Physical Sciences and Mathematics Commons^™

A Uav Based Cmos Ku-Band Metasurface Fmcw Radar System For Low-Altitude Snowpack Sensing, Adrian Tang, Nacer Chahat, Yangyho Kim, Arhison Bharathan, Gabriel Virbila, Hans-Peter Marshall, Thomas Van Der Weide, Gaurangi Gupta, Raunika Anand, Goutam Chattopadhyay, Mau-Chung Frank Chang

Geosciences Faculty Publications and Presentations

This article presents development of a UAV based frequency modulated continuous wave (FMCW) radar system for remotely sensing the water contained within snowpacks. To make the radar system compatible with the payload requirements of small UAV platforms, the radar electronics are implemented with CMOS technology, and the antenna is implemented as an extremely compact and lightweight metasurface (MTS) antenna. This article will discuss how the high absorption losses of snowpacks lead to dynamic range requirements much stricter than FMCW radars used for automotive and other sensing applications, and how these requirements are met through antenna isolation, leakage calibration and exploitation …

Integrating Remote Sensing With Ground-Based Observations To Quantify The Effects Of An Extreme Freeze Event On Black Mangroves (Avicennia Germinans) At The Landscape Scale, Melinda Martinez, Michael J. Osland, James B. Grace, Nicholas M. Enwright, Camille L. Stagg, Camille L. Stagg, Simen Kaalstad, Gordon H. Anderson, Elena A. Flores, Alejandro Fierro-Cabo

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Climate change is altering the frequency and intensity of extreme weather events. Quantifying ecosystem responses to extreme events at the landscape scale is critical for understanding and responding to climate-driven change but is constrained by limited data availability. Here, we integrated remote sensing with ground-based observations to quantify landscape-scale vegetation damage from an extreme climatic event. We used ground- and satellite-based black mangrove (Avicennia germinans) leaf damage data from the northern Gulf of Mexico (USA and Mexico) to examine the effects of an extreme freeze in a region where black mangroves are expanding their range. The February 2021 …

Enhancing Urban Water Quality Through Biological-Chemical Treatment: Aquatic Macroinvertebrate Community And Temporal Chlorophyll-A Response, Matthew Chaffee

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

With a growing human population, urbanization is impeding a plethora of natural waterways. Of these, urban ponds play a vital role in nutrient sequestration, flood prevention, and habitat sanctuaries. However, nutrient loading can reduce habitat effectiveness and promote harmful algae blooms. To reduce internal nutrient loads, a biological-chemical treatment strategy consisting of floating treatment wetlands (FTWs) and lanthanum were applied to two urban retention ponds, Densmore and Wilderness Ridge Ponds. To measure effectiveness, chlorophyll-a samples were collected and correlated with Sentinel-2. A novel band algorithm termed 3BR1 produced a strong correlation (R² = 0.72) to physical chlorophyll-a …

Causes And Effects Of Shisper Glacial Lake Outburst Flood Event In Karakoram In 2022, Sandeep Kumar Mondal, Vatsal D. Patel, Rishikesh Bharti, Ramesh P. Singh

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Karakoram Himalayas are vulnerable to glacial lake outburst floods (GLOFs), which cause catastrophic floods in the surrounding areas. The increasing natural and anthropogenic activities, especially in the Indo-Gangetic Plains at the southern flank of the towering Himalayas, could be the cause of climate change affecting the frequency of the natural hazards in the Himalayas. In the present study, a detailed analysis of the Shisper Lake breach of 7 May 2022 is carried out using satellite remote sensing. A decreasing trend in the glacial mass balance is observed between 2017 and 2021; in this period, frequent GLOF episodes occurred. A pronounced …

Snowpack Relative Permittivity And Density Derived From Near-Coincident Lidar And Ground-Penetrating Radar, Randall Bonnell, Daniel Mcgrath, Andrew R. Hedrick, Ernesto Trujillo, Tate G. Meehan, Keith Williams, Hans-Peter Marshall, Graham Sexstone, John Fulton, Michael J. Ronayne, Steven R. Fassnacht, Ryan Webb, Katherine E. Hale

Geosciences Faculty Publications and Presentations

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. …

A Neural-Network-Based Landscape Search Engine: Lse Wisconsin, Matthew Haffner, Matthew Dewitte, Papia F. Rozario, Gustavo A. Ovando-Montejo

