This page has only limited features, please log in for full access.
Circle-fitting methods are commonly used to estimate diameter at breast height (DBH) of trees from horizontal cross-section of point clouds. In this paper, we addressed the problem of cross-section thickness optimization regarding DBH estimation bias and accuracy. DBH of 121 European beeches (Fagus sylvatica L.) and 43 Sessile oaks (Quercus petraea (Matt.) Liebl.) was estimated from cross-sections with thicknesses ranging from 1 to 100 cm. The impact of cross-section thickness on the bias, standard error, and accuracy of DBH estimation was statistically significant. However, the biases, standard errors, and accuracies of DBH estimation were not significantly different among 1–10-cm cross-sections, except for oak DBH estimation accuracy from an 8-cm cross-section. DBH estimations from 10–100-cm cross-sections were considerably different. These results provide insight to the influence of cross-section thickness on DBH estimation by circle-fitting methods, which is beneficial for point cloud data acquisition planning and processing. The optimal setting of cross-section thickness facilitates point cloud processing and DBH estimation by circle-fitting algorithms.
Milan Koreň; Milan Hunčaga; Juliana Chudá; Martin Mokroš; Peter Surový. The Influence of Cross-Section Thickness on Diameter at Breast Height Estimation from Point Cloud. ISPRS International Journal of Geo-Information 2020, 9, 495 .
AMA StyleMilan Koreň, Milan Hunčaga, Juliana Chudá, Martin Mokroš, Peter Surový. The Influence of Cross-Section Thickness on Diameter at Breast Height Estimation from Point Cloud. ISPRS International Journal of Geo-Information. 2020; 9 (9):495.
Chicago/Turabian StyleMilan Koreň; Milan Hunčaga; Juliana Chudá; Martin Mokroš; Peter Surový. 2020. "The Influence of Cross-Section Thickness on Diameter at Breast Height Estimation from Point Cloud." ISPRS International Journal of Geo-Information 9, no. 9: 495.
The benchmarking project of image-based point cloud for forest inventory (SFM-Forest-Benchmark) was initiated in 2019 and supported by ISPRS Scientific Initiative 2019. The main goal of the project was the evaluation of the applicability of terrestrial image-based point clouds for forest inventories, the clarification of the potential and limitations of the state-of-the-art techniques, and the exploration of the best practices in practical field inventories. In the project, related tree parameter (i.e. tree position diameter at breast height - DBH) were derived from 14 algorithms and evaluated using field inventory data as a reference. In order to clarify the potential of terrestrial image-based point clouds, the results from the image-based point clouds were also compared to results derived from the best available point clouds obtained by terrestrial laser scanning (TLS).
The project is consisted of two phases. In the first phase, we established two research plots in each country (Austria, China, Czech, Finland and Slovakia), ten plots in total. The stem density ranged from 272 to 875 stems/ha and plot size ranged approximately from 700 to 2500 m2. Dominant tree species across research plots were Norway spruce, European beech, bald cypress, Chinese tulip poplar, Scots pine, European silver fir and sessile oak. TLS, images and reference data acquisition were performed on each study site, where TLS data were acquired through multi-scan approach, images were taken in the stop-and-go mode, and tree positions and the DBHs were measured with a tachymeter and a calliper as field references. Images were processed with structure from motion algorithm within Agisoft Metashape software to final point clouds. The TLS data was pre-processed with RiProcess software. And, the co-registration of all three data sources (TLS, SFM, and reference data) was done with OPALS software.
In the benchmarking phase, we distributed point clouds to participants of the benchmark. Altogether 14 different research groups processed the data with own algorithms. The individual results are evaluated through the reference to clarify the applicability of the image-point clouds in deriving tree parameters, were compared to each other to reveal the state-of-the-art of technologies, and were benchmarked to the up-to-data the most accurate data from TLS to explore the strength and weakness of the image-based point cloud. In this presentation the first benchmark results will be presented and discussed.
All images and point clouds collected for this project will be available as open access data for non-commercial uses.
