Browsing by Subject "LIDAR"

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  • Sievänen, Risto; Raumonen, Pasi; Perttunen, Jari; Nikinmaa, Eero Heikki; Kaitaniemi, Pekka Juhani (2018)
    Background and Aims: Functional-structural plant models (FSPMs) allow simulation of tree crown development as the sum of modular (e.g. shoot-level) responses triggered by the local environmental conditions. The actual process of space filling by the crowns can be studied. Although the FSPM simulations are at organ scale, the data for their validation have usually been at more aggregated levels (whole-crown or whole-tree). Measurements made by terrestrial laser scanning (TLS) that have been segmented into elementary units (internodes) offer a phenotyping tool to validate the FSPM predictions at levels comparable with their detail. We demonstrate the testing of different formulations of crown development of Scots pine trees in the LIGNUM model using segmented TLS data. Methods: We made TLS measurements from four sample trees growing in a forest on a relatively poor soil from sapling size to mature stage. The TLS data were segmented into intenodes. The segmentation also produced information on whether needles were present in the internode. We applied different formulations of crown development (flushing of buds and length of growth of new internodes) in LIGNUM. We optimized the parameter values of each formulation using genetic algorithms to observe the best fit of LIGNUM simulations to the measured trees. The fitness function in the estimation combined both tree-level characteristics (e.g. tree height and crown length) and measures of crown shape (e.g. spatial distribution of needle area). Key Results: Comparison of different formulations against the data indicates that the Extended Borchert- Honda model for shoot elongation works best within LIGNUM. Control of growth by local density in the crown was important for all shoot elongation formulations. Modifying the number of lateral buds as a function of local density in the crown was the best way to accomplish density control. Conclusions: It was demonstrated how segmented TLS data can be used in the context of a shoot-based model to select model components.
  • Tanhuanpaa, Topi; Saarinen, Ninni; Kankare, Ville; Nurminen, Kimmo; Vastaranta, Mikko; Honkavaara, Eija; Karjalainen, Mika; Yu, Xiaowei; Holopainen, Markus; Hyyppa, Juha (Springer International Publishing AG, 2017)
    Lecture Notes in Geoinformation and Cartography
    During the past decade, airborne laser scanning (ALS) has established its status as the state-of-the-art method for detailed forest mapping and monitoring. Current operational forest inventory widely utilizes ALS-based methods. Recent advances in sensor technology and image processing have enabled the extraction of dense point clouds from digital stereo imagery (DSI). Compared with ALS data, the DSI-based data are cheap and the point cloud densities can easily reach that of ALS. In terms of point density, even the high-altitude DSI-based point clouds can be sufficient for detecting individual tree crowns. However, there are significant differences in the characteristics of ALS and DSI point clouds that likely affect the accuracy of tree detection. In this study, the performance of high-altitude DSI point clouds was compared with low-density ALS in detecting individual trees. The trees were extracted from DSI-and ALS-based canopy height models (CHM) using watershed segmentation. The use of both smoothed and unsmoothed CHMs was tested. The results show that, even though the spatial resolution of the DSI-based CHM was better, in terms of detecting the trees and the accuracy of height estimates, the low-density ALS performed better. However, utilizing DSI with shorter ground sample distance (GSD) and more suitable image matching algorithms would likely enhance the accuracy of DSI-based approach.
  • Pyorala, Jiri; Liang, Xinlian; Saarinen, Ninni; Kankare, Ville; Wang, Yunsheng; Holopainen, Markus; Hyyppa, Juha; Vastaranta, Mikko (2018)
    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.