Environment and Society Faculty Publications

The task of image retrieval is common in the world of data science and deep learning, but it has received less attention in the field of remote sensing. The authors seek to fill this gap in research through the presentation of a web-based landscape search engine for the US state of Wisconsin. The application allows users to select a location on the map and to find similar locations based on terrain and vegetation characteristics. It utilizes three neural network models—VGG16, ResNet-50, and NasNet—on digital elevation model data, and uses the NDVI mean and standard deviation for comparing vegetation data. The …

Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which …

Coupling Dendroecological And Remote Sensing Techniques To Assess The Biophysical Traits Of Juniperus Virginiana And Pinus Ponderosa Within The Semi-Arid Grasslands Of The Nebraska Sandhills, R. Allen, Anastasios Mazis, Brian Wardlow, P. Cherubini, J. Hiller, David A. Wedin, Tala Awada

School of Natural Resources: Faculty Publications

Woody species encroachment is occurring within the semi-arid grasslands of the Nebraska Sandhills U.S., primarily driven by native Juniperus virginiana and Pinus ponderosa, altering ecosystems and the services they provide. Effective, low cost, and cross-scale monitoring of woody species growth and performance is necessary for integrated grassland and forest management in the face of climate variability and change. In this study, we sought to establish a relationship between remote sensing-derived vegetation indices (VIs), tree dendrochronological (raw and standardized tree ring width) measurements, and the abiotic environment [(precipitation, temperature, Palmer Drought Severity Index (PDSI), and soil water content (0–300 cm …

A Satellite-Based Monitoring System For Quantifying Surface Water And Mesic Vegetation Dynamics In A Semi-Arid Region, N. E. Kolarik, A. Roopsind, A. Pickens, J. S. Brandt

Human-Environment Systems Research Center Faculty Publications and Presentations

Semi-arid and arid systems cover one third of the earth’s land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water …

Increased Floodplain Inundation In The Amazon Since 1980, Ayan Fleischmann, Fabrice Papa, Stephen K. Hamilton, Alice Fassoni-Andrade, Sly Wongchuig, Jhan Carlo Espinoza, Rodrigo Paiva, John Melack, Etienne Fluet-Chouinard, Rafael M. Almeida

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Extensive floodplains throughout the Amazon basin support important ecosystem services and influence global water and carbon cycles. A recent change in the hydroclimatic regime of the region, with increased rainfall in the northern portions of the basin, has produced record-breaking high water levels on the Amazon River mainstem. Yet, the implications for the magnitude and duration of floodplain inundation across the basin remain unknown. Here we leverage state-of-the-art hydrological models, supported by in situ and remote sensing observations, to show that the maximum annual inundation extent along the central Amazon increased by 26% since 1980. We further reveal increased flood …

Dynamic Mass Loss From Greenland's Marine-Terminating Peripheral Glaciers (1985–2018), Katherine E. Bollen, Ellyn M. Enderlin, Rebecca Muhlheim

Geosciences Faculty Publications and Presentations

Global glacier mass balance decreased rapidly over the last two decades, exceeding mass loss from the Greenland and Antarctic Ice Sheets. In Greenland, peripheral glaciers and ice caps (GICs) cover only ~5% of Greenland's area but contributed ~20% of the island's ice mass loss between 2000 and 2018. Although Greenland GIC mass loss due to surface meltwater runoff has been estimated using atmospheric models, mass lost to changes in ice discharge into oceans (i.e., dynamic mass loss) remains unquantified. We use the flux gate method to estimate discharge from Greenland's 585 marine-terminating peripheral glaciers between 1985 and 2018, and compute …

Revisiting The Carbon–Biodiversity Connection, John Gamon

School of Natural Resources: Faculty Publications

This article is a Commentary on Schuldt et al., https://doi.org/10.1111/gcb.16697

The link between biodiversity and ecosystem function has long been a subject of intense interest and debate among biologists, going back to the time of Charles Darwin, whose ideas on species interactions presaged subsequent discussions of biodiversity and ecosystem function (Peterson et al., 1998). Since then, many considerations of community diversity have centered on the importance of species or functional diversity for maintaining system resilience in the face of disturbance, analogous to the way that interwoven threads maintain the function and integrity of fabric. While our language, concepts, and methods …