Martin Mokros; Markus Hollaus; Yunsheng Wang; Xinlian Liang. SFM-Forest-Benchmark project: The benchmarking of image-based point cloud for forest inventory. 2020, 1 .
AMA StyleMartin Mokros, Markus Hollaus, Yunsheng Wang, Xinlian Liang. SFM-Forest-Benchmark project: The benchmarking of image-based point cloud for forest inventory. . 2020; ():1.
Chicago/Turabian StyleMartin Mokros; Markus Hollaus; Yunsheng Wang; Xinlian Liang. 2020. "SFM-Forest-Benchmark project: The benchmarking of image-based point cloud for forest inventory." , no. : 1.
Annual trunk increments are essential for short-term analyses of the response of trees to various factors. For instance, based on annual trunk increments, it is possible to develop and calibrate forest growth models. We investigated the possibility of estimating annual trunk increments from the terrestrial structure from motion (SfM) photogrammetry. Obtaining the annual trunk increments of mature trees is challenging due to the relatively small growth of trunks within one year. In our experiment, annual trunk increments were obtained by two conventional methods: measuring tape (perimeter increment) at heights of 0.8, 1.3, and 1.8 m on the trunk and increment borer (diameter increment) at a height of 1.3 m on the trunk. The following tree species were investigated: Fagus sylvatica L. (beech), Quercus petraea (Matt.) Liebl. (oak), Picea abies (L.) H. Karst (spruce), and Abies alba Mill (fir). The annual trunk increments ranged from 0.9 cm to 2.4 cm (tape/perimeter) and from 0.7 mm to 3.1 mm (borer/diameter). The data were collected before- and after-vegetation season, besides the data collection increment borer. When the estimated perimeters from the terrestrial SfM photogrammetry were compared to those obtained using the measuring tape, the root mean square error (RMSE) was 0.25–1.33 cm. The relative RMSE did not exceed 1% for all tree species. No statistically significant differences were found between the annual trunk increments obtained using the measuring tape and terrestrial SfM photogrammetry for beech, spruce, and fir. Only in the case of oak, the difference was statistically significant. Furthermore, the correlation coefficient between the annual trunk increments collected using the increment borer and those derived from terrestrial SfM photogrammetry was positive and equal to 0.6501. Terrestrial SfM photogrammetry is a hardware low-demanding technique that provides accurate three-dimensional data that can, based on our results, even detect small temporal tree trunk changes.
Martin Mokroš; Jozef Výbošťok; Alžbeta Grznárová; Michal Bosela; Vladimír Šebeň; Ján Merganič. Non-destructive monitoring of annual trunk increments by terrestrial structure from motion photogrammetry. PLOS ONE 2020, 15, e0230082 .
AMA StyleMartin Mokroš, Jozef Výbošťok, Alžbeta Grznárová, Michal Bosela, Vladimír Šebeň, Ján Merganič. Non-destructive monitoring of annual trunk increments by terrestrial structure from motion photogrammetry. PLOS ONE. 2020; 15 (3):e0230082.
Chicago/Turabian StyleMartin Mokroš; Jozef Výbošťok; Alžbeta Grznárová; Michal Bosela; Vladimír Šebeň; Ján Merganič. 2020. "Non-destructive monitoring of annual trunk increments by terrestrial structure from motion photogrammetry." PLOS ONE 15, no. 3: e0230082.
Carbon cycling in forest ecosystems is affected by a number of interacting environmental factors. Here we analyse carbon sequestration in temperate forests composed of three common Central European species: Norway spruce, European beech and oak along an extended environmental gradient across Central Europe using long-term monitoring data and process-based modelling of forest dynamics. For the analyses we used selected ICP forest monitoring plots, long-term forest research plots from thinning trials, and highly-equipped intensively monitored plots from five central European countries: Croatia, Hungary, Slovakia, Poland and the Czech Republic. Their temporal development was simulated using a process-based model Biome-BGCMuSo, which is sensitive to soil and climate conditions. Since such models of forest growth dynamics implicitly describe relationships between forest productivity and environmental conditions, their implementation can reveal the main factors affecting carbon cycling in forests along the gradients of latitude, altitude, or other environmental factors as long as they are included in the models. The study indicates that by linking long-term monitoring data and forest growth modelling we can not only test the model capacity to simulate forest dynamics, but above all we can increase our capacity to address main challenges faced by the central European forestry with respect to the global climate change.
