Remote Sensing Commons^™

The Role Of Remote Sensing And Gis Techniques In Analysis Of Southern California Wildfires In January 2025, Sara Fitzgibbon

Dissertations, Master's Theses and Master's Reports

California is no stranger to wildfires, with them becoming more frequent and larger in recent years. In early January 2025, Southern California had one of the worst outbreaks of wildfires in its history, with the Pacific Palisades and Eaton Fires being considered among the most destructive in state history. To help prevent this from happening in the future, remote sensing techniques are essential in combating these issues. In this report, geographic information systems (GIS) and remote sensing data utilized to explore fire behavior by various organizations were investigated. By using this data, this study aims to predict and monitor fire …

Aboveground Biomass Density Estimation Using Deep Learning: Insight From Neon Ground-Truth Data And Simulated Gedi Waveform, Ashish Mahaur

Dissertations, Master's Theses and Master's Reports

Accurately estimating Aboveground Biomass Density (AGBD) is crucial for managing Earth's carbon cycle and informing climate strategies. NASA's GEDI mission advances global forest mapping, but traditional linear models often yield less reliable AGBD estimates. This study enhances AGBD estimation using deep learning models with NEON ground-truth data and simulated GEDI waveforms. We compared 1D CNNs, LSTMs, and pre-trained CNNs to traditional models. The ResNet152 model outperformed linear regression, achieving an R² of 0.68, demonstrating a 17% improvement. Our experiments also demonstrate the importance of large, diverse datasets, particularly for training deep learning models.

Using Uncrewed Aerial Vehicles For Identifying The Extent Of Invasive Phragmites Australis In Treatment Areas Enrolled In An Adaptive Management Program, Colin Brooks, Charlotte Weinstein, Andrew Poley, Amanda Grimm, Nicholas Marion, Laura Bourgeau-Chavez, Dana Hansen, Kurt Kowalski

Michigan Tech Publications, Part 1

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image …

Multi-Source Eo For Dynamic Wetland Mapping And Monitoring In The Great Lakes Basin, Michael Battaglia, Sarah Banks, Amir Behnamian, Laura Bourgeau-Chavez

Michigan Tech Publications, Part 1

Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to monitor wetlands over time, covering large areas like the Great Lakes and providing information to those making management and policy decisions. In this paper we describe ongoing efforts to monitor dynamic changes in wetland vegetation, surface water extent, and water level change. Included are assessments of simulated Radarsat Constellation Mission data to determine feasibility of continued monitoring …

Mapping Kenyan Grassland Heights Across Large Spatial Scales With Combined Optical And Radar Satellite Imagery, Olivia S. B. Spagnuolo, Julie C. Jarvey, Michael Battaglia, Zachary Laubach, Mary Ellen Miller, Kay E. Holekamp, Laura Bourgeau-Chavez

Michigan Tech Publications, Part 1

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using …

Regional Scale Dryland Vegetation Classification With An Integrated Lidar-Hyperspectral Approach, Hamid Dashti, Andrew Poley, Nancy Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Et. Al.

Michigan Tech Publications, Part 1

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral …

Determining Remote Sensing Spatial Resolution Requirements For The Monitoring Of Harmful Algal Blooms In The Great Lakes, John Lekki, Eric Deutsch, Michael Sayers, Karl Bosse, Robert Anderson, Roger Tokars, Reid W. Sawtell

Michigan Tech Publications, Part 1

Harmful algal blooms (HABs) have become a major health and environmental concern in the Great Lakes. In 2014, severe HABs prompted the State of Ohio to request NASA Glenn Research Center (GRC) to assist with monitoring algal blooms in Lake Erie. The most notable species of HAB is Microcystis aeruginosa, a hepatotoxin producing cyanobacteria that is responsible for liver complications for humans and other fauna that come in contact with these blooms. NASA GRC conducts semiweekly flights in order to gather up-to-date imagery regarding the blooms' spatial extents and concentrations. Airborne hyperspectral imagery is collected using two hyperspectral imagers, HSI-2 …

Real Time Habs Mapping Using Nasa Glenn Hyperspectral Imager, Reid W. Sawtell, Robert Anderson, Roger Tokars, John D. Lekki, Robert Shuchman, Karl Bosse, Michael Sayers

Michigan Tech Publications, Part 1

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015 to 2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 h) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument …

Spatial And Temporal Variability Of Inherent And Apparent Optical Properties In Western Lake Erie: Implications For Water Quality Remote Sensing, Michael Sayers, Karl Bosse, Robert Shuchman, Steven A. Ruberg, Gary L. Fahnenstiel, George Leshkevich, Et Al.

