Environmental Sciences Commons^™

A Comprehensive Review Of Carbon Sequestration And Its Assessment Techniques Using Remote Sensing And Geospatial Methods, Imen Ben Salem

All Works

Global warming has elevated carbon sequestration as a critical strategy for mitigating climate change, while enhancing sustainability in productivity. Agricultural land use systems contribute substantially to CO2 emissions due to crop residues, shifting cultivation practices, low-biomass crops, land degradation, and deforestation. The significant rise in CO2 emissions over the past thirty years is associated with burning fossil fuels, leading to substantial environmental changes, including global warming. Remote sensing (RS) and Geographic Information Systems (GIS) are advanced geospatial technologies that facilitate the rapid evaluation of terrestrial carbon stock over extensive regions. An integrated RS-GIS approach for carbon stock estimation and precision …

Don't Let Lead Lead On Environmental Justice: A Simulative Approach To Lead Remediation In The Big Data Era, Charles C. Knoble Ii

Theses, Dissertations and Culminating Projects

Environmental justice, as both a movement and a theoretical construct, continues to evolve in response to shifting societal, environmental, and technological conditions. This dissertation investigates the integration of big data, such as social media, remote sensing imagery, and internet search frequencies, into the identification, analysis, and remediation of environmental injustices. Framing environmental justice through the lenses of distributive and data justice, the project explores both the promises and pitfalls of using emergent data sources to enhance the spatial and temporal precision of environmental equity investigations. Through a combination of systematic literature review, spatial analysis, system dynamics simulation, and policy evaluation, …

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

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

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

A Machine Learning Model To Predict Wildfire Burn Severity For Pre-Fire Risk Assessments, Utah, Usa, Kipling B. Klimas, Larissa L. Yocom, Brendan P. Murphy, Scott R. David, Patrick Belmont, James A. Lutz, R. Justin Derose, Sara A. Wall

Wildland Resources Student Research

Background

High-severity burned areas can have lasting impacts on vegetation regeneration, carbon dynamics, hydrology, and erosion. While landscape models can predict erosion from burned areas using the differenced normalized burn ratio (dNBR), post-fire erosion modeling has predominantly focused on areas that have recently burned. Here, we developed and validated a predictive burn severity model that produces continuous dNBR predictions for recently unburned forest land in Utah.

Results

Vegetation productivity, elevation, and canopy fuels were the most important predictor variables in the model, highlighting the strong control of fuels and vegetation on burn severity in Utah. Final model out-of-bag R^{2 …}

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

School of Natural Resources: Faculty Publications

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

Devising Optimized Maize Nitrogen Stress Indices In Complex Field Conditions From Uav Hyperspectral Imagery, J. Li, Y. Ge, J. Gamon, L. A. Puntel Et Al.

School of Natural Resources: Faculty Publications

No abstract provided.

Spatiotemporal Economic Impact Analysis Of The Taal Volcano Eruption Using Electricity Consumption And Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano

SOSE Affiliate: Manila Observatory

Analyzing the spatiotemporal dimension of the economic impacts of disasters is critical for providing timely and proportionate support. However, traditional economic impact measures often lack spatiotemporal details. Hence, we estimated the daily electricity consumption (EC) loss using high-frequency EC data and analyzed temporal dimensions of the immediate impacts of Taal Volcano’s eruption on January 12, 2020. Subsequently, we computed the nighttime light (NTL) change using high-resolution NTL data to analyze the spatial distribution of these impacts. The temporal analysis revealed two EC loss peaks. The first peak coincided with the power outage and decreased energy demand, followed by a brief …

Bridging The Gap Between Plot-Level And Landscape-Scale Analysis For Wildfire Risk Assessment, Vanessa Leigh Niemczyk

Graduate Student Theses, Dissertations, & Professional Papers

Remote sensing technology has advanced greatly over the past couple of decades proving its ability to aid in wildfire risk assessment and improve our understanding of forest structure and fuel inventory across the landscape. While some aerial and satellite sensors perform better than others, they all have a common weakness, their reduced ability to capture understory fuels with high detail. Terrestrial laser scanning is an emerging solution due to its understory perspective. This research leverages the beneficial aspects of both terrestrial laser scanning and various aerial- or satellite-based remote sensing platforms (aerial laser scanning, digital aerial photogrammetry, and Sentinel-2) to …

Impacts Of Southern Pine Beetle (Dendroctonus Frontalis Zimmerman) On Loblolly Pine (Pinus Taeda L.) Canopy And Water Use In The Homochitto National Forest, Mississippi, Usa, Sasha Goodnow, Yun Yang, Hui Liu, Ashley Schulz