Katarina Merganicova; Roland Hollos; Zoltan Barcza; Jan Merganic; Zuzana Sitkova; Daniel Kurjak; Martin Mokros; Peter Fleischer; Hrvoje Marjanovic; Dora Hidy; Katarina Strelcova; Tomas Hlasny. Integrating multi-source data and model projections to address carbon cycling in central European forests. 2020, 1 .
AMA StyleKatarina Merganicova, Roland Hollos, Zoltan Barcza, Jan Merganic, Zuzana Sitkova, Daniel Kurjak, Martin Mokros, Peter Fleischer, Hrvoje Marjanovic, Dora Hidy, Katarina Strelcova, Tomas Hlasny. Integrating multi-source data and model projections to address carbon cycling in central European forests. . 2020; ():1.
Chicago/Turabian StyleKatarina Merganicova; Roland Hollos; Zoltan Barcza; Jan Merganic; Zuzana Sitkova; Daniel Kurjak; Martin Mokros; Peter Fleischer; Hrvoje Marjanovic; Dora Hidy; Katarina Strelcova; Tomas Hlasny. 2020. "Integrating multi-source data and model projections to address carbon cycling in central European forests." , no. : 1.
The forest inventory is an important instrument for sustainable forest management. Canopy Height Model (CHM) and Digital Surface Model (DSM) created from high-resolution UAV (unmanned aerial vehicle) imagery provide possibility to determine tree crown diameters for the whole stand at fast. The goal of this paper is to identify the influence of tree species on the accuracy of estimation of crown diameter from high-resolution UAV imagery. In Plot 1 with coniferous tree species we identified 21 trees from total of 22 trees that leads to a detection rate of 95%. In Plot 1 with deciduous trees species we identified 24 trees from total 34 trees that leads to a detection rate of 71%. The RMSE errors calculated between the reference crown diameters and estimated crown diameters by IWS on Plot 1and Plot 2 were calculated as 0.80 m (RMSE% = 21.85) and 1.89 m (RMSE% = 21.54), respectively. The results didn’t show the significant influence of tree species on the accuracy of estimation of crown diameter from high-resolution UAV imagery. However, result showed the significant influence of tree species on the detection number trees on the plot. The detection of number trees on the plot by method Inverese Watersed Segmentation in software ArcGis is higher for coniferous tree species. It is mainly due to the overlapping crowns.
A. Grznárová; M. Mokroš; P. Surový; M. Slavík; M. Pondelík; J. Merganič. THE CROWN DIAMETER ESTIMATION FROM FIXED WING TYPE OF UAV IMAGERY. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-2/W13, 337 -341.
AMA StyleA. Grznárová, M. Mokroš, P. Surový, M. Slavík, M. Pondelík, J. Merganič. THE CROWN DIAMETER ESTIMATION FROM FIXED WING TYPE OF UAV IMAGERY. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-2/W13 ():337-341.
Chicago/Turabian StyleA. Grznárová; M. Mokroš; P. Surový; M. Slavík; M. Pondelík; J. Merganič. 2019. "THE CROWN DIAMETER ESTIMATION FROM FIXED WING TYPE OF UAV IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13, no. : 337-341.
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.
Livia Piermattei; Wilfried Karel; Di Wang; Martin Wieser; Martin Mokroš; Peter Surový; Milan Koreň; Julián Tomaštík; Norbert Pfeifer; Markus Hollaus. Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data. Remote Sensing 2019, 11, 950 .
AMA StyleLivia Piermattei, Wilfried Karel, Di Wang, Martin Wieser, Martin Mokroš, Peter Surový, Milan Koreň, Julián Tomaštík, Norbert Pfeifer, Markus Hollaus. Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data. Remote Sensing. 2019; 11 (8):950.
