Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification

Marselis, S.; Yebra, Marta; Jovanovic, Tom; Dijk, Albert I. J. M. van

doi:10.1016/j.envsoft.2016.04.025

Cited by 40 publications

(27 citation statements)

References 38 publications

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“…Forest in situ measurements by non-professional users, such as forest owners, has become feasible by using, e.g., a cellphone camera. Moreover, in recent years, mobile and personal mapping have become available (Liang et al 2014;Bauwens et al 2016;Forsman et al 2016b;Marselis et al 2016;Juraj et al 2017;Oveland et al 2017) and many efforts have been invested to new Mobile Laser Scanning (MLS)-relevant data processing Liang et al 2018b;Luo et al 2018). Mobile and personal mapping systems integrating the LS/camera sensors, the kinematic platforms, and/or the navigation sensors, are capable to measure the forest plots 3-20 times faster than stationary systems (Liang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

et al. 2019

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Background: Lately, terrestrial point clouds have drawn attention as a new data source for in situ forest investigations. So far, terrestrial laser scanning (TLS) has the highest data quality among all terrestrial point cloud data in terms of geometric accuracy and level of detail (IEEE Transact Geosci Remote Sens 53: 5117-5132, 2015). The TLS point clouds processed by automated algorithms can provide certain individual tree parameters at close to required accuracy in practical applications. However, all terrestrial point clouds face a general challenge, which is the occlusions of upper tree crowns. An emerging technology called unmanned-aerial-vehicle (UAV) -borne laser scanning (ULS) potentially combines the strengths of above and under canopy surveys.Results: The performance of ULS are evaluated in 22 sample plots of various forest stand conditions in a boreal forest. The forest parameter estimates are benchmarked through a comparison with state-of-the-art terrestrial mechanisms from both static terrestrial and mobile laser scanning. The results show that in easy forest stand conditions, the performance of ULS point cloud is comparable with the terrestrial solutions. Conclusions: This study gives the first strict evaluation of ULS in situ observations in varied forest conditions. The study also acts as a benchmarking of available active remote sensing techniques for forest in situ mensuration. The results indicate that the current off-the-shelf ULS has an excellent tree height/tops measurement performance. Although the geometrical accuracy of the ULS data, especially at the stem parts, does not yet reach the level of other terrestrial point clouds, the unbeatable high mobility and fast data acquisition make the ULS a very attractive option in forest investigations.

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Section: Introductionmentioning

confidence: 99%

Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

et al. 2019

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“…The TLS method is one of the most progressively developing methods of 3D data acquisition. Nowadays, the TLS can be applied in various fields: topographical measurements [20], exploration of landslides [21,22], forest inventory [23][24][25], documentation of the actual state of buildings [26][27][28], mapping of industrial sites and underground areas [20,29], monitoring of ground surface erosion [30], documentation of cultural heritage [31][32][33][34], and many others.…”

Section: Introductionmentioning

confidence: 99%

Detecting Forest Road Wearing Course Damage Using Different Methods of Remote Sensing

et al. 2018

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Currently, a large part of forest roads with a bituminous surface course constructed in the Czech Republic in the second half of the last century has been worn out. The aim of the study is to verify the possibility and the accuracy of the road wearing course damage detected by four different remote sensing methods: close range photogrammetry, terrestrial laser scanning, mobile laser scanning and airborne laser scanning. At the beginning of verification, cross sections of the road surface were surveyed geodetically and then compared with the cross sections created in the DTMs which were acquired using the four methods mentioned above. The differences calculated between particular models and geodetic measurements show that close range photogrammetry achieved an RMSE of 0.0110 m and the RMSE of terrestrial laser scanning was 0.0243 m. Based on these results, we can conclude that these two methods are sufficient for the monitoring of the asphalt wearing course of forest roads. These methods allow precise and objective localization, size and quantification of the road damage. By contrast, mobile laser scanning with an RMSE of 0.3167 m does not reach the required precision for the damage detection of forest roads due to the vegetation that affects the precision of the measurements. Similar results are achieved by airborne laser scanning, with an RMSE of 0.1392 m. As regards the time needed, close range photogrammetry appears to be the most appropriate method for damage detection of forest roads.

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“…A mobile terrestrial system consists of a set of sensors that are carried by a human operator, for instance on a backpack, to acquire measurements of the environment while walking [21]. Mobile laser scanning systems for deriving canopy height profiles or the classical inventory parameters have recently been tested by [5,9,17,[22][23][24][25][26][27]. However, providing complete and accurate data under the heterogeneous and complex forest conditions remains a major challenge for mobile laser scanning systems [5].…”

Section: Introductionmentioning

confidence: 99%

Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

et al. 2019

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The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

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Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification

Cited by 40 publications

References 38 publications

Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

Detecting Forest Road Wearing Course Damage Using Different Methods of Remote Sensing

Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

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