Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

Real-time model-based video stabilization for microaerial vehicles

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.Peer ReviewedPostprint (author's final draft

UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition

Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition. In principle, techniques like deblurring and super-resolution should yield improvements by de-emphasizing noise and increasing signal in an input image. But the historically divergent goals of the computational photography and visual recognition communities have created a significant need for more work in this direction. To facilitate new research, we introduce a new benchmark dataset called UG^2, which contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 160,000 annotated frames forhundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches. Further, current image restoration and enhancement techniques are evaluated by determining whether or not theyimprove baseline classification performance. Results showthat there is plenty of room for algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset: https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or

Integration of LIDAR and IFSAR for mapping

LiDAR and IfSAR data is now widely used for a number of applications, particularly those needing a digital elevation model. The data is often complementary to other data such as aerial imagery and high resolution satellite data. This paper will review the current data sources and the products and then look at the ways in which the data can be integrated for particular applications. The main platforms for LiDAR are either helicopter or fixed wing aircraft, often operating at low altitudes, a digital camera is frequently included on the platform, there is an interest in using other sensors such as 3 line cameras of hyperspectral scanners. IfSAR is used from satellite platforms, or from aircraft, the latter are more compatible with LiDAR for integration. The paper will examine the advantages and disadvantages of LiDAR and IfSAR for DEM generation and discuss the issues which still need to be dealt with. Examples of applications will be given and particularly those involving the integration of different types of data. Examples will be given from various sources and future trends examined

Unmanned Aerial Vehicle (UAV) for monitoring soil erosion in Morocco

This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents