Tania E. Romero-Gonzalez, Vanessa Hatje, Jana Friedrich, Sigit D. Sasmito, Kenta Watanabe, Inés Mazarrasa, Dorte Krause-Jensen, Janine B. Adams, Miguel Cifuentes-Jara, Ariane Arias-Ortiz, Andre S. Rovai, Milica Stankovic, Kirsten Isensee, Ana M. Queirós, Luzhen Chen, Jorge Herrera-Silveira, Catriona L. Hurd, Rashid Ismail, Ken W. Krauss, Anna Lafratta, Maria M. Palacios, William E.N. Austin

Research outputs 2022 to 2026

Blue carbon ecosystems, classically defined as mangroves, tidal marshes and seagrasses, but increasingly expanded to include ecosystems such as tidal flats, macroalgal forests and shelf sediments, contribute to climate change mitigation and biodiversity support. Here, seven years after the last global assessment of research priorities, we conducted a priority-setting exercise to identify persistent knowledge and implementation gaps, and the strategic priorities that must be addressed to enable scalable, high-integrity and equitable management of blue carbon ecosystems in a rapidly evolving policy and finance landscape. The highest priority focuses on managing blue carbon ecosystems to support coastal communities while integrating traditional …

Zero-Shot Segmentation Of Estuary Mudflats Using The Segment Anything Model, Jaren Unzen

Honors Theses and Capstones

Estuary mudflats are ecologically sensitive environments that require consistent monitoring. Traditional satellite-based classification workflows are often constrained by the high cost and labor-intensive nature of manual data annotation. This study evaluates the utility of Segment Anything Model 3 (SAM 3), a foundational computer vision model, to automate mudflat segmentation without domain-specific fine-tuning. By leveraging the model’s text-prompting capabilities alongside specialized pre- and post-processing techniques, we generated segmentation masks in a zero-shot framework. Our approach achieved an F1- score of 0.51, demonstrating the inherent challenges of spectrally complex coastal features. Despite this, the results highlight a promising pathway for adapting large-scale …

Land Cover Classification Using Optimized Imagery Resolution And Machine Learning Algorithms For A Long-Term Monitoring And Restoration Project, Jessica R. Suoja

Cal Poly Humboldt theses and projects

Ecosystem services and functions are prone to water resource exploitation resulting in cascading effects that include decrease of biodiversity and loss of riparian vegetation, a keystone habitat in desert riparian ecosystems. Mono Lake is a prime example of overexploitation of water resources leading to legal action and eventually a legally mandated long-term monitoring and restoration project. Monitoring and restoration projects can benefit from techniques such as remote sensing and machine learning algorithms to generate accurate land cover classification maps for calculating land cover change over time. However, the spatial resolution of remote sensing imagery and the machine learning algorithms chosen …

Direct And Satellite-Based Quantification Of Greenhouse Gas Fluxes In Small Ponds., Mark Christopher Tierney

Electronic Theses and Dissertations

As evidenced by their historic losses due to anthropogenic activities, wetlands are undervalued ecosystems that provide numerous ecosystem services, from recreational opportunities to carbon (C) storage. However, these wetlands are also the largest natural source of atmospheric methane (CH₄). Additionally, small water bodies (< 10,000 m²) are disproportionately large emitters relative to larger lakes and reservoirs. As construction and restoration efforts proceed, it is crucial to identify wetlands that need restoration and to understand the factors driving both greenhouse gas (GHG) fluxes and the C balance of these systems. This dissertation investigates the drivers of greenhouse gas fluxes …

Global, Continental, And Local Scale Paleoenvironmental Reconstruction Methods For Interpreting The History Of Miocene North America And The Great Plains, Willow Hue Nguy

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

The Miocene of North America is characterized by dynamic changes in herbivore communities, habitat composition, and global climate. Long-term cooling and drying trends influenced large changes in floral and faunal communities. The Great Plains, North America shifted from closed-canopy biomes to open-canopy biomes with C3-grasses to biomes with C4-grasses. This shift affected the herbivore communities as browsing-adapted ungulates were replaced by grazing-adapted ungulates. Much focus has centered on the Great Plains, so it is not well understood how the changes in biomes compare to other regions of North America. The models used for vegetation density indicate open-canopy biomes since at …

The Relationship Between Lineaments And Cave Passages In Unfaulted Gently-Folded Strata, Snail Shell Cave, Central Tennessee, Usa, Mark Abolins, Joseph Firkaly-Paciera, Blake King, Cris Ocasio-Valentin, Jackson Slighter, Teagan Sullivan, Justin Whited