Chicago/Turabian StyleLivia Piermattei; Wilfried Karel; Di Wang; Martin Wieser; Martin Mokroš; Peter Surový; Milan Koreň; Julián Tomaštík; Norbert Pfeifer; Markus Hollaus. 2019. "Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data." Remote Sensing 11, no. 8: 950.
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.
Julián Tomaštík; Martin Mokroš; Peter Surový; Alžbeta Grznárová; Ján Merganič. UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas? Remote Sensing 2019, 11, 721 .
AMA StyleJulián Tomaštík, Martin Mokroš, Peter Surový, Alžbeta Grznárová, Ján Merganič. UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas? Remote Sensing. 2019; 11 (6):721.
Chicago/Turabian StyleJulián Tomaštík; Martin Mokroš; Peter Surový; Alžbeta Grznárová; Ján Merganič. 2019. "UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?" Remote Sensing 11, no. 6: 721.
Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.
Juraj Čerňava; Martin Mokroš; Ján Tuček; Michal Antal; Zuzana Slatkovská. Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data. Remote Sensing 2019, 11, 615 .
AMA StyleJuraj Čerňava, Martin Mokroš, Ján Tuček, Michal Antal, Zuzana Slatkovská. Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data. Remote Sensing. 2019; 11 (6):615.
Chicago/Turabian StyleJuraj Čerňava; Martin Mokroš; Ján Tuček; Michal Antal; Zuzana Slatkovská. 2019. "Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data." Remote Sensing 11, no. 6: 615.
An active gully-related landslide system is located in a deep valley under forest canopy cover. Generally, point clouds from forested areas have a lack of data connectivity, and optical parameters of scanning cameras lead to different densities of point clouds. Data noise or systematic errors (missing data) make the automatic identification of landforms under tree canopy problematic or impossible. We processed, analyzed, and interpreted data from a large-scale landslide survey, which were acquired by the light detection and ranging (LiDAR) technology, remotely piloted aircraft system (RPAS), and close-range photogrammetry (CRP) using the ‘Structure-from-Motion’ (SfM) method. LAStools is a highly efficient Geographic Information System (GIS) tool for point clouds pre-processing and creating precise digital elevation models (DEMs). The main landslide body and its landforms indicating the landslide activity were detected and delineated in DEM-derivatives. Identification of micro-scale landforms in precise DEMs at large scales allow the monitoring and the assessment of these active parts of landslides that are invisible in digital terrain models at smaller scales (obtained from aerial LiDAR or from RPAS) due to insufficient data density or the presence of many data gaps.
František Chudý; Martina Slámová; Julián Tomaštík; Roberta Prokešová; Martin Mokroš. Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models. Geosciences 2019, 9, 117 .
AMA StyleFrantišek Chudý, Martina Slámová, Julián Tomaštík, Roberta Prokešová, Martin Mokroš. Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models. Geosciences. 2019; 9 (3):117.
Chicago/Turabian StyleFrantišek Chudý; Martina Slámová; Julián Tomaštík; Roberta Prokešová; Martin Mokroš. 2019. "Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models." Geosciences 9, no. 3: 117.
Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In our study, we focused on the reconstruction of trees separately within the forest stand. We investigated the influence of camera lens, tree species, and height of diameter on the accuracy of the tree perimeter and diameter estimation. Furthermore, we investigated the variance of the perimeter and diameter reference measurements. We chose four tree species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst. and Abies alba Mill.). The perimeters and diameters were measured at three height levels (0.8 m, 1.3 m, and 1.8 m) and two types of lenses were used. The data acquisition followed a circle around the tree at a 3 m radius. The highest accuracy of the perimeter estimation was achieved when a fisheye lens was used at a height of 1.3 m for Fagus sylvatica (root mean square error of 0.25 cm). Alternatively, the worst accuracy was achieved when a non-fisheye lens was used at 1.3 m for Quercus petraea (root mean square error of 1.27 cm). The tree species affected the estimation accuracy for both diameters and perimeters.
