Browsing by Subject "LiDAR"

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  • 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.
  • Hovi, Aarne (Helsingfors universitet, 2011)
    Understory trees often emerging beneath dominant tree layer in even-aged stands have significance for timber harvesting operations, forest regeneration, landscape and visibility analysis, biodiversity and carbon balance. Airborne laser scanning (ALS) has proven to be an efficient remote sensing method in inventory of mature forest stands. Recent introduction of ALS to operational forest inventory systems could potentially enable cost-efficient acquisition of information on understory tree layer. In this study, accurate field reference and discrete return LiDAR data (1–2 km flying altitude, 0.9–9.7 pulses m-2) were used. The LiDAR data were obtained with Optech ALTM3100 and Leica ALS50-II sensors. The field reference plots represented typical commercially managed, even-aged pine stands in different developmental stages. Aims of the study were 1) to study the LiDAR signal from understory trees at pulse level and the factors affecting the signal, and 2) to explore what is the explanatory power of area-based LiDAR features in predicting the properties of understory tree layer. Special attention was paid in studying the effect of transmission losses to upper canopy layers on the obtained signal and possibilities to make compensations for transmission losses to the LiDAR return intensity. Differences in intensity between understory tree species were small and varied between data sets. Thus, intensity is of little use in tree species classification. Transmission losses increased noise in intensity observations from understory tree layer. Compensations for transmission losses were made to the 2nd and 3rd return data. The compensations decreased intensity variation within targets and improved classification accuracy between targets. In classification between ground and most abundant understory tree species using 2nd return data, overall classification accuracies were 49.2–54.9 % and 57.3–62.0 %, and kappa values 0.03–0.13 and 0.10–0.22, before and after compensations, respectively. The classification accuracy improved also in 3rd return data. The most important variable explaining the transmission losses was the intensity from previous echoes and pulse intersection geometry with upper canopy layer had a minor effect. The probability of getting an echo from an understory tree was studied, and differences between tree species were observed. Spruce produced an echo with a greater probability than broadleaved trees. If the pulse was subject to transmission losses, the differences were increased. The results imply that area-based LiDAR height distribution metrics could depend on tree species. There were differences in intensity data between sensors, which are a problem if multiple LiDAR data sets are used in inventory systems. Also the echo probabilities differed between sensors, which caused minor changes in LiDAR height distribution metrics. Area-based predictors for stem number and mean height of understory trees were detected if trees with height < 1 m were not included. In general, predictions for stem number were more accurate than for mean height. Explanatory power of the studied features did not markedly decrease with decreasing pulse density, which is important for practical applications. Proportion of broadleaved trees could not be predicted. As a conclusion, discrete return LiDAR data could be utilized e.g. in detecting the need for initial clearings before harvesting operations. However, accurate characterization of understory trees (e.g. detection of tree species) or detection of the smallest seedlings seems to be out of reach. Additional research is needed to generalize the results to different forests.
  • Tanhuanpää, Topi; Kankare, Ville; Setälä, Heikki; Yli-Pelkonen, Vesa; Vastaranta, Mikko; Niemi, Mikko T.; Raisio, Juha; Holopainen, Markus (2017)
    Assessment of the amount of carbon sequestered and the value of ecosystem services provided by urban trees requires reliable data. Predicting the proportions and allometric relationships of individual urban trees with models developed for trees in rural forests may result in significant errors in biomass calculations. To better understand the differences in biomass accumulation and allocation between urban and rural trees, two existing biomass models for silver birch (Betula pendula Roth) were tested for their performance in assessing the above-ground biomass (AGB) of 12 urban trees. In addition, the performance of a volume-based method utilizing accurate terrestrial laser scanning (TLS) data and stem density was evaluated in assessing urban tree AGB. Both tested models underestimated the total AGB of single trees, which was mainly due to a substantial underestimation of branch biomass. The volume-based method produced the most accurate estimates of stem biomass. The results suggest that biomass models originally based on sample trees from rural forests should not be used for urban, open-grown trees, and that volume-based methods utilizing TLS data are a promising alternative for non-destructive assessment of urban tree AGB. (C) 2017 Elsevier GmbH. All rights reserved.
  • 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.
