Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

The application of small unmanned aerial systems for precision agriculture: a review

The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000–2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.

Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint

The production of topographic datasets is of increasing interest and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods have evolved. Despite the range of available methods, the production of high resolution, high quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware and/or software. However, image-based methods such as digital photogrammetry have been decreasing in costs. Developed for the purpose of rapid, inexpensive and easy three-dimensional surveys of buildings or small objects, the ‘structure from motion’ photogrammetric approach (SfM) is an image-based method which could deliver a methodological leap if transferred to geomorphic applications, requires little training and is extremely inexpensive. Using an online SfM program, we created high-resolution digital elevation models of a river environment from ordinary photographs produced from a workflow that takes advantage of free and open source software. This process reconstructs real world scenes from SfM algorithms based on the derived positions of the photographs in three-dimensional space. The basic product of the SfM process is a point cloud of identifiable features present in the input photographs. This point cloud can be georeferenced from a small number of ground control points collected in the field or from measurements of camera positions at the time of image acquisition. The georeferenced point cloud can then be used to create a variety of digital elevation products. We examine the applicability of SfM in the Pedernales River in Texas (USA), where several hundred images taken from a hand-held helikite are used to produce DEMs of the fluvial topographic environment. This test shows that SfM and low-altitude platforms can produce point clouds with point densities comparable with airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels. Copyright © 2012 John Wiley & Sons, Ltd.

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Topographic structure from motion: a new development in photogrammetric measurement

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

Rangeland comprises as much as 70% of the Earth's land surface area. Much of this vast space is in very remote areas that are expensive and often impossible to access on the ground. Unmanned Aerial Vehicles (UAVs) have great potential for rangeland management. UAVs have several advantages over satellites and piloted aircraft: they can be deployed quickly and repeatedly; they are less costly and safer than piloted aircraft; they are flexible in terms of flying height and timing of missions; and they can obtain imagery at sub-decimeter resolution. This hyperspatial imagery allows for quantification of plant cover, composition, and structure at multiple spatial scales. Our experiments have shown that this capability, from an off-the-shelf mini-UAV, is directly applicable to operational agency needs for measuring and monitoring. For use by operational agencies to carry out their mandated responsibilities, various requirements must be met: an affordable and reliable platform; a capability for autonomous, low altitude flights; takeoff and landing in small areas surrounded by rugged terrain; and an easily applied data analysis methodology. A number of image processing and orthorectification challenges have been or are currently being addressed, but the potential to depict the land surface commensurate with field data perspectives across broader spatial extents is unrivaled.

Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management.

The use of unmanned aerial vehicles (UAVs) for natural resource applications has increased considerably in recent years due to their greater availability, the miniaturization of sensors, and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory, assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semiautomated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between imageand ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace, the results of this study show that UAVs can be used successfully to obtain imagery for rangeland monitoring, and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the UAV mission, image processing and analysis, and accuracy assessment.

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Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring.

Rangelands cover about 50% of the earth's land surface, are in remote areas and have low population densities, all of which provide an ideal opportunity for remote sensing applications from unmanned aircraft systems (UAS). In this article, we describe a proven workflow for UAS-based remote sensing, and discuss geometric errors of image mosaics and classification accuracies at different levels of detail. We report on several UAS missions over rangelands in Idaho and New Mexico, USA, where we acquired 6–8 cm resolution aerial photography and concurrent field measurements. The geometric accuracies of the image mosaics were in the 1–2 m range, and overall classification accuracies for vegetation maps ranged from 78–92%. Despite current FAA regulations that restrict UAS operations to distances within line-of-sight of the UAS, our results show that UAS are a viable platform for obtaining very high-resolution remote sensing products for applied vegetation mapping of rangelands.

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UAS remote sensing missions for rangeland applications

Electronic tracking provides a unique way to document behaviour by cows on a continuous basis. Over 2 years 17 beef cows with calves were fitted with global positioning system (GPS) devices programmed to record uncorrected GPS locations at 1-s intervals in a semi-desert rangeland. Each cow was periodically observed during daylight hours and foraging, walking and stationary (standing/lying) activity times were recorded across days and individual cows to calculate a mean travel rate for each activity. Data without observers present were collected immediately preceding and following the abrupt weaning of calves at between 223 and 234 days of age to evaluate the potential of classifying various travel rates into foraging, walking and stationary activity. The three activities were further characterised within a 24-h period based on the sun’s angle with respect to the horizon. Only data from cows whose equipment acquired ≥ 90% of the potential GPS positional data among consecutive days were analysed. Due to problems with the equipment, data from two cows in 2009 and two cows in 2011 met these criteria. The interval evaluated consisted of four 24-h periods before abrupt weaning and seven 24-h periods following weaning. Results suggested that uncorrected 1-s positional GPS data are satisfactory to classify the behaviour by free-ranging beef cows into foraging, walking and stationary activities. Furthermore, abrupt weaning caused cows to change their spatial and temporal behaviour across and within days. Overall, travel by cows increased post-weaning with subtle within-day behavioural changes. Further research will be required to fully understand the biological importance of spatio-temporal behaviour to optimise cattle and landscape management goals.

Characterising the Spatial and Temporal Activities of Free-Ranging Cows from GPS Data

Digital aerial photography acquired with unmanned aerial vehicles (UAVs) has great value for resource management due to flexibility and relatively low cost for image acquisition. The very high resolution imagery (5 cm) allows for mapping bare soil and vegetation types, structure and patterns in great detail. While image acquisition is relatively straightforward, the creation of orthorectified, GIS-ready image mosaics presents multiple challenges. Those include relatively small image footprints, image distortion due to the use of low-cost digital cameras, difficulty in locating ground control points and in automatic generation of tie points, and relatively large errors in exterior orientation (camera position and attitude information from the UAV’s GPS/IMU). We developed an automated procedure to improve the accuracy of the exterior orientation by matching the UAV images to an orthorectified reference image. Using the UAV reported exterior orientation and camera geometry, combined with the reference image and DEM, the algorithm simulates image acquisition and then computes the covariance between camera image and simulated image pixels. With this evaluation function, a heuristic search algorithm finds successive improvements to the external orientation, ultimately producing a corrected exterior orientation that allows orthorectification with minimal input of tie points and/or ground control points. The RMS error for a 5-cm resolution, 257-image mosaic was 48 cm. Cost and turnaround time for production of orthorectified mosaics from UAV imagery are considerably reduced due to less time and money spent on ground control point and manual tie point collection.

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Craig Winters

Papers

Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management.

Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring.

UAS remote sensing missions for rangeland applications

Characterising the Spatial and Temporal Activities of Free-Ranging Cows from GPS Data

A procedure for orthorectification of sub-decimeter resolution imagery obtained with an unmanned aerial vehicle (uav)