Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success

Moudrý, Vítězslav; Gdulová, Kateřina; Fogl, Michal; Klápště, Petr; Urban, R.; Komárek, Jan; Moudrá, Lucie; Štroner, Martin; Barták, Vojtěch; Solský, Milič

doi:10.1016/j.apgeog.2019.02.002

Cited by 72 publications

(52 citation statements)

References 58 publications

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“…Guerra-Hernández et al [12] studied terrain under a Eucalyptus plantation with canopy cover higher than 60% and reported terrain height overestimation over two meters. Moudrý et al [51] mapped a post-mining site under leaf-off conditions and achieved point cloud accuracy between 0.11 and 0.19 m. Similar team of authors [52] reported a DTM acquired in forest during the leaf-off season as the most accurate when compared with aquatic vegetation and steppe ecosystems. Through application of the Best Available Pixel Compositing (BATP) on multi-temporal UAV imagery, Goodbody et al [53] were able to obtain DTMs with a 0.01 m mean error, a standard deviation of 0.14m and a relative coverage of 86.3% compared with the reference DTM.…”

Section: Discussionmentioning

confidence: 88%

UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?

et al. 2019

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Mapping hard-to-access and hazardous parts of forests by terrestrial surveying methods is a challenging task. Remote sensing techniques can provide an alternative solution to such cases. Unmanned aerial vehicles (UAVs) can provide on-demand data and higher flexibility in comparison to other remote sensing techniques. However, traditional georeferencing of imagery acquired by UAVs involves the use of ground control points (GCPs), thus negating the benefits of rapid and efficient mapping in remote areas. The aim of this study was to evaluate the accuracy of RTK/PPK (real-time kinematic, post-processed kinematic) solution used with a UAV to acquire camera positions through post-processed and corrected measurements by global navigation satellite systems (GNSS). To compare this solution with approaches involving GCPs, the accuracies of two GCP setup designs (4 GCPs and 9 GCPs) were evaluated. Additional factors, which can significantly influence accuracies were also introduced and evaluated: type of photogrammetric product (point cloud, orthoimages and DEM) vegetation leaf-off and leaf-on seasonal variation and flight patterns (evaluated individually and as a combination). The most accurate results for both horizontal (X and Y dimensions) and vertical (Z dimension) accuracies were acquired by the UAV RTK/PPK technology with RMSEs of 0.026 m, 0.035 m and 0.082 m, respectively. The PPK horizontal accuracy was significantly higher when compared to the 4GCP and 9GCP georeferencing approach (p < 0.05). The PPK vertical accuracy was significantly higher than 4 GCP approach accuracy, while PPK and 9 GCP approach vertical accuracies did not differ significantly (p = 0.96). Furthermore, the UAV RTK/PPK accuracy was not influenced by vegetation seasonal variation, whereas the GCP georeferencing approaches during the vegetation leaf-off season had lower accuracy. The use of the combined flight pattern resulted in higher horizontal accuracy; the influence on vertical accuracy was insignificant. Overall, the RTK/PPK technology in combination with UAVs is a feasible and appropriately accurate solution for various mapping tasks in forests.

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Section: Discussionmentioning

confidence: 88%

UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?

et al. 2019

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“…Despite that, UAVs are advantageous due to their (a) spatial resolution, which offers a solution for local scale analysis at the level of individual trees [25]; and (b) temporal resolution where rapid deployment is crucial [26]. Miniaturized UAV-specific sensors thus represent a state-of-the-art solution for many recent environmental applications [22,27] and deriving forestry parameters [28,29]. Despite this, only few studies have focused on bark beetle detection using UAVs [30,31].…”

Section: Introductionmentioning

confidence: 99%

The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

et al. 2019

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The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue and presents a major challenge for forest management. A timely detection of bark beetle infestation is therefore necessary to reduce losses. Besides wood production, a bark beetle outbreak affects the forest ecosystem in many other ways including the water cycle, nutrient cycle, or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed in the unmanaged zone of the Krkonoše Mountains National Park in the northern part of the Czech Republic where the natural spreading of bark beetle is slow and, therefore, allow us to continuously monitor the infested trees that are, in contrast to managed forests, not being removed. The aim of this work is to evaluate possibilities of unmanned aerial vehicle (UAV)-mounted low-cost RGB and modified near-infrared sensors for detection of different stages of infested trees at the individual level, using a retrospective time series for recognition of still green but already infested trees (so-called green attack). A mosaic was created from the UAV imagery, radiometrically calibrated for surface reflectance, and five vegetation indices were calculated; the reference data about the stage of bark beetle infestation was obtained through a combination of field survey and visual interpretation of an orthomosaic. The differences of vegetation indices between infested and healthy trees over four time points were statistically evaluated and classified using the Maximum Likelihood classifier. Achieved results confirm our assumptions that it is possible to use a low-cost UAV-based sensor for detection of various stages of bark beetle infestation across seasons; with increasing time after infection, distinguishing infested trees from healthy ones grows easier. The best performance was achieved by the Greenness Index with overall accuracy of 78%–96% across the time periods. The performance of the indices based on near-infrared band was lower.

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“…In TLS and photogrammetry, these also depend on the distance of the sensor to the measured object. Large-scale photogrammetry or lidar surveying of the ground surface is characterized by RMSE of more than 10 mm [11,20,21,36]. For small-scale surveying, the measurement accuracy of a few mm and high density of points can be expected.…”

Section: Discussionmentioning

confidence: 99%

“…Generally, in the field of geosciences and mining industry, many studies deal with the issue of surface modeling. For example, Blistan et al successfully used UAV photogrammetry to model rock outcrops in the surface quarry also used in this research [17]; Gallay et al used the combination of TLS technology and digital 3D modeling for surface reconstruction to derive geomorphic properties of underground cave spaces [18]; Hofierka et al defined a workflow to process massive data from terrestrial and airborne laser scanning to derive accurate digital models representing surface and subsurface geomorphological features [19]; airborne laser scanning was also used to map and model slope deformations in a badly accessible terrain by Fraštia et al [20]; digital terrain models derived from LiDAR and UAV data were successfully used for safety, remediation, and ecological problems by Moudrý et al [21].…”

Section: Introductionmentioning

confidence: 99%

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

et al. 2020

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A frequently recurring problem in the extraction of mineral resources (especially heterogeneous mineral resources) is the rapid operative determination of the extracted quantity of raw material in a surface quarry. This paper deals with testing and analyzing the possibility of using unconventional methods such as digital close-range photogrammetry and terrestrial laser scanning in the process of determining the bulk density of raw material under in situ conditions. A model example of a heterogeneous deposit is the perlite deposit Lehôtka pod Brehmi (Slovakia). Classical laboratory methods for determining bulk density were used to verify the results of the in situ method of bulk density determination. Two large-scale samples (probes) with an approximate volume of 7 m 3 and 9 m 3 were realized in situ. 6 point samples (LITH) were taken for laboratory determination. By terrestrial laser scanning (TLS) measurement from 2 scanning stations, point clouds with approximately 163,000/143,000 points were obtained for each probe. For Structure-from-Motion (SfM) photogrammetry, 49/55 images were acquired for both probes, with final point clouds containing approximately 155,000/141,000 points. Subsequently, the bulk densities of the bulk samples were determined by the calculation from in situ measurements by TLS and SfM photogrammetry. Comparison of results of the field in situ measurements (1841 kg·m −3 ) and laboratory measurements (1756 kg·m −3 ) showed only a 4.5% difference in results between the two methods for determining the density of heterogeneous raw materials, confirming the accuracy of the used in situ methods. For the determination of the loosening coefficient, the material from both large-scale samples was transferred on a horizontal surface. Their volumes were determined by TLS. The loosening coefficient for the raw material of 1.38 was calculated from the resulting values.

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Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success

Cited by 72 publications

References 58 publications

UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?

UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?

The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

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