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Full-Text Articles in Engineering
Leveraging Unmanned Aerial System Remote Sensing To Inform Energy And Water Balance Models For Spatial Soil Water Content Monitoring And Irrigation Management, Mitchell S. Maguire
Leveraging Unmanned Aerial System Remote Sensing To Inform Energy And Water Balance Models For Spatial Soil Water Content Monitoring And Irrigation Management, Mitchell S. Maguire
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
Irrigation has provided a means to produce more food and fiber throughout the world, converting low producing land into high yielding cropping systems in certain scenarios. The managing of irrigation has taken on various approaches as different locations have been constrained by different factors. Certain areas have significant ground and surface water available for irrigation while other areas struggle to meet irrigation demands due to limited water resources. These factors, along with the desire to increase crop water use efficiency, has provided the motivation to better understand crop water demands spatially within a field. A sub-field scale irrigation management study …
Characterizing Soil Stiffness Using Thermal Remote Sensing And Machine Learning, Jordan Ewing, T. Oommen, Paramsothy Jayakumar, Russell Alger
Characterizing Soil Stiffness Using Thermal Remote Sensing And Machine Learning, Jordan Ewing, T. Oommen, Paramsothy Jayakumar, Russell Alger
Michigan Tech Publications
Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict …
Using Remote Sensing And Environmental Precursors To Detect And Predict Cyanobacteria Harmful Algal Blooms In Northeastern Us Waterbodies, Amanda Craver
Environmental & Water Resources Engineering Masters Projects
Cyanobacteria harmful algal blooms (cyanoHABs) are a global problem with human health, environmental, and economic concerns. The severity and frequency of toxic cyanoHABs are expected to increase with climate change. Remote sensing has proven to be a useful tool in monitoring cyanoHABs. This study uses remote sensing observations from Sentinel-3Ocean Land Color Imager (OLCI)combined with the Spectral Shape Algorithm (SSA) to detect the presence of cyanobacteria in numerous waterbodies throughout the Northeast United States over 2016to 2020. The ACOLITE processor was used for the atmospheric correction of the Sentinel-3 OLCI data, as it has been shown to provide more accurate …
Deploying And Analyzing Air Quality Sensors In Mongolian Gers, Lehi Alcantara
Deploying And Analyzing Air Quality Sensors In Mongolian Gers, Lehi Alcantara
ScholarsArchive Data
This is work done for a thesis that would be way too long to attach to the thesis document. It contains code, spreadsheets, data.
Rapid Quantification Of Biofouling With An Inexpensive, Underwater Camera And Image Analysis, Matthew R. First, Scott C. Riley, Kazi Aminul Islam, Victoria Hill, Jiang Li, Richard C. Zimmerman, Lisa A. Drake
Rapid Quantification Of Biofouling With An Inexpensive, Underwater Camera And Image Analysis, Matthew R. First, Scott C. Riley, Kazi Aminul Islam, Victoria Hill, Jiang Li, Richard C. Zimmerman, Lisa A. Drake
Electrical & Computer Engineering Faculty Publications
To reduce the transport of potentially invasive species on ships' submerged surfaces, rapid-and accurate-estimates of biofouling are needed so shipowners and regulators can effectively assess and manage biofouling. This pilot study developed a model approach for that task. First, photographic images were collected in situ with a submersible, inexpensive pocket camera. These images were used to develop image processing algorithms and train machine learning models to classify images containing natural assemblages of fouling organisms. All of the algorithms and models were implemented in a widely available software package (MATLAB©). Initially, an unsupervised clustering model was used, and three …