Browsing by Subject "LiDAR"

Sort by: Order: Results:

Now showing items 1-20 of 36
  • Masiero, A.; Dabove, P.; Di Pietra, V; Piragnolo, M.; Vettore, A.; Guarnieri, A.; Toth, C.; Gikas, V; Perakis, H.; Chiang, K-W; Ruotsalainen, L. M.; Goel, S.; Gabela, J. (ISPRS, 2022)
    The international archives of the photogrammetry, remote sensing and spatial information sciences
    Despite the availability of GNSS on consumer devices enabled personal navigation for most of the World population in most of the outdoor conditions, the problem of precise pedestrian positioning is still quite challenging when indoors or, more in general, in GNSS-challenging working conditions. Furthermore, the covid-19 pandemic also raised of pedestrian tracking, in any environment, but in particular indoors, where GNSS typically does not ensure sufficient accuracy for checking people distance. Motivated by the mentioned needs, this paper investigates the potential of UWB and LiDAR for pedestrian positioning and tracking. The two methods are compared in an outdoor case study, nevertheless, both are usable indoors as well. The obtained results show that the positioning performance of the LiDAR-based approach overcomes the UWB one, when the pedestrians are not obstructed by other objects in the LiDAR view. Nevertheless, the presence of obstructions causes gaps in the LiDAR-based tracking: instead, the combination of LiDAR and UWB can be used in order to reduce outages in the LiDAR-based solution, whereas the latter, when available, usually improves the UWB-based results.
  • 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.
  • Imangholiloo, Mohammad; Yrttimaa, Tuomas; Mattsson, Teppo; Junttila, Samuli; Holopainen, Markus; Saarinen, Ninni; Savolainen, Pekka; Hyyppä, Juha; Vastaranta, Mikko (Elsevier BV, 2022)
    ISPRS Journal of Photogrammetry and Remote Sensing
    Silvicultural tending of seedling stands is important to producing quality timber. However, it is challenging to allocate where and when to apply these silvicultural tending actions. Here, we tested and evaluated two methodological modifications of the ordinary area-based approach (ABAOrdinary) that could be utilized in the airborne laser scanning-based forest inventories and especially seedling stand characterization. We hypothesize that ABA with added individual tree detection-derived features (ABAITD) or correcting edge-tree effects (ABAEdge) would display improved performance in estimating the tree density and mean tree height of seedling stands. We tested this hypothesis using single-photon laser (SPL) and linear-mode laser (LML) scanning data covering 89 sample plots. The obtained results supported the hypothesis as the methodological modifications improved seedling stand characterization. Compared to the performance of ABAordinary, relative bias in tree density estimation decreased from 17.2% to 10.1% when we applied ABAITD. In the case of mean height estimation, the relative root mean square error decreased from 19.5% to 16.3% when we applied ABAEdgeITD. The SPL technology provided practically comparable or, in some cases, enhanced performance in seedling stand characterization when compared to conventional LML technology. Based on the obtained findings, it seems that the tested methodological improvements should be carefully considered when ALS-based inventories supporting forest management and silvicultural decision-making are developed further.
  • Imangholiloo, Mohammad; Yrttimaa, Tuomas; Mattsson, Teppo; Junttila, Samuli; Holopainen, Marjut; Saarinen, Ninni; Savolainen, P.; Hyyppä, J.; Vastaranta, Mikko (2022)
    Silvicultural tending of seedling stands is important to producing quality timber. However, it is challenging to allocate where and when to apply these silvicultural tending actions. Here, we tested and evaluated two methodological modifications of the ordinary area-based approach (ABAOrdinary) that could be utilized in the airborne laser scanning-based forest inventories and especially seedling stand characterization. We hypothesize that ABA with added individual tree detection-derived features (ABAITD) or correcting edge-tree effects (ABAEdge) would display improved performance in estimating the tree density and mean tree height of seedling stands. We tested this hypothesis using single-photon laser (SPL) and linear-mode laser (LML) scanning data covering 89 sample plots.The obtained results supported the hypothesis as the methodological modifications improved seedling stand characterization. Compared to the performance of ABAordinary, relative bias in tree density estimation decreased from 17.2% to 10.1% when we applied ABAITD. In the case of mean height estimation, the relative root mean square error decreased from 19.5% to 16.3% when we applied ABAEdgeITD. The SPL technology provided practically comparable or, in some cases, enhanced performance in seedling stand characterization when compared to conventional LML technology. Based on the obtained findings, it seems that the tested methodological improvements should be carefully considered when ALS-based inventories supporting forest management and silvicultural decision-making are developed further.