Martin Mokroš; Jozef Výbošťok; Julián Tomaštík; Alžbeta Grznárová; Peter Valent; Martin Slavík; Ján Merganič. High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry. Forests 2018, 9, 696 .
AMA StyleMartin Mokroš, Jozef Výbošťok, Julián Tomaštík, Alžbeta Grznárová, Peter Valent, Martin Slavík, Ján Merganič. High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry. Forests. 2018; 9 (11):696.
Chicago/Turabian StyleMartin Mokroš; Jozef Výbošťok; Julián Tomaštík; Alžbeta Grznárová; Peter Valent; Martin Slavík; Ján Merganič. 2018. "High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry." Forests 9, no. 11: 696.
The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources.
Xinlian Liang; Juha Hyyppä; Harri Kaartinen; Matti Lehtomäki; Jiri Pyörälä; Norbert Pfeifer; Markus Holopainen; Gábor Brolly; Pirotti Francesco; Jan Hackenberg; Huabing Huang; Hyun-Woo Jo; Masato Katoh; Luxia Liu; Martin Mokroš; Jules Morel; Kenneth Olofsson; Jose Poveda-Lopez; Jan Trochta; Di Wang; Jinhu Wang; Zhouxi Xi; Bisheng Yang; Guang Zheng; Ville Kankare; Ville Luoma; Xiaowei Yu; Liang Chen; Mikko Vastaranta; Ninni Saarinen; Yunsheng Wang. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 144, 137 -179.
AMA StyleXinlian Liang, Juha Hyyppä, Harri Kaartinen, Matti Lehtomäki, Jiri Pyörälä, Norbert Pfeifer, Markus Holopainen, Gábor Brolly, Pirotti Francesco, Jan Hackenberg, Huabing Huang, Hyun-Woo Jo, Masato Katoh, Luxia Liu, Martin Mokroš, Jules Morel, Kenneth Olofsson, Jose Poveda-Lopez, Jan Trochta, Di Wang, Jinhu Wang, Zhouxi Xi, Bisheng Yang, Guang Zheng, Ville Kankare, Ville Luoma, Xiaowei Yu, Liang Chen, Mikko Vastaranta, Ninni Saarinen, Yunsheng Wang. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 144 ():137-179.
Chicago/Turabian StyleXinlian Liang; Juha Hyyppä; Harri Kaartinen; Matti Lehtomäki; Jiri Pyörälä; Norbert Pfeifer; Markus Holopainen; Gábor Brolly; Pirotti Francesco; Jan Hackenberg; Huabing Huang; Hyun-Woo Jo; Masato Katoh; Luxia Liu; Martin Mokroš; Jules Morel; Kenneth Olofsson; Jose Poveda-Lopez; Jan Trochta; Di Wang; Jinhu Wang; Zhouxi Xi; Bisheng Yang; Guang Zheng; Ville Kankare; Ville Luoma; Xiaowei Yu; Liang Chen; Mikko Vastaranta; Ninni Saarinen; Yunsheng Wang. 2018. "International benchmarking of terrestrial laser scanning approaches for forest inventories." ISPRS Journal of Photogrammetry and Remote Sensing 144, no. : 137-179.
The potential of close-range photogrammetry (CRP) to compete with terrestrial laser scanning (TLS) to produce dense and accurate point clouds has increased in recent years. The use of CRP for estimating tree diameter at breast height (DBH) has multiple advantages over TLS. For example, point clouds from CRP are similar to TLS, but hardware costs are significantly lower. However, a number of data collection issues need to be clarified before the use of CRP in forested areas is considered effective. In this paper we focused on different CRP data collection methods to estimate DBH. We present seven methods that differ in camera orientation, shooting mode, data collection path, and other important factors. The methods were tested on a research plot comprised of European beeches (Fagus sylvatica L.). The circle-fitting algorithm was used to estimate DBH. Four of the seven methods were capable of producing a dense point cloud. The tree detection rate varied from 49% to 81%. Estimates of DBH produced a root mean square error that varied from 4.41 cm to 5.98 cm. The most accurate method was achieved using a vertical camera orientation, stop-and-go shooting mode, and a path leading around the plot with two diagonal paths through the plot. This method also had the highest rate of tree detection (81%).