Multispectral Image Analysis Using Convolution Neural Networks, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Machine learning (ML) techniques are used often to classify pixels in multispectral images. Recently, there is growing interest in using Convolution Neural Networks (CNNs) for classifying multispectral images. CNNs are preferred because of high performance, advances in hardware such as graphical processing units (GPUs), and availability of several CNN architectures. In CNN, units in the first hidden layer view only a small image window and learn low level features. Deeper layers learn more expressive features by combining low level features. In this paper, we propose a novel approach to classify pixels in a multispectral image using deep convolution neural networks …

The Impact Of Climate Change On Environmental Sustainability And Human Mortality, Xingzhi Mara Chen, Andrew Sharma, Hua Liu

Political Science & Geography Faculty Publications

Climate dictates the critical aspects of human environmental conditions. The frequency and intensity of extreme weather conditions due to human-induced climate change have alarmingly increased. Consequently, climate change directly affects environmental sustainability and human mortality in the short term and creates prolonged and complicated long-term indirect grave risks. This paper examines three-level environmental impact risks associated with climate change on human mortality. It proposes a conceptual framework for developing an empirical event-based human mortality database related to climate change and communication strategies to enhance global environmental adaptation, resilience, and sustainability.

Biophysical Interactions Control The Progression Of Harmful Algal Blooms In Chesapeake Bay: A Novel Lagrangian Particle Tracking Model With Mixotrophic Growth And Vertical Migration, Jilian Xiong, Jian Shen, Qubin Qin, Michelle C. Tomlinson, Yinglong J. Zhang, Xun Cai, Fei Yi, Linlin Cui, Margaret R. Mulholland

OES Faculty Publications

Climate change and nutrient pollution contribute to the expanding global footprint of harmful algal blooms. To better predict their spatial distributions and disentangle biophysical controls, a novel Lagrangian particle tracking and biological (LPT-Bio) model was developed with a high-resolution numerical model and remote sensing. The LPT-Bio model integrates the advantages of Lagrangian and Eulerian approaches by explicitly simulating algal bloom dynamics, algal biomass change, and diel vertical migrations along predicted trajectories. The model successfully captured the intensity and extent of the 2020 Margalefidinium polykrikoides bloom in the lower Chesapeake Bay and resolved fine-scale structures of bloom patchiness, demonstrating a reliable …

The Vulnerability And Resilience Of Seagrass Ecosystems To Marine Heatwaves In New Zealand: A Remote Sensing Analysis Of Seascape Metrics Using Planetscope Imagery, Ken Joseph E. Clemente, Mads S. Thomsen, Richard C. Zimmerman

OES Faculty Publications

Seagrasses are foundation species that provide ecosystem functions and services, including increased biodiversity, sediment retention, carbon sequestration, and fish nursery habitat. However, anthropogenic stressors that reduce water quality, impose large-scale climate changes, and amplify weather patterns, such as marine heatwaves, are altering seagrass meadow configurations. Quantifying large-scale trends in seagrass distributions will help evaluate the impacts of climate drivers on their functions and services. Here, we quantified spatiotemporal dynamics in abundances and configurations of intertidal and shallow subtidal seagrass (Zostera muelleri) meadows in 20 New Zealand (NZ) estuaries that span a 5-year period (mid/late 2016–early 2022) just before, …

A Workshop On Using Nasa Airs Data To Monitor Drought For The U.S. Drought Monitor, Alireza Farahmand,, Sharon Ray, Heidar Thrastarson, Stephen Licata, Stephanie Granger, Brian Fuchs

Drought Mitigation Center: Faculty Publications

Recent studies indicate that drought indicators based on near-surface air relative humidity (RH), air temperature (T), and air vapor pressure deficit (VPD), derived from the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA’s Aqua satellite can detect the onset of drought earlier than other drought indicators, specifically standardized precipitation index (SPI), which is widely used for drought onset detection. A recent study showed that standardized relative humidity index (SRHI) can detect drought signals earlier than SPI (Farahmand et al. 2015). Relative humidity is a climate variable defined as the ratio of air vapor pressure to saturated vapor pressure. Precipitation and relative …

Utilizing Remote Sensing Technology To Relocate Lubra Village And Visualize Flood Damages, Ronan Wallace

