Remote Sensing Commons^™

Use And Improvement Of Remote Sensing And Geospatial Technologies In Support Of Crop Area And Yield Estimations In The West African Sahel, Kaboro Samasse

Electronic Theses and Dissertations

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability …

Quantifying The Impacts Of Land Use, Management And Climate Change On Water Resources In Missouri River Basin, Arun Bawa

Electronic Theses and Dissertations

A location-specific evaluation of hydrological landscape responses concerning past and projected climate and land use land cover (LULC) changes can provide a powerful intellectual basis for developing efficient and profitable agroecosystems, and overcoming uncertain and detrimental consequences of LULC and climate shifts. This dissertation assessed the impacts of land use, management, and climate change on water resources in the Missouri River Basin (MRB) through four specific studies that included: (i) to study the responses of leached nutrient concentrations and soil health to winter rye cover crop (CC) under no-till corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation, (ii) to …

Assessing And Forecasting Chlorophyll Abundances In Minnesota Lake Using Remote Sensing And Statistical Approaches, Ben Von Korff

All Graduate Theses, Dissertations, and Other Capstone Projects

Harmful algae blooms (HABs) can negatively impact water quality, lake aesthetics, and can harm human and animal health. However, monitoring for HABs is rare in Minnesota. Detecting blooms which can vary spatially and may only be present briefly is challenging, so expanding monitoring in Minnesota would require the use of new and cost efficient technologies. Unmanned aerial vehicles (UAVs) were used for bloom mapping using RGB and near-infrared imagery. Real time monitoring was conducted in Bass Lake, in Faribault County, MN using trail cameras. Time series forecasting was conducted with high frequency chlorophyll-a data from a water quality sonde. Normalized …

A Karst Feature Prediction Model For Prince Of Wales Island, Alaska Based On High Resolution Lidar Imagery, Alexander Lyles

Master's Theses or Doctor of Nursing Practice

Investigation into surface karst formation is significant to hazard prediction, hydrogeologic drainage, and land management. Southeast Alaska contains over 600,000 acres of mapped carbonate bedrock, and some of the fastest recorded karst dissolution in the world. The objectives of this study are to develop and compare multiple semi-automated models to map and delineate karst features from bare-earth LiDAR imagery using ArcGIS Desktop 10.7, and to apply a preliminary geostatistical analysis of sinkhole morphometric parameters to highlight potential spatial patterns of karst evolution on Prince of Wales Island, Alaska. A semi-automated approach of mapping karst features provides a dataset that minimizes …

Investigating The Spatial Behavior And Habitat Use Of The Matschie’S Tree-Kangaroo (Dendrolagus Matschiei) Using Gps Collars And Unmanned Aircraft Systems (Uas), Jonathan B. Byers

Graduate Student Theses, Dissertations, & Professional Papers

Understanding the movement patterns and habitat needs of the endangered Matschie’s tree-kangaroo (Dendrolagus matschiei) is important for their conservation and management. Endemic to the montane cloud forests of the Huon Peninsula in northeastern Papua New Guinea, these elusive arboreal marsupials are tremendously challenging to study using traditional observational methods.

This study is an assessment of novel techniques to overcome the significant challenges to in-situ data collection in remote and rugged tropical cloud forests. Animal locations are remotely tracked using purpose built altitude and motion logging GPS collars and habitat structure data is measured using photogrammetry from small Unmanned Aircraft …

The Kiwanis Site: A Multi-Method Geophysical Approach To Investigating Mound Features, Luke Burds

All Graduate Theses, Dissertations, and Other Capstone Projects

Subtle mound-like landforms can be genetically ambiguous features within a landscape. A variety of geomorphological and anthropological processes can result in these equifinal forms being difficult to interpret. Being able to reliably and noninvasively differentiate them is important for legal as well as cultural and spiritual reasons. A suite of non-invasive geophysical methods were thus used on mounds at the Kiwanis site in western Wisconsin in order to determine if culturally diagnostic indicators could be recorded in geophysical data. Genesis of these mounds is ambiguous given the presence of aeolian landforms in immediate proximity. As a control, the same geophysical …

An Application Of The Sleuth Model: Future Urbanization Of Davidson County, Tennessee, Caitlyn J. Linehan

Theses and Dissertations

As the urban population is expected to grow in the coming decades, there is pressure on urban areas to expand to accommodate this growth. Often times, these expansions are not sustainable, they consume valuable lands and cause significant environmental, social, and economic burdens, a theory which is known as urban sprawl. Therefore it is often imperative to model areas more likely to urbanize in order for cities to plan for their expansion in the most sustainable way possible. Nashville, Tennessee located within Davidson County is an area that has been experiencing a significant period of urban growth. The objective of …

