Automated Structural-level Alignment of Multi-view TLS and ALS Point Clouds in Forestry

Access to highly detailed models of heterogeneous forests from the near surface to above the tree canopy at varying scales is of increasing demand as it enables more advanced computational tools for analysis, planning, and ecosystem management. LiDAR sensors available through different scanning platforms including terrestrial, mobile and aerial have become established as one of the primary technologies for forest mapping due to their inherited capability to collect direct, precise and rapid 3D information of a scene. However, their scalability to large forest areas is highly dependent upon use of effective and efficient methods of co-registration of multiple scan sources. Surprisingly, work in forestry in GPS denied areas has mostly resorted to methods of co-registration that use reference based targets (e.g., reflective, marked trees), a process far from scalable in practice. In this work, we propose an effective, targetless and fully automatic method based on an incremental co-registration strategy matching and grouping points according to levels of structural complexity. Empirical evidence shows the method's effectiveness in aligning both TLS-to-TLS and TLS-to-ALS scans under a variety of ecosystem conditions including pre/post fire treatment effects, of interest to forest inventory surveyors

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

Improving Tree Crown Mapping using Airborne LiDAR with Genetic Algorithms

Landscape-scale mapping of individual trees derived from LiDAR (Light Detection And Ranging) data have been found to be valuable for a wide range of environmental analyses including carbon inventories; fuel estimations for wildfire risk assessment and management. These mapping efforts use individual tree crown (ITC) recognition algorithms applied to LiDAR point clouds, which have complex parameter sets. Genetic algorithms (GA) have been demonstrated to be excellent function optimizers for very complex search spaces and perform well for parameter tuning. Here, we use GAs to identify the best of a set of published ITC models and their optimal parameters for airborne LiDAR of forested plots in the Sierra Nevada Mountains of California. We assessed the accuracy of these ITC models in terms of the F-score and percentage bias for tree crown prediction. GA-optimization generally improved on ITC default parameters and showed that these models typically perform better for detecting overstory trees

Forest structure from terrestrial laser scanning – in support of remote sensing calibration/validation and operational inventory

Forests are an important part of the natural ecosystem, providing resources such as timber and fuel, performing services such as energy exchange and carbon storage, and presenting risks, such as fire damage and invasive species impacts. Improved characterization of forest structural attributes is desirable, as it could improve our understanding and management of these natural resources. However, the traditional, systematic collection of forest information – dubbed “forest inventory” – is time-consuming, expensive, and coarse when compared to novel 3-D measurement technologies. Remote sensing estimates, on the other hand, provide synoptic coverage, but often fail to capture the fine- scale structural variation of the forest environment. Terrestrial laser scanning (TLS) has demonstrated a potential to address these limitations, but its operational use has remained limited due to unsatisfactory performance characteristics vs. budgetary constraints of many end-users. To address this gap, my dissertation advanced affordable mobile laser scanning capabilities for operational forest structure assessment. We developed geometric reconstruction of forest structure from rapid-scan, low-resolution point cloud data, providing for automatic extraction of standard forest inventory metrics. To augment these results over larger areas, we designed a view-invariant feature descriptor to enable marker-free registration of TLS data pairs, without knowledge of the initial sensor pose. Finally, a graph-theory framework was integrated to perform multi-view registration between a network of disconnected scans, which provided improved assessment of forest inventory variables. This work addresses a major limitation related to the inability of TLS to assess forest structure at an operational scale, and may facilitate improved understanding of the phenomenology of airborne sensing systems, by providing fine-scale reference data with which to interpret the active or passive electromagnetic radiation interactions with forest structure. Outputs are being utilized to provide antecedent science data for NASA’s HyspIRI mission and to support the National Ecological Observatory Network’s (NEON) long-term environmental monitoring initiatives