  • Luoma, Ville; Saarinen, Ninni; Wulder, Michael A.; White, Joanne C.; Vastaranta, Mikko; Holopainen, Markus; Hyyppä, Juha (2017)
    Forest resource information has a hierarchical structure: individual tree attributes are summed at the plot level and then in turn, plot-level estimates are used to derive stand or large-area estimates of forest resources. Due to this hierarchy, it is imperative that individual tree attributes are measured with accuracy and precision. With the widespread use of different measurement tools, it is also important to understand the expected degree of precision associated with these measurements. The most prevalent tree attributes measured in the field are tree species, stem diameter-at-breast-height (dbh), and tree height. For dbh and height, the most commonly used measuring devices are calipers and clinometers, respectively. The aim of our study was to characterize the precision of individual tree dbh and height measurements in boreal forest conditions when using calipers and clinometers. The data consisted of 319 sample trees at a study area in Evo, southern Finland. The sample trees were measured independently by four trained mensurationists. The standard deviation in tree dbh and height measurements was 0.3 cm (1.5%) and 0.5 m (2.9%), respectively. Precision was also assessed by tree species and tree size classes; however, there were no statistically significant differences between the mensurationists for dbh or height measurements. Our study offers insights into the expected precision of tree dbh and height as measured with the most commonly used devices. These results are important when using sample plot data in forest inventory applications, especially now, at a time when new tree attribute measurement techniques based on remote sensing are being developed and compared to the conventional caliper and clinometer measurements.
  • Liu, Jingbin; Liang, Xinlian; Hyyppä, Juha; Yu, Xiaowei; Lehtomäki, Matti; Pyörälä, Jiri; Zhu, Lingli; Wang, Yunsheng; Chen, Ruizhi (2017)
    Terrestrial laser scanning has been widely used to analyze the 3D structure of a forest in detail and to generate data at the level of a reference plot for forest inventories without destructive measurements. Multi-scan terrestrial laser scanning is more commonly applied to collect plot-level data so that all of the stems can be detected and analyzed. However, it is necessary to match the point clouds of multiple scans to yield a point cloud with automated processing. Mismatches between datasets will lead to errors during the processing of multi-scan data. Classic registration methods based on flat surfaces cannot be directly applied in forest environments; therefore, artificial reference objects have conventionally been used to assist with scan matching. The use of artificial references requires additional labor and expertise, as well as greatly increasing the cost. In this study, we present an automated processing method for plot-level stem mapping that matches multiple scans without artificial references. In contrast to previous studies, the registration method developed in this study exploits the natural geometric characteristics among a set of tree stems in a plot and combines the point clouds of multiple scans into a unified coordinate system. Integrating multiple scans improves the overall performance of stem mapping in terms of the correctness of tree detection, as well as the bias and the root-mean-square errors of forest attributes such as diameter at breast height and tree height. In addition, the automated processing method makes stem mapping more reliable and consistent among plots, reduces the costs associated with plot-based stem mapping, and enhances the efficiency. (C) 2016 The Authors. Published by Elsevier B.V.
  • Nurminen, Kimmo; Litkey, Paula; Honkavaara, Eija; Vastaranta, Mikko; Holopainen, Markus; Lyytikäinen-Saarenmaa, Päivi; Kantola, Tuula; Lyytikäinen, Minna (2015)
    Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs) generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS)-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1 parts per thousand with respect to flying height for data sets collected since the 1980s, and 0.2 parts per thousand for older data sets. The results show that of the studied land cover classes, "peatland without trees" changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way.
  • Imangholiloo, Mohammad; Saarinen, Ninni; Markelin, Lauri; Rosnell, Tomi; Nasi, Roope; Hakala, Teemu; Honkavaara, Eija; Holopainen, Markus; Hyyppa, Juha; Vastaranta, Mikko (2019)
    Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.
  • Pyörälä, Jiri; Kankare, Ville; Vastaranta, Mikko; Rikala, Juha; Holopainen, Markus; Sipi, Marketta; Hyyppä, Juha; Uusitalo, Jori (2018)
    While X-ray scanning is increasingly used to measure the interior quality of logs, terrestrial laser scanning (TLS) could be used to collect information on external tree characteristics. As branches are one key indicator of wood quality, we compared TLS and X-ray scanning data in deriving whorl locations and each whorl's maximum branch and knot diameters for 162 Scots pine (Pinus sylvestris L.) log sections. The mean number of identified whorls per tree was 37.25 and 22.93 using X-ray and TLS data, respectively. The lowest TLS-derived whorl in each sample tree was an average 5.56 m higher than that of the X-ray data. Whorl-to-whorl mean distances and the means of the maximum branch and knot diameters in a whorl measured for each sample tree using TLS and X-ray data had mean differences of -0.12 m and -6.5 mm, respectively. One of the most utilized wood quality indicators, tree-specific maximum knot diameter measured by X-ray, had no statistically significant difference to the tree-specific maximum branch diameter measured from the TLS point cloud. It appears challenging to directly derive comparative branch structure information using TLS and X-ray. However, some features that are extractable from TLS point clouds are potential wood quality indicators.
  • White, Joanne C.; Saarinen, Ninni; Kankare, Ville; Wulder, Michael A.; Hermosilla, Txomin; Coops, Nicholas C.; Pickell, Paul D.; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2018)
    Landsat time series (LTS) enable the characterization of forest recovery post-disturbance over large areas; however, there is a gap in our current knowledge concerning the linkage between spectral measures of recovery derived from LTS and actual manifestations of forest structure in regenerating stands. Airborne laser scanning (ALS) data provide useful measures of forest structure that can be used to corroborate spectral measures of forest recovery. The objective of this study was to evaluate the utility of a spectral index of recovery based on the Normalized Burn Ratio (NBR): the years to recovery, or Y2R metric, as an indicator of the return of forest vegetation following forest harvest (clearcutting). The Y2R metric has previously been defined as the number of years required for a pixel to return to 80% of its pre-disturbance NBR (NBRpre) value. In this study, the Composite2Change (C2C) algorithm was used to generate a time series of gap-free, cloud-free Landsat surface reflectance composites (1985–2012), associated change metrics, and a spatially-explicit dataset of detected changes for an actively managed forest area in southern Finland (5.3 Mha). The overall accuracy of change detection, determined using independent validation data, was 89%. Areas of forest harvesting in 1991 were then used to evaluate the Y2R metric. Four alternative recovery scenarios were evaluated, representing variations in the spectral threshold used to define Y2R: 60%, 80%, and 100% of NBRpre, and a critical value of z (i.e. the year in which the pixel's NBR value is no longer significantly different from NBRpre). The Y2R for each scenario were classified into five groups: recovery within 17 years, and not recovered. Measures of forest structure (canopy height and cover) were obtained from ALS data. Benchmarks for height (>5 m) and canopy cover (>10%) were applied to each recovery scenario, and the percentage of pixels that attained both of these benchmarks for each recovery group, was determined for each Y2R scenario. Our results indicated that the Y2R metric using the 80% threshold provided the most realistic assessment of forest recovery: all pixels considered in our analysis were spectrally recovered within the analysis period, with 88.88% of recovered pixels attaining the benchmarks for both cover and height. Moreover, false positives (pixels that had recovered spectrally, but not structurally) and false negatives (pixels that had recovered structurally, but not spectrally) were minimized with the 80% threshold. This research demonstrates the efficacy of LTS-derived assessments of recovery, which can be spatially exhaustive and retrospective, providing important baseline data for forest monitoring.
  • Vauhkonen, Jari (2020)
    Key Message Tree-level forest inventory data are becoming increasingly available, which motivates the use of these data for decision-making. However, airborne inventories carried out tree-by-tree typically include systematic errors, which can propagate to objective function variables used to determine optimal forest management. Effects of under-detection focused on the smallest trees on predicted immediate harvest profits and future expectation values were assessed assuming different sites and interest rates. Management decisions based on the erroneous information caused losses of 0-17% of the total immediate and future expected income of Scots pine stands. Context Optimal decisions on how to manage forest stands can depend on the absence or presence of intermediate and understory trees. Yet, these tree strata are likely prone to inventory errors. Aims The aim of this study is to examine implications of making stand management decisions based on data that include systematic errors resembling those typically observed in airborne inventories carried out tree-by-tree. Methods Stand management instructions were developed based on theoretical diameter distribution functions simulated to have different shape, scale, and frequency parameters corresponding to various degrees of under-detection focused on the smallest trees. Immediate harvest income and future expectation value were derived based on various management alternatives simulated. Results Errors in diameter distributions affected the predicted harvest profits and future expectation values differently between the simulated alternatives and depending on site type and interest rate assumptions. As a result, different alternatives were considered as optimal management compared to the use of the error-free reference distributions. In particular, the use of no management or most intensive management alternatives became preferred over alternatives with intermediate harvesting intensities. Certain harvesting types such as thinning from below became preferred more often than what was optimal. The errors did not affect the selection of the management alternative in 71% of the simulations, whereas in the remaining proportion, relying on the erroneous information would have caused losing 2%, on average, and 17% at maximum, of the total immediate and future expected income. Conclusion The effects above might not have been discovered, if the results were validated for inventory totals instead of separately considering the immediate and future income and losses produced by the erroneous decisions. It is recommended not to separate but to integrate the inventory and planning systems for well-informed decisions.
  • Luoma, Ville; Vastaranta, Mikko; Eyvindson, Kyle; Kankare, Ville; Saarinen, Ninni; Holopainen, Markus; Hyyppa, Juha (Springer International Publishing AG, 2017)
    Lecture Notes in Geoinformation and Cartography
    Currently the forest sector in Finland is looking towards the next generation's forest resource information systems. Information used in forest planning is currently collected by using an area-based approach (ABA) where airborne laser scanning (ALS) data are used to generalize field-measured inventory attributes over an entire inventory area. Inventories are typically updated at 10-year interval. Thus, one of the key challenges is the age of the inventory information and the cost-benefit trade-off between using the old data and obtaining new data. Prediction of future forest resource information is possible through growth modelling. In this paper, the error sources related to ALS-based forest inventory and the growth models applied in forest planning to update the forest resource information were examined. The error sources included (i) forest inventory, (ii) generation of theoretical stem distribution, and (iii) growth modelling. Error sources (ii) and (iii) stem from the calculations used for forest planning, and were combined in the investigations. Our research area, Evo, is located in southern Finland. In all, 34 forest sample plots (300 m(2)) have been measured twice tree-by-tree. First measurements have been carried out in 2007 and the second measurements in 2014 which leads to 7 year updating period. Respectively, ALS-based forest inventory data were available for 2007. The results showed that prediction of theoretical stem distribution and forest growth modelling affected only slightly to the quality of the predicted stem volume in short-term information update when compared to forest inventory error.
  • Kansanen, Kasper; Vauhkonen, Jari; Lahivaara, Timo; Seppanen, Aku; Maltamo, Matti; Mehtatalo, Lauri (2019)
    Errors in individual tree detection and delineation affect diameter distribution predictions based on crown attributes extracted from the detected trees. We develop a methodology for circumventing these problems. The method is based on matching cumulative distribution functions of field measured tree diameter distributions and crown radii distributions extracted from airborne laser scanning data through individual tree detection presented by Vauhkonen and Mehtatalo (2015). In this study, empirical distribution functions and a monotonic, nonlinear model curve are introduced. Tree crown radius distribution produced by individual tree detection is corrected by a method taking into account that all trees cannot be detected. The evaluation is based on the ability of the developed model sequence to predict quadratic mean diameter and total basal area. The studied data consists of 36 field plots in a typical boreal managed forest area in eastern Finland. The suggested enhancements to the model sequence produce improved results in most of the test cases. Most notably, in leaveone-out cross-validation experiments the modified models improve RMSE of basal area 13% in the full data and RMSE of quadratic mean diameter and basal area 69% and 11%, respectively, in pure pine plots. Better modeling of the crown radius distribution and improved matching between crown radii and stem diameters add the operational premises of the full distribution matching.