Martin Mokroš; Xinlian Liang; Peter Surový; Peter Valent; Juraj Čerňava; František Chudý; Daniel Tunák; Šimon Saloň; Ján Merganič. Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters. ISPRS International Journal of Geo-Information 2018, 7, 93 .
AMA StyleMartin Mokroš, Xinlian Liang, Peter Surový, Peter Valent, Juraj Čerňava, František Chudý, Daniel Tunák, Šimon Saloň, Ján Merganič. Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters. ISPRS International Journal of Geo-Information. 2018; 7 (3):93.
Chicago/Turabian StyleMartin Mokroš; Xinlian Liang; Peter Surový; Peter Valent; Juraj Čerňava; František Chudý; Daniel Tunák; Šimon Saloň; Ján Merganič. 2018. "Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters." ISPRS International Journal of Geo-Information 7, no. 3: 93.
Milan Koreň; Martin Mokroš; Tomáš Bucha. Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods. International Journal of Applied Earth Observation and Geoinformation 2017, 63, 122 -128.
AMA StyleMilan Koreň, Martin Mokroš, Tomáš Bucha. Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods. International Journal of Applied Earth Observation and Geoinformation. 2017; 63 ():122-128.
Chicago/Turabian StyleMilan Koreň; Martin Mokroš; Tomáš Bucha. 2017. "Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods." International Journal of Applied Earth Observation and Geoinformation 63, no. : 122-128.
Strong wind disturbances can affect large forested areas and often occur irregularly within a forest. Due to this, identifying damaged sites and estimating the extent of these losses are crucial for the harvesting management of salvage logging. Furthermore, the location should be surveyed as soon as possible after the disturbance to prevent the degradation of fallen trees. A fixed-wing type of unmanned aircraft system (UAS) with a compact digital camera was used in this study. The imagery was acquired on approximately 200 hectares where five large windthrow areas had occurred. The objective of the study was to determine the location of the windthrow areas using a semi-automatic approach based on the UAS imagery, and on the combination of UAS imagery with airborne laser scanning (ALS). The results were compared with reference data measured by global navigation satellite system (GNSS) devices. At the same time, windthrow areas were derived from Landsat imagery to investigate whether the UAS imagery would have significantly more accurate results. GNSS measurements and Landsat imagery are currently used in forestry on an operational level. The salvage logging was estimated for each forest stand based on the estimated areas and volume per hectare obtained from the forest management plan. The results from the UAS (25.09 ha) and the combined UAS/ALS (25.56 ha) methods were statistically similar to the reference GNSS measurements (25.39 ha). The result from Landsat, at 19.8 ha, was not statistically similar to the reference GNSS measurements or to the UAS and UAS/ALS methods. The estimate of salvage logging for the whole area, from UAS imagery and the forest management plan, overestimated the actual salvage logging measured by foresters by 4.93% (525 m3), when only the most represented tree species were considered. The UAS/ALS combination improved the preliminary results of determining windthrow areas which lead to decreased editing time for all operators. The UAS imagery shows potential for application to early-stage surveys of windthrow areas in forests. The advantages of this method are that it provides the ability to conduct flights immediately after the disturbance, the foresters do not need to walk within the affected areas which decreases the risk of injury, and allows flights to be conducted on cloudy days. The orthomosaic of the windthrow areas, as a by-product of data processing in combination with forest maps and forest road maps, can be used as a tool to plan salvage logging.
Martin Mokroš; Jozef Výbošťok; Ján Merganič; Markus Hollaus; Iván Barton; Milan Koreň; Julián Tomaštík; Juraj Čerňava. Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery. Forests 2017, 8, 306 .
AMA StyleMartin Mokroš, Jozef Výbošťok, Ján Merganič, Markus Hollaus, Iván Barton, Milan Koreň, Julián Tomaštík, Juraj Čerňava. Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery. Forests. 2017; 8 (9):306.
