Remote Sensing Commons^™

Leveraging Of Hyperspectral Remote Sensing On Estimating Biomass Yield Of Moringa Oleifera Lam. Medicinal Plant, Thulani Tshabalala, Elfatih M. Abdel-Rahman, Bhekumthetho Ncube, Ashwell R. Ndhlala, Onisimo Mutanga

All Peer-Reviewed Publications

Moringa oleifera Lam. is a functional plant considered to be high in nutrients as well as medicinal properties, largely utilised in most of the developing countries. Therefore, early prediction of M. oleifera biomass yield is a valuable pre- and post-harvest planning strategy for ensuring a reliable supply of the plant products and for marketing purposes. Subsequently, the objective of the current study was to explore the potential use of hyperspectral data in predicting biomass yield of different cultivars of M. oleifera. Canopy hyperspectral data were collected on five M. oleifera cultivars when they were one month and two months old …

Relationship Between Image Spectroscopy Spatial Resolution And Crown Level Tree Species Classification Accuracy, Andrew Richard Fritter

Dissertations and Theses

Hyperspectral imagery has become a common remote sensing data type used in tree species classifications because of its rich spectral signals that allow the detection of the variations in canopy reflectance. While high spatial resolution hyperspectral imagery provides fine spatial resolution for discerning surface objects, it has the inherent drawbacks of expensive acquisition costs, large data sizes, and can be computationally taxing to use. This study attempts to determine a relationship between crown level tree species classification accuracy and hyperspectral spatial resolution. Future tree species classification projects can make use of this relationship by targeting a spatial resolution that best …

An Operational Overview Of The Export Processes In The Ocean From Remote Sensing (Exports) Northeast Pacific Field Deployment, David A. Siegel, Ivona Cetinić, Jason R. Graff, Craig M. Lee, Norman Nelson, Mary Jane Perry, Inia Soto Ramos, Deborah K. Steinberg, Ken Buesseler, Roberta Hamme, Andrea J. Fassbender, David Nicholson, Melissa M. Omand, Marie Robert, Andrew Thompson, Vinicius Amaral, Michael Behrenfeld, Claudia Benitez-Nelson, Kelsey Bisson, Emmanuel Boss, Philip W. Boyd, Mark Brzezinski, Kristen Buck

Faculty Publications

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with …

Analyzing Satellite Ocean Color Match-Up Protocols Using The Satellite Validation Navy Tool (Savant) At Moby And Two Aeronet-Oc Sites, Adam Lawson, Jennifer Bowers, Sherwin Ladner, Richard Crout, Christopher Wood, Robert Arnone, Paul Martinolich, David Lewis

Faculty Publications

The satellite validation navy tool (SAVANT) was developed by the Naval Research Laboratory to help facilitate the assessment of the stability and accuracy of ocean color satellites, using numerous ground truth (in situ) platforms around the globe and support methods for match-up protocols. The effects of varying spatial constraints with permissive and strict protocols on match-up uncertainty are evaluated, in an attempt to establish an optimal satellite ocean color calibration and validation (cal/val) match-up protocol. This allows users to evaluate the accuracy of ocean color sensors compared to specific ground truth sites that provide continuous data. Various match-up constraints may …

Deep Learning Reveals Extent Of Archaic Native American Shell-Ring Building Practices, Dylan Davis, Gino Capsari, Carl P. Lipo, Matthew Sanger

Anthropology Faculty Scholarship

In the mid-Holocene (5000 - 3000 cal B.P.), Native American groups constructed shell rings, a type of circular midden, in coastal areas of the American Southeast. These deposits provide important insights into Native American socioeconomic organization but are also quite rare: only about 50 such rings have been documented to date. Recent work using automated LiDAR analysis demonstrates that many more shell rings likely exist than are currently recorded in state archaeological databases. Here, we use deep learning, a form of machine intelligence, to detect shell ring deposits and identify their geographic range in LiDAR data from South Carolina. We …

Deep Learning Approaches For Seagrass Detection In Multispectral Imagery, Kazi Aminul Islam

Electrical & Computer Engineering Theses & Dissertations

Seagrass forms the basis for critically important marine ecosystems. Seagrass is an important factor to balance marine ecological systems, and it is of great interest to monitor its distribution in different parts of the world. Remote sensing imagery is considered as an effective data modality based on which seagrass monitoring and quantification can be performed remotely. Traditionally, researchers utilized multispectral satellite images to map seagrass manually. Automatic machine learning techniques, especially deep learning algorithms, recently achieved state-of-the-art performances in many computer vision applications. This dissertation presents a set of deep learning models for seagrass detection in multispectral satellite images. It …

