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Hello, I'm a PhD candidate in Geography at University of Calgary. My research focuses on drone-based pre- and post-disaster mapping for supporting damage assessments. Broadly, I am interested in the intersection of geospatial methods/technologies and disaster/emergency management (in Canada and abroad).
Small (< 25 kg) aerial drones have expanded the remote sensing toolkit for disaster management activities. Here, we provide a critical review of drone-based remote sensing of natural hazard-related disasters to highlight research trends, biases, and expose new opportunities. We performed a systematic literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, resulting in 635 relevant articles from which we derived statistics relating to geography, drone hardware, disaster management application, and drone remote sensing data type and analysis method. Key findings include a bias towards: (i) mass movement hazards (38%); (ii) small (< 1 km2) (76%) and rural (79%) study areas in high-income countries and territories (64%); (iii) image-based observations of features from the natural environment (77%); and (iv) support of mitigation-related vulnerability assessment and risk modeling (54%) and environmental recovery (23%). We recommend that future studies focus on: (i) earthquakes, floods, and cyclones and other windstorms due to higher loss of life and economic impacts; (ii) larger and urban study areas in low, lower-middle, and upper-middle income countries and territories to support vulnerable populations; (iii) under-demonstrated (and especially response-related) disaster management activities, which generally require observations of built features from urban environments; and (iv) data standards for integrating drone-based remote sensing with international disaster management methodologies.
Maja Kucharczyk; Chris H. Hugenholtz. Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities. Remote Sensing of Environment 2021, 264, 112577 .
AMA StyleMaja Kucharczyk, Chris H. Hugenholtz. Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities. Remote Sensing of Environment. 2021; 264 ():112577.
Chicago/Turabian StyleMaja Kucharczyk; Chris H. Hugenholtz. 2021. "Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities." Remote Sensing of Environment 264, no. : 112577.
Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial); (ii) best practices informed by research; and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.
Maja Kucharczyk; Geoffrey Hay; Salar Ghaffarian; Chris Hugenholtz. Geographic Object-Based Image Analysis: A Primer and Future Directions. Remote Sensing 2020, 12, 2012 .
AMA StyleMaja Kucharczyk, Geoffrey Hay, Salar Ghaffarian, Chris Hugenholtz. Geographic Object-Based Image Analysis: A Primer and Future Directions. Remote Sensing. 2020; 12 (12):2012.
Chicago/Turabian StyleMaja Kucharczyk; Geoffrey Hay; Salar Ghaffarian; Chris Hugenholtz. 2020. "Geographic Object-Based Image Analysis: A Primer and Future Directions." Remote Sensing 12, no. 12: 2012.
We report a case study using drone-based imagery to develop a pre-disaster 3-D map of downtown Victoria, British Columbia, Canada. This represents the first drone mapping mission over an urban area approved by Canada's aviation authority. The goal was to assess the quality of the pre-disaster 3-D data in the context of geospatial accuracy and building representation. The images were acquired with a senseFly eBee Plus fixed-wing drone with real-time kinematic/post-processed kinematic functionality. Results indicate that the spatial accuracies achieved with this drone would allow for sub-meter building collapse detection, but the non-gimbaled camera was insufficient for capturing building facades.
Maja Kucharczyk; Chris H. Hugenholtz. Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada. Natural Hazards and Earth System Sciences 2019, 19, 2039 -2051.
AMA StyleMaja Kucharczyk, Chris H. Hugenholtz. Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada. Natural Hazards and Earth System Sciences. 2019; 19 (9):2039-2051.
Chicago/Turabian StyleMaja Kucharczyk; Chris H. Hugenholtz. 2019. "Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada." Natural Hazards and Earth System Sciences 19, no. 9: 2039-2051.
Maja Kucharczyk. Author responses to Referee #2 comments. 2019, 1 .
AMA StyleMaja Kucharczyk. Author responses to Referee #2 comments. . 2019; ():1.
Chicago/Turabian StyleMaja Kucharczyk. 2019. "Author responses to Referee #2 comments." , no. : 1.
Maja Kucharczyk. Author responses to Referee #1 comments. 2019, 1 .
AMA StyleMaja Kucharczyk. Author responses to Referee #1 comments. . 2019; ():1.
Chicago/Turabian StyleMaja Kucharczyk. 2019. "Author responses to Referee #1 comments." , no. : 1.
We examined the horizontal and vertical accuracy of LiDAR data acquired from an unmanned aerial vehicle (UAV) at a field site with six vegetation types: coniferous trees, deciduous trees, short grass (0–0.3 m height), tall grass (>0.3 m height), short shrubs (0–1 m height), and tall shrubs (>1 m height). The objective was to assess positional accuracy of the ground surface in the context of digital mapping standards, and to determine how different vegetation types affect vertical accuracy. The data were acquired from a single-rotor vertical takeoff and landing UAV equipped with a Riegl VUX-1UAV laser scanner, KVH Industries 1750 IMU, and dual NovAtel GNSS receivers. Reference measurements of ground surface elevation were acquired with conventional field surveying techniques. Accuracy was evaluated using methods in the 2015 American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data. Results show that horizontal accuracy and vegetated vertical accuracy at the 95% confidence level were 0.05 and 0.24 m, respectively. Median vertical errors significantly differed among 10 of 15 vegetation type pairs, highlighting the need to account for variations of vegetation structure. According to the 2015 ASPRS standards, the reported errors fulfill the requirements for mapping at the 2 and 8 cm horizontal and vertical class levels, respectively.
