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Crop scouting is essential to manage crops such as potatoes and to detect stresses. In fact, conventional approaches require a lot of time and staff. The rise of unmanned aerial vehicle imagery offers interesting perspectives in this area. However, the development of interpreted and generalizable cartographic products that can be used directly by producers still poses challenges in terms of processing complexity and production time. The purpose of this study was to develop a tool for the phytosanitary surveillance of potato crops using scouting maps in support of conventional methods. The approach is based on relatively simple and efficient preprocessing methods when no radiometric correction data are available. The first step establishes the best correlations between some biophysical parameters directly related to phytosanitary problems and vegetation indices in the visible and infrared domains. The second step allows the development of an approach to classify the following three stresses: pests, diseases, and development problems. Validated by scouting field sites, the developed approach makes it possible to quickly produce accurate scouting maps that producers can use directly thanks to their high potential for generalization to other areas and crop productions.

In this paper, we present a novel smoothing approach for ultra-wideband (UWB) aided unmanned aerial vehicle (UAV) positioning. Existing works based on smoothing or filtering estimate 3D position of UAV by updating a solution for each single 1D low-dimensional UWB range measurement. However, a low-dimensional single range measurement merely acts as a weak constraint in a solution space for UAV position estimation, and thus it can often lead to incorrect estimation in unfavorable conditions. Inspired by the idea that the multilateration outcome can be utilized as a measurement providing a strong constraint, we utilize two types of UWB-based measurements: (i) each single 1D range as a high-rate measurement with a weak constraint, and (ii) multilateration outcome as a low-rate measurement with a strong constraint. We propose an incremental smoothing-based method that seamlessly integrates these two types of UWB-based measurements and inertial measurement into a unified factor graph framework. Through experiments under a variety of scenarios, we demonstrate the effectiveness of the proposed method.

In this paper, the procedure of developing and evaluating a UAV-borne mapping system is described. The system is equipped with both a LiDAR and a camera. The system mounting parameters, as well as the intrinsic parameters of the individual sensors, are calibrated rigorously. Simultaneous calibration of the LiDAR intrinsic parameters and the LiDAR-camera mounting parameters is performed in a self-calibrating bundle adjustment with additional relative orientation constraints. A visual-inertial approach is proposed to georeference the laser scans without using a GNSS receiver. This approach is motivated not only by the interest of users in low-cost systems but also by the fact that the integrity of GNSS signals might be affected under several environmental conditions, e.g., indoors, in urban canyons, under tree canopies. It is shown that a low-cost inertial measurement unit not equipped with a dual-frequency, real-time kinematic GNSS receiver is still useful for georeferencing the laser scanning data with cm-level accuracy. The scans are also textured using the images captured by the camera, which enriches the LiDAR point clouds with spectral information.

In the last decade, applications of unmanned aerial vehicles (UAVs), as remote-sensing platforms, have extensively been investigated for fine-scale mapping, modeling and monitoring of the environment. In few recent years, integration of 3D laser scanners and cameras onboard UAVs has also received considerable attention as these two sensors provide complementary spatial/spectral information of the environment. Since lidar performs range and bearing measurements in its body-frame, precise GNSS/INS data are required to directly geo-reference the lidar measurements in an object-fixed coordinate system. However, such data comes at the price of tactical-grade inertial navigation sensors enabled with dual-frequency RTK-GNSS receivers, which also necessitates having access to a base station and proper post-processing software. Therefore, such UAV systems equipped with lidar and camera (UAV-LiCam Systems) are too expensive to be accessible to a wide range of users. Hence, new solutions must be developed to eliminate the need for costly navigation sensors. In this paper, a two-fold solution is proposed based on an in-house developed, low-cost system: 1) a multi-sensor self-calibration approach for calibrating the Li-Cam system based on planar and cylindrical multi-directional features; 2) an integrated sensor orientation method for georeferencing based on unscented particle filtering which compensates for time-variant IMU errors and eliminates the need for GNSS measurements.

Unmanned aerial vehicles (UAVs) have become popular platforms for collecting various types of geospatial data for various mapping, monitoring and modelling applications. With the advancement of imaging and computing technologies, a vast variety of photogrammetric, computer-vision and, nowadays, end-to-end learning workflows are introduced to produce three-dimensional (3D) information in form of digital surface and terrain models, textured meshes, rectified mosaics, CAD models, etc. These 3D products might be used in applications where accuracy and precision play a vital role, e.g. structural health monitoring. Therefore, extensive tests against data with relevant characteristics and reliable ground-truth are required to assess and ensure the performance of 3D modelling workflows. This article describes the images collected by a customized unmanned aerial vehicle (UAV) system from an open-pit gravel mine accompanied with additional data that will allow implementing and evaluating any structure-from-motion or photogrammetric approach for sparse or dense 3D reconstruction. This dataset includes total of 158 high-quality images captured with more than 80% endlap and spatial resolution higher than 1.5 cm, the 3D coordinates of 109 ground control points and checkpoints, 2D coordinates of more than 40K corresponding points among the images, a subset of 25 multi-view stereo images selected from an area of approximately 30 m × 40 m within the scene accompanied with a dense point cloud measured by a terrestrial laser scanner. Keywords: Unmanned aerial vehicle, Structure from motion, Dense matching, Bundle adjustment, Stereo

Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.

Wildlife management is based on various measurements representative of the health of populations and their habitats. Some agencies are focusing on animal surveys to manage species such as white-tailed deer (Odocoileus virginianus). Current survey methods are faced with the challenge of reduced operating costs as well as estimating and correcting detection biases. Our pilot study (data collected on 6 Nov 2012 at Saint-David-de-Falardeau, QC, Canada) assessed the potential of a new approach detect and count deer based on visible and thermal infrared image processing at very-high spatial resolutions using an unmanned aerial system (UAS). Supervised and unsupervised pixel-based image classification approaches as well as object-based image analysis (OBIA) were assessed for different spatial resolutions and with different combinations of spectral bands. None of the pixel-based approaches were effective for detecting deer. The OBIA approach detected deer with a rate of up to 100% under the best conditions by using a combination of visible and thermal infrared imagery at a spatial resolution of 0.8 cm/pixel. Overall, this approach had an average detection rate of 0.5, which is comparable to conventional aerial surveys. Visual obstructions by coniferous canopy and the spectral confusion associated with certain elements (e.g., bare soil, rocks) are problems that remain unresolved. Using UASs with image processing for surveys of deer and other species of large mammals is promising, but currently limited by the flight range of unmanned aerial vehicles and the associated regulations. © 2016 The Wildlife Society.

Along with the advancement of unmanned aerial vehicles (UAVs), improvement of high-resolution cameras and development of vision-based mapping techniques, unmanned aerial imagery has become a matter of remarkable interest among researchers and industries. These images have the potential to provide data with unprecedented spatial and temporal resolution for three-dimensional (3D) modelling. In this paper, we present our theoretical and technical experiments regarding the development, implementation and evaluation of a UAV-based photogrammetric system for precise 3D modelling. This system was preliminarily evaluated for the application of gravel-pit surveying. The hardware of the system includes an electric powered helicopter, a 16-megapixels visible camera and inertial navigation system. The software of the system consists of the in-house programs built for sensor calibration, platform calibration, system integration and flight planning. It also includes the algorithms developed for structure from motion (SfM) computation including sparse matching, motion estimation, bundle adjustment and dense matching.

The specific requirements of UAV-photogrammetry necessitate particular solutions for system development, which have mostly been ignored or not assessed adequately in recent studies. Accordingly, this paper presents the methodological and experimental aspects of correctly implementing a UAV-photogrammetry system. The hardware of the system consists of an electric-powered helicopter, a high-resolution digital camera and an inertial navigation system. The software of the system includes the in-house programs specifically designed for camera calibration, platform calibration, system integration, on-board data acquisition, flight planning and on-the-job self-calibration. The detailed features of the system are discussed, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The developed system is extensively tested for precise modeling of the challenging environment of an open-pit gravel mine. The accuracy of the results is evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy are assessed. The experiments demonstrated that 1.55 m horizontal and 3.16 m vertical absolute modeling accuracy could be achieved via direct geo-referencing, which was improved to 0.4 cm and 1.7 cm after indirect geo-referencing.