  • 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.
  • Suomalainen, Juha (Finnish Geodetic Institute, 2012)
    Publications of the Finnish Geodetic Institute
    The reflectance factor is a quantity describing the efficiency of a surface to reflect light and affecting the observed brightness of reflected light. It is a complex property that varies with the view and illumination geometries as well as the wavelength and polarization of the light. The reflectance factor response is a peculiar property of each target surface. In optical remote sensing, the observed reflectance properties of natural surfaces are used directly for, e.g., classifying targets. Also, it is possible to extract target physical properties from observations, but generally this requires an understanding and modeling of the reflectance properties of the target. The most direct way to expand our understanding of the reflectance properties of natural surfaces is through empirical measurements. This thesis presents three original measurement setups for obtaining the reflectance properties of natural surfaces and some of the results acquired using them. The first instrument is the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO); an instrument for measuring the view angle dependency of polarized hyperspectral reflectance factor on small targets. The second instrument is an unmanned aerial vehicle (UAV) setup with a consumer camera used for taking measurements. The procedure allows 2D-mapping of the reflectance factor view angle dependency over larger areas. The third instrument is a virtual hyperspectral LiDAR, i.e. a setup for acquiring laser scanner point clouds with 3D-referenced reflectance spectra ([x,y,z,R(λ)]). During the research period 2005 2011, the FIGIFIGO was used to measure the angular reflectance properties of nearly 400 remote sensing targets, making the acquired reflectance library one of the largest of its kind in the world. These data have been exploited in a number of studies, including studies dealing with the vicarious calibration of airborne remote sensing sensors and satellite imagery and the development and characterization of reflectance reference targets for airborne remote sensing sensors, and the reflectance measurements have been published as a means of increasing the general understanding of the scattering of selected targets. The two latter instrument prototypes demonstrate emerging technologies that are being used in a novel way in remote sensing. Both measurement concepts have shown promising results, indicating that, in some cases, it can be beneficial to use such a methodology in place of the traditional remote sensing methods. Thus, the author believes that such measurement concepts will be used more widely in the near future. Heijastuskerroin on kullekin kohteelle yksilöllinen ominaisuus joka kuvaa kohteesta heijastuneen valon määrää. Heijastuskertoimen arvo riippuu havainto- ja valaistusgeometriasta sekä valon aallonpituudesta ja polarisaatiosta. Useimmissa optisen kaukokartoituksen menetelmissä mitataan kohteiden heijastuskerrointa. Näitä heijastuskerroinhavaintoja käytetään suoraan esim. kohteiden luokittelussa. Kehittyneemmissä menetelmissä havainnoista on myös mahdollista irrottaa joitain kohteen fysikaalisia ominaisuuksia, mutta yleensä tämä edellyttää kohteen ymmärtämistä sekä valonsironnan mallintamista. Suorin tapa laajentaa ymmärrystä luonnon pintojen valonsironnasta on tehdä empiirisiä mittauksia. Tässä väitöskirjassa esitellään kolme mittalaitetta luonnon pintojen valonsironnan mittaamiseksi sekä näillä laitteilla kerättyjä tuloksia. Ensimmäinen esiteltävä mittalaite on Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO), jolla voidaan mitata kohteen sirottaman valon suuntariippuvuutta valon aallonpituuden sekä polarisaation funktiona. Toinen mittalaite on automaattinen miehittämätön helikopteri. Kopteriin asennetun kameran sekä kuvien yhdistämismenetelmän avulla maaston valonsironnan suuntariippuvuutta voidaan kartoittaa laajemmilla alueilla kuin FIGIFIGO:a käyttäen. Kolmas mittalaite on virtuaalinen valkean valon LiDAR, jolla voidaan mitata laboratoriokohteen 3D rakenne yhdessä heijastusspektrien kanssa ([x,y,z,R(λ)]). Tutkimusjakson (2005 2011) aikana FIGIFIGO:a on käytetty lähