Experimental analysis of different software packages for orientation and digital surface modelling from UAV images

Sona, Giovanna; Pinto, Livio; Pagliari, Diana; Passoni, D.; Gini, Rossana

doi:10.1007/s12145-013-0142-2

Cited by 130 publications

(90 citation statements)

References 16 publications

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“…These errors result in overestimation or underestimation of canopy cover, height, and 3D canopy surface area, whereas poor penetration of point clouds and shadows cast by dense canopies lead to incomplete information on the middle and lower canopy layers. Similar variations were found in other studies that used different software packages, which suggests that these variations are not related to matching algorithms [19,62,66]. In contrast to Müller et al [56] and Baltsavias et al [50], who conducted their studies in mountainous forest environments, we did not observe a clear relationship between ground slope and RMSD values.…”

Section: Influence Of Forest Structural Properties and Topographic Cosupporting

confidence: 84%

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

2018

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Unmanned aerial vehicles (UAVs) and digital photogrammetric techniques are two recent advances in remote sensing (RS) technology that are emerging as alternatives to high-cost airborne laser scanning (ALS) data sources. Despite the potential of UAVs in forestry applications, very few studies have included detailed analyses of UAV photogrammetric products at larger scales or over a range of forest types, including mixed conifer-broadleaf forests. In this study, we assessed the performance of fixed-wing UAV photogrammetric products of a mixed conifer-broadleaf forest with varying levels of canopy structural complexity. We demonstrate that fixed-wing UAVs are capable of efficiently collecting image data at local scales and that UAV imagery can be effectively utilized with digital photogrammetric techniques to provide detailed automated reconstruction of the three-dimensional (3D) canopy surface of mixed conifer-broadleaf forests. When combined with an accurate digital terrain model (DTM), UAV photogrammetric products are promising for producing reliable structural measurements of the forest canopy. However, the performance of UAV photogrammetric products is likely to be influenced by the structural complexity of the forest canopy. Furthermore, we highlight the potential of fixed-wing UAVs in operational forest management at the forest management compartment level, for acquiring high-resolution imagery at low cost. A future direction of this research would be to address the issue of how well the photogrammetric products can predict the actual structure of mixed conifer-broadleaf forests.

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Section: Influence Of Forest Structural Properties and Topographic Cosupporting

confidence: 84%

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

2018

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“…Agisoft Photoscan 1.0 Professional, which is now relatively common in the UAS community, was used to perform photogrammetric surveys with proven efficiency (Dandois and Ellis 2013;2010;Sona et al 2014). Every flight dataset was processed by following the workflow described in Fig.…”

Section: General Photogrammetric Workflowmentioning

confidence: 99%

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

198

179

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Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.The comparison of various scales of image analysis identified the smallest object-based image analysis (OBIA) objects (ca. 1 m 2 ) as the most relevant scale. Variables derived from spectral information (bands ratios) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for site 1 and site 2). The classification scenario regarding the health condition of the black alders of the site 1 performed the best (90.6 %).The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multi-spectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metric datasets derived from those dense time series.

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“…In total, 8667 UAV photos were used for point cloud production in Agisoft Photoscan Professional v1.2.4. Photoscan was used because it produces the most accurate surface models, and can deal with shadows better than other SfM packages [23]. The workflow recommended by the manufacturers was followed (also see Table 2 in [38]), and where possible, the same GCPs were used to georectify each model (one target was selected as a GCP per 20 m × 20 m grid cell of the survey site).…”

Section: Dtm Production (Site 2)mentioning

confidence: 99%

“…The accuracy of DTMs from SfM techniques is affected by the amount of image overlap, camera calibration and sensor size, image quality [22], photogrammetry algorithm [23], UAV flying altitude [24,25], camera viewing angle [22,26,27], ground control point accuracy, and ground cover heterogeneity (i.e., featureless surfaces can cause additional error [22,26,28,29]). Fortunately, modern photogrammetric software can automatically detect tie points between photos, regardless of changes in camera orientation or survey distance, providing there is sufficient overlap between photos (50%-90%).…”

Section: Introductionmentioning

confidence: 99%

Tropical Peatland Burn Depth and Combustion Heterogeneity Assessed Using UAV Photogrammetry and Airborne LiDAR

et al. 2016

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Abstract:We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra, Indonesia. Tropical peat soils are the result of thousands of years of dead biomass accumulation, and when burned are globally significant net sources of carbon emissions. The El Niño year of 2015 saw huge areas of Indonesia affected by tropical peatland fires, more so than any year since 1997. However, the Depth of Burn (DoB) of these 2015 fires has not been assessed, and indeed has only previously been assessed in few tropical peatland burns in Kalimantan. Therefore, DoB remains arguably the largest uncertainty when undertaking fire emissions calculations in these tropical peatland environments. We apply a SfM photogrammetric methodology to map this DoB metric, and also investigate combustion heterogeneity using orthomosaic photography collected using the UAV system. We supplement this information with pre-burn airborne light detection and ranging (LiDAR) data, reducing uncertainty by estimating pre-burn soil height more accurately than from interpolation of adjacent unburned areas alone. Our pre-and post-fire Digital Terrain Models (DTMs) show accuracies of 0.04 and 0.05 m (root-mean-square error, RMSE) respectively, compared to ground-based global navigation satellite system (GNSS) surveys. Our final DoB map of a 5.2 ha degraded peat swamp forest area neighboring Berbak National Park (Sumatra, Indonesia) shows burn depths extending from close to zero to over 1 m, with a mean (±1σ) DoB of 0.23 ± 0.19 m. This lies well within the range found by the few other studies available (on Kalimantan; none are available on Sumatra). Our combustion heterogeneity analysis suggests the deepest burns, which extend to~1.3 m, occur around tree roots. We use these DoB data within the Intergovernmental Panel on Climate Change (IPCC) default equation for fire emissions to estimate mean carbon emissions as 134 ± 29 t·C·ha −1 for this peatland fire, which is in an area that had not had a recorded fire previously. This is amongst the highest per unit area fuel consumption anywhere in the world for landscape fires. Our approach provides significant uncertainty reductions in such emissions calculations via the reduction in DoB uncertainty, and by using the UAV SfM approach this is accomplished at a fraction of the cost of airborne LiDAR-albeit over limited sized areas at present. Deploying this approach at locations across Indonesia, sampling a variety of fire-affected landscapes, would provide new and important DoB statistics for producing optimized carbon and greenhouse gas (GHG) emissions estimates from peatland fires.

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Experimental analysis of different software packages for orientation and digital surface modelling from UAV images

Cited by 130 publications

References 16 publications

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Tropical Peatland Burn Depth and Combustion Heterogeneity Assessed Using UAV Photogrammetry and Airborne LiDAR

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