Common Approach to Geoprocessing of Uav Data Across Application Domains

Percivall, George; Reichardt, Mark; Taylor, Trevor

doi:10.5194/isprsarchives-xl-1-w4-275-2015

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…To overcome these challenges and ensure repeatable results, this study was based on direct collaboration between remote sensing analysts, geomorphologists and permafrost scientists. We also leveraged RTK/PPK-grade UAVs that depend less on ground control networks, applied minimum sensor requirements (e.g., 1 inch sensor, 16-20 megapixels) and implemented a standardized methodology that specified high side-and forward image overlaps, a minimum and optimized GNSS-based GCP/CP network, common horizontal and vertical coordinate systems and datums, DTM uncertainty analyses, and a regional high-resolution ALS-derived topographic surface to tie in fine-scale UAV surveys [94,95].…”

Section: Discussionmentioning

confidence: 99%

Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging

et al. 2018

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Unmanned Aerial Vehicle (UAV) systems, sensors, and photogrammetric processing techniques have enabled timely and highly detailed three-dimensional surface reconstructions at a scale that bridges the gap between conventional remote-sensing and field-scale observations. In this work 29 rotary and fixed-wing UAV surveys were conducted during multiple field campaigns, totaling 47 flights and over 14.3 km2, to document permafrost thaw subsidence impacts on or close to road infrastructure in the Northwest Territories, Canada. This paper provides four case studies: (1) terrain models and orthomosaic time series revealed the morphology and daily to annual dynamics of thaw-driven mass wasting phenomenon (retrogressive thaw slumps; RTS). Scar zone cut volume estimates ranged between 3.2 × 103 and 5.9 × 106 m3. The annual net erosion of RTS surveyed ranged between 0.35 × 103 and 0.39 × 106 m3. The largest RTS produced a long debris tongue with an estimated volume of 1.9 × 106 m3. Downslope transport of scar zone and embankment fill materials was visualized using flow vectors, while thermal imaging revealed areas of exposed ground ice and mobile lobes of saturated, thawed materials. (2) Stratigraphic models were developed for RTS headwalls, delineating ground-ice bodies and stratigraphic unconformities. (3) In poorly drained areas along road embankments, UAV surveys detected seasonal terrain uplift and settlement of up to 0.5 m (>1700 m2 in extent) as a result of injection ice development. (4) Time series of terrain models highlighted the thaw-driven evolution of a borrow pit (6.4 × 105 m3 cut volume) constructed in permafrost terrain, whereby fluvial and thaw-driven sediment transfer (1.1 and 3.9 × 103 m3 a−1 respectively) was observed and whereby annual slope profile reconfiguration was monitored to gain management insights concerning site stabilization. Elevation model vertical accuracies were also assessed as part of the case studies and ranged between 0.02 and 0.13 m Root Mean Square Error, whereby photogrammetric models processed with Post-processed Kinematic image solutions achieved similar accuracies without ground control points over much larger and complex areas than previously reported. The high resolution of UAV surveys, and the capacity to derive quantitative time series provides novel insights into permafrost processes that are otherwise challenging to study. The timely emergence of these tools bridges field-based research and applied studies with broad-scale remote-sensing approaches during a period when climate change is transforming permafrost environments.

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Section: Discussionmentioning

confidence: 99%

Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging

et al. 2018

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“…The UAV is equipped with GPS, for inflight recording with accuracy of +/-1.5 meter. During each flight, the camera was fixed on a two-axis gimbal, pointing vertically downwards covering the entire field to generate the ortho-mosaic images in post-processing (Percivall et al, 2015 andHe et al, 2012). Absolute camera position and orientation uncertainties obtained during the flight was within the error limit (1-3 meters).…”

Section: Sensor Platform and Sensorsmentioning

confidence: 99%

Hierarchical Classification for Assessment of Horticultural Crops in Mixed Cropping Pattern Using Uav-Borne Multi-Spectral Sensor

Handique

Goswami

Gupta

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Assessment of horticultural crops under mixed cropping system has been a challenge, both for horticulturists and also to the remote sensing communities. But the recent developments in wide range of sensors onboard Unmanned Aerial Vehicles (UAVs) has opened up new possibilities in identification, mapping and monitoring of horticultural crops. This paper presents the results made from a pilot exercise on horticultural crop discrimination using Parrot Sequoia multi-spectral sensor onboard a UAV. This exercise was carried out in Nongkhrah village, Ri-Bhoi district of Meghalaya state located in the north eastern part of India having mixed horticultural crops. A two level hierarchical classification system was followed for identification and delineation of the major horticultural crops in the village. Parrot Sequoia multi-spectral sensor having four bands has been found to be effective in discrimination of horticultural crops based on variation in spectral response of six horticultural crops viz., pineapple, banana, orange, papaya, ginger and turmeric using three commonly used indices viz., Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI). NDVI and GNDVI showed nearly similar spectral response, whereas separability among the horticultural crops significantly improved with the use of NDRE. The first level of classification involving the five broad land cover classes has resulted an overall accuracy of about 91%, whereas the second level of classification for delineating the five selected horticultural crops has provided an overall accuracy of 79.8%.

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“…In this research, UAV images are processed under conventional photogrammetry method to extract high accuracy spatial data. The following diagram showing the work flow adopted under this method [10] (Dig-1).…”

Section: Uav Surveymentioning

confidence: 99%

Combined Geospatial Approch to Extract Spatial Data from UAV Imageries

Stalin¹,

Gnanaprakasam²

2017

IJERT

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Evaluation of UAV have opened the whole new world of survey with less cost. Vertical and Oblique aerial imageries took from UAV's are used to generate various geospatial data like high resolution ortho photo, color point clouds, DSM, DTM and 3D models [1]. The main objectives of this paper is to understand the UAV systems, components, image acquisition technique and high precision geo data extraction method from UAV imageries for better spatial decision support system [2] .

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Common Approach to Geoprocessing of Uav Data Across Application Domains

Cited by 6 publications

References 6 publications

Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging

Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging

Hierarchical Classification for Assessment of Horticultural Crops in Mixed Cropping Pattern Using Uav-Borne Multi-Spectral Sensor

Combined Geospatial Approch to Extract Spatial Data from UAV Imageries

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