Effects Of Training Set Size On Supervised Machine-Learning Land-Cover Classification Of Large-Area High-Resolution Remotely Sensed Data, Christopher A. Ramezan, Timothy A. Warner, Aaron E. Maxwell, Bradley S. Price

Faculty & Staff Scholarship

The size of the training data set is a major determinant of classification accuracy. Neverthe- less, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algo- rithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic …

Effects Of Bark Beetle Outbreaks On Forest Landscape Pattern In The Southern Rocky Mountains, U.S.A., Kyle C. Rodman, Robert A. Andrus, Cori L. Butkiewicz, Teresa B. Chapman, Nathan S. Gill, Brian J. Harvey, Dominik Kulakowski, Niko J. Tutland, Thomas T. Veblen, Sarah J. Hart

Geography

Since the late 1990s, extensive outbreaks of native bark beetles (Curculionidae: Scolytinae) have affected coniferous forests throughout Europe and North America, driving changes in carbon storage, wildlife habitat, nutrient cycling, and water resource provisioning. Remote sensing is a cru-cial tool for quantifying the effects of these disturbances across broad landscapes. In particular, Landsat time series (LTS) are increasingly used to characterize outbreak dynamics, including the presence and severity of bark beetle-caused tree mortality, though broad-scale LTS-based maps are rarely informed by detailed field validation. Here we used spatial and temporal information from LTS products, in combination with extensive field data …

Operationalizing Resilience: Co-Creating A Framework To Monitor Hard, Natural, And Nature-Based Shoreline Features In New York State, Katinka Wijsman, D. S. Novem Auyeung, Pippa Brashear, Brett F. Branco, Joseph H. Graziano, Kathryn Graziano, Peter M. Groffman, Helen Cheng, Dylan Corbett

Advanced Science Research Center

There is growing interest in the application of nature-based solutions to adapt to climate change and promote resilience, yet barriers exist to their implementation. These include a perceived lack of evidence of their functioning in comparison to conventional solutions and an inability for existing design, policy, and assessment processes to capture the multiple benefits of these solutions. Positing this as a challenge of operationalizing and measuring resilience, we argue that the concept of resilience needs to be given concrete meaning in applied management contexts. Starting with shoreline vulnerability as a policy problem and natural and nature-based shoreline features as a …

Sensing Population Distribution From Satellite Imagery Via Deep Learning:Model Selection, Neighboring Effects, And Systematic Biases, Xiao Huang, Di Zhu, Fan Zhang, Tao Liu, Xiao Li, Lei Zou

Geosciences Faculty Publications and Presentations

The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted. This article marks the first attempt to cross-compare performances of popular state-of-the-art deep learning models in estimating population distribution from remote sensing images, investigate the contribution of neighboring effect, and explore the potential systematic population estimation biases. We conduct an end-to-end training of four popular deep learning architectures, i.e., VGG, ResNet, Xception, and DenseNet, by establishing a mapping between Sentinel-2 image patches and …

Spatiotemporal Observations Of Water Stress In Kansas Winter Wheat And Corn From Remotely Sensed Evapotranspiration And Ndwi, Lindi Diane Oyler

Masters Theses

"Optimizing water use is a growing concern, especially in agricultural communities where water use is high. An important challenge in agricultural water optimization is knowing when and where crop water stress is occurring, particularly on large scales where in-situ measurements are no longer practical to obtain. In an effort to combat this challenge, this study utilizes remotely sensed evapotranspiration (ET) and Normalized Difference Water Index (NDWI) to evaluate the responses of integrated satellite datasets to water-stressed conditions over fields of irrigated corn, irrigated winter wheat, and rainfed winter wheat from 2007 to 2017 in southwestern Kansas. Using two different ET …

Evaluating St. Catherines Island's Shoreline, Vegetation Line, And The Locations Of Loggerhead Sea Turtle Nests, Sydney O. Davis

College of Graduate Studies: Theses & Dissertations

St. Catherines Island is a highly dynamic barrier island on the Georgia coast that is also federally listed critical nesting habitat for loggerhead sea turtles. Understanding how St. Catherines’ shoreline and vegetation is changing over time is geographically important as a potential template for other barrier islands. Measuring sea turtle nest locations will provide insight into their natural patterns and how they adjust those locations on a changing barrier island. Analyzing Moving Boundaries Using R (AMBUR) is implemented in this research to assess the movement of the vegetation and shorelines from 2005-2017 using the End Point Rate (EPR) and Linear …

Gis And Remote Sensing Groundwater Potentiality Investigation Of Gulu District, Uganda Using Synthetic Aperture Radar And Magnetic Geophysics, Rachel Ann Jones