Wildlife aerial surveys require time and significant resources. Multispecies detection could reduce costs to a single census for species that coexist spatially. Traditional methods are demanding for observers in terms of concentration and are not adapted to multispecies censuses. The processing of multispectral aerial imagery acquired from an unmanned aerial vehicle (UAV) represents a potential solution for multispecies detection. The method used in this study is based on a multicriteria object-based image analysis applied on visible and thermal infrared imagery acquired from a UAV. This project aimed to detect American bison, fallow deer, gray wolves, and elks located in separate enclosures with a known number of individuals. Results showed that all bison and elks were detected without errors, while for deer and wolves, 0–2 individuals per flight line were mistaken with ground elements or undetected. This approach also detected simultaneously and separately the four targeted species even in the presence of other untargeted ones. These results confirm the potential of multispectral imagery acquired from UAV for wildlife census. Its operational application remains limited to small areas related to the current regulations and available technology. Standardization of the workflow will help to reduce time and expertise requirements for such technology.

In this paper, we propose a robust technique using genetic algorithm for detecting inliers and estimating accurate motion parameters from putative correspondences containing any percentage of outliers. The proposed technique aims to increase computational efficiency and modelling accuracy in comparison with the state-of-the-art via the following contributions: i) guided generation of initial populations for both avoiding degenerate solutions and increasing the rate of useful hypotheses, ii) replacing random search with evolutionary search, iii) possibility of evaluating the individuals of every population by parallel computation, iv) being performable on images with unknown internal orientation parameters, iv) estimating the motion model via detecting a minimum, however more than enough, set of inliers, v) ensuring the robustness of the motion model against outliers, degeneracy and poorperspective camera models, vi) making no assumptions about the probability distribution of inliers and/or outliers residuals from the estimated motion model, vii) detecting all the inliers by setting the threshold on their residuals adaptively with regard to the uncertainty of the estimated motion model and the position of the matches. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC, MSAC, MLESAC, Least Trimmed Squares and Least Median of Squares. Experimental results proved that the proposed approach perform better than others in terms of accuracy of motion estimation, accuracy of inlier detection and the computational efficiency.

This paper presents a workflow for robust structure-from-motion (SfM) and 3D reconstruction, which is applied to unmanned aerial imagery acquired from an open-pit mine. A robust method based on gen...

Unmanned aerial vehicles have become popular platforms for remote-sensing applications, particularly when spaceborne technology, manned airborne techniques, and in situ methods are not as efficient...

Recent studies have shown that ice duration in lakes and rivers over the Northern Hemisphere has decreased over the 19th and 20th centuries in response to global warming. However, lake ice trends have not been well documented in Canada. Because of its size, considerable variability may exist in both freeze-up and break-up dates across the country. In this paper, results of the analysis of recent trends (1951–2000) in freeze-up and break-up dates across Canada are presented. Trends toward earlier break-up dates are observed for most lakes during the time periods of analysis which encompass the 1990s. Freeze-up dates, on the other hand, show few significant trends and a low degree of temporal coherence when compared with break-up dates. These results are compared with trends in autumn and spring 0 °C isotherm dates over the time period 1966–95. Similar spatial and temporal patterns are observed, with generally significant trends toward earlier springs/break-up dates over most of western Canada and little change in isotherm and freeze-up dates over the majority of the country in autumn. Strong correlations ( r> 0Ð5) between 0 °C isotherm dates and freeze-up/break-up dates at many locations across the country reveal the high synchrony of these variables. These results are also consistent with more recent observations of other cryospheric and atmospheric variables that indicate, in particular, a general trend toward earlier springs in the latter part of the 20th century. The results of this study provide further evidence of the robustness of lake ice as a proxy indicator of climate variability and change. Copyright  2006 John Wiley & Sons, Ltd.

A one-dimensional thermodynamic lake ice model (Canadian Lake Ice Model or CLIMo) is used to simulate ice phenology on Great Slave Lake (GSL) in the Mackenzie River basin, Northwest Territories, Canada. Model simulations are validated against freeze-up and break-up dates, as well as ice thickness and on-ice snow depth measurements made in situ at three sites on GSL (Back Bay near Yellowknife, 1960-91; Hay River, 1965-91; Charlton Bay near Fort Reliance, 1977-90). Freeze-up and break-up dates from the lake ice model are also compared with those derived from SSM/I 85 GHz passive microwave imagery over the entire lake surface (1988-99). Results show a very good agreement between observed and simulated ice thickness and freeze-up/break-up dates over the 30-40 years of observations, particularly for the Back Bay and Hay River sites. CLIMo simulates the ice thickness and annual freeze-up/break-dates with a mean error of 7 cm and 4 days respectively. However, some limitations have been identified regarding the rather simplistic approach used to characterize the temporal evolution of snow cover on ice. Future model improvements will therefore focus on this particular aspect, through linkage or coupling to a snow model.