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

Direct measurement of tree height provides different results on the assessment of LiDAR accuracy

open8noopenSibona, Emanuele; Vitali, Alessandro; Meloni, Fabio; Caffo, Lucia; Dotta, Alberto; Lingua, Emanuele; Motta, Renzo; Garbarino, MatteoSibona, Emanuele; Vitali, Alessandro; Meloni, Fabio; Caffo, Lucia; Dotta, Alberto; Lingua, Emanuele; Motta, Renzo; Garbarino, Matte

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

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

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

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

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

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