  • Gaudel, Rabins (Helsingin yliopisto, 2019)
    Canopy gaps and their characteristic features (e.g. area and shape) influence the availability of nutrients, moisture and light in a forest ecosystem, and consequently affect the regeneration process and species composition in the forest. Most of the earlier research on canopy gap used field measurement and conventional remote sensing to quantify gap and these methods have limitations and accuracy problems. However, the development in Light Detecting and Ranging (LiDAR) technology has been effective in overcoming limitations and challenges associated with conventional remote sensing. The ability of LiDAR to represent the three-dimensional structure of the canopies and the sub-canopy resulting in high-resolution topographic maps, highly accurate estimated of vegetation height, cover and canopy structure makes it suitable technology for gap studies. LiDAR-based digital surface model (DSM) and digital elevation model (DEM) were used to quantify the canopy gaps over 5124ha of University of Tokyo Chichibu Forests (UTCF) consisting of three forest-types; primary, secondary and plantation forest. Disturbance driven canopy gaps might have spatial and characteristic variation due to differences in disturbance history, nature, frequency and intensity in different forest and land-types. Quantifying gap characteristics and studying variation and size distribution in different forest types and topography help to understand the different gap dynamics and their ecological perspectives. In this study, a gap was defined as an opening with a maximum height of 2m and minimum area threshold of 10m2. The minimum area threshold, which represents the gap area created by the death of at least a single tree, was determined through a random sampling of 100 tree crowns at UTCF using high resolution aerial photographs. Gap size distribution was analyzed in different forest types and land types. Spatial autocorrelation of gap occurrence was studied using semivariance analysis and distance to the nearest gap (DNG), which is the distance to the nearest gap for an individual gap. Canopy gap size frequency distribution in different forest-types was investigated using power-law. The negative exponent (α), which is also the scaling component of the power-law distribution, was compared between forest-types. Altogether, 6179 gaps with area 10-11603 m2 were found. Gap size distribution in UTCF showed skewness with a high frequency of smaller gaps and a few large gaps. Half of the gaps were smaller than 19 m2 and less than one percent of gaps (0.73 %) were larger than 400 m2. Primary forest contained high gap density (1.85 gaps per ha), shortest mean-DNG (22m) and second-largest gap-area fraction (0.72 %) after plantation forest area (0.76 %). Secondary forest had the lowest gap density (1.03 gaps per hectare) but had the larger mean gap-area (43 m2) than in primary forest (39 m2). The Kolmogorov–Smirnov test showed differences (p<0.05) in gap size distribution between primary and secondary forest. However, the gap size distribution in primary forest show similarity (p=0.59) with plantation forest area. In primary and plantation forest there was a high frequency of small gaps and few very large gaps (2000-10500 m2), whereas very large gaps (>2400 m2) were absent in the secondary forest. Gap size frequency distribution followed a power-law distribution only in plantation forest area (p>0.1, α =2.27). The scaling parameter in the primary and secondary forest was 2.56 (p=0.01) and 2.20 (p=0.02), respectively. Gap distribution showed some spatial autocorrelation in primary and secondary forest at least with distance up to 1300m. Most of the gaps in the primary forest were concentrated in the valley and middle slope, whereas the upper and middle slope had fewest gaps.
  • Kantola, Tuula; Vastaranta, Mikko; Lyytikäinen-Saarenmaa, Päivi; Holopainen, Markus; Kankare, Ville; Talvitie, Mervi; Hyyppä, Juha (2013)
    Forest disturbances caused by pest insects are threatening ecosystem stability, sustainable forest management and economic return in boreal forests. Climate change and increased extreme weather patterns can magnify the intensity of forest disturbances, particularly at higher latitudes. Due to rapid responses to elevating temperatures, forest insect pests can flexibly change their survival, dispersal and geographic distributions. The outbreak pattern of forest pests in Finland has evidently changed during the last decade. Projection of shifts in distributions of insect-caused forest damages has become a critical issue in the field of forest research. The Common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini has resulted in severe growth loss and mortality of Scots pine (Pinus sylvestris L.) (Pinaceae) in eastern Finland. In this study, tree-wise defoliation was estimated for five different needle loss category classification schemes and for 10 different simulated airborne laser scanning (ALS) pulse densities. The nearest neighbor (NN) approach, a nonparametric estimation method, was used for estimating needle loss of 701 Scots pines, using the means of individual tree features derived from ALS data. The Random Forest (RF) method was applied in NN-search. For the full dense data (~20 pulses/m2), the overall estimation accuracies for tree-wise defoliation level varied between 71.0% and 86.5% (kappa-values of 0.56 and 0.57, respectively), depending on the classification scheme. The overall classification accuracies for two class estimation with different ALS pulse densities varied between 82.8% and 83.7% (kappa-values of 0.62 and 0.67, respectively). We conclude that ALS-based estimation of needle losses may be of acceptable accuracy for individual trees. Our method did not appear sensitive to the applied pulse densities.
