The performance of a sensor platform for environmental or industrial monitoring is sensitive to the cost and performance of the individual sensor elements. Thus, the detection limits, accuracy, and precision of commercially available, low-cost carbon dioxide and methane gas concentration sensors were evaluated by precise measurements at known gas concentrations. Sensors were selected based on market availability, cost, power consumption, detection range, and accuracy. A specially constructed gas mixing chamber, coupled to a precision bench-top analyzer, was used to characterize each sensor during a controlled exposure to known gas concentrations. For environmental monitoring, the selected carbon dioxide sensors were characterized around 400 ppm. For methane, the sensor response was first monitored at 0 ppm, close to the typical environmental background. The selected sensors were then evaluated at gas concentrations of several thousand ppm. The determined detection limits accuracy, and precision provides a set of matrices that can be used to evaluate and select sensors for integration into a sensor platform for specific applications.

Methane (CH₄) contributes significantly to global warming. However, accurate identification of CH₄ sources for reducing CH₄ emissions is often hampered by inadequate accuracy and spatiotemporal coverage of CH₄ detection, and lack of accurate CH₄ forward modeling used in top‐down inversion systems. In this study, a field experiment was conducted in Pampa, Texas using two CH₄ sensors (LI‐COR and OGI camera) to detect CH₄ releases. We investigated whether high‐resolution simulations using the Weather Research and Forecasting (WRF) model with greenhouse gases (WRF‐GHG) could accurately simulate the CH₄ plumes in the presence of evolving atmospheric boundary layer from sunrise to noon. CH₄ plumes showed substantial variation in time. At a release rate of ∼17.5 kg hr⁻¹, the maximum enhancement of CH₄ measured by LI‐COR was 2.6 ppm at sunrise (7:36 a.m.), 250 m from the release location. Within half an hour after sunrise, this enhancement decreased to 0.3–0.4 ppm. The enhancement was 0.2 ppm by 10:00 a.m. and further dropped to less than 0.1 ppm after 11:30 a.m. Due to the low temperature at sunrise, the OGI camera failed to detect the CH₄ plume. The WRF‐GHG large‐eddy simulation (LES) with 32 m grid spacing successfully reproduced these CH₄ enhancements. In situ measurements together with numerical simulations illustrate the impact of the transition from a stable boundary layer in the early morning to a convective boundary layer at noon on the dispersion of CH₄ plumes. Additionally, CH₄ plumes from a cattle farm in Oklahoma are briefly examined using the same modeling approach.

Moon watching is a method of quantifying nocturnal bird migration by focusing a telescope on the moon and recording observations of flying birds silhouetted against the lunar surface. Although simple and well-established, researchers use moon watching infrequently due in part to the hours of late night observation it requires. To reduce the labor entailed in moon watching, we designed a low-cost system called LunAero that can track and record video of the moon at night. Here we present a proof-of-concept prototype that can serve as a platform for citizen scientists interested in observing nocturnal bird migration. We tested the video recording on clear nights from February 2018 to May 2019 when the moon was full or nearly full. Manual analysis of a 1.5 h sample of video revealed a total of 450 birds, which is a much higher detection rate than previous moon watching efforts have yielded. The hardware described here is part of a larger effort involving software development (currently underway) to automate recorded video analysis. We argue that LunAero can reduce the labor involved in moon watching, offer improved data quality over traditional moon watching, and provide insights into social behavior and wind-drift compensation in migrating birds.

Quantification of nocturnal migration of birds through moon watching is a technique ripe for modernization with superior computational power. In this paper, collected by a motorized telescope mount was data analyzed using both video observations by trained observers and modernized approaches using computer vision. The more advanced data extraction used the OpenCV library of computer vision tools to identify bird silhouettes by means of image stabilization and background subtraction. The silhouettes were sanitized and analyzed in sequence to produce stacked relationships between temporally close contours, discriminating birds from noise based on the assumption that birds migrate in stable paths. The flight ceiling of the birds was determined by extracting relevant correlation coefficient data from doppler radar co-located with the LunAero instrument in Norman, OK, USA using a method with low-computational overhead. The bird paths and flight ceiling were combined with lunar ephemera to provide input for the original method used for nocturnal migration quantification as well as an enhanced version of the same method with more advanced computational tools. We found that the manual quantification of migration activity detected 16,300 birds/km•h heading northwest from 110°, whereas the automated analysis reported a density of 43,794 birds/km•h heading northwest from 106.67°. Hence, there was agreement with regard to flight direction, but the automated method overestimated migration density by approximately three times. The reasons for the discrepancy between flight path detection appeared to be due to a substantial amount of noise in the video data as well as a tendency for the computer vision analysis to split single flight paths into two or more segments. The authors discuss ongoing innovations aimed at addressing these methodological challenges.

Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang–Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal “false positive” noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods.