Laser-scanned tree stem filtering for forest inventories measurements

International audienceWith specific flora and fauna, regional landscapes and forests constitute an important part of the cultural heritage. Several natural environments have already been classified as national or regional parks. The UNESCO World Heritage covers 13% of the protected forests in the world. Thus, preserving those sites represents a crucial issue. Such a safeguarding involves a detailed knowledge of the sites and forestry management plans. The management of a natural forest is traditionally based on forest plot inventories in which several features of the trees are measured. The set of data collected during these inventories represents the starting point of forest monitoring, flora preservation and risks prevention. Traditionally, measurements are made manually by operators. However, during the last decade, terrestrial laser scanning has become a new and promising way of measuring such attributes. This instrument provides a fine three dimensional point cloud virtual representation of the scanned scene. Trees location, stem diameter, and stem taper can be extracted from these point clouds using pattern recognition algorithms. In this paper we present a novel two steps way to improve the quality of tree branching detection in a three dimensional point cloud acquired by terrestrial laser scanner. This method was developed in order to enhance the results of a previous study. Our approach is based on the combination of a simplification step (using particle simulation), followed by a shape detection (discrete arcs of circle detection). It identifies the lack of accuracy in tree stem diameter measurements at branching junctions for further more detailed analysis

Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads

The collection of field-reference data is a key task in remote sensing-based forest inventories. However, traditional methods of collection demand extensive personnel resources. Thus, field-reference data collection would benefit from more automated methods. In this study, we proposed a method for individual tree detection (ITD) and stem attribute estimation based on a car-mounted mobile laser scanner (MLS) operating along forest roads. We assessed its performance in six ranges with increasing mean distance from the roadside. We used a Riegl VUX1LR sensor operating with high repetition rate, thus providing detailed cross sections of the stems. The algorithm we propose was designed for this sensor configuration, identifying the cross sections (or arcs) in the point cloud and aggregating those into single trees. Furthermore, we estimated diameter at breast height (DBH), stem profiles, and stem volume for each detected tree. The accuracy of ITD, DBH, and stem volume estimates varied with the trees' distance from the road. In general, the proximity to the sensor of branches 0-10 m from the road caused commission errors in ITD and over estimation of stem attributes in this zone. At 50-60 m from roadside, stems were often occluded by branches, causing omissions and underestimation of stem attributes in this area. ITD's precision and sensitivity varied from 82.8% to 100% and 62.7% to 96.7%, respectively. The RMSE of DBH estimates ranged from 1.81 cm (6.38%) to 4.84 cm (16.9%). Stem volume estimates had RMSEs ranging from 0.0800 m(3) (10.1%) to 0.190 m(3) (25.7%), depending on the distance to the sensor. The average proportion of detected reference volume was highly affected by the performance of ITD in the different zones. This proportion was highest from 0 to 10 m (113%), a zone that concentrated most ITD commission errors, and lowest from 50 to 60 m (66.6%), mostly due to the omission errors in this area. In the other zones, the RMSE ranged from 87.5% to 98.5%. These accuracies are in line with those obtained by other state-of-the-art MLS and terrestrial laser scanner (TLS) methods. The car-mounted MLS system used has the potential to collect data efficiently in large-scale inventories, being able to scan approximately 80 ha of forests per day depending on the survey setup. This data collection method could be used to increase the amount of field-reference data available in remote sensing based forest inventories, improve models for area-based estimations, and support precision forestry development

Mapping Forest Regeneration from Terrestrial Laser Scans

Az erdei újulati foltok helye, kiterjedése, borítottsága és törzsszáma kulcsfontosságú tényezők az erdődinamikai folyamatok feltárásában és a többkorú faállományok kezelésében. A fatermési modellek előállítása, az üzemi gyakorlatban végzett erdőművelés valamint erdőfeltárás pontos és objektív módszereket kíván az újulat helyének meghatározására. A földi lézeres letapogatás kiválóan alkalmas törzstérképek előállítására, ám az adatok feldolgozásához szükséges eljárásokat eddig csak szálerdőkre fejlesztettek ki. A tanulmány olyan automatikus eljárást mutat be, ami 3–6 méter magasságú faegyedek lézeres letapogatás adataiból történő azonosítását teszi lehetővé. Három, különböző jellegű újulati foltban létesített mintaterületen a ponthalmaz vizuális interpretációjával azonosított törzsek 79–90%-át sikerült automatikus úton felismerni. Az eljárás teljesítményét a vizsgált állományjellemzők közül elsősorban a törzsszám befolyásolta, míg az ágak mennyiségének hatása elenyésző. Az elért eredmények rámutatnak, hogy a földi lézeres letapogatás alkalmas az újulat mennyiségének felmérésére, így a folyamatos borítású erdők leírásának ígéretes eszköze lehet

On the use of rapid-scan, low point density terrestrial laser scanning (TLS) for structural assessment of complex forest environments

Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (LAD), which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS

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

An automated approach for extracting forest inventory data from individual trees using a handheld mobile laser scanner

Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near )natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83-0.99, p> hard plotso << located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in "easy plots". By improving the algorithmic performances, the accuracy levels can be further increased by future research

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

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

Estimation of breast height diameter and trunk curvature with linear and single-photon LiDARs

