Remote Sensing Commons^™

Viability And Application Of Mounting Personal Pid Voc Sensors To Small Unmanned Aircraft Systems, Cheryl Lynn Marcham, Scott Burgess, Joseph Cerreta, Patti J. Clark, James P. Solti, Brandon Breault, Joshua G. Marcham

Publications

Using a UAS-mounted sensor to allow for a rapid response to areas that may be difficult to reach or potentially dangerous to human health can increase the situational awareness of first responders of an aircraft crash site through the remote detection, identification, and quantification of airborne hazardous materials. The primary purpose of this research was to evaluate the remote sensing viability and application of integrating existing commercial-off-the-shelf (COTS) sensors with small unmanned aircraft system (UAS) technology to detect potentially hazardous airborne contaminants in emergency leak or spill response situations. By mounting the personal photoionization detector (PID) with volatile organic compound …

Rapid Mapping Of Landslides In The Western Ghats (India) Triggered By 2018 Extreme Monsoon Rainfall Using A Deep Learning Approach, Sansar Raj Meena, Omid Ghorbanzadeh, Cees J. Van Westen, Thimmaiah Gudiyangada Nachappa, Thomas Blaschke, Ramesh P. Singh, Raju Sarkar

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the …

A 3d Point Cloud Deep Learning Approach Using Lidar To Identify Ancient Maya Archaeological Sites, Heather Richards-Rissetto, David Newton, Aziza Al Zadjali

Department of Anthropology: Faculty Publications

Airborne light detection and ranging (LIDAR) systems allow archaeologists to capture 3D data of anthropogenic landscapes with a level of precision that permits the identification of archaeological sites in difficult to reach and inaccessible regions. These benefits have come with a deluge of LIDAR data that requires significant and costly manual labor to interpret and analyze. In order to address this challenge, researchers have explored the use of state-of-the-art automated object recognition algorithms from the field of deep learning with success. This previous research, however, has been limited to the exploration of deep learning processes that work with only 2D …

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 …

Modelling Acoustics In Ancient Maya Cities: Moving Towards A Synesthetic Experience Using Gis & 3d Simulation, Graham Goodwin, Heather Richards-Rissetto

Department of Anthropology: Faculty Publications

Archaeological analyses have successfully employed 2D and 3D tools to measure vision and movement within cityscapes; however, built environments are often designed to invoke synesthetic experiences. GIS and Virtual Reality (VR) now enable archaeologists to also measure the acoustics of ancient spaces. To move toward an understanding of synesthetic experience in ancient Maya cities, we employ GIS and 3D modelling to measure sound propagation and reverberation using the main civic-ceremonial complex in ancient Copán as a case study. For the ancient Maya, sight and sound worked in concert to create ritually-charged atmospheres and architecture served to shape these experiences. Together …

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 …

Classification And Evaluation Of Extended Pics (Epics) On A Global Scale For Calibration And Stability Monitoring Of Optical Satellite Sensors, Juliana Maria Fajardo Rueda

Electronic Theses and Dissertations

As targets for the calibration and monitoring of optical satellite sensors, historically stable areas across North Africa have been used, known as Pseudo Invariant Calibration Sites PICS. However, two major drawbacks exist for these sites; first is the dependency on a single location to be always invariant, and second is the limited amount of observation achieved using these sites. As a result, longer time periods are needed to construct a dense data set to assess the radiometric performance of on-orbit optical sensors, and be convinced that the change detected is sensor-specific rather than site-specific. This work presents a global land …

Using New And Long-Term Multi-Scale Remotely Sensed Data To Detect Recurrent Fires And Quantify Their Relationship To Land Cover/Use In Indonesian Peatlands, Yenni Vetrita

Electronic Theses and Dissertations

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. …

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 …

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 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 Hand-Held Structure From Motion Photogrammetric Approach To Riparian And Stream Asseessment And Monitoring, Joseph M. Dehnert, Joseph Dehnert

Graduate Student Theses, Dissertations, & Professional Papers

Two of the biggest weaknesses in stream restoration and monitoring are: 1) subjective estimation and subsequent comparison of changes in channel form, vegetative cover, and in-stream habitat; and 2) the high costs in terms of financing, human resources, and time necessary to make these estimates. Remote sensing can be used to remedy these weaknesses and save organizations focused on restoration both money and time. However, implementing traditional remote sensing approaches via autonomous aerial systems or light detection and ranging systems is either prohibitively expensive or impossible along small streams with dense vegetation. Hand-held Structure from Motion Multi-view Stereo (SfM-MVS) photogrammetric …

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 …

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 …

Assessment Of Empirical And Semi-Analytical Algorithms Using Modis-Aqua For Representing In-Situ Chromophoric Dissolved Organic Matter (Cdom) In The Bering, Chukchi, And Western Beaufort Seas Of The Pacific Arctic Region, Melishia I. Santiago, Karen E. Frey

Geography

We analyzed a variety of satellite-based ocean color products derived using MODIS-Aqua to investigate the most accurate empirical and semi-analytical algorithms for representing in-situ chromophoric dissolved organic matter (CDOM) across a large latitudinal transect in the Bering, Chukchi, and western Beaufort Seas of the Pacific Arctic region. In particular, we compared the performance of empirical (CDOM index) and several semi-analytical algorithms (quasi-analytical algorithm (QAA), Carder, Garver-Siegel-Maritorena (GSM), and GSM-A) with field measurements of CDOM absorption (aCDOM) at 412 nanometers (nm) and 443 nm. These algorithms were compared with in-situ CDOM measurements collected on cruises during July 2011, 2013, 2014, 2015, …

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 …

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 …

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 …

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 …

Remote Sensing Applications In Population Estimation, Regression Analysis, And Urban Growth Simulation Modeling For A Middle Eastern City, Elaf Amer Alyasiri

Graduate Research Theses & Dissertations

Due to a change in government regime and population migration, the City of Hillah in Iraq has been facing several urban issues, particularly population estimating and urban growth planning. Since the last census conducted in 1997, population in Iraq has been estimated based on the annual growth, without considering migration as factor of the population growth. Therefore, the aim of this dissertation study was to develop a population estimation method and an urban growth simulation method for the City of Hillah. Population data were estimated for Hillah, using the Normalized Difference Built-up Index (NDBI) derived from Remote Sensing (RS) and …

Extended Pseudo Invariant Calibration Site-Based Trend-To-Trend Cross-Calibration Of Optical Satellite Sensors, Prathana Khakurel

Electronic Theses and Dissertations

Satellite sensors have been extremely useful and are in massive demand in the understanding of the Earth’s surface and monitoring of changes. For quantitative analysis and acquiring consistent measurements, absolute radiometric calibration is necessary. The most common vicarious approach of radiometric calibration is cross-calibration, which helps to tie all the sensors to a common radiometric scale for consistent measurement. One of the traditional methods of cross-calibration is performed using temporally and spectrally stable pseudo-invariant calibration sites (PICS). This technique is limited by adequate cloud-free acquisitions for cross-calibration which would require a longer time to study the differences in sensor measurements. …

Detection Of Change Points In Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery, Neha Khadka

Electronic Theses and Dissertations

The remote sensing community has extensively used Pseudo-Invariant Calibration Sites (PICS) to monitor the long-term in-flight radiometric calibration of Earth-observing satellites. The use of the PICS has an underlying assumption that these sites are invariant over time. However, the site’s temporal stability has not been assured in the past. This work evaluates the temporal stability of PICS by not only detecting the trend but also locating significant shifts (change points) lying behind the time series. A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal …

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 …

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 …

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 …

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 …