International Journal of Speleology

Near Snail Shell Cave in central Tennessee, USA, seven image interpreters independently mapped lineaments using a 3DEP LiDAR DTM, revealing geomorphic and structural differences in and near different parts of the cave. MANOVA comparison of lineament and cave passage trend for Western, Central, and Eastern Snail Shell caves reveals statistically significant differences (p <0.05). The Western cave (mean depth 44 m) is in the gently-dipping limb of the Snail Shell anticline, and the Central cave (mean depth 31 m) is in the anticline hinge. In contrast, many Eastern cave passages (mean depth 40 m) cut across the anticline hinge and part of the limb of the Overall syncline. For both the Western and Central caves, a post-hoc Tukey’s HSD test fails to show that lineament trend within 235 m of the cave differs significantly from passage trend, and the test fails to show that the two differ significantly within 235 and 433 m of the Eastern cave. Additional statistical testing fails to show a difference between Western passage trend and bed strike, indicating the likelihood that lineaments and passages developed within strike-joints. However, testing shows a statistically significant difference between passage trend and bed strike for the Central and Eastern caves. The strike of joints inside the Eastern cave shows that it is within a joint zone which penetrates to depths of at least 80 m and is expressed at the surface as an ~4.2 km-long belt of elevated lineament line density. The lack of a statistically significant difference between Eastern passage trend and the trend of a nearby topographic escarpment suggests that many joints are stress release fractures formed during relatively recent erosion. For the Eastern cave, intersections between lineaments and structures would be attractive targets for groundwater exploration at depths typical of wells in carbonates.

Dynamics Of Meandering River Cutoffs And Post-Cutoff Sedimentation, Dylan T. Shoemaker

LSU Doctoral Dissertations

Meander cutoffs are pivotal events in the life cycle of rivers, abruptly adjusting flow fields and reshaping riparian environments. The consequences of cutoffs extend beyond geomorphology, influencing aquatic ecosystems, sediment storage within the floodplain, nutrient and contaminant cycling, and risks to human infrastructure. Yet, research of cutoffs has not produced a clear conceptual understanding of the persistence of oxbow lakes, and the dynamics of post-cutoff planform change that aligns with the observations of cutoff events on a global scale. This research aims to use satellite imagery to further clarify our understanding of the post-cutoff morphometrics and processes that constrain the …

Predict Social Economic Outcomes By Transferred Knowledge With Satellite Imagery, Yang Tang, Shih-Fen Cheng, Yunqiang Zhu, Yichen Yang, Zhiqiang Zou

Research Collection School Of Computing and Information Systems

Traditional deep learning methods and econometric models have played a crucial role in the field of data mining, particularly in the prediction of socioeconomic outcomes. However, socio-economic information is unable to be directly extracted from remote sensing data. So, in this paper, we propose a method to leverage transfer learning to predict socioeconomic indicators (outcomes) through satellite imagery. Specifically, we use road network types as a proxy for socioeconomic factors, which is more effective and stable than using nightlight. We have extracted eleven distinct road topological features to generate reasonable road network types. Given the unique characteristics of road networks, …

Establishing A Framework Of Best Management Practices For Sustainable Water Quality: Integration Of Technology And Remote Sensing, Mason L. Marcantel

LSU Master's Theses

Water quality security is a complex management issue that is constantly challenged by factors of seasonal variability in climate change, urban population growth, and agricultural intensification. These factors warrant the need for innovative technological adaptations to management practices that integrate remote monitoring, predictive modeling, and sustainable resource utilization. This study establishes a framework for sustainable water quality practices through the integration of remote sensing technology for urban wastewater treatment and agricultural irrigation systems. This dual-site research study explores seasonal water quality dynamics and technological interventions to create a more proactive approach to water quality security.

In the first study, water …

Mapping Seagrass Extent Using Sentinel-2 Satellite Imagery: A Case Study In Salt River Bay, United States Virgin Islands, Chelsea O. Bryant

USF Tampa Graduate Theses and Dissertations

Seagrass beds provide important services in many coastal ecosystems. They provide habitat and food for a wide range of marine species, stabilize sediments, and prevent coastal erosion. These services support local fisheries and tourism-driven economies. Yet, seagrass ecosystems are threatened by changes in water quality, sedimentation, and other stressors. This study investigated the interannual variability in seagrass extent in Salt River Bay, United States Virgin Islands (USVI), from 2018 to 2024. The goals were to better understand temporal dynamics in seagrass extent and evaluate the influence of turbidity as a driver of seagrass health and distribution. The overarching research question …

Deep Learning For Land Use Classification: A Systematic Review Of Hs-Lidar Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, David Blake, Anwaar Ulhaq, Naeem Janjua

Research outputs 2022 to 2026

Remote sensing (RS) technologies have significantly advanced Earth observation capabilities, enhancing the characterization and identification of surface materials through both spaceborne and airborne systems. These advancements are crucial for improving environmental monitoring and urban planning. As RS datasets have become more accessible, their increased complexity has necessitated a shift from traditional machine learning techniques to more robust deep learning approaches, particularly convolutional neural networks (CNNs) and transformer-based models known for their superior feature extraction capabilities. This systematic review focuses on the application of these deep learning techniques in land use classification, emphasizing the fusion of hyperspectral (HS) and LiDAR data. …

Spectral Mixture Analyses Of Hematite-Rich And Clay-Bearing Terrains In Meridiani And Oxia Planum, Mars, Thomas Jake Condus

