Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

BackgroundCurrent automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost.ResultsIn the experiment, an approximately 0.5ha forest was covered in ca. 10min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2-4cm RMSE of the diameter at the breast height estimates, and a 4-7cm RMSE of the stem curve estimates.ConclusionsResults of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.Peer reviewe

Terrestrial laser scanning in forest inventories

AbstractDecision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique

Carisma workshop: from 21 to 23 August 2017 in Helsinki, Finland

Abstraktit201

Measuring tree growth using terrestrial laser scanning

Forests are dynamic ecosystems that are constantly changing. The most common natural reasons for change in forests are the growth and death of trees, as well as the damage occurring to them. Tree growth appears as an increment of its structural dimensions, such as stem diameter, height, and crown volume, which all affect the structure of a tree. Repeated measurements of tree characteristics enable observations of the respective increments indicating tree growth. According to current knowledge, the tree growth process follows the priority theory, where trees aim to achieve sufficient lightning conditions for the tree crown through primary growth, whereas increment in diameter results from the secondary growth. Tree growth is known to have an effect on the carbon sequestration potential of trees as well as on the quality of timber. To improve the understanding of the underlying cause–effect relations driving tree growth, methods to quantify structural changes in trees and forests are needed. The use of terrestrial laser scanning (TLS) has emerged during the recent decade as an effective tool to determine attributes of individual trees. However, the capacity of TLS point cloud-based methods to measure tree growth remains unexplored. This thesis aimed at developing new methods to measure tree growth in boreal forest conditions by utilizing two-date TLS point clouds. The point clouds were also used to investigate how trees allocate their growth and how the stem form of trees develops, to deepen the understanding of tree growth processes under different conditions and over the life cycle of a tree. The capability of the developed methods was examined during a five- to nine-year monitoring period with two separate datasets consisting of 1315 trees in total. Study I demonstrated the feasibility of TLS point clouds for measuring tree growth in boreal forests. In studies II and III, an automated point cloud-based method was further developed and tested for measuring tree growth. The used method could detect trees from two-date point clouds, with the detected trees representing 84.5% of total basal area. In general, statistically significant changes in the examined attributes, such as diameter at breast height, tree height, stem volume, and logwood volume, were detected during the monitoring periods. Tree growth and stem volume allocation seemed to be more similar for trees growing in similar structural conditions. The findings obtained in this thesis demonstrate the capabilities of repeatedly acquired TLS point clouds to be used for measuring the growth of trees and for characterizing the structural changes in forests. This thesis showed that TLS point cloud-based methods can be used for enhancing the knowledge of how trees allocate their growth, and thus help discover the underlying reasons for processes driving changes in forests, which could generate benefits for ecological or silvicultural applications where information on tree growth and forest structural changes is needed

Puiden kasvun mittaaminen maastolaserkeilauksella

Forests are dynamic ecosystems that are constantly changing. The most common natural reasons for change in forests are the growth and death of trees, as well as the damage occurring to them. Tree growth appears as an increment of its structural dimensions, such as stem diameter, height, and crown volume, which all affect the structure of a tree. Repeated measurements of tree characteristics enable observations of the respective increments indicating tree growth. According to current knowledge, the tree growth process follows the priority theory, where trees aim to achieve sufficient lightning conditions for the tree crown through primary growth, whereas increment in diameter results from the secondary growth. Tree growth is known to have an effect on the carbon sequestration potential of trees as well as on the quality of timber. To improve the understanding of the underlying cause–effect relations driving tree growth, methods to quantify structural changes in trees and forests are needed. The use of terrestrial laser scanning (TLS) has emerged during the recent decade as an effective tool to determine attributes of individual trees. However, the capacity of TLS point cloud-based methods to measure tree growth remains unexplored. This thesis aimed at developing new methods to measure tree growth in boreal forest conditions by utilizing two-date TLS point clouds. The point clouds were also used to investigate how trees allocate their growth and how the stem form of trees develops, to deepen the understanding of tree growth processes under different conditions and over the life cycle of a tree. The capability of the developed methods was examined during a five- to nine-year monitoring period with two separate datasets consisting of 1315 trees in total. Study I demonstrated the feasibility of TLS point clouds for measuring tree growth in boreal forests. In studies II and III, an automated point cloud-based method was further developed and tested for measuring tree growth. The used method could detect trees from two-date point clouds, with the detected trees representing 84.5% of total basal area. In general, statistically significant changes in the examined attributes, such as diameter at breast height, tree height, stem volume, and logwood volume, were detected during the monitoring periods. Tree growth and stem volume allocation seemed to be more similar for trees growing in similar structural conditions. The findings obtained in this thesis demonstrate the capabilities of repeatedly acquired TLS point clouds to be used for measuring the growth of trees and for characterizing the structural changes in forests. This thesis showed that TLS point cloud-based methods can be used for enhancing the knowledge of how trees allocate their growth, and thus help discover the underlying reasons for processes driving changes in forests, which could generate benefits for ecological or silvicultural applications where information on tree growth and forest structural changes is needed