  • Seitsonen, Oula; Ikäheimo, Janne (2021)
    Open access airborne laser scanning (ALS) data have been available in Finland for over a decade and have been actively applied by the Finnish archaeologists in that time. The low resolution of this laser scanning 2008-2019 dataset (0.5 points/m(2)), however, has hindered its usability for archaeological prospection. In the summer of 2020, the situation changed markedly, when the Finnish National Land Survey started a new countrywide ALS survey with a higher resolution of 5 points/m(2). In this paper we present the first results of applying this newly available ALS material for archaeological studies. Finnish LIDARK consortium has initiated the development of semi-automated approaches for visualizing, detecting, and analyzing archaeological features with this new dataset. Our first case studies are situated in the Alpine tundra environment of Sapmi in northern Finland, and the assessed archaeological features range from prehistoric sites to indigenous Sami reindeer herding features and Second Word War-era German military structures. Already the initial analyses of the new ALS-5p data show their huge potential for locating, mapping, and assessing archaeological material. These results also suggest an imminent burst in the number of known archaeological sites, especially in the poorly accessible and little studied northern wilderness areas, when more data become available.
  • Tanhuanpää, Topi; Yu, Xiaowei; Luoma, Ville; Saarinen, Ninni; Raisio, Juha; Hyyppä, Juha; Kumpula, Timo; Holopainen, Markus (2019)
    Urban forests consist of patches of recreational areas, parks, and single trees on roadsides and other forested urban areas. Large number of tree species and heterogeneous growing conditions result in diverse canopy structure. High variation can be found both at level of single tree crowns and in canopy characteristics of larger areas. As urban forests are typically managed with small-scale, even tree-level operations, there is a need for detailed forest information. In this study, the effect of varying canopy conditions was tested on nine individual tree detection (ITD) methods. All methods utilized airborne laser scanning (ALS)-derived canopy height models (CHM) and different modifications of watershed segmentation (WS). The performance of mapping methods was compared in three strata with varying mean height and canopy cover. The results showed considerable variation between the methods when tested in varying canopy conditions. Especially, presence of large broadleaved trees affected the accuracy of detecting individual trees. The best performing methods for the three strata were G0.7, F2 and Gadapt. The areas with low canopy cover turned out problematic for all ITD methods tested as co-occurrence of small trees and large deciduous trees affected the accuracy significantly. Overall, The results show that stratification can be used to enhance the quality of ITD in urban park areas. However, heterogeneous canopy structure and varying growth patterns typical for urban parks lower the accuracy of tree detection. Also, according to our results we suggest that canopy height and canopy cover alone are insufficient attributes for stratifying urban canopy conditions.
  • 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).