  • Saarinen, Ninni; Kankare, Ville; Vastaranta, Mikko; Luoma, Ville; Pyörälä, Jiri; Tanhuanpää, Topi; Liang, Xinlian; Kaartinen, Harri; Kukko, Antero; Jaakkola, Anttoni; Yu, Xiaowei; Holopainen, Markus; Hyyppä, Juha (2017)
    Interest in measuring forest biomass and carbon stock has increased as a result of the United Nations Framework Convention on Climate Change, and sustainable planning of forest resources is therefore essential. Biomass and carbon stock estimates are based on the large area estimates of growing stock volume provided by national forest inventories (NFIs). The estimates for growing stock volume based on the NFIs depend on stem volume estimates of individual trees. Data collection for formulating stem volume and biomass models is challenging, because the amount of data required is considerable, and the fact that the detailed destructive measurements required to provide these data are laborious. Due to natural diversity, sample size for developing allometric models should be rather large. Terrestrial laser scanning (TLS) has proved to be an efficient tool for collecting information on tree stems. Therefore, we investigated how TLS data for deriving stem volume information from single trees should be collected. The broader context of the study was to determine the feasibility of replacing destructive and laborious field measurements, which have been needed for development of empirical stem volume models, with TLS. The aim of the study was to investigate the effect of the TLS data captured at various distance (i.e. corresponding 25%, 50%, 75% and 100% of tree height) on the accuracy of the stem volume derived. In addition, we examined how multiple TLS point cloud data acquired at various distances improved the results. Analysis was carried out with two ways when multiple point clouds were used: individual tree attributes were derived from separate point clouds and the volume was estimated based on these separate values (multiple scan A), and point clouds were georeferenced as a combined point cloud from which the stem volume was estimated (multiple-scan B). This permitted us to deal with the practical aspects of TLS data collection and data processing for development of stem volume equations in boreal forests. The results indicated that a scanning distance of approximately 25% of tree height would be optimal for stem volume estimation with TLS if a single scan was utilized in boreal forest conditions studied here and scanning resolution employed. Larger distances increased the uncertainty, especially when the scanning distance was greater than approximately 50% of tree height, because the number of successfully measured diameters from the TLS point cloud was not sufficient for estimating the stem volume. When two TLS point clouds were utilized, the accuracy of stem volume estimates was improved: RMSE decreased from 12.4% to 6.8%. When two point clouds were processed separately (i.e. tree attributes were derived from separate point clouds and then combined) more accurate results were obtained; smaller RMSE and relative error were achieved compared to processing point clouds together (i.e. tree attributes were derived from a combined point cloud). TLS data collection and processing for the optimal setup in this study required only one sixth of time that was necessary to obtain the field reference. These results helped to further our knowledge on TLS in estimating stem volume in boreal forests studied here and brought us one step closer in providing best practices how a phase-shift TLS can be utilized in collecting data when developing stem volume models. (C) 2016 The Authors. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
  • Liang, Xinlian; Hyyppä, Juha; Kaartinen, Harri; Lehtomäki, Matti; Pyörälä, Jiri; Pfeifer, Norbert; Holopainen, Markus; Brolly, Gábor; Francesco, Pirotti; Hackenberg, Jan; Huang, Huabing; Jo, Hyun-Woo; Katoh, Masato; Liu, Luxia; Mokroš, Martin; Morel, Jules; Olofsson, Kenneth; Poveda-Lopez, Jose; Trochta, Jan; Wang, Di; Wang, Jinhu; Xi, Zhouxi; Yang, Bisheng; Zheng, Guang; Kankare, Ville; Luoma, Ville; Yu, Xiaowei; Chen, Liang; Vastaranta, Mikko; Saarinen, Ninni; Wang, Yunsheng (2018)
    The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources.
  • Wang, Yunsheng; Lehtomäki, Matti; Liang, Xinlian; Pyörälä, Jiri Kristian; Kukko, Antero; Jaakkola, Anttoni; Liu, Jingbin; Feng, Ziyi; Chen, Ruizhi; Hyyppä, Juha (2019)
    Quantitative comparisons of tree height observations from different sources are scarce due to the difficulties in effective sampling. In this study, the reliability and robustness of tree height observations obtained via a conventional field inventory, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) were investigated. A carefully designed non-destructive experiment was conducted that included 1174 individual trees in 18 sample plots (32 m x 32 m) in a Scandinavian boreal forest. The point density of the ALS data was approximately 450 points/m(2). The TLS data were acquired with multi-scans from the center and the four quadrant directions of the sample plots. Both the ALS and TLS data represented the cutting edge point cloud products. Tree heights were manually measured from the ALS and TLS point clouds with the aid of existing tree maps. Therefore, the evaluation results revealed the capacities of the applied laser scanning (LS) data while excluding the influence of data processing approach such as the individual tree detection. The reliability and robustness of different tree height sources were evaluated through a cross-comparison of the ALS-, TLS-, and field- based tree heights. Compared to ALS and TLS, field measurements were more sensitive to stand complexity, crown classes, and species. Overall, field measurements tend to overestimate height of tall trees, especially tall trees in codominant crown class. In dense stands, high uncertainties also exist in the field measured heights for small trees in intermediate and suppressed crown class. The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. The highest uncertainty in ALS-based tree heights came from trees in intermediate crown class, due to the difficulty of identifying treetops. When using TLS, reliable tree heights can be expected for trees lower than 15-20 m in height, depending on the complexity of forest stands. The advantage of LS systems was the robustness of the geometric accuracy of the data. The greatest challenges of the LS techniques in measuring individual tree heights lie in the occlusion effects, which lead to omissions of trees in intermediate and suppressed crown classes in ALS data and incomplete crowns of tall trees in TLS data.
  • Adhikari, Hari; Valbuena, Ruben; Pellikka, Petri; Heiskanen, Janne (2020)
    Tropical montane forests are important reservoirs of carbon and biodiversity and have a central role in the hydrological cycle. They are, however, very fragmented and degraded, leaving isolated remnants across the landscape. These montane forest remnants have considerable differences in forest structure, depending on factors such as tree species composition and degree of forest degradation. Our objectives were (1) to analyse the reliability of airborne laser scanning (ALS) in modelling forest structural heterogeneity, as described by the Gini coefficient (GC) of tree size inequality; (2) to determine whether models are improved by including tree species-sensitive spectral-temporal metrics from the Landsat time series (LTS); and (3) to evaluate differences between three forest remnants and different forest types using the resulting maps of predicted GC. The study area was situated in Taita Hills, Kenya, where indigenous montane forests have been partly replaced by single-species plantations. The data included field measurements from 85 sample plots and two ALS data sets with different pulse densities (9.6 and 3.1 pulses m(-2)). GC was modeled using beta regression. We found that GC was predicted more accurately by the ALS data set with a higher point density (a cross-validated relative root mean squared error (rRMSE(CV)) 13.9%) compared to ALS data set with lower point density (rRMSE(CV) 15.1%). Furthermore, important synergies exist between ALS and LTS metrics. When combining ALS and LTS metrics, rRMSE(CV) was improved to 12.5% and 13.0%, respectively. Therefore, if the LTS metrics are included in models, ALS data with lower pulse density are sufficient to yield similar accuracy to more expensive, higher pulse density data acquired from the lower altitude. In Ngangao and Yale, forest canopy has multiple layers of variable tree sizes, whereas elfin forests in Vuria are of more equal tree size, and the GC value ranges of the indigenous forests are 0.42-0.71, 0.20-0.74, and 0.17-0.76, respectively. The single-species plantations of cypress and pine showed lower values of GC than indigenous forests located in the same remnants in Yale, whereas Eucalyptus plantations showed GC values more similar to the indigenous forests. These results show the usefulness of GC maps for identifying and separating forest types as well as for assessing their distinctive ecologies.