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)

Developing Allometric Equations for Teak Plantations Located in the Coastal Region of Ecuador from Terrestrial Laser Scanning Data

Traditional studies aimed at developing allometric models to estimate dry above-ground biomass (AGB) and other tree-level variables, such as tree stem commercial volume (TSCV) or tree stem volume (TSV), usually involves cutting down the trees. Although this method has low uncertainty, it is quite costly and inefficient since it requires a very time-consuming field work. In order to assist in data collection and processing, remote sensing is allowing the application of non-destructive sampling methods such as that based on terrestrial laser scanning (TLS). In this work, TLS-derived point clouds were used to digitally reconstruct the tree stem of a set of teak trees (Tectona grandis Linn. F.) from 58 circular reference plots of 18 m radius belonging to three different plantations located in the Coastal Region of Ecuador. After manually selecting the appropriate trees from the entire sample, semi-automatic data processing was performed to provide measurements of TSCV and TSV, together with estimates of AGB values at tree level. These observed values were used to develop allometric models, based on diameter at breast height (DBH), total tree height (h), or the metric DBH2 × h, by applying a robust regression method to remove likely outliers. Results showed that the developed allometric models performed reasonably well, especially those based on the metric DBH2 × h, providing low bias estimates and relative RMSE values of 21.60% and 16.41% for TSCV and TSV, respectively. Allometric models only based on tree height were derived from replacing DBH by h in the expression DBH2 x h, according to adjusted expressions depending on DBH classes (ranges of DBH). This finding can facilitate the obtaining of variables such as AGB (carbon stock) and commercial volume of wood over teak plantations in the Coastal Region of Ecuador from only knowing the tree height, constituting a promising method to address large-scale teak plantations monitoring from the canopy height models derived from digital aerial stereophotogrammetry

Branch information extraction from Norway spruce using handheld laser scanning point clouds in Nordic forests

We showed that a mobile handheld laser scanner (HHLS) provides useful features concerning the wood quality-influencing external structures of trees. When linked with wood properties measured at a sawmill utilizing state-of-the-art X-ray scanners, these data enable the training of various wood quality models for use in targeting and planning future wood procurement. A total of 457 Norway spruce sample trees (Picea abies (L.) H. Karst.) from 13 spruce-dominated stands in southeastern Finland were used in the study. All test sites were recorded with a ZEB Horizon HHLS, and the sample trees were tracked to a sawmill and subjected to X-rays. Two branch extraction techniques were applied to the HHLS point clouds: 1) a method developed in this study that was based on the density-based spatial clustering of applications with noise (DBSCAN) and 2) segmentation-based quantitative structure model (treeQSM). On average, the treeQSM method detected 46% more branches per tree than the DBSCAN did. However, compared with the X-rayed references, some of the branches detected by the treeQSM may either be false positives or so small in size that the X-rays are unable to detect them as knots, as the method overestimated the whorl count by 19% when compared with the X-rays. On the other hand, the DBSCAN method only detected larger branches and showed a −11% bias in whorl count. Overall, the DBSCAN underestimated knot volumes within trees by 6%, while the treeQSM overestimated them by 25%. When we input the HHLS features into a Random Forest model, the knottiness variables measured at the sawmill were predicted with R2s of 0.47–0.64. The results were comparable with previous results derived with the static terrestrial laser scanners. The obtained stem branching data are relevant for predicting wood quality attributes but do not provide data that are directly comparable with the X-ray features. Future work should combine terrestrial point clouds with dense above-canopy point clouds to overcome the limitations related to vertical coverage