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

Laserkeilaus puutason biomassan, puutavaralajien sekä laatutiedon ennustamisessa

The precise knowledge of forest structural attributes play an essential role in decision-making, forest management procedure planning and in wood supply chain optimization. Laser scanning (LS) is one of the most promising remote sensing techniques, which can be used to estimate forest attributes at all levels, from single trees to global applications. The main objectives of the present thesis were to develop LS-based methodologies for mapping and measuring single trees. More specifically, new high-density LS-based models and methodologies were developed for the prediction of aboveground biomass (AGB), logging recovery, stem curve and external tree quality estimation. Multisource remote sensing methodologies were additionally introduced for the detailed next generation forest-inventory process. Substudies I and II concentrated on developing LS-based biomass models. Total AGB was estimated with the relative root mean squared errors (RMSE%) of 12.9% and 11.9% for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H.Karst.), respectively using terrestrial LS (TLS) -derived predictors in multiple regression modelling. TLS-based AGB models significantly improved the estimation accuracy of AGB components compared to state-of-the-art allometric biomass models. Airborne LS (ALS) resulted in slightly higher RMSE% values of 26.3% and 36.8% for Scots pine and Norway spruce compared to results obtained with TLS. The goal of substudies III and IV was to predict timber assortment and tree quality information using high-density LS data. Sawlog volumes were estimated with RMSE% of 17.5% and 16.8% with TLS and a combination of TLS and ALS, respectively. Results in IV showed that trees could be successfully classified in different quality classes based on TLS-measured attributes with accuracies between 76.4% and 83.6% depending on the amount of quality classes. Substudies V and VI presented new automatic processing tools for TLS data and a multisource approach for the more detailed prediction of diameter distribution. Automatic processing of TLS data was demonstrated to be effective and accurate and could be utilized to make future TLS measurements more efficient. Accuracies of ~1 cm were achieved using the automatic stem curve procedure. The multisource single-tree inventory approach combined accurate treemaps produced automatically from the TLS data, and ALS individual tree detection technique. Results from diverse forest conditions were promising, resulting in diameter prediction accuracies between 1.4 cm and 4.7 cm depending on tree density and main tree species. Each substudy (I VI) presented new methods and results for single-tree AGB modelling, external tree quality classification, automatic stem reconstruction and multisource approaches.Yksityiskohtainen metsävaratieto on merkittävässä roolissa metsänomistajien päätöksenteon, metsänhoidon suunnittelun sekä puunhankintaketjun optimoinnin tukena. Laserkeilaus on yksi lupaavimmista kaukokartoitustekniikoista, jolla on mahdollista ennustaa metsävaratietoa yksittäisen puun tasolta laajoihin alueisiin. Väitöskirjatyön päätavoitteina oli kehittää laserkeilauspohjaisia menetelmiä yksittäisten puiden kartoitukseen ja mittaukseen. Osajulkaisuissa I ja II selvitettiin laserkeilausmenetelmien tarkkuutta puutason biomassaositteiden mallinnuksessa. Kokonaisbiomassan mallinnustarkkuus maastolaserkeilaukseen perustuen oli männylle 12,9 % ja kuuselle 11,9 %. Maastolaserkeilauksen hyödyntäminen biomassan mallintamisessa tarkensi erityisesti latvusbiomassan mallinnustarkkuutta verrattuna läpimittaan ja pituuteen perustuviin biomassamalleihin. Lentolaserkeilauksen tarkkuudet olivat hieman heikompia verrattuna maastolaserkeilaukseen. Kokonaisbiomassan tarkkuudet olivat männylle 26,3 % ja kuuselle 36,8 %. Osajulkaisuissa III ja IV tavoitteena oli ennustaa puutavaralajikohtaisia tilavuuksia sekä laatutietoa maastolaserkeilauksen ja lentolaserkeilauksen avulla. Tukkipuun tilavuuden ennustetarkkuudet olivat maastolaserkeilausta käytettäessä 17,5 % ja maasto- ja lentolaserkeilauksen yhdistelmää käytettäessä 16,8 %. Puuston laatu on erittäin tärkeä tekijä metsikköä arvioitaessa. Maastolaserkeilauksen avulla yksittäiset puut luokiteltiin puunhankinnan kannalta tärkeisiin laatuluokkiin 76,4 % 83,6 % tarkkuudella. Osajulkaisuissa V ja VI tavoitteena oli kehittää uusia automaattisia menetelmiä maastolaserkeilausaineiston käsittelyyn, sekä monilähdemenetelmiä läpimittajakauman ennustamiseen. Automaattisten menetelmien avulla maastolaserkeilausaineiston käsittelyä on mahdollista tehostaa ja jopa tarkentaa manuaaliseen aineiston käsittelyyn verrattuna. Osajulkaisussa V runkokäyrän mittaustarkkuus oli automaattista aineistonkäsittelymenetelmää käytettäessä ~ 1 cm. Osajulkaisussa VI hyödynnettiin monilähdemenetelmää, jossa tarvittu puukartta mitattiin automaattisesti maastolaserkeilausaineistosta ja läpimittajakaumat ennustettiin maasto- ja lentolaserkeilausaineistojen avulla. Läpimitan ennustetarkkuus oli 1,4 cm ja 4,7 cm välillä puustoltaan hyvin vaihtelevissa metsiköissä. Väitöskirjatyön osajulkaisuissa kehitetyt menetelmät ja esitetyt tulokset osoittivat laserkeilausmenetelmien olevan varteenotettava vaihtoehto yksittäisten puiden kartoitukselle ja mittaamiselle tulevaisuudessa

Detection and elimination of rock face vegetation from terrestrial LIDAR data using the virtual articulating conical probe algorithm

A common use of terrestrial lidar is to conduct studies involving change detection of natural or engineered surfaces. Change detection involves many technical steps beyond the initial data acquisition: data structuring, registration, and elimination of data artifacts such as parallax errors, near-field obstructions, and vegetation. Of these, vegetation detection and elimination with terrestrial lidar scanning (TLS) presents a completely different set of issues when compared to vegetation elimination from aerial lidar scanning (ALS). With ALS, the ground footprint of the lidar laser beam is very large, and the data acquisition hardware supports multi-return waveforms. Also, the underlying surface topography is relatively smooth compared to the overlying vegetation which has a high spatial frequency. On the other hand, with most TLS systems, the width of the lidar laser beam is very small, and the data acquisition hardware supports only first-return signals. For the case where vegetation is covering a rock face, the underlying rock surface is not smooth because rock joints and sharp block edges have a high spatial frequency very similar to the overlying vegetation. Traditional ALS approaches to eliminate vegetation take advantage of the contrast in spatial frequency between the underlying ground surface and the overlying vegetation. When the ALS approach is used on vegetated rock faces, the algorithm, as expected, eliminates the vegetation, but also digitally erodes the sharp corners of the underlying rock. A new method that analyzes the slope of a surface along with relative depth and contiguity information is proposed as a way of differentiating high spatial frequency vegetative cover from similar high spatial frequency rock surfaces. This method, named the Virtual Articulating Conical Probe (VACP) algorithm, offers a solution for detection and elimination of rock face vegetation from TLS point cloud data while not affecting the geometry of the underlying rock surface. Such a tool could prove invaluable to the geotechnical engineer for quantifying rates of vertical-face rock loss that impact civil infrastructure safety --Abstract, page iii

Fine-scale Inventory of Forest Biomass with Ground-based LiDAR

Biomass measurement provides a baseline for ecosystem valuation required by modern forest management. The advent of ground-based LiDAR technology, renowned for 3D sampling resolution, has been altering the routines of biomass inventory. The thesis develops a set of innovative approaches in support of fine-scale biomass inventory, including automatic extraction of stem statistics, robust delineation of plot biomass components, accurate classification of individual tree species, and repeatable scanning of plot trees using a lightweight scanning system. Main achievements in terms of accuracy are a relative root mean square error of 11% for stem volume extraction, a mean classification accuracy of 0.72 for plot wood components, and a classification accuracy of 92% among seven tree species. The results indicate the technical feasibility of biomass delineation and monitoring from plot-level and multi-species point cloud datasets, whereas point occlusion and lack of fine-scale validation dataset are current challenges for biomass 3D analysis from ground.S.G.S. International Tuition Award from the University of Lethbridge The Dean's Scholarship from the University of Lethbridge Campus Alberta Innovates Program NSERC Discovery Grants Progra