Endeavors: Mississippi State Undergraduate Research Journal

Abiotic and biotic forest disturbances can have many impacts to forest ecosystem services, including to forest water use. Studies on impacts to forest evapotranspiration have been conducted on the mountain pine beetle (Dendroctonus ponderosae) in western North America, but not on the southern pine beetle (Dendroctonus frontalis), which is a native pest of loblolly pine (Pinus taeda) and shortleaf pine (Pinus echinata), in the southeastern United States. Stressed pine trees produce pheromones that attract southern pine beetles and, with enough stressed trees, beetle populations can quickly grow to epidemic levels and attack healthy trees, which results in widespread tree mortality. …

Analysis Of Aerosols In The Asian Monsoon Anticyclone As Observed By The Atmospheric Chemistry Experiment, M. Lecours, R. Dodangodage, C. D. Boone, P. F. Bernath

Chemistry & Biochemistry Faculty Publications

During the Asian summer monsoon season, pollutants from the lower troposphere are transported through deep convection to the upper troposphere and lower stratosphere. Surface pollutants such as CO are transported upward and trapped in the anticyclone during this unique atmospheric phenomenon. Associated with the anticyclone is a layer of enhanced aerosols located near the tropopause often referred to as the Asian tropopause aerosol layer (ATAL). The chemical and physical properties of aerosols in the ATAL are not yet fully understood as direct observations of the aerosols are limited. The Atmospheric Chemistry Experiment (ACE) is a satellite mission that provides high-resolution …

Small Area Estimation Of Forest Biomass Via A Two-Stage Model For Continuous Zero-Inflated Data, Grayson W. White, Josh K. Yamamoto, Dinan H. Elsyad, Julian F. Schmitt, Niels H. Korsgaard, Jie Hu, George C. Gaines Iii, Tracey S. Frescino, Kelly S. Mcconville

Faculty Journal Articles

Nationwide Forest Inventories (NFIs) collect data on and monitor the trends of forests across the globe. Users of NFI data are increasingly interested in monitoring forest attributes such as biomass at fine geographic and temporal scales, resulting in a need for assessment and development of small area estimation techniques in forest inventory. We implement a small area estimator and parametric bootstrap estimator that account for zero-inflation in biomass data via a two-stage model-based approach and compare the performance to a Horvitz–Thompson estimator, a post-stratified estimator, and to the unit- and area-level empirical best linear unbiased prediction (EBLUP) estimators. We conduct …

Development Of An Quadcopter Unmanned Aerial Vehicle For Atmospheric Remote Sensing, Omar J. Addasi

Dissertations and Theses

This paper explores the development of a quadcopter unmanned aerial vehicle (UAV, a.k.a. drone) for atmospheric remote sensing of temperature, pressure, humidity, and PM2.5 particulate matter quantities. A 3-D printed drone body is designed and flight tuning is performed. An optimal length for an upward extending mast for the drone body is determined. Low cost, lightweight sensors are compared against higher precision sensors in both static and dynamic conditions. In addition, an initial investigation into the design of a pressure sensor based anemometer is performed.

Mapping Kirtland’S Warbler (Setophaga Kirtlandii) Stationary Non-Breeding Habitat: Characterizing Land Cover Within The South-Central Bahamas And Evaluating The Impacts Of Sea Level Rise, Cole Anthony Scrivner

Antioch University Dissertations & Theses

Understanding the spatial distribution of current and future habitat for the Kirtland’s Warbler (Setophaga kirtlandii) and other threatened species is critical for guiding conservation planning in The Bahamas. Our study mapped land cover and impacts of sea level rise across the south-central Bahamian islands, as an initial step to determine suitable areas for Kirtland’s Warblers in the region. We used a Random Forest (RF) classification of multispectral satellite image bands and indices from Landsat and Sentinel-2 image composites to predict land cover. The classification identified tropical dry forest communities (broadleaved, semi-evergreen trees and shrubs known as coppice) as …

An Integrated Ecosystem Monitoring Technology For Coal Mining Subsidence Areas And Its Application In The Shendong Mining Area, Cheng Yang, Liu Wei, Zhang Na, Li Guanjie, Liu Kai, Zhang Chengye, Li Jun