Michigan Tech Publications, Part 1

Lake Erie has experienced dramatic changes in water quality over the past several decades requiring extensive monitoring to assess effectiveness of adaptive management strategies. Remote sensing offers a unique potential to provide synoptic monitoring at daily time scales complementing in-situ sampling activities occurring in Lake Erie. Bio-optical remote sensing algorithms require knowledge about the inherent optical properties (IOPs) of the water for parameterization to produce robust water quality products. This study reports new IOP and apparent optical property (AOP) datasets for western Lake Erie that encapsulate the May–October period for 2015 and 2016 at weekly sampling intervals. Previously reported IOP …

Evaluating Visible Derivative Spectroscopy By Varimax-Rotated, Principal Component Analysis Of Aerial Hyperspectral Images From The Western Basin Of Lake Erie, Joseph D. Ortiz, Dulci M. Avouris, Stephan J. Schiller, Jeffrey C. Luvall, John D. Lekki, Roger P. Tokars, Robert C. Anderson, Robert Shuchman, Michael Sayers, Richard Becker

Michigan Tech Publications, Part 1

The Kent State University (KSU) spectral decomposition method provides information about the spectral signals present in multispectral and hyperspectral images. Pre-processing steps that enhance signal to noise ratio (SNR) by 7.37–19.04 times, enables extraction of the environmental signals captured by the National Aeronautics and Space Administration (NASA) Glenn Research Center's, second generation, Hyperspectral imager (HSI2) into multiple, independent components. We have accomplished this by pre-processing of Level 1 HSI2 data to remove stripes from the scene, followed by a combination of spectral and spatial smoothing to further increase the SNR and remove non-Lambertian features, such as waves. On average, …

Satellite Monitoring Of Harmful Algal Blooms In The Western Basin Of Lake Erie: A 20-Year Time-Series, Michael Sayers, Amanda Grimm, Robert Shuchman, Karl Bosse, Gary L. Fahnenstiel, Steven A. Ruberg, George A. Leshkevich

Michigan Tech Publications, Part 1

Blooms of harmful cyanobacteria (cyanoHABs) have occurred on an annual basis in western Lake Erie for more than a decade. Previously, we developed and validated an algorithm to map the extent of the submerged and surface scum components of cyanoHABs using MODIS ocean-color satellite data. The algorithm maps submerged cyanoHABs by identifying high chlorophyll concentrations (>18 mg/m3) combined with water temperature >20 °C, while cyanoHABs surface scums are mapped using near-infrared reflectance values. Here, we adapted this algorithm for the SeaWiFS sensor to map the annual areal extents of cyanoHABs in the Western Basin of Lake Erie for the …

Spatial-Temporal Variability Of In Situ Cyanobacteria Vertical Structure In Western Lake Erie: Implications For Remote Sensing Observations, Karl Bosse, Michael Sayers, Robert Shuchman, Gary L. Fahnenstiel, Steven A. Ruberg, David L. Fanslow, Dack G. Stuart, Thomas H. Johengen, Ashley M. Burtner

Michigan Tech Publications, Part 1

Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatio-temporal variability …

Oil Spills Detection By Means Of Uas And Low-Cost Airborne Thermal Sensors, A. Al-Shammari, Eugene Levin, R. Shults

School of Technology Publications

This paper provides an overview of oil spill scenarios and the remote sensing methods used for detection and mapping the spills. It also discusses the different kinds of thermal sensors used in oil spills detection. As UAS is becoming an important player in the oil and gas industry for the low operating costs involved, this research involved working with a cheap thermal airborne sensor mounted on DJI Phantom 4 system. Data were collected in two scenarios, first scenario is collecting data in Michigan’s Upper Peninsula at a petroleum company location and the second scenario was an indoor experiment simulating an …

Semi-Automated Surface Water Detection With Synthetic Aperture Radar Data: A Wetland Case Study, Amir Behnamian, Sarah Banks, Lori White, Brian Brisco, Koreen Millard, Jon Pasher, Zhaohua Chen, Jason Duffe, Laura Bourgeau-Chavez, Michael Battaglia

Michigan Tech Publications, Part 1

In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via a combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing the statistical distribution of backscatter values applied to the mean of each superpixel. Higher-order texture measures, such as variance, are used to improve accuracy by removing false positives via an additional thresholding process used to identify the boundaries of water bodies. Results applied to quad-polarized RADARSAT-2 data show that the threshold value for the variance texture measure …

Novel Methods For Quantifying Spatio-Temporal Change In Glaciated And Subaqueous Environments, Jordan Mertes

Dissertations, Master's Theses and Master's Reports

In many scientific fields, it is important to actively develop new approaches to monitoring and quantifying changes within different systems. Often adapting existing tools or applying techniques from alternative fields can greatly improve our ability to monitor spatial and temporal changes. In this dissertation, I present four studies aimed at demonstrating new innovative ways at improving our ability to observe and quantify changes occurring on glaciers, submerged cultural resources (SCRs) and supraglacial lakes by using technology such as Structure from Motion + Multi-view stereo photogrammetry (SfM) and ground penetrating radar (GPR) surveying combined with facies analysis.