  • Yrttimaa, Tuomas; Saarinen, Ninni; Kankare, Ville; Viljanen, Niko; Hynynen, Jari; Huuskonen, Saija; Holopainen, Markus; Hyyppa, Juha; Honkavaara, Eija; Vastaranta, Mikko (2020)
    Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (H-g) and mean stem volume (V-mean). Most notably, the root-mean-square-error (RMSE) in H-g improved from 0.8 to 0.58 m and the bias improved from -0.75 to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, V-mean, H-g, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.
  • Yrttimaa, Tuomas; Saarinen, Ninni; Kankare, Ville; Hynynen, Jari; Huuskonen, Saija; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2020)
    There is a limited understanding of how forest structure affects the performance of methods based on terrestrial laser scanning (TLS) in characterizing trees and forest environments. We aim to improve this understanding by studying how different forest management activities that shape tree size distributions affect the TLS-based forest characterization accuracy in managed Scots pine (Pinus sylvestris L.) stands. For that purpose, we investigated 27 sample plots consisting of three different thinning types, two thinning intensities as well as control plots without any treatments. Multi-scan TLS point clouds were collected from the sample plots, and a point cloud processing algorithm was used to segment individual trees and classify the segmented point clouds into stem and crown points. The classified point clouds were further used to estimate tree and forest structural attributes. With the TLS-based forest characterization, almost 100% completeness in tree detection, 0.7 cm (3.4%) root-mean-square- error (RMSE) in diameter-at-breast-height measurements, 0.9–1.4 m (4.5–7.3%) RMSE in tree height measure-ments, and <6% relative RMSE in the estimates of forest structural attributes (i.e. mean basal area, number of trees per hectare, mean volume, basal area-weighted mean diameter and height) were obtained depending on the applied thinning type. Thinnings decreased variation in horizontal and vertical forest structure, which especially favoured the TLS-based tree detection and tree height measurements, enabling reliable estimates for forest structural attributes. A considerably lower performance was recorded for the control plots. Thinning intensity was noticed to affect more on the accuracy of TLS-based forest characterization than thinning type. The number of trees per hectare and the proportion of suppressed trees were recognized as the main factors affecting the accuracy of TLS-based forest characterization. The more variation there was in the tree size distribution, the more challenging it was for the TLS-based method to capture all the trees and derive the tree and forest structural attributes. In general, consistent accuracy and reliability in the estimates of tree and forest attributes can be expected when using TLS for characterizing managed boreal forests.
  • Luoma, Ville; Yrttimaa, Tuomas; Kankare, Ville; Saarinen, Ninni; Pyorala, Jiri; Kukko, Antero; Kaartinen, Harri; Hyyppa, Juha; Holopainen, Markus; Vastaranta, Mikko (2021)
    Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.
  • Yrttimaa, Tuomas; Luoma, Ville; Saarinen, Ninni; Kankare, Ville; Junttila, Samuli; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2020)
    Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS inquantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindricalcharacteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and foreststands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99–1.22 cm for diameter at breast height (∆dbh), 44.14–55.49 cm2 for basal area (∆g), and 1.91–4.85 m for tree height (∆h). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60–1.13 cm was recorded for changes in basal area-weighted meandiameter (∆Dg), 0.81–2.26 m for changes in basal area-weighted mean height (∆Hg), 1.40–2.34 m2 /ha for changes in mean basal area (∆G), and 74–193 n/ha for changes in the number of trees per hectare (∆TPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.