Coal Geology & Exploration

Background The ecosystem monitoring of arid and semi-arid coal mining subsidence areas acts as a significant prerequisite for regional ecosystem conservation and management, holding critical significance for accelerating green mine construction. Methods Based on the Chinese government's regulatory requirements for mine ecosystems, this study analyzed the difficulties in ecosystem monitoring in the coal mining subsidence area of the Shendong mining area (also referred to as the Shendong coal mining subsidence area). By detailing the integrated coal-rock-water-soil-air-vegetation-carbon monitoring technology system, this study determined the factors, principal methods, and technology roadmap for the integrated ecosystem monitoring in the Shendong coal mining subsidence …

Estimating Medium-Term Regional Monthly Economic Activity Reductions During The Covid-19 Pandemic Using Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano

SOSE Affiliate: Manila Observatory

Economic impact estimates of the initial lockdowns due to the COVID-19 pandemic showed a significant reduction in economic activities globally. However, the succeeding impacts and their spatiotemporal distribution within countries remain unknown. Studies showed that nighttime light data (NTL) has effectively revealed the spatiotemporal dimensions of the economic effects of COVID-19. Thus, this study used NTL data to determine the medium-term regional monthly economic impacts of the pandemic in the Philippines in terms of the Economic Activity Reduction (EAR) index. We generated a spatial error model, regressing pre-pandemic NTL on mean temperature, maximum rainfall, and built-up area. This model explained …

Landslide Inventory And Unstable Slope Monitoring Along Highways In Eastern Tennessee, Robert Mcsweeney

Electronic Theses and Dissertations

This research introduces an unstable slope management program (USMP) for Tennessee based on federal slope management standards, along with improved methods for landslide monitoring with unmanned aerial systems (UAS) lidar and photogrammetry. In mountainous regions, monitoring slope hazards is a critical function of transportation management. A mobile field assessment form created with Survey123 was used to collect 22 unstable slope ratings in eastern Tennessee. Location points were appended with photographs, notes, and site information. Landslide scores ranged from 325 (Fair) to 1005 (Poor). UAS monitoring of a slow-moving soil landslide along I-40 near Rockwood, TN, produced high-resolution lidar and photogrammetry …

Key Largo Mangrove Population Monitoring: A Remote Sensing Analysis And Classification Methodology Review, David Lackajs

Geography and the Environment: Graduate Student Capstones

Mangrove forests are some of the world's most bio-diverse habitats, providing essential services to the surrounding coasts. Removal of these habitats has a devastating impact on the ecosystems within them. The Florida Keys are some of the last areas in the United States with extensive mangrove populations. One specific area, John Pennekamp Coral Reef State Park in Key Largo, has been under state protection since 1959. For that reason, mangrove forest habitats there are less fragmented. This study uses remotely sensed imagery to quantify and analyze mangrove populations in this area using two methods: sub-pixel analysis and supervised classification. The …

Mapping Urban Tree Canopy Using Publicly Available Satellite Data, Rosemary Mcguinness

Theses and Dissertations

This project addresses the need for accessible, cost-effective tools for quantifying spatial and temporal changes in tree canopy cover in urban areas. Urban tree canopy provides a wide range of ecosystem services, including lowering air temperatures, reducing pollution, and mitigating stormwater runoff. Cities around the world have placed the expansion of their urban forests at the center of their sustainability goals. Consistent and timely data on urban tree canopy is essential for urban greening initiatives to succeed. Existing methods of accessing information about urban tree canopy are highly technical, costly, and labor-intensive, while the freely available source of tree canopy …

Applications Of Artificial Intelligence On Drought Impact Monitoring And Assessment, Beichen Zhang

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

Drought, a prevalent and consequential natural disaster, poses widespread, indirect challenges across environmental and societal dimensions. Despite considerable focus on monitoring meteorological and hydrological drought and studying their characteristics, there is a gap in assessing its multifaceted impacts, especially on societal sectors. The dissertation comprises three research essays utilizing artificial intelligence to quantitatively study multi-dimensional drought impacts. The first essay leveraged deep learning and natural language processing to predict multi-dimensional drought impacts from textual datasets, including social media, news media, and citizen scientist reports. The findings demonstrate superior performance over traditional methods and unveil the spatial and temporal heterogeneity of …

Remotely Sensed Early Warning Of Algal Blooms In An Eastern Nebraska Reservoir: A Comparison Of Temporal And Spatial Indicators, Mercy Kipenda

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

Cyanobacterial harmful algal blooms (CyanoHABs) detrimentally affect human, animal, and ecosystem health. Remotely sensed early warning systems for cyanoHABs in inland lakes could contribute to more proactive water quality monitoring and help mitigate negative impacts. Advances in freely available remote sensing imagery, with finer spatial, temporal, and spectral resolutions, present new opportunities for the development and comparative analysis of methods to detect sudden deterioration in lake water quality. In this thesis, I compared and tested for temporal and spatial early warning signals of cyanoHABs in field-based and remotely sensed datasets from 2019 to 2023 in Pawnee Lake in southeast Nebraska, …