Chicago/Turabian StyleMartin Mokroš; Jozef Výbošťok; Ján Merganič; Markus Hollaus; Iván Barton; Milan Koreň; Julián Tomaštík; Juraj Čerňava. 2017. "Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery." Forests 8, no. 9: 306.
This study focuses on the horizontal and vertical accuracy of point-clouds based on unmanned aerial vehicle (UAV) imagery. The DJI Phantom 3 Professional unmanned aerial vehicle and Agisoft PhotoScan Professional software were used for the evaluation. Three test sites with differing conditions (canopy openness, slope, terrain complexity, etc.) were used for comparison. The accuracy evaluation was aimed on positions of points placed on the ground. This is often disregarded under forest conditions as it is not possible to photogrammetrically reconstruct terrain that is covered by a fully-closed forest canopy. Therefore, such a measurement can only be conducted when there are gaps in the canopy or under leaf-off conditions in the case of deciduous forests. The reported sub-decimetre horizontal accuracy and vertical accuracy lower than 20 cm have proven that the method is applicable for survey, inventory, and various other tasks in forests. An analysis of ground control point (GCP) quantity and configuration showed that the quantity had only a minor effect on the accuracy in cases of plots with ~1-hectare area when using the aforementioned software. Therefore, methods increasing quality (precision, accuracy) of GCP positions should be preferred over the increase of quantity alone.
Julián Tomaštík; Martin Mokroš; Šimon Saloň; František Chudý; Daniel Tunák. Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy. Forests 2017, 8, 151 .
AMA StyleJulián Tomaštík, Martin Mokroš, Šimon Saloň, František Chudý, Daniel Tunák. Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy. Forests. 2017; 8 (5):151.
Chicago/Turabian StyleJulián Tomaštík; Martin Mokroš; Šimon Saloň; František Chudý; Daniel Tunák. 2017. "Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy." Forests 8, no. 5: 151.
Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives.
We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud.
While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.
Robert Niederheiser; Martin Mokroš; Julia Lange; Helene Petschko; Guenther Prasicek; Sander Oude Elberink. DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B5, 685 -692.
AMA StyleRobert Niederheiser, Martin Mokroš, Julia Lange, Helene Petschko, Guenther Prasicek, Sander Oude Elberink. DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B5 ():685-692.
Chicago/Turabian StyleRobert Niederheiser; Martin Mokroš; Julia Lange; Helene Petschko; Guenther Prasicek; Sander Oude Elberink. 2016. "DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5, no. : 685-692.
The rapid development of unmanned aerial vehicles is a challenge for applied research. Many technologies are developed and then researcher are looking up for their application in different sectors. Therefore, we decided to verify the use of the unmanned aerial vehicle for wood chips pile monitoring.
We compared the use of GNSS device and unmanned aerial vehicle for volume estimation of four wood chips piles. We used DJI Phantom 3 Professional with the built-in camera and GNSS device (geoexplorer 6000). We used Agisoft photoscan for processing photos and ArcGIS for processing points.
Volumes calculated from pictures were not statistically significantly different from amounts calculated from GNSS data and high correlation between them was found (p = 0.9993). We conclude that the use of unmanned aerial vehicle instead of the GNSS device does not lead to significantly different results. Tthe data collection consumed from almost 12 to 20 times less time with the use of UAV. Additionally, UAV provides documentation trough orthomosaic.
Martin Mokroš; M. Tabačák; M. Lieskovsky; M. Fabrika. UNMANNED AERIAL VEHICLE USE FOR WOOD CHIPS PILE VOLUME ESTIMATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B1, 953 -956.
AMA StyleMartin Mokroš, M. Tabačák, M. Lieskovsky, M. Fabrika. UNMANNED AERIAL VEHICLE USE FOR WOOD CHIPS PILE VOLUME ESTIMATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B1 ():953-956.
Chicago/Turabian StyleMartin Mokroš; M. Tabačák; M. Lieskovsky; M. Fabrika. 2016. "UNMANNED AERIAL VEHICLE USE FOR WOOD CHIPS PILE VOLUME ESTIMATION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1, no. : 953-956.