Feasibility of Terrestrial Laser Scanning for Plotwise Forest Inventories

Osajulkaisut: Publication 1: Liang, X., Litkey, P., Hyyppä, J., Kaartinen, H., Vastaranta, M., Holopainen, M., 2012. Automatic stem mapping using single-scan terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 50(2): 661–670. Publication 2: Liang, X., Litkey, P., Hyyppä, J., Kaartinen, H., Kukko, A., Holopainen, M., 2011. Automatic plot-wise tree location mapping using single-scan terrestrial laser scanning. The Photogrammetric Journal of Finland, 22(2): 37–48. Publication 3: Liang, X., Hyyppä, J., 2013. Automatic stem mapping by merging several terrestrial laser scans at the feature and decision levels. Sensors, 13(2): 1614–1634. Publication 4: Liang, X., Kankare, V., Yu, X., Hyyppä, J., Holopainen, M. Automated stem curve measurement using terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, DOI 10.1109/TGRS.2013.2253783. Publication 5: Yu, X., Liang, X., Hyyppä, J., Kankare, V., Vastaranta, M., Holopainen, M., 2013. Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds. Remote Sensing Letters, 4(4): 344-353. Publication 6: Liang, X., Hyyppä, J., Kaartinen, H., Holopainen, M., Melkas, T., 2012. Detecting changes in forest structure over time with bi-temporal terrestrial laser scanning data. ISPRS International Journal of Geo-Information, 1(3): 242-255.Detailed, up-to-date forest information is increasingly important in quantitative forest inventories. The accuracy of the information retrieval is highly dependent on the quality and quantity of the reference data collected on field sample plots. In practice, the plotwise forest data are used as a reference for the calibration of large-area inventory data measured by aerial and space-borne remote sensing techniques. Field reference data are conventionally collected at the sample plot level by manual measurements. Because of the high costs and labor intensity of manual measurements, the number of tree attributes collected is limited. Some of the most important tree attributes are not even measured or sampled. Terrestrial laser scanning (TLS) has been recently shown to be a promising technique for forest-related studies. Many tree attributes have been correlated with measurements from TLS data. Numerous TLS methods have been proposed. 6However, the feasibility of applying TLS in plotwise forest inventories is still unclear. The major missing factor is automation of data processing. Other factors hampering the acceptance of the technology include the relatively high cost of the TLS instrument, the low measurement accuracy achieved using the automated data processing currently available, and the shortage of experimental results related to the retrieval of advanced stem attributes (e.g., stem curve) and to different forest conditions. In this study, a series of methods to map sample plots were developed, and their applicability in plotwise forest inventories was analyzed. The accuracy of stem mapping, the efficiency of data collection, and the limitations of the techniques were discussed. The results indicate that TLS is capable of documenting a forest sample plot in detail and that automated mapping methods yield accurate measurements of the most important tree attributes, such as diameter at breast height and stem curve. The fully-automated TLS data processing that was developed in this study resulted in measurement accuracy similar to that of manual measurements using conventional tools or models and of manual measurements from point cloud data. The results of this study support the feasibility of TLS for practical forest field inventories. Further research is needed to explore new protocols for the application of TLS in field inventories. Three possible new directions are the integration of detailed tree attributes (e.g., stem curve, volume, and biomass) in large-area inventories, the utilization of TLS field plots in national forest inventories, and the mapping of large sample plots, e.g., in operational harvest planning. More studies need to be performed on sample plots under different forest conditions (development class, tree species, and amount of ground vegetation).Tarkka ja ajantasainen metsävaratieto on yhä tärkeämpää metsätaloudessa. Laajojen metsäalueiden inventointi ja seuranta perustuu maastomittauksiin ja kaukokartoitustulkintaan. Maastossa mitattuja koealoja hyödynnetään kaukokartoituksen referenssi- tai kalibrointiaineistoina. Tällöin tulkinnassa käytettävien referenssi- tai kalibrointikoealojen mittaustarkkuus on ratkaisevan tärkeää. Perinteisesti maastoreferenssi on kerätty koealoilta manuaalisilla mittauksilla, mikä on työlästä. Korkeiden työvoimakustannusten vuoksi mitattavien puutunnusten määrä on rajallinen, ja joitakin tärkeitä puutunnuksia ei voida operatiivisesti edes mitata. Maastolaserkeilaus (Terrestrial Laser Scanning, TLS) on viime aikoina antanut lupauksia puiden mittaamiseen. Monet puutunnukset korreloivat hyvin TLS-piirteiden kanssa, ja useita menetelmiä puiden mittaukseen on esitetty. TLS:n soveltuvuus koealoihin perustuvaan metsävarojen inventointiin on kuitenkin edelleen epäselvää. Suurin ongelma on TLS-aineiston automaattinen käsittely ja tulkinta. Muita uuden tekniikan käyttöönottoa rajoittavia tekijöitä ovat TLS-laitteiston suhteellisen korkea hinta, tarjolla olevien automaattisten menetelmien huonot mittaustarkkuudet sekä käytännön testien puuttuminen (esim. runkokäyrän mittaus) erilaisissa puustoissa ja metsiköissä. Tutkimuksessa kehitettiin useita TLS-menetelmiä koealojen kartoitukseen ja mittaukseen. Lisäksi menetelmien soveltuvuutta koealoihin perustuvassa metsävarojen inventoinnissa analysoitiin ottaen huomioon runkojen paikannuksen tarkkuus, aineiston keruun tehokkuus sekä tekniikan rajoitukset. Tulosten mukaan TLS-mittaukset ovat soveltuvia metsikkökoealan tarkkaan kartoitukseen ja automaattiset menetelmät tuottivat tarkkoja mittaustuloksia tärkeimmistä puutunnuksista, kuten puiden rinnankorkeusläpimitasta ja runkokäyrästä. Täysin automaattinen TLS-aineiston käsittelymenetelmä, joka tutkimuksessa kehitettiin, tuotti samantasoista mittaustarkkuutta kuin perinteiset metsässä tehtävät mittausmenetelmät tai TLS-pistepilvestä suoritetut manuaaliset mittaukset. Tulokset osoittavat TLS-mittausten olevan potentiaalinen menetelmä operatiiviseen metsävarojen maastoinventointiin. Jatkotutkimuksia tarvitaan operatiivisen TLS-inventointimenetelmän kehittämiseen. Kolme mahdollista tutkimuslinjaa ovat TLS:llä mitattujen tarkkojen puutunnusten (esim. runkokäyrä, tilavuus ja biomassa) integrointi laajojen alueiden inventointeihin, TLS-koealojen hyödyntäminen operatiivisessa valtakunnan metsien inventoinnissa (VMI) sekä laajojen koealojen mittaaminen TLS:llä, esimerkiksi operatiivisen leimikkosuunnittelun yhteydessä. Lisäksi tarvitaan edelleen jatkotutkimuksia TLS-mittausten tarkkuudesta erilaisissa metsiköissä (kehitysluokka, puulaji, aluskasvillisuuden määrä)

TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest

In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a ‘bridge’. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R2 = 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle.This work was financially supported in part by the National Natural Science Foundation of China [grant numbers 41471281 and 31670718] and in part by the SRF for ROCS, SEM, China. (41471281 - National Natural Science Foundation of China; 31670718 - National Natural Science Foundation of China; SRF for ROCS, SEM, China)http://www-tandfonline-com.ezproxy.bu.edu/doi/abs/10.1080/17538947.2016.1247473?journalCode=tjde20Published versio

Hand-Held Personal Laser Scanning – Current Status and Perspectives for Forest Inventory Application

The emergence of hand-held Personal Laser Scanning (H-PLS) systems in recent years resulted in initial research on the possibility of its application in forest inventory, primarily for the estimation of the main tree attributes (e.g. tree detection, stem position, DBH, tree height, etc.). Research knowledge acquired so far can help to direct further research and eventually include H-PLS into operational forest inventory in the future. The main aims of this review are: Þ to present the current state of the art for H-PLS systems Þ briefly describe the fundamental concept and methods for H-PLS application in forest inventory Þ provide an overview of the results of previous studies Þ emphasize pros and cons for H-PLS application in forest inventory in relation to conventional field measurements and other similar laser scanning systems Þ highlight the main issues that should be covered by further H-PLS-based forest inventory studies

Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests

Terrestrial laser scanning (TLS) has proven to accurately represent individual trees, while the use of TLS for plot-level forest characterization has been studied less. We used 91 sample plots to assess the feasibility of TLS in estimating plot-level forest inventory attributes, namely the stem number (N), basal area (G), and volume (V) as well as the basal area weighed mean diameter (Dg) and height (Hg). The effect of the sample plot size was investigated by using different-sized sample plots with a fixed scan set-up to also observe possible differences in the quality of point clouds. The Gini coefficient was used to measure the variation in tree size distribution at the plot-level to investigate the relationship between stand heterogeneity and the performance of the TLS-based method. Higher performances in tree detection and forest attribute estimation were recorded for sample plots with a low degree of tree size variation. The TLS-based approach captured 95% of the variation in Hg and V, 85% of the variation in Dg and G, and 67% of the variation in N. By increasing the sample plot size, the tree detection rate was decreased, and the accuracy of the estimates, especially G and N, decreased. This study emphasizes the feasibility of TLS-based approaches in plot-level forest inventories in varying southern boreal forest conditions

3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness

Quantitative Assessment of Scots Pine (Pinus Sylvestris L.) Whorl Structure in a Forest Environment Using Terrestrial Laser Scanning

State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter > 15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67 degrees (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2 degrees (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.Peer reviewe

Assessing branching structure for biomass and wood quality estimation using terrestrial laser scanning point clouds

Terrestrial laser scanning (TLS) accompanied by quantitative tree-modeling algorithms can potentially acquire branching data non-destructively from a forest environment and aid the development and calibration of allometric crown biomass and wood quality equations for species and geographical regions with inadequate models. However, TLS's coverage in capturing individual branches still lacks evaluation. We acquired TLS data from 158 Scots pine (Pinus sylvestris L.) trees and investigated the performance of a quantitative branch detection and modeling approach for extracting key branching parameters, namely the number of branches, branch diameter (b(d)) and branch insertion angle (b) in various crown sections. We used manual point cloud measurements as references. The accuracy of quantitative branch detections decreased significantly above the live crown base height, principally due to the increasing scanner distance as opposed to occlusion effects caused by the foliage. b(d) was generally underestimated, when comparing to the manual reference, while b was estimated accurately: tree-specific biases were 0.89cm and 1.98 degrees, respectively. Our results indicate that full branching structure remains challenging to capture by TLS alone. Nevertheless, the retrievable branching parameters are potential inputs into allometric biomass and wood quality equations.Peer reviewe

Detecting changes in forest structure over time with bi-temporal terrestrial laser scanning data

Peer reviewe

Detecting and characterizing downed dead wood using terrestrial laser scanning

Dead wood is a key forest structural component for maintaining biodiversity and storing carbon. Despite its important role in a forest ecosystem, quantifying dead wood alongside standing trees has often neglected when investigating the feasibility of terrestrial laser scanning (TLS) in forest inventories. The objective of this study was therefore to develop an automatic method for detecting and characterizing downed dead wood with a diameter exceeding 5 cm using multi-scan TLS data. The developed four-stage algorithm included (1) RANSAC-cylinder filtering, (2) point cloud rasterization, (3) raster image segmentation, and (4) dead wood trunk positioning. For each detected trunk, geometry-related quality attributes such as dimensions and volume were automatically determined from the point cloud. For method development and validation, reference data were collected from 20 sample plots representing diverse southern boreal forest conditions. Using the developed method, the downed dead wood trunks were detected with an overall completeness of 33% and correctness of 76%. Up to 92% of the downed dead wood volume were detected at plot level with mean value of 68%. We were able to improve the detection accuracy of individual trunks with visual interpretation of the point cloud, in which case the overall completeness was increased to 72% with mean proportion of detected dead wood volume of 83%. Downed dead wood volume was automatically estimated with an RMSE of 15.0 m(3)/ha (59.3%), which was reduced to 6.4 m(3)/ha (25.3%) as visual interpretation was utilized to aid the trunk detection. The reliability of TLS-based dead wood mapping was found to increase as the dimensions of dead wood trunks increased. Dense vegetation caused occlusion and reduced the trunk detection accuracy. Therefore, when collecting the data, attention must be paid to the point cloud quality. Nevertheless, the results of this study strengthen the feasibility of TLS-based approaches in mapping biodiversity indicators by demonstrating an improved performance in quantifying ecologically most valuable downed dead wood in diverse forest conditions.Peer reviewe