Maja Kucharczyk; Chris H. Hugenholtz; Xueyang Zou. UAV–LiDAR accuracy in vegetated terrain. Journal of Unmanned Vehicle Systems 2018, 6, 212 -234.
AMA StyleMaja Kucharczyk, Chris H. Hugenholtz, Xueyang Zou. UAV–LiDAR accuracy in vegetated terrain. Journal of Unmanned Vehicle Systems. 2018; 6 (4):212-234.
Chicago/Turabian StyleMaja Kucharczyk; Chris H. Hugenholtz; Xueyang Zou. 2018. "UAV–LiDAR accuracy in vegetated terrain." Journal of Unmanned Vehicle Systems 6, no. 4: 212-234.
We report a case study using drone-based imagery to develop a pre-disaster 3D map of downtown Victoria, British Columbia, Canada. This represents the first drone mapping mission over an urban area approved by Canada’s aviation authority. The goal was to assess the quality of the pre-disaster 3D data in the context of geospatial accuracy and building representation. The images were acquired with a senseFly eBee Plus fixed-wing drone with real-time kinematic/post-processed kinematic functionality. Results indicate that the spatial accuracies achieved with this drone would allow for sub-meter building collapse detection, but the non-gimbaled camera was insufficient for capturing building facades.
Maja Kucharczyk; Chris H. Hugenholtz. Pre-disaster mapping with drones: an urban case study in Victoria, BC, Canada. 2018, 2018, 1 -17.
AMA StyleMaja Kucharczyk, Chris H. Hugenholtz. Pre-disaster mapping with drones: an urban case study in Victoria, BC, Canada. . 2018; 2018 ():1-17.
Chicago/Turabian StyleMaja Kucharczyk; Chris H. Hugenholtz. 2018. "Pre-disaster mapping with drones: an urban case study in Victoria, BC, Canada." 2018, no. : 1-17.
Fluvial deposits are highly heterogeneous and inherently challenging to map in outcrop due to a combination of lateral and vertical variability along with a lack of continuous exposure. Heavily incised landscapes, such as badlands, reveal continuous three-dimensional (3-D) outcrops that are ideal for constraining the geometry of fluvial deposits and enabling reconstruction of channel morphology through time and space. However, these complex 3-D landscapes also create challenges for conventional field mapping techniques, which offer limited spatial resolution, coverage, and/or lateral contiguity of measurements. To address these limitations, we examined an emerging technique using images acquired from a small unmanned aerial vehicle (UAV) and structure-from-motion (SfM) photogrammetric processing to generate a 3-D digital outcrop model (DOM). We applied the UAV-SfM technique to develop a DOM of an Upper Cretaceous channel-belt sequence exposed within a 0.52 km2 area of Dinosaur Provincial Park (southeastern Alberta, Canada). Using the 3-D DOM, we delineated the lower contact of the channel-belt sequence, created digital sedimentary logs, and estimated facies with similar conviction to field-based estimations (±4.9%). Lateral accretion surfaces were also recognized and digitally traced within the DOM, enabling measurements of accretion direction (dip azimuth), which are nearly impossible to obtain accurately in the field. Overall, we found that measurements and observations derived from the UAV-SfM DOM were commensurate with conventional ground-based mapping techniques, but they had the added advantage of lateral continuity, which aided interpretation of stratigraphic surfaces and facies. This study suggests that UAV-SfM DOMs can complement traditional field-based methods by providing detailed 3-D views of topographically complex outcrop exposures spanning intermediate to large spatial extents.
Paul Nesbit; Paul R. Durkin; Christopher H. Hugenholtz; Stephen Hubbard; Maja Kucharczyk. 3-D stratigraphic mapping using a digital outcrop model derived from UAV images and structure-from-motion photogrammetry. Geosphere 2018, 1 .
AMA StylePaul Nesbit, Paul R. Durkin, Christopher H. Hugenholtz, Stephen Hubbard, Maja Kucharczyk. 3-D stratigraphic mapping using a digital outcrop model derived from UAV images and structure-from-motion photogrammetry. Geosphere. 2018; ():1.
Chicago/Turabian StylePaul Nesbit; Paul R. Durkin; Christopher H. Hugenholtz; Stephen Hubbard; Maja Kucharczyk. 2018. "3-D stratigraphic mapping using a digital outcrop model derived from UAV images and structure-from-motion photogrammetry." Geosphere , no. : 1.