  • Männistö, Sameli (Helsingin yliopisto, 2020)
    As a result of urbanization and climate change, cities are facing various ecological and social challenges. For instance, flooding, pollution, urban heat island, decreased biodiversity, and mental stress of city dwellers are well recognized challenges of urban spaces. Urban green spaces are increasingly important in mitigating the adverse effects of climate change, such as flooding due to precipitation extremes, and also providing various other ecosystem services. In order to ensure sustainable land use and provision of ecosystem services, it is essential to develop methods for effective urban green space mapping. As a result, there is a growing demand for micro-scale land cover maps for urban areas. Emerging technologies, such as Object Based Image Analysis, OBIA, and light detection and ranging, LiDAR, offer promising possibilities for efficient mapping of green spaces in the urban environment. The aim of this thesis was to develop a semi-automatic method for urban green space mapping and classification. The other major task was to study the added benefits of light detection and ranging technology. Three research sites of varying degree of urbanization from the city of Helsinki were chosen for the study; from the city core in Itä-Pasila to appartment area with blocks of flats in Pihlajamäki and small-house residential area in Veräjämäki. The classification process was executed with an image analysis program called Definiens Developer. Main input data for classification was LiDAR data and VHR (very high resolution) aerial images. In the classification process, normalized vegetation index (NDVI) was used to detect live vegetation; assignation to different classes was based on height information derived from LIDAR data. Finally, an accuracy assessment was performed on the classified images to determine how well the classification process accomplished the task. The accuracy was assessed by comparing the classification images to the reference images of each catchment. Results demonstrate well the potential of OBIA for extracting urban green spaces. The downtown area of high land use intensity (Itä-Pasila) had the smallest green space coverage (31%), consisting mostly of urban parks and planted trees along the streets. The small-house area of low land use intensity (Veräjämäki) had the highest proportion (65%) of green spaces, consisting of forests and gardens. In the intermediate land use intensity with block of flats (Pihlajamäki)ts, a little under half of the coverage is green spaces. The highest accuracy of detecting green spaces was reached in low land use intensity area (92%), followed by the high and intermediate land use areas with 82% and 78%, respectively. The most common problem for classification was shaded areas, which reflect only limited spectral information and therefore the calculating of NDVI index becomes impossible. I found the object-based image analysis together with LiDAR data fusion to provide good means for urban green space mapping and classification. The presented method allowed a quick data acquisition with good overall accuracy, while avoiding the problems previously related to more traditional pixel-based methods. The addition of LiDAR data created the possibility of extracting vegetation height and using it in the classification process in order to divide vegetation into four different classes.
  • 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.
  • Kemppinen, Julia; Niittynen, Pekka; Riihimaki, Henri; Luoto, Miska (2018)
    Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non-climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape-scale soil moisture variation by utilizing high-resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high-latitude landscape of mountain tundra in north-western Finland. We measured the plots three times during growing season 2016 with a hand-held time-domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R-2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R-2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R-2 = 0.47 and RMSE 9.34 VWC%, and for the latter R-2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high-resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1m(2) digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine-scale soil moisture variation. In the temporal variation models, the strongest predictor was the field-quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright (c) 2017 John Wiley & Sons, Ltd.
  • Vastaranta, Mikko; Saarinen, Ninni; Kankare, Ville; Holopainen, Markus; Kaartinen, Harri; Hyyppa, Juha; Hyyppa, Hannu (2014)
  • Holopainen, Markus; Vastaranta, Mikko; Hyyppa, Juha (2014)
  • 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.
  • Wang, Di; Puttonen, Eetu; Casella, Eric (Elsevier BV, 2022)
    International Journal of Applied Earth Observation and Geoinformation
    The mechanisms involved in organ motions are central to our understanding of how plants develop and respond to environmental stimuli such as light quality, gravity, and water availability throughout time. Recent studies have shown that motions in plants such as circadian rhythms and growth patterns, can be recorded and quantified from time series of terrestrial laser scans (TLS). However, most works monitored the changes of certain functional traits such as height and volume to detect and analyze structural dynamics. A generic method for retrieving fine-scale three-dimensional (3D) motion fields of plant structural movements is still missing. We present PlantMove, a new fully automatic tool to quantify 3D motion fields of plant structural movements with varied magnitudes using TLS point clouds acquired over different time periods. The method uses spatio-temporal point cloud registration embedded in a progressive and coarse-to-fine framework, enabling an efficient processing of large datasets with complex structures. PlantMove was first demonstrated on synthetic plant datasets, displaying millimeter to centimeter level accuracy of retrieved motion fields. In addition, PlantMove was used to assess circadian rhythms on a birch tree from TLS data acquired over the course of one night with about one-hour time intervals, and growth patterns on an English oak from a four-year TLS survey. PlantMove can help to better monitor plant phenotypic plasticity with fine level of details, and can contribute to improve our understanding in plant dynamics across various spatial and temporal scales.