Stem-Level Bucking Pattern Optimization in Chainsaw Bucking Based on Terrestrial Laser Scanning Data

Cross-cutting of a tree into a set of assortments (»bucking pattern«) presents a large potential for optimizing the volume and value recovery; therefore, bucking pattern optimization has been studied extensively in the past. However, it has not seen widespread adoption in chainsaw bucking, where time consuming and costly manual measurement of input parameters is required for taper curve estimation. The present study investigated an alternative approach, where taper curves are fit based on terrestrial laser scanning data (TLS), and how deviations from observed taper curves (REF) affect the result of bucking pattern optimization. In addition, performance of TLS was compared to a traditional, segmental taper curve estimation approach (APP) and an experienced chainsaw operator’s solution (CHA). A mature Norway Spruce stand was surveyed by stationary terrestrial laser scanning. In TLS, taper curves were fit by a mixed-effects B-spline regression approach to stem diameters extracted from 3D point cloud data. A network analysis technique algorithm was used for bucking pattern optimization during harvesting. Stem diameter profiles and the chainsaw operator’s bucking pattern were obtained by manual measurement. The former was used for post-operation fit of REF taper curves by the same approach as in TLS. APP taper curves were fit based on part of the data. For 35 trees, TLS and APP taper curves were compared to REF on tree, trunk and crown section level. REF and APP bucking patterns were optimized with the same algorithm as in TLS. For 30 trees, TLS, APP and CHA bucking patterns were compared to REF on operation and tree level. Taper curves were estimated with high accuracy and precision (underestimated by 0.2 cm on average (SD=1.5 cm); RMSE=1.5 cm) in TLS and the fit outperformed APP. Volume and value recovery were marginally higher in TLS (0.6%; 0.9%) than in REF on operation level, while substantial differences were observed for APP (–6.1%; –4.1%). Except for cumulated nominal length, no significant differences were observed between TLS and REF on tree level, while APP result was inferior throughout. Volume and value recovery in CHA was significantly higher (2.1%; 2.4%), but mainly due to a small disadvantage of the optimization algorithm. The investigated approach based on terrestrial laser scanning data proved to provide highly accurate and precise estimations of the taper curves. Therefore, it can be considered a further step towards increased accuracy, precision and efficiency of bucking pattern optimization in chainsaw bucking

Automatic tree detection and attribute characterization using portable terrestrial lidar

Currently, the implementation of portable laser scanners (PLS) in forest inventories is being studied, since they allow for significantly reduced field-work time and costs when compared to the traditional inventory methods and other LiDAR systems. However, it has been shown that their operability and efficiency are dependent upon the species assessed, and therefore, there is a need for more research assessing different types of stands and species. Additionally, a few studies have been conducted in Eucalyptus stands, one of the tree genus that is most commonly planted around the world. In this study, a PLS system was tested in a Eucalyptus globulus stand to obtain different metrics of individual trees. An automatic methodology to obtain inventory data (individual tree positions, DBH, diameter at different heights, and height of individual trees) was developed using public domain software. The results were compared to results obtained with a static terrestrial laser scanner (TLS). The methodology was able to identify 100% of the trees present in the stand in both the PLS and TLS point clouds. For the PLS point cloud, the RMSE of the DBH obtained was 0.0716, and for the TLS point cloud, it was 0.176. The RMSE for height for the PLS point cloud was 3.415 m, while for the PLS point cloud, it was 10.712 m. This study demonstrates the applicability of PLS systems for the estimation of the metrics of individual trees in adult Eucalyptus globulus stands.Agencia Estatal de Investigación | Ref. PID2019-111581RB-I00Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/02054Universidade de Vigo/CISU