A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, Kyle Knipper, Martha Anderson, Nicholas Bambach, Forrest Melton, Zac Ellis, Yun Yang, John Volk, Andrew J. Mcelrone, William Kustas, Matthew Roby, Will Carrara, Sebastian Castro, Ayse Kilic, Joshua B. Fisher, Anderson Ruhoff, Gabriel B. Senay, Charles Morton, Sebastian Saa, Richard G. Allen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in …

From Pixels To Plants: Remote Sensing Of California Invasive Plants, Kenneth Rangel

Master's Projects and Capstones

Invasive plants cause significant impacts to ecosystems, the economy, and human health. California has experienced significant plant invasions and is well suited to future invasion because of its Mediterranean climate and human disturbance. Eradication or control of invasive plant species requires a detailed understanding of their spatial distribution, which typically involves on the ground surveys that can be expensive or inconsistent. Remote sensing offers a potential alternative or supplement to in-person invasive plant mapping. This study performed a comparative analysis of 41 remote sensing studies that mapped the distribution of California invasive plants. I found that while high spectral resolution …

Agroecology And Soil Stewardship: Values And Techniques Of Smallholder Farmers In Bernalillo County, Stephanie Olivas

Geography ETDs

Agrarian movements around the world use agroecology to build sovereignty and steward dynamic ecosystems. Research has shown that agroecological farmers steward more resilient crops, more resilient soil biomes, and greater biodiversity than conventional agriculture. GIS and remote sensing offer many tools to detect the impacts of these farmers on the environment, but it is less clear how such technologies fit into agroecological goals. This study asks: what values, experiences and knowledge do smallholder producers in Bernalillo County embody in their soil stewardship practices? Also, what experience or knowledge do smallholder producers in Bernalillo County have about remote sensing, and would …

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos

Institute for ECHO Articles and Research

The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …

Relocating Lubra Village And Visualizing Himalayan Flood Damages With Remote Sensing, Ronan Wallace, Yungdrung Tsewang Gurung, Ryan Kastner

Journal of Critical Global Issues

As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community-driven solutions. In Himalayan Mustang, Nepal, indigenous Lubra Village faces threats of increasing flash flooding. After every flood, residual muddy sediment hardens across the riverbed like concrete, 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 Lubra community witnessed floods swallowing several agricultural fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as …

A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, K. Knipper, M. Anderson, N. Bambach, F. Melton, Z. Ellis, Y. Yang, J. Volk, A. J. Mcelrone, W. Kustas, M. Roby, W. Carrara, S. Castro, Ayse Kilic

School of Natural Resources: Faculty Publications

No abstract provided.

Planet’S Biomass Proxy For Monitoring Aboveground Agricultural Biomass And Estimating Crop Yield, T. E. Franz

School of Natural Resources: Faculty Publications

No abstract provided.

Road Extraction On Remote Sensing Imagery: Historical Mapping Of The Brazilian Amazon, Jonas Paiva Botelho Jr

Graduate Theses/Dissertations

This work proposes an artificial intelligence model based on U-Net architecture to map road networks in the Brazilian Amazon. Over the years, the Amazon region has been heavily exploited, leading to increased deforestation rates, contributing to CO2 emissions, amplifying global warming, and causing a disturbance in local fauna and flora. The expansion into the forest by illegal miners, loggers, and land grabbers can be tracked down by the construction of roads, which we can refer to as the arteries of deforestation. Previous works on the matter proposed algorithms that use high-resolution imagery to map roads precisely. However, this work approach …

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, & Marine Sciences Faculty Publications

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 …

Shading The Future: A Gis-Based Approach To Optimal Tree Planting For Urban Heat Mitigation At Northern Michigan University, Mary Kelly

Conspectus Borealis

Increasing tree canopy cover is a powerful tool for mitigating the effects of urban heat on the natural and built environment, specifically the effects of ecosystem stress and air quality. Heat can also place stress on human health. Northern Michigan University (NMU) is proposing to build a new tree nursery on campus, which would help alleviate urban heat in Marquette. Finding suitable areas at NMU to plant new trees from this nursery is an essential component of this effort. A goal of my project was to provide this helpful information. Using GIS and remote sensing techniques in ArcGIS Pro and …