Mathematics, Statistics, and Computer Science Honors Projects

As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community and habitat-conscious solutions. In Himalayan Mustang, Nepal, indigenous Lubra village faces threats of increasing flash flooding. After every flood, residual concrete-like sediment hardens across the riverbed, causing the riverbed elevation to rise. As elevation increases, sediment encroaches on Lubra’s agricultural fields and homes, magnifying flood vulnerability. In the last monsoon season alone, the village witnessed floods swallowing several fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as eight centuries …

Transformers In Remote Sensing: A Survey, Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long-range dependencies. Inspired by recent advances in computer vision, remote sensing community has also witnessed an increased exploration of vision transformers for a diverse set of tasks. Although a number of surveys have focused on transformers in computer vision in general, to the best of our knowledge we are …

Coupling Dendrochronology And Remote Sensing Techniques To Assess The Biophysical Traits Of Juniperus Virginiana And Pinus Ponderosa Within Grassland Communities In The Semi-Arid Grasslands Of The Nebraska Sandhills, Reece Allen

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

Woody species encroachment is occurring within the sandhills region in Nebraska, primarily driven by Juniperus virginiana and Pinus ponderosa, altering ecosystems and the services they provide. Effective, low cost, and cross-scale monitoring of woody species growth and performance is necessary for integrated grassland and forest management in the face of climate variability and change. In this study, we sought to establish a relationship between remote sensing-derived vegetation indices (VIs) and dendrochronological (raw and standardized tree ring width) measurements to assess the performance of encroaching woody J. virginiana and P. ponderosa located within the Nebraska National Forest in the sandhills. …

Titaniferous-Vanadiferous, Magnetite-Ilmenite Mineralization In A Mafic Suite Within The Chhotanagpur Gneissic Complex, Bihar, India, Ashmeer Mohammad, Anup K. Prasad, Kehe-U Wetsah, Mohammad Azad, Vivek Aryan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Titanium or vanadium metals or their alloys are important industrial metals/alloys. Because these resources are in short supply, the investigation of potential titaniferous-vanadiferous deposits needs special attention to bridge the supply-demand gap. The study integrates geological, geochemical, remote sensing, and geophysical data for assessing the potentiality of titaniferous-vanadiferous, magnetite-ilmenite mineralization in and around the Sudamakund and Paharpur areas, Gaya and Jehanabad districts, Bihar, India, and delineation of specific targets for detailed exploration. Field visits for large scale mapping on (1:12,500 scale) were used to conduct a reconnaissance survey for magnetite-ilmenite mineralization in parts of toposheet number 72G/04 in the Gaya …

Response Of Surface And Atmospheric Parameters Associated With The Iran M 7.3 Earthquake, Feng Jing, Ramesh P. Singh

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Multiparameter observed from satellite, including microwave brightness temperature, skin temperature, air temperature, and carbon monoxide, have been analyzed to identify the anomalous signals associated with the M 7.3 Iran earthquake of November 12, 2017. Besides removing the multiyear variability of parameters as background, the effect of surface and atmosphere of a dust storm event in Middle East region during October 29–November 1 is considered to distinguish the possible anomalies associated with the earthquake. The characteristic behaviors of surface and atmospheric parameters clearly show the signals associated with the M 7.3 earthquake and the dust storm event. The multiple parameters at …

Climatology Of Rainfall Distribution And Asymmetries Of Tropical Cyclones: A Global Perspective, Oscar Guzman Rey

FIU Electronic Theses and Dissertations

Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling states is an important aspect of the TC forecast that directly affects the level of response from emergency managers in coastal areas. This research analyses the spatial distribution of the rainfall magnitude in tropical cyclones (TCs) at different stages over global oceans. The research’s central hypothesis is that TC rainfall exhibits distinct features in the long-term satellite dataset due to the evolution of the spatial distribution, radial variation, and asymmetries at the stages before, during, and after landfall. The resulting patterns are analyzed through a statistical approach that takes …

Osm-Gan: Using Generative Adversarial Networks For Detecting Change In High-Resolution Spatial Images, Lasith Niroshan, James Carswell