Arts & Sciences Graduate Student Theses and Dissertations

Minerals have characteristic absorption features in the visible and near-infrared wavelength range, which can be detected in remotely sensed observations using methods such as spectral parameter maps or modeled via spectral mixtures. My dissertation uses both methods on orbital and ground-based single-scattering albedo data to assess the surface compositions of Meridiani Planum and Oxia Planum, regions of Mars that are rich in secondary minerals. A probabilistic approach to spectral unmixing was adopted to account for the nonuniqueness of model solutions, with endmember abundance and grain size maps being produced for Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) orbital images. For …

Assessing Greenhouse Gas Emissions From Michigan’S Drowned River Mouths Using In Situ And Remote Sensing Methods, Jillian A. Greene

Masters Theses

Freshwater estuaries are natural contributors to the carbon cycle including production and emission of methane (CH4) and carbon dioxide (CO2), potent greenhouse gases (GHGs); however, estimates of their contribution to regional and global GHG emissions is largely unconstrained. Few studies have examined the quantification and drivers of lake CH4 and CO2 production in the Great Lakes region and how it may differ in response to anthropogenic development. In this study, CH4 and CO2 emissions were measured from three drowned river mouth estuaries (DRMs) along the eastern shore of Lake Michigan in 2024. The DRMs exist along a latitudinal gradient ranging …

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 …

A Study On Application Of Landsat Imagery To Grassland Resource Surveys, Xia Jing Xin, Chia Shen Siu, Chang Xiao Guo

IGC Proceedings (1977-2023)

The emergence of remote sensing has provided an advanced means for surveys and planning of grassland resources. In 1979, China started and since then has developed technical cooperations in agricultural remote sensing with FAO and UNDP respectively. With the cooperation, National Training and Application Center for Agricultural Remote Sensing was established at Beijing Agricultural University. We study on application of remote sensing to grassland resource surveys total area of 50,000 km2. All those indicate that remote sensing provides a mean of grassland surveys to achieve greater, faster, better and more economical results and also bears extensive prospects for use on …

The Application Of Remote Sensing Technology To The Resource Investigation Of Inner Mongolia, Chen Kai, Li Bo

IGC Proceedings (1977-2023)

This paper is a comprehensive report and explanation of a large scale research project carried out for the investigation of grassland in the Inner Mongolia Autonomous Region, China. In the paper, the nature of the grassland resources in Inner Mongolia, the purposes of the research project, the organization of the research forces, the methods applied in the research, the chief results of the project, and the problems to be further solved have all been briefly discussed.

Modeling Mars Chaos Terrains As Fracture Mosaics, Aidan C. Buie

Earth and Planetary Sciences ETDs

Chaos terrain exists on several planetary bodies but most notably on Mars. With 30 different named terrains and hundreds of chaotic or ”chaos-like” terrains, they are one of the most prominent and visible features on the planet. Chaos terrains have many visible similarities to each other: they all are comprised of polygonal blocks separated by fractures or channels. These blocks vary from angular to rounded and from flat plateaus to small hills, and the fractures vary from flat floored to ”v” shaped. Often, a single chaos will contain all of these morphologies. Most of the 30 named chaoses on Mars …

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.

Leveraging Weekly Snow Cover Time Series For Improved Glacier Monitoring And Modeling, Rainey Aberle, Ellyn M. Enderlin, David R. Rounce, Shad O'Neel, Brandon Tober, Alexandra Friel

Boise State University Publications and Presentations

Seasonal snow and ice melt strongly influence glacier mass balance, yet sparse sub-annual observations limit our understanding of seasonal dynamics. Here we construct and analyze weekly snow cover time series for 200 glaciers across western North America from 2013 to 2023 using an automated image processing pipeline. Snow cover varied widely across the region: snow minima timing varied with latitude — from August from 62 to 64N to October from 48 to 50N—and accumulation area ratios ranged from near-zero to 0.92 (median of 0.52). A comparison of snowlines from observations and the PyGEM glacier mass balance model revealed seasonally evolving …

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 …

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 …

3d Solid Models, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

3D solid models for model vehicles, target panels, objects, and the desert terrain model in STL file format.

Ground Truth Images, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Laboratory and scenario ground truth images for the Model Desert Terrain Monochromatic DoT dataset.

Groundwater Flooding On Highways In The Nebraska Sandhills: Applying Remote Sensing, Precipitation And Groundwater Modeling To Determine Depth, Cause And Frequency, Aaron R. Mittelstet

Nebraska Department of Transportation: Research Reports

Climate change significantly impacts infrastructure sustainability, particularly through shifts in precipitation patterns, intensity, and duration. These precipitation dynamics may increase the occurrence of surface water and groundwater flooding. Groundwater in the Nebraska Sand Hills (NSH), including the thickest portions of the High Plains Aquifer, extends close to the land surface in interdunal areas, making the NSH vulnerable to groundwater flooding. Recent events involving heavy precipitation, snow melt, and rising groundwater levels have caused prolonged highway flooding, disrupting transportation networks. This study estimates groundwater flood inundation depth, duration and frequency in the highways of NSH using remote sensing techniques and groundwater …

Polarimetric Data: Scenario 01, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric data Scenario 01 collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.