This data is a large suite of coincident surface water observations for many different physical, chemical, and biological properties of the water in a reservoir in Southwest Ohio (Harsha Lake) at the time a flyover occurred to take hyperspectral images of the lake’s surface. 44 sites were visited across the reservoir within 2 hours. The data could be useful to others with interest in remote sensing of water quality or studying spatial patterns of water quality at one particular point in time. This dataset is associated with the following publication: Beck, R., M. Xu, S. Zhan, R. Johansen, E. Emery, M. Reif, C. Nietch, D. Macke, R. Stumpf, and M. Martin. Comparison of Satellite Reflectance Algorithms for Estimating Turbidity and Cyanobacterial Concentrations in Productive Freshwaters Using Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 45(3): 413-433, (2019).

This layer contains the areas identified as areas of anomalous surface temperature from ASTER satellite imagery. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. Areas that had temperature greater than 2o, and areas with temperature equal to 1o to 2o, were considered ASTER modeled very warm and warm surface exposures (thermal anomalies), respectively Note: 'o' is used in place of lowercase sigma in this description.

This dataset consists of four sets of flower images, from three different species: apple, peach, and pear, and accompanying ground truth images. The images were acquired under a range of imaging conditions. These datasets support work in an accompanying paper that demonstrates a flower identification algorithm that is robust to uncontrolled environments and applicable to different flower species. While this data is primarily provided to support that paper, other researchers interested in flower detection may also use the dataset to develop new algorithms. Flower detection is a problem of interest in orchard crops because it is related to management of fruit load. Funding provided through ARS Integrated Orchard Management and Automation for Deciduous Tree Fruit Crops.

This a report for the project "Mapping Fracture Network Creation with Microseismicity During EGS Demonstrations". Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are often employed to identify microearthquakes in geothermal regions. However, most commonly used algorithms may miss events if the seismic signal of an earthquake is small relative to the background noise level or if a microearthquake occurs within the coda of a larger event. Consequently, we have developed a set of algorithms that provide improved microearthquake detection. Our objective is to investigate the microseismicity at the DOE Newberry EGS site to better image the active regions of the underground fracture network during and immediately after the EGS stimulation. Detection of more microearthquakes during EGS stimulations will allow for better seismic delineation of the active regions of the underground fracture system. This improved knowledge of the reservoir network will improve our understanding of subsurface conditions, and allow improvement of the stimulation strategy that will optimize heat extraction and maximize economic return. This project is the FY14 continuation of FY13 AOP project 25728, which had its origins as the ARRA lab project AID 19981.

Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada" project. This software is licensed as free to use, modify, and distribute with attribution. Full license details are included within the archive. See "documentation.zip" for setup instructions and file trees annotated with module descriptions.