Articles

Detecting changes to built environment objects such as buildings/roads/etc. in aerial/satellite (spatial) imagery is necessary to keep online maps and various value-added LBS applications up-to-date. However, recognising such changes automatically is not a trivial task, and there are many different approaches to this problem in the literature. This paper proposes an automated end-to-end workflow to address this problem by combining OpenStreetMap (OSM) vectors of building footprints with a machine learning Generative Adversarial Network (GAN) model - where two neural networks compete to become more accurate at predicting changes to building objects in spatial imagery. Notably, our proposed OSM-GAN architecture achieved …

Using Lidar To Estimate Carbon Sequestration Of Evergreen Trees At Eastern Washington University (Ewu) Campus, Cheney, Washington, Kristy A. Snyder

2022 Symposium

EWU contains a variety of deciduous and evergreen trees across its campus, providing several benefits. However, no comprehensive record exists of the total number, location, species, or ages of these trees. This knowledge can inform facilities of proper care for individual trees and can be used to estimate carbon sequestration on campus. Traditional on-the-ground methods for assessing trees require tree cores or clinometers, making trees susceptible to pests or disease and leading to inaccurate results. Remote sensing using lidar data is a noninvasive, more precise method to measure tree height and subsequently assess tree age. This poster explores using point …

Multi-Criteria Evaluation Model For Classifying Marginal Cropland In Nebraska Using Historical Crop Yield And Biophysical Characteristics, Andrew Laws

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

Marginal cropland is suboptimal due to historically low and variable productivity and limiting biophysical characteristics. To support future agricultural management and policy decisions in Nebraska, U.S.A, it is important to understand where cropland is marginal for its two most economically important crops: corn (Zea mays) and soybean (Glycine max). As corn and soybean are frequently planted in a crop rotation, it is important to consider if there is a relationship with cropland marginality. Based on the current literature, there exists a need for a flexible yet robust methodology for identifying marginal land at different scales, which …

Post-Analysis Of Osm-Gan Spatial Change Detection, Lasith Niroshan Kottawa Hewa Manage, James Carswell

Conference Papers

Keeping crowdsourced maps up-to-date is important for a wide range of location-based applications (route planning, urban planning, navigation, tourism, etc.).We propose a novelmap updatingmechanism that combines the latest freely available remote sensing data with the current state of online vector map data to train a Deep Learning (DL) neural network. It uses a GenerativeAdversarial Network (GAN) to perform image-to-image translation, followed by segmentation and raster-vector comparison processes to identify changes to map features (e.g. buildings, roads, etc.) when compared to existing map data. This paper evaluates various GAN models trained with sixteen different datasets designed for use by our change …

Learning Enriched Features For Fast Image Restoration And Enhancement, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao

Computer Vision Faculty Publications

Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computational photography, surveillance, autonomous vehicles, and remote sensing. Significant advances in image restoration have been made in recent years, dominated by convolutional neural networks (CNNs). The widely-used CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatial details are preserved but the contextual information cannot be precisely encoded. In the latter case, generated outputs are semantically reliable but spatially less accurate. This paper presents a new architecture with a holistic goal …

Surface Urban Heat Island In South Korea’S New Towns With Different Urban Planning, Kyungil Lee, Yoonji Kim, Hyun Chan Sung, Seung Hee Kim, Seong Woo Jeon

Institute for ECHO Articles and Research

A new town is strategically built within a short period compared to naturally developed cities. It is considered an appropriate study area for analyzing the urban climate problems such as surface urban heat islands (SUHIs) that is differently generated according to urban planning and development. In this study, we suggest comprehensive method for determining and comparing changes in surface UHI distribution during 1989–2048 in two new towns with different urban planning. First, a substantial increase in built-up areas was observed from 1989 (< 5%) to 2018 (> 40%) in both new towns. However, SUHI phenomenon-increasing patterns were different of about 12.25% depending on urban …

Using Remote Sensing Technologies In Relocating Lubrak Village And Visualizing Flood Damages, Ronan Wallace

Independent Study Project (ISP) Collection

As weather patterns change across the world, there are communities impacted by climate change that are left unnoticed. In the Himalayan mountain range, communities have suffered, experiencing an increase in flash flooding and droughts. For Lubrak Village in Lower Mustang, the community faces the threats of flash flooding. Over the last ten years, the amount of flash flooding has increased, occurring more than once each monsoon season. After every flood, concrete-like sediment is left behind, hardening across the riverbed and increasing its elevation. As the riverbed elevation increases, this sediment encroaches on Lu-brak Village’s agricultural fields and ancient mud buildings, …