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This paper describes the image acquisition and processing methodology, including surface emissivity and atmospheric corrections, for generating surface temperatures of two active hydrothermal systems in Yellowstone National Park. Airborne thermal infrared (8-12 μm) images were obtained annually from 2007 to 2012 using a FLIR SC640 thermal infrared camera system. Thermal infrared image acquisitions occurred under clear-sky conditions after sunset to meet the objective of providing high-spatial resolution, georectified imagery for hydrothermal monitoring. Comparisons of corrected radiative temperature maps with measured ground and water kinetic temperatures at flight times provided an assessment of temperature accuracy. A repeatable, time-sequence of images for Hot Spring Basin (2007) and Norris Geyser Basin (2008 documented fracture-related changes in temperature and fluid flow for both hydrothermal systems, highlighting the utility of methods for synoptic monitoring of Yellowstone National Park's hydrothermal systems.

Abstract. Soybean aphids are serious pests, causing negative yield impacts in the crop. Assessing their population is essential for making appropriate pesticide application decisions. Manual identification and counting, which is commonly performed to determine the economic threshold level, is time-consuming, laborious, and causes visual fatigue. In this study, an automatic image processing method was developed to identify and count aphids as well as exoskeletons and leaf spots on soybean leaves based on shape analysis. Aphid-infested soybean trifoliates were obtained at three infestation rates (low, medium, and high). Images of the front sides of the leaves were captured in the laboratory with three cameras (digital single-lens reflex or DSLR, consumer-grade digital, and smartphone) under two lighting conditions (direct and indirect). The shape parameters considered were area, perimeter, convex area, eccentricity, aspect ratio, solidity, hollowness, and roundness. Among the shape parameters tested, hollowness was the best in identifying aphids and was therefore used for developing the object classification algorithm. Of the three cameras tested, images from the consumer-grade digital camera produced the best identification accuracy (>82.4%), followed by the DSLR camera (>81.2%) and smartphone camera (>37.9%). Statistical analysis revealed that the accuracies did not differ significantly under different lighting conditions (p = 0.43), but the accuracies differed for the smartphone camera compared to the DSLR and consumer-grade digital cameras (p = 8.87 × 10-10). The results of automatic and manual counting were very well correlated (r = 0.92). The automatic image processing method achieved more rapid counting (<2 s per image after loading the image) compared to manual counting (~5 min per image). The developed approach for aphid identification and counting can be easily applied to other pest identification issues with minor modifications to the algorithm. Keywords: Aphids, Classification, Image processing, Segmentation, Shape analysis, Soybean.

Monitoring Yellowstone National Park's hydrothermal systems and establishing hydrothermal baselines are the main goals of an ongoing collaborative effort between Yellowstone National Park's Geology program and Utah State University's Remote Sensing Services Laboratory. During the first years of this research effort, improvements were made in image acquisition, processing and calibration. In 2007, a broad-band, forward looking infrared (FLIR) camera (8-12 microns) provided reliable airborne images for a hydrothermal baseline of the Hot Spring Basin hydrothermal system. From 2008 to 2011, night-time, airborne thermal infrared image acquisitions during September yielded temperature maps that established the temporal variability of the hydrothermal system. A March 2012 airborne image acquisition provided an initial assessment of seasonal variability. The consistent, high-spatial resolution imagery (~1 m) demonstrates that the technique is robust and repeatable for generating corrected (atmosphere and emissivity) and calibrated temperature maps of the Hot Spring Basin hydrothermal system. Atmospheric conditions before and at flight-time determine the usefulness of the thermal infrared imagery for geohydrologic applications, such as hydrothermal monitoring. Although these ground-surface temperature maps are easily understood, quantification of OPEN ACCESSRemote Sens. 2013, 5 6588 radiative heat from the Hot Spring Basin hydrothermal system is an estimate of the system's total energy output. Area is a key parameter for calculating the hydrothermal system's heat output. Preliminary heat calculations suggest a radiative heat output of ~56 MW to 62 MW for the central Hot Spring Basin hydrothermal system. Challenges still remain in removing the latent solar component within the calibrated, atmospherically adjusted, and emissivity corrected night-time imagery.