  • Saukkola, Atte; Melkas, Timo; Riekki, Kirsi; Sirparanta, Sanna; Peuhkurinen, Jussi; Holopainen, Markus; Hyyppa, Juha; Vastaranta, Mikko (2019)
    The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015-16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m(2)). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (D-g) and basal-area weighted mean height (H-g) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10-11% and 6-8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254-761 m(2). Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m(2). Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m(2) reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.
  • Pyörälä, Jiri; Liang, Xinlian; Vastaranta, Mikko; Saarinen, Ninni; Kankare, Ville; Wang, Yunsheng; Holopainen, Markus; Hyyppä, Juha (2018)
    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.
  • Cuenca-Garcia, Carmen; Risbol, Ole; Bates, C. Richard; Stamnes, Arne Anderson; Skoglund, Fredrik; Odegard, Oyvind; Viberg, Andreas; Koivisto, Satu; Fuglsang, Mikkel; Gabler, Manuel; Mauritsen, Esben Schlosser; Perttola, Wesa; Solem, Dag-Oyvind (2020)
    In August 2018, a group of experts working with terrestrial/marine geophysics and remote sensing methods to explore archaeological sites in Denmark, Finland, Norway, Scotland and Sweden gathered together for the first time at the Workshop 'Sensing Archaeology in The North'. The goal was to exchange experiences, discuss challenges, and consider future directions for further developing these methods and strategies for their use in archaeology. After the event, this special journal issue was arranged to publish papers that are based on the workshop presentations, but also to incorporate work that is produced by other researchers in the field. This paper closes the special issue and further aims to provide current state-of-the-art for the methods represented by the workshop. Here, we introduce the aspects that inspired the organisation of the meeting, a summary of the 12 presentations and eight paper contributions, as well as a discussion about the main outcomes of the workshop roundtables, including the production of two searchable databases (online resources and equipment). We conclude with the position that the 'North', together with its unique cultural heritage and thriving research community, is at the forefront of good practice in the application and development of sensing methods in archaeological research and management. However, further method development is required, so we claim the support of funding bodies to back research efforts based on testing/experimental studies to: explore unknown survey environments and identify optimal survey conditions, as well as to monitor the preservation of archaeological remains, especially those that are at risk. It is demonstrated that remote sensing and geophysics not only have an important role in the safeguarding of archaeological sites from development and within prehistorical-historical research, but the methods can be especially useful in recording and monitoring the increased impact of climate change on sites in the North.
  • Maeda, Eduardo; Nunes, Matheus; Calders, Kim; Mendes de Moura, Yhasmin; Raumonen, Pasi; Tuomisto, Hanna; Verley, Philippe; Vincent, Gregoire; Zuquin, Gabriela; Camargo, José Luis (2022)
    Forest edges are an increasingly common feature of Amazonian landscapes due to human-induced forest frag-mentation. Substantial evidence shows that edge effects cause profound changes in forest biodiversity and productivity. However, the broader impacts of edge effects on ecosystem functioning remain unclear. Assessing the three-dimensional arrangement of forest elements has the potential to unveil structural traits that are scalable and closely linked to important functional characteristics of the forest. Using over 600 high-resolution terrestrial laser scanning measurements, we present a detailed assessment of forest structural metrics linked to ecosystem processes such as energy harvesting and light use efficiency. Our results show a persistent change in forest structural characteristics along the edges of forest fragments, which resulted in a significantly lower structural diversity, in comparison with the interior of the forest fragments. These structural changes could be observed up to 35 m from the forest edges and are likely to reflect even deeper impacts on other ecosystem variables such as microclimate and biodiversity. Traits related to vertical plant material allocation were more affected than traits related to canopy height. We demonstrate a divergent response from the forest understory (higher vegetation density close to the edge) and the upper canopy (lower vegetation density close to the edge), indicating that assessing forest disturbances using vertically integrated metrics, such as total plant area index, can lead to an erroneous interpretation of no change. Our results demonstrate the strong potential of terrestrial laser scanning for benchmarking broader-scale (e.g. airborne and space-borne) remote sensing assessments of forest distur-bances, as well as to provide a more robust interpretation of biophysical changes detected at coarser resolutions.