UAV incidents were analyzed using data from Transport Canada’s Civil Aviation Daily Occurrence Reporting System (CADORS). Between 5 November 2005 and 31 December 2016 a total of 355 incidents were reported in Canadian airspace. The largest number involved UAV sightings (66.5%) and close encounters with piloted aircraft (22.3%). These incidents increased markedly after 2013, with the highest number in British Columbia, followed by Ontario, Quebec, Alberta, and Manitoba. The vast majority of UAV incident reports were filed by pilots of piloted aircraft. Typically, airspace at altitudes greater than 400 feet above ground level (AGL) is off limits to UAVs; however, of the 270 incidents in the CADORS database with UAV altitude reported, 80.4% were above 400 feet AGL and 62.6% were above 1000 feet AGL. Of the 268 incidents with reported horizontal distance to the nearest aerodrome, 74.6% occurred or likely occurred within five nautical miles (M), and of those 92.4% and 76.6% were reported above 100 and 300 feet AGL, respectively. Collectively, the CADORS data indicate that the overwhelming majority of UAV incidents reported in Canada were airspace violations. These results can guide future risk mitigation measures, hardware and software solutions, and educational campaigns to increase airspace safety.
Paul R. Nesbit; Thomas E. Barchyn; Chris H. Hugenholtz; Sterling Cripps; Maja Kucharczyk. Reported UAV incidents in Canada: analysis and potential solutions. Journal of Unmanned Vehicle Systems 2017, 5, 51 -61.
AMA StylePaul R. Nesbit, Thomas E. Barchyn, Chris H. Hugenholtz, Sterling Cripps, Maja Kucharczyk. Reported UAV incidents in Canada: analysis and potential solutions. Journal of Unmanned Vehicle Systems. 2017; 5 (2):51-61.
Chicago/Turabian StylePaul R. Nesbit; Thomas E. Barchyn; Chris H. Hugenholtz; Sterling Cripps; Maja Kucharczyk. 2017. "Reported UAV incidents in Canada: analysis and potential solutions." Journal of Unmanned Vehicle Systems 5, no. 2: 51-61.
Mapping with unmanned aerial vehicles (UAVs) typically involves the deployment of ground control points (GCPs) to georeference the images and topographic model. An alternative approach is direct geo ref er encing, whereby the onboard Global Navigation Satellite System (GNSS) and inertial measurement unit are used without GCPs to locate and orient the data. This study compares the spatial accuracy of these approaches using two nearly identical UAVs. The onboard GNSS is the one difference between them, as one vehicle uses a survey-grade GNSS/RTK receiver (RTK UAV), while the other uses a lower-grade GPS receiver (non-RTK UAV). Field testing was performed at a gravel pit, with all ground measurements and aerial sur vey ing completed on the same day. Three sets of orthoimages and DSMs were produced for comparing spa tial accuracies: two sets were created by direct georeferencing images from the RTK UAV and non-RTK UAV and one set was created by using GCPs during the external orientation of the non-RTK UAV images. Spatial accuracy was determined from the horizontal (X,Y) and vertical (Z) residuals and root-mean-square-errors (RMSE) relative to 17 horizontal and 180 vertical check points measured with a GNSS/RTK base station and rover. For the two direct georeferencing datasets, the horizontal and vertical accuracy improved substantially with the survey-grade GNSS/RTK receiver onboard the RTK UAV, effectively reducing the RMSE values in X, Y and Z by 1 to 2 orders of magnitude compared to the lower grade GPS receiver onboard the non-RTK UAV. Importantly, the horizontal accuracy of the RTK UAV data processed via direct georeferencing was equivalent to the horizontal accuracy of the non-RTK UAV data processed with GCPs, but the vertical error of the DSM from the RTK UAV data was 2 to 3 times greater than the DSM from the non-RTK data with GCPs. Overall, results suggest that direct georeferencing with the RTK UAV can achieve horizontal accuracy comparable to that obtained with a network of GCPs, but for topographic meas urements requiring the highest achievable accuracy, researchers and practitioners should use GCPs.
Chris Hugenholtz; Owen Brown; Jordan Walker; Thomas Barchyn; Paul Nesbit; Maja Kucharczyk; Steve Myshak. Spatial Accuracy of UAV-Derived Orthoimagery and Topography: Comparing Photogrammetric Models Processed with Direct Geo-Referencing and Ground Control Points. Geomatica 2016, 70, 21 -30.
AMA StyleChris Hugenholtz, Owen Brown, Jordan Walker, Thomas Barchyn, Paul Nesbit, Maja Kucharczyk, Steve Myshak. Spatial Accuracy of UAV-Derived Orthoimagery and Topography: Comparing Photogrammetric Models Processed with Direct Geo-Referencing and Ground Control Points. Geomatica. 2016; 70 (1):21-30.
Chicago/Turabian StyleChris Hugenholtz; Owen Brown; Jordan Walker; Thomas Barchyn; Paul Nesbit; Maja Kucharczyk; Steve Myshak. 2016. "Spatial Accuracy of UAV-Derived Orthoimagery and Topography: Comparing Photogrammetric Models Processed with Direct Geo-Referencing and Ground Control Points." Geomatica 70, no. 1: 21-30.