Towards An Integrated Assessment Of Sea-Level Observations Along The U.S. Atlantic Coast, Brett A. Buzzanga

OES Theses and Dissertations

Sea levels are rising globally due to anthropogenic climate change. However, local sea levels that impact coastal ecosystems often differ from the global trend, sometimes by a factor of two or more. Improved understanding of this regional variability provides insights into geophysical processes and has implications for coastal communities developing resilience to ongoing sea-level rise. This dissertation conducts an investigation of sea level and its contributing processes at multiple spatial scales. Focusing on primarily interannual time-scales and data-driven approaches, new data sources and technologies are utilized to reduce current uncertainties.

First, sea-level trends are assessed over the global ocean and …

Geolocation Of Monitoring Wells Using Small Unmanned Aircraft Systems, Joel Deyoung

Graduate Theses and Dissertations

Groundwater monitoring wells are commonly installed on a property as part of an environmental investigation to observe hydrological subsurface conditions, facilitate the collection of groundwater samples, and predict the flow of groundwater across a site. In addition to their installation, monitoring wells should be surveyed or mapped as accurately as possible. Traditional surveying techniques have employed the use of global navigation satellite systems (GNSS) technologies or other surveying equipment. A common surveying approach is to use real-time kinematic (RTK) GNSS to accurately measure the coordinates of each monitoring well on the site.In recent years, drones, or small unmanned aircraft systems …

Economically Optimal Nitrogen Side-Dressing Based On Vegetation Indices From Satellite Images Through On-Farm Experiments, Qianqian Du

Department of Agricultural Economics: Dissertations, Theses, and Student Research

Optimal N fertilizer rates for corn (Zea mays L.) vary substantially within and among fields, and by corn growth stages. Improving N side-dressing management can improve fertilizer use efficiency, farmers’ profitability, and the sustainability of crop production. The objective of this study is to introduce a framework along with a methodology that can find the site-specific economically optimal N rates (EONRs) within one field for a particular growing season. An on-farm experiment was conducted in the 2019 corn growing season. A base N rate was applied uniformly on the field. NDRE images from the Sentinel-2 satellite were observed during …

Assessing Impacts Of Winter-Hay Feeding On Soil And Forage Nutrient Dynamics In A Rotationally-Grazed Pasture System In Arkansas, Lawrence Gordon Berry Iv

Graduate Theses and Dissertations

More than 38 % of United States’ rural land area was used for grazing (i.e., pastureland or rangeland) ruminant animals in 2017, constituting the largest private land use group. The expansive nature of these lands means that grazing and pasture management decisions have potential to impact water quality as well as profit margins. As a result, beef producers are under increased pressure from economic and environmental standpoints to limit application of nutrients beyond those required to grow the forage needed for animal consumption. At the same time, a large amount of nutrients is recycled back to pasture systems directly from …

Machine Learning & Big Data Analyses For Wildfire & Air Pollution Incorporating Gis & Google Earth Engine, Abdullah Al Saim

Graduate Theses and Dissertations

The climatic condition, the vegetation type, and the landscape of the United States have made it susceptible to wildfires. This research is divided into two parts based on the analysis of two different aspects of wildfires of two distinct regions. The first part of the study investigates the wildfire susceptibility in Arkansas. Arkansas is a natural state, and it is heavily dependent on its forest and agricultural resources. During the last 30 years, more than 1,000 wildfires occurred in Arkansas and caused more than 10,000 acres of burned areas. Therefore, identifying wildfire-susceptible areas is crucial for ensuring sustainable forest and …

Examining Melt Pond Dynamics And Light Availability In The Arctic Ocean Via High Resolution Satellite Imagery, Austin Wesley Abbott

OES Theses and Dissertations

As the Arctic experiences consequences of climate change, a shift from thicker, multi-year ice to thinner, first-year ice has been observed. First-year ice is prone to extensive pools of meltwater (“melt ponds”) forming on its surface, which enhance light transmission to the ocean. Changes in the timing and distribution of melt pond formation and associated increases in under-ice light availability are the primary drivers for seasonal progression of water column primary production and warming. Observations of melt pond development and distribution require meter scale resolution and have traditionally been limited to airborne images. However, recent advances in high spatial resolution …

A Fused Radar–Optical Approach For Mapping Wetlands And Deepwaters Of The Mid–Atlantic And Gulf Coast Regions Of The United States, Brian T. Lamb, Maria A. Tzortziou, Kyle C. Mcdonald

