Multi-Source Eo For Dynamic Wetland Mapping And Monitoring In The Great Lakes Basin,
2021
Michigan Technological University
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 …
Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data,
2021
Old Dominion University
Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data, Mohammed Alqawba, Norou Diawara, Kwasi G. Afrifa, Mohamed I. Elbakary, Mecit Cetin, Khan Iftekharuddin
Mathematics & Statistics Faculty Publications
In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear …
Towards Advancing The Earthquake Forecasting By Machine Learning Of Satellite Data,
2021
China Earthquake Administration
Towards Advancing The Earthquake Forecasting By Machine Learning Of Satellite Data, Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huyui Zhou
Mathematics, Physics, and Computer Science Faculty Articles and Research
Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for …
Fine-Grained, Spatiotemporal Datasets Measuring 200 Years Of Land Development In The United States,
2021
University of Colorado, Boulder
Fine-Grained, Spatiotemporal Datasets Measuring 200 Years Of Land Development In The United States, Johannes H. Uhl, Stefan Leyk, Caitlin M. Mcshane, Anna E. Braswell, Dylan S. Connor, Deborah Balk
Publications and Research
The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth’s surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250m spatial and 5-year temporal resolution). We create these time series using …
Viability And Application Of Mounting Personal Pid Voc Sensors To Small Unmanned Aircraft Systems,
2021
Embry-Riddle Aeronautical University
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,
2021
University of Salzburg
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,
2021
University of Nebraska-Lincoln
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,
2021
Missouri University of Science and Technology
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,
2021
University of California, Merced
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,
2021
Georgia Southern University
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 …
Extended Pseudo Invariant Calibration Site-Based Trend-To-Trend Cross-Calibration Of Optical Satellite Sensors,
2021
South Dakota State University
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,
2021
South Dakota State University
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 …
A Hand-Held Structure From Motion Photogrammetric Approach To Riparian And Stream Asseessment And Monitoring,
2021
University of Montana
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 …
Classification And Evaluation Of Extended Pics (Epics) On A Global Scale For Calibration And Stability Monitoring Of Optical Satellite Sensors,
2021
South Dakota State University
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,
2021
South Dakota State University
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,
2021
South Dakota State University
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,
2021
South Dakota State University
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,
2021
Minnesota State University, Mankato
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 …
The Kiwanis Site: A Multi-Method Geophysical Approach To Investigating Mound Features,
2021
Minnesota State University, Mankato
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 …
Effects Of Training Set Size On Supervised Machine-Learning Land-Cover Classification Of Large-Area High-Resolution Remotely Sensed Data,
2021
West Virginia University
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 …