International audienceContext: Precision forestry together with new sensor technologies implies Digital Forest Inventories for estimation of volume and quality of trees in a stand.Aims: This study compared commercial LiDAR, new prototype SPAD LiDAR, and manual methods for measuring tree quality attributes, i.e., diameter at breast height (DBH) and trunk curvature in the forest stand.Methods: We measured 7 Scots pine trees (Pinus sylvestris) with commercial LiDAR (Zeb Horizon by GeoSLAM), prototype SPAD LiDAR, and manual devices. We compared manual measurements to the DBH and curvature values estimated based on LiDAR data. We also scanned a densely branched Picea abies to compare penetrability of the LiDARs and detectability of the obstructed trunk.Results: The DBH values deviated 1–3 cm correlating to the specified accuracies of the employed devices, showing close to acceptable results. The curvature values deviated 1–6 cm implying distorted range measurements from the top part of the trunks and inaccurate manual measurement method, leaving space for improvement. The most important finding was that the SPAD LiDAR outperformed conventional LiDAR in detecting tree stem of the densely branched spruce.Conclusion: These results represent preliminary but clear evidence that LiDAR technologies are already close to acceptable level in DBH measurements, but not yet satisfactory for curvature measurements. In addition, terrestrial SPAD LiDAR has a great potential to outperform conventional LiDARs in forest measurements of densely branched trees

Puiden paikannus ja lajitunnistaminen maalaserkeilausaineistosta

The requirements for more accurate and up-to-date spatial data increases constantly due to changes occurring in the environment. In addition, there is a technical and economical need to map trees, tree ages and sizes, as well in wide forest areas as park areas in cities by modern scanning techniques. The aim of this thesis was to investigate different positioning methods for terrestrial laser scanned trees. The second aim was to examine different techniques to identify the species of the positioned trees. Laser scans from two separate relatively small woodlands were acquired for the thesis. These scans were utilised for tree locating and species identification. Tree positioning was based on the cylinder fitting method performed for tree stems provided by the scans. The results achieved by the positioning were analyzed based on the comparison to the manually measured reference values. To identify the tree species, the tree intensities and structure parameters extracted from the point clouds were used. According to the study results, the classification of some tree species was relatively well succeeded. However, the identification of some other species did not succeed as expected. The best classification correctness of 80 percent was achieved using the combination of tree intensities and the structure parameters, as well as by the structure parameters only. Classification using the intensities only provided considerably more unreliable results. Instead of that, one tree species (spruce) identification succeeded perfectly in each case. However, tree positioning succeeded obviously well, so the tree locations deviated slightly from the reference values. This examination indicated that a reliable evaluation of the tree classification results did not fully succeed with the relatively small tree sample size used in this thesis. To obtain more reliable estimate of success rate for the results provided by terrestrial laser scanning data, a larger sample size may be required. Furthermore, the laser scans for this work were performed in autumn when there were no leaves in the trees. This, of course, affected the intensity-based tree classification. However, modern tree positioning and classification methods appear quite promising. The future use of these techniques require further development and examination work.Yhä tarkemman ja ajantasaisen paikkatiedon tarve kasvaa jatkuvasti ympäristössä tapahtuvien nopeiden muutosten myötä. Tämä näkyy myös teknistaloudellisena tarpeena kartoittaa puulajeja, niiden ikää ja kokoa mm. erilaisilla nykyajan keilausmenetelmillä, niin laajoilla metsäalueilla kuin kaupunkien puistoalueilla. Tämän työn tavoitteena oli tutkia maastolaserkeilattujen puiden erilaisia paikannusmenetelmiä. Toisena tavoitteena oli tarkastella paikannettujen puiden lajitunnistusmenetelmiä. Työn toteuttamiseksi suoritettiin laserkeilauksia kahdella erillisellä pienehköllä metsäalueella. Näitä keilausaineistoja käytettiin puiden paikantamiseen ja lajitunnistukseen. Paikannus perustui keilausten tuloksena saaduille puun rungoille tehtyyn sylinterisovitusmenetelmään. Laskennalla saatuja tuloksia analysoitiin vertaamalla niitä referenssiarvoihin, jotka saatiin pistepilvistä manuaalisesti mittaamalla. Lajitunnistuksessa käytettiin puista saatujen pistepilvien intensiteettejä ja rakenneparametreja. Suoritetun tarkastelun perusteella joidenkin puulajien tunnistaminen onnistui melko hyvin. Kaikkien puiden tunnistaminen ei kuitenkaan onnistunut odotetulla tavalla. Käyttäen pistepilvien intensiteettien ja pistepilvistä saatujen puiden rakenneparametrien yhdistelmää, sekä pelkkiä rakenneparametreja, tulkittiin parhaimmillaankin noin 80 prosenttia tuloksista oikein. Pelkkiä intensiteettejä käyttäen saatiin huomattavasti epäluotettavampi tulos. Sen sijaan yhden puulajin (kuusen) tunnistaminen onnistui kaikissa tapauksissa täydellisesti. Toisaalta, suoritetussa tarkastelussa puiden paikantaminen onnistui kokonaisuudessaan hyvin, sillä laskennalla puille saadut sijainnit poikkesivat referenssiarvoista kauttaaltaan varsin vähän. Tarkastelu osoitti, että maalaserkeilattujen puiden tunnistaminen tässä työssä käytetyllä suhteellisen pienellä otoskoolla ei täysin onnistunut. Tarkempi arvio maalaserkeilausaineistosta saatujen tulosten onnistumisprosentista olisi edellyttänyt suurempaa otoskokoa. Lisäksi puiden laserkeilaukset tehtiin syksyllä, jolloin puissa ei ollut lehtiä. Tämä tietenkin vaikutti puiden tunnistamiseen intensiteettien avulla. Nykyiset puiden tunnistusmenetelmät vaikuttavat kuitenkin kokonaisuudessaan varsin lupaavilta. Menetelmien hyödyntäminen edellyttää yhä tutkimus- ja kehitystyötä