Publications and Research

Tidal wetlands are critically important ecosystems that provide ecosystem services including carbon sequestration, storm surge mitigation, water filtration, and wildlife habitat provision while supporting high levels of biodiversity. Despite their importance, monitoring these systems over large scales remains challenging due to difficulties in obtaining extensive up-to-date ground surveys and the need for high spatial and temporal resolution satellite imagery for effective space-borne monitoring. In this study, we developed methodologies to advance the monitoring of tidal marshes and adjacent deepwaters in the Mid-Atlantic and Gulf Coast United States. We combined Sentinel-1 SAR and Landsat 8 optical imagery to classify marshes and …

Mapping Complex Land Use Histories And Urban Renewal Using Ground Penetrating Radar: A Case Study From Fort Stanwix, Tyler Stumpf, Daniel P. Bigman, Dominic J. Day

Anthropology Graduate Research

Fort Stanwix National Monument, located in Rome, NY, is a historic park with a complex use history dating back to the early Colonial period and through the urban expansion and recent economic revitalization of the City of Rome. The goal of this study was to conduct a GPR investigation over an area approximately 1 acre in size to identify buried historic features (particularly buildings) so park management can preserve these resources and develop appropriate educational programming and management plans. The GPR recorded reflection events consistent with our expectations of historic structures. Differences in size, shape, orientation, and depth suggest that …

Creating A Field-Wide Forage Canopy Model Using Uavs And Photogrammetry Processing, Cameron Minch, Joseph S. Dvorak, Joshua J. Jackson, Stuart Tucker Sheffield

Biosystems and Agricultural Engineering Faculty Publications

Alfalfa canopy structure reveals useful information for managing this forage crop, but manual measurements are impractical at field-scale. Photogrammetry processing with images from Unmanned Aerial Vehicles (UAVs) can create a field-wide three-dimensional model of the crop canopy. The goal of this study was to determine the appropriate flight parameters for the UAV that would enable reliable generation of canopy models at all stages of alfalfa growth. Flights were conducted over two separate fields on four different dates using three different flight parameters. This provided a total of 24 flights. The flight parameters considered were the following: 30 m altitude with …

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

Monitoring War Destruction From Space Using Machine Learning, Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, Joan Serrat

Economics Faculty Articles and Research

Satellite imagery is becoming ubiquitous. Research has demonstrated that artificial intelligence applied to satellite imagery holds promise for automated detection of war-related building destruction. While these results are promising, monitoring in real-world applications requires high precision, especially when destruction is sparse and detecting destroyed buildings is equivalent to looking for a needle in a haystack. We demonstrate that exploiting the persistent nature of building destruction can substantially improve the training of automated destruction monitoring. We also propose an additional machine-learning stage that leverages images of surrounding areas and multiple successive images of the same area, which further improves detection significantly. …

Mapping The Suitability Of Cal Poly's Insulated Solar Electric Cookers (Isec) In Ghana, Togo, And Jamaica, Julia G. Kraatz

Social Sciences

The World Health Organization estimates that 3 billion people depend on biomass fuels for cooking, heating, and other day-to-day activities, which causes approximately 4.3 million people annually to die from illnesses attributable to indoor air pollution. The issue is especially pressing for women and children in developing countries, because women care for the home and are consequently responsible for attaining household fuels and cooking. In 2015, ISECs (Insulated Solar Electric Cookers) were developed at California Polytechnic State University, San Luis Obispo, as a technology that utilizes solar electricity to directly cook food in a well-insulated chamber. They are capable of …

Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco, Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …

El Atlas De Las Carreteras Propuestas En La Zona Transfronteriza Ucayali, Perú-Acre, Brasil, David S. Salisbury, Stephanie A. Spera, Elspeth Collard*, Anna Frisbie*, M. R. Place*, Yunuen Reygadas Langarica, Elizabeth Zizzamia

Multimedia

El Atlas de las Carreteras Propuestas en la Zona Transfronteriza Ucayali, Perú-Acre, Brasil incluye una serie de 15 mapas de dos carreteras propuestas: 1) Pucallpa, Perú-Cruzeiro do Sul, Brasil; 2) Nuevo Italia-Puerto Breu, Perú. El objetivo del atlas es presentar los mapas, posters, e información geográfica para dar una perspectiva geográfica de las propuestas de carreteras y entender mejor los posibles impactos socio-ambientales en estas áreas fronterizas con altos índices en diversidad ambiental y cultural. Los mapas y posters son de acceso público.