Doctoral Dissertations

“Developing countries have few resources for ground-based hydrological investigations to determine optimal placement of boreholes for community water access. Remote sensing data are available at a variety of resolutions and sense different parameters, and are useful inputs for hydrologic models, but these data are rarely obtainable in developing countries with the parameters or resolutions necessary for hydrologic applications. This research seeks to use and improve existing remote sensing and GIS techniques to identify areas of optimal water supply in locations with limited geologic or hydrologic information, such as Gulu District, Uganda. Fusing different remotely sensed data sets can produce higher …

Assessing Synthetic Aperture Radar (Sar)-Derived Temporal Patterns And Digital Terrain Data For Palustrine Wetland Mapping, Jaimee L. Pyron

Graduate Theses, Dissertations, and Problem Reports (ETD)

Palustrine wetland systems are important ecosystems and provide numerous ecosystems services to support society. Unfortunately, they remain under constant threat of devastation due to land use practices and global climate change, which underscores the need to identify, map, and monitor these landscape features. This study explores harmonic coefficients and seasonal median values derived from Sentinel-1 synthetic aperture radar (SAR) data, as well as digital elevation model (DEM)-derived terrain variables, to predict palustrine wetland locations in the Vermont counties of Bennington, Chittenden, and Essex. Support vector machine (SVM) and random forest (RF) machine learning models were used with various combinations of …

State Changes: Insights From The U.S. Long Term Ecological Research Network, Julie C. Zinnert, Jesse B. Nippert, Jennifer A. Rudgers, Steven C. Pennings, Grizelle González, Merryl Alber, Sara G. Baer, John M. Blair, Adrian Burd, Scott L. Collins, Christopher Craft, Daniela Di Iorio, Walter K. Dodds, Peter M. Groffman, Ellen Herbert, Christine Hladik, Fan Li, Marcy E. Litvak, Seth Newsome, John O’Donnell, William T. Pockman, John Schalles, Donald R. Young

Advanced Science Research Center

Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long-term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER) network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in 50 or 100 yr based on long-term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and …

Time Lags: Insights From The U.S. Long Term Ecological Research Network, Edward B. Rastetter, Mark D. Ohman, Katherine J. Elliott, J. S. Rehage, Victor H. Rivera-Monroy, R. E. Boucek, Edward Castañeda-Moya, Tess M. Danielson, Laura Gough, Peter M. Groffman, C. Rhett Jackson, Chelcy Ford Miniat, Gaius R. Shaver

Advanced Science Research Center

Ecosystems across the United States are changing in complex ways that are difficult to predict. Coordinated long-term research and analysis are required to assess how these changes will affect a diverse array of ecosystem services. This paper is part of a series that is a product of a synthesis effort of the U.S. National Science Foundation’s Long Term Ecological Research (LTER) network. This effort revealed that each LTER site had at least one compelling scientific case study about “what their site would look like” in 50 or 100 yr. As the site results were prepared, themes emerged, and the case …

Deepmapper : Automatic Updating Crowdsourced Maps, Lasith Niroshan Hewa Manage, James Carswell

Other

To get accurate information returned from location-based services (e.g., LBS info on nearby restaurants, retail outlets, points-of-interest, etc.), the underlying map (spatial data) must be up-to-date. However, the built environment (e.g., roads, buildings, bike paths, etc.) can change quickly over time, either through planned developments or as the result of natural/manmade disasters. The problem is that keeping online crowdsourced maps like Open Street Map (OSM) updated is still very much a manual process. As such, it can take considerable time to sync the online maps used by LBS with up-to-date spatial data in "real-time".

Our case study considers the Grangegorman …

An Assessment Of The Hydrological Trends Using Synergistic Approaches Of Remote Sensing And Model Evaluations Over Global Arid And Semi-Arid Regions, Wenzhao Li, Hesham El-Askary, Rejoice Thomas, Surya Prakash Tiwari, Karuppasamy Manikandan, Thomas Piechota, Daniele Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of …

Synergies Among Environmental Science Research And Monitoring Networks: A Research Agenda, J. A. Jones, Peter M. Groffman, J. Blair, F. W. Davis, H. Dugan, E. E. Euskirchen, S. D. Frey, T. K. Harms, E. Hinckley, M. Kosmala, S. Loberg, S. Malone, K. Novick, S. Record, A. V. Rocha, B. L. Ruddell, E. H. Stanley, C. Sturtevant, A. Thorpe, T. White, W. R. Wieder, L. Zhai, K. Zhu

Advanced Science Research Center

Many research and monitoring networks in recent decades have provided publicly available data documenting environmental and ecological change, but little is known about the status of efforts to synthesize this information across networks. We convened a working group to assess ongoing and potential cross-network synthesis research and outline opportunities and challenges for the future, focusing on the US-based research network (the US Long-Term Ecological Research network, LTER) and monitoring network (the National Ecological Observatory Network, NEON). LTER-NEON cross-network research synergies arise from the potentials for LTER measurements, experiments, models, and observational studies to provide context and mechanisms for interpreting NEON …