Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model, Geomorphology (2013) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. m, while the vertical RMSE of the DTM was 0.29 m, which is equivalent to the RMSE of a bare earth LiDAR DTM for the same site. The combined error from both datasets was used to define a threshold of the minimum elevation difference that could be reliably attributed to erosion or deposition in the seven years separating the sUAS and LiDAR datasets. Overall, our results suggest that sUAS-acquired imagery may provide a low-cost, rapid, and flexible alternative to airborne LiDAR for geomorphological mapping. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT

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Small unmanned aircraft systems (UASs) are often suited to applications where the cost, resolution, and (or) operational inflexibility of conventional remote sensing platforms is limiting. Remote sensing with small UASs is still relatively new, and there is limited understanding of how the data are acquired and used for scientific purposes and decision making. This paper provides practical guidance about the opportunities and limitations of small UAS-based remote sensing by highlighting a small sample of scientific and commercial case studies. Case studies span four themes: (i) mapping, which includes case studies to measure aggregate stockpile volumes and map river habitat; (ii) feature detection, which includes case studies on grassland image classification and detection of agricultural crop infection; (iii) wildlife and animal enumeration, with case studies describing the detection of fish concentrations during a major salmon spawning event, and cattle enumeration at a concentrated animal feeding operation; (iv) landscape dynamics with a case study of arctic glacier change. Collectively, these case studies only represent a fraction of possible remote sensing applications using small UASs, but they provide insight into potential challenges and outcomes, and help clarify the opportunities and limitations that UAS technology offers for remote sensing of the environment.Key words: UAS, remote sensing of the environment, case studies, unmanned aerial vehicles, remotely piloted aircraft, remote sensing.Résumé : Les petits systèmes d'aéronef sans pilote (UAS) sont principalement utilisés pour des applications dont les coûts, la résolution et (ou) l'inflexibilité opérationnelle des plateformes de télédétection conventionnelles sont limités. La télédétection à l'aide de petits UAS est encore relativement récente et on connaît mal la façon dont les données sont recueillies puis utilisées à des fins scientifiques et décisionnelles. Le présent exposé fournit des conseils pratiques sur les possibilités et les limites de la télédétection à partir de petits UAS en se servant d'un petit échantillon d'études scientifiques et commerciales. Quatre thèmes sont examinés dans ces différentes études de cas : (i) la cartographie, afin de mesurer les volumes d'aire de stockage des agrégats et établir une représentation graphique de l'habitat d'un cours d'eau; (ii) la détection des caractéristiques, afin d'effectuer la classification des images de dunes et la détection de l'infection de terres agricoles; (iii) le dénombrement de la faune et du bétail, afin de décrire la détection des concentrations de poissons lors de Mots-clés : UAS, télédétection de l'environnement, études de cas, véhicules aériens sans pilote, aéronef téléguidé, télédétection.

[1] This paper reports the results of laboratory and field tests that evaluate the performance of a new laser particle counter for measuring aeolian sand transport. The Wenglor® model YH03PCT8 ("Wenglor") consists of a laser (655 nm), photo sensor, and switching circuit. When a particle passes through the 0.6 mm diameter, 30 mm long laser beam, the sensor outputs a digital signal. Laboratory tests with medium sand and a vertical gravity flume show that the Wenglor count rate scales approximately linearly with mass flux up to the saturation point of the sensor, after which the count rate decreases despite increasing mass flux. Saturation depends on the diameter and concentration of particles in the airstream and may occur during extreme events in the field. Below saturation sensor performance is relatively consistent; the mean difference between average count rate response was between 50 and 100 counts. Field tests provide a complimentary frame of reference for evaluating the performance of the Wenglor under varying environmental conditions and to gauge its performance with respect to a collocated piezoelectric impact sensor (Sensit H11-B). During 136.5 h of deployment on an active sand dune the relative proportion of time sand transport recorded by two Wenglors was 0.09% and 0.79%, compared to 4.68% by the Sensit H11-B. The weak performance of the Wenglors is attributed to persistent lens contamination from adhesion of sand grains on the sensors after rainfall. However, during dry and windy conditions the Wenglor performance improved substantially; sensors measured a concentration of sand particles in the airstream more than seven times greater than that measured by the Sensit. Between the two Wenglors, the mean absolute count rate difference was 6.16 counts per second, with a standard deviation of 8.53 counts per second. For short-term measurement campaigns in dry conditions, therefore, the Wenglor is relatively consistent and can outperform the Sensit in detecting particles in the airstream. The Sensit, however, is more reliable in detecting particle transport during longer unattended deployments. Two additional field tests show that the sensor is well-suited to the measurement of snow drifting but could be ineffective in dusty settings because of lens contamination. Overall, the main advantages of the Wenglor include (1) insensitivity to particle momentum; (2) low measurement variability; (3) low cost ($210 USD); and perhaps most important of all, (4) a consistent design that will improve comparison of results between investigations. At present, no other particle detector used in aeolian research can claim all these characteristics.Citation: Hugenholtz, C. H., and T. E. Barchyn (2011), Laboratory and field performance of a laser particle counter for measuring aeolian sand transport,

Fugitive methane emissions from the oil and gas industry are targeted using leak detection and repair (LDAR) programs. Until recently, only a limited number of measurement standards have been permitted by most regulators, with emphasis on close-range methods (e.g. Method-21, optical gas imaging). Although close-range methods are essential for source identification, they can be labor-intensive. To improve LDAR efficiency, there has been a policy shift in Canada and the United States towards incorporating alternative technologies. However, the suitability of these technologies for LDAR remains unclear. In this paper, we systematically review and compare six technology classes for use in LDAR: handheld instruments, fixed sensors, mobile ground labs (MGLs), unmanned aerial vehicles (UAVs), aircraft, and satellites. These technologies encompass broad spatial and temporal scales of measurement. Minimum detection limits for technology classes range from <1 g h−1 for Method 21 instruments to 7.1 × 106 g h−1 for the GOSAT satellite, and uncertainties are poorly constrained. To leverage the diverse capabilities of these technologies, we introduce a hybrid screening-confirmation approach to LDAR called a comprehensive monitoring program. Here, a screening technology is used to rapidly tag high-emitting sites to direct close-range source identification. Currently, fixed sensors, MGLs, UAVs, and aircraft could be used as screening technologies, but their performances must be evaluated under a range of environmental and operational conditions to better constrain detection effectiveness. Methane-sensing satellites are improving rapidly and may soon be ready for facility-scale screening. We conclude with a speculative discussion of the future of LDAR, touching on integration, analytics, incentivization, and regulatory pathways.