I have successfully reconstructed …

Identification Of Woodland Vernal Pools With Seasonal Change Palsar Data For Habitat Conservation, Laura Bourgeau-Chavez, Yu Man Lee, Michael Battaglia, Sarah L. Endres, Zachary Laubach, Kirk Scarbrough

Michigan Tech Publications, Part 1

Woodland vernal pools are important, small, cryptic, ephemeral wetland ecosystems that are vulnerable to a changing climate and anthropogenic influences. To conserve woodland vernal pools for the state of Michigan USA, vernal pool detection and mapping methods were sought that would be efficient, cost-effective, repeatable and accurate. Satellite-based L-band radar data from the high (10 m) resolution Japanese ALOS PALSAR sensor were evaluated for suitability in vernal pool detection beneath forest canopies. In a two phase study, potential vernal pool (PVP) detection was first assessed with unsupervised PALSAR (LHH) two season change detection (spring when flooded—summer when dry) and validated …

Exploration Of The Mtsat2 Satellite Capabilities For Real Time Detection And Characterization Of Volcanic Emissions, Nicholas R. Stewart

Dissertations, Master's Theses and Master's Reports - Open

In this report, we attempt to define the capabilities of the infrared satellite remote sensor, Multifunctional Transport Satellite-2 (MTSAT-2) (i.e. a geosynchronous instrument), in characterizing volcanic eruptive behavior in the highly active region of Indonesia. Sulfur dioxide data from NASA's Ozone Monitoring Instrument (OMI) (i.e. a polar orbiting instrument) are presented here for validation of the processes interpreted using the thermal infrared datasets. Data provided from two case studies are analyzed specifically for eruptive products producing large thermal anomalies (i.e. lava flows, lava domes, etc.), volcanic ash and SO₂ clouds; three distinctly characteristic and abundant volcanic emissions. Two primary …

Object-Based Classification Of Earthquake Damage From High-Resolution Optical Imagery Using Machine Learning, James Bialas

Dissertations, Master's Theses and Master's Reports - Open

Object-based approaches to the segmentation and supervised classification of remotely-sensed images yield more promising results compared to traditional pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods and trial and error are often used, but time consuming and yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time sensitive applications such as earthquake induced damage assessment.

Our research takes a systematic approach to evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely-sensed imagery using Trimble’s eCognition …

Assessment Of Land Cover And Riparian Zones For The Ford Research Forest - Hickey Creek/Sturgeon River And Falls River Subwatersheds, Fay Dearing

Dissertations, Master's Theses and Master's Reports - Open

For landowners, knowing the contents of their land is always a primary concern. Traditional field based assessments can be challenging and expensive for large landholdings as well. However, by utilizing remote sensing and GIS models the land cover/land use and riparian areas can be more easily identified on a larger landscape level. To this end two remote sensing techniques were explored to create a land cover/land use map for the study area. The first, utilizing object-oriented techniques and high spatial resolution generated an overall accuracy of 71.97% which indicated a moderate agreement with the classified image and the field truthed …

Beyond Roots Alone: Novel Methodologies For Analyzing Complex Soil And Minirhizotron Imagery Using Image Processing And Gis Tools, Justina A. Silva

Dissertations, Master's Theses and Master's Reports - Open

Quantifying belowground dynamics is critical to our understanding of plant and ecosystem function and belowground carbon cycling, yet currently available tools for complex belowground image analyses are insufficient. We introduce novel techniques combining digital image processing tools and geographic information systems (GIS) analysis to permit semi-automated analysis of complex root and soil dynamics. We illustrate methodologies with imagery from microcosms, minirhizotrons, and a rhizotron, in upland and peatland soils. We provide guidelines for correct image capture, a method that automatically stitches together numerous minirhizotron images into one seamless image, and image analysis using image segmentation and classification in SPRING or …

Implementation Of Photogrammetry To Improve Proactive Assessment Of Retaining Walls Along Transportation Corridors, Daniel J. Cerminaro

Dissertations, Master's Theses and Master's Reports - Open

Retaining walls are important assets in the transportation infrastructure and assessing their condition is important to prolong their performance and ultimately their design life. Retaining walls are often overlooked and only a few transportation asset management programs consider them in their inventory. Because these programs are few, the techniques used to assess their condition focus on a qualitative assessment as opposed to a quantitative approach. The work presented in this thesis focuses on using photogrammetry to quantitatively assess the condition of retaining walls. Multitemporal photogrammetry is used to develop 3D models of the retaining walls, from which offset displacements are …

Application Of An Imputation Method For Geospatial Inventory Of Forest Structural Attributes Across Multiple Spatial Scales In The Lake States, U.S.A., Ram K. Deo

Dissertations, Master's Theses and Master's Reports - Open

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable …

Deep Learning Methods For Multiband Explosive Hazard Detection Using L-Band And X-Band Forward-Looking Ground-Penetrating Radar, John T. Becker

Dissertations, Master's Theses and Master's Reports - Open

Explosive hazards are one of the most deadly threats in modern conflicts. The U.S. Army is interested in a reliable way to detect these hazards at range. A promising way of accomplishing this task is using a forward-looking ground-penetrating radar (FLGPR) system. Recently, the Army has been testing a system that utilizes both L-band and X-band radar arrays on a vehicle mounted platform. Using data from this system, we sought to improve the performance of a constant false-alarm-rate (CFAR) prescreener through the use of three deep learning architechtures; deep belief networks (DBNs), stacked denoising autoencoders (SDAEs), and convolutional neural networks …

A Study Of So2 Emissions And Ground Surface Displacements At Lastarria Volcano, Antofagasta Region, Northern Chile, Lucie Guihem Krewcun

Dissertations, Master's Theses and Master's Reports - Open

Lastarria volcano (Chile) is located at the North-West margin of the `Lazufre' ground inflation signal (37x45 km²), constantly uplifting at a rate of ~2.5 cm/year since 1996 (Pritchard and Simons 2002; Froger et al. 2007). The Lastarria volcano has the double interest to be superimposed on a second, smaller-scale inflation signal and to be the only degassing area of the Lazufre signal. In this project, we compared daily SO2 burdens recorded by AURA's OMI mission for 2005-2010 with Ground Surface Displacements (GSD) calculated from the Advanced Synthetic Aperture Radar (ASAR) images for 2003-2010.

We found a constant maximum displacement rate …

Tandem-X High Resolution Dems And Their Applications To Flow Modeling, Kelly M. Wooten

Dissertations, Master's Theses and Master's Reports - Open

Lava flow modeling can be a powerful tool in hazard assessments; however, the ability to produce accurate models is usually limited by a lack of high resolution, up-to-date Digital Elevation Models (DEMs). This is especially obvious in places such as Kilauea Volcano (Hawaii), where active lava flows frequently alter the terrain. In this study, we use a new technique to create high resolution DEMs on Kilauea using synthetic aperture radar (SAR) data from the TanDEM-X (TDX) satellite. We convert raw TDX SAR data into a geocoded DEM using GAMMA software [Werner et al., 2000]. This process can be completed in …

Characterizing The First Historic Eruption Of Nabro, Eritrea: Insights From Thermal And Uv Remote Sensing, Christine Ruth Sealing

Dissertations, Master's Theses and Master's Reports - Open

June 2011 saw the first historic eruption of Nabro volcano, one of an ongoing sequence of eruptions in the Afar-Red Sea region since 2005. It halted air travel in northern Africa, contaminated food and water sources, and displaced thousands from their homes. Due to its remote location, little was known about this event in terms of the quantity of erupted products and the timing and mechanisms of their emplacement. Geographic isolation, previous quiescence and regional civil unrest meant that this volcano was effectively unmonitored at the time of eruption, and opportunities for field study are limited. Using free, publicly available …

Application Of Remote Sensing In Aquatic Ecosystems, Foad Yousef

Dissertations, Master's Theses and Master's Reports - Open

I utilized state the art remote sensing and GIS (Geographical Information System) techniques to study large scale biological, physical and ecological processes of coastal, nearshore, and offshore waters of Lake Michigan and Lake Superior. These processes ranged from chlorophyll a and primary production time series analysies in Lake Michigan to coastal stamp sand threats on Buffalo Reef in Lake Superior. I used SeaWiFS (Sea-viewing Wide Field-of-view Sensor) satellite imagery to trace various biological, chemical and optical water properties of Lake Michigan during the past decade and to investigate the collapse of early spring primary production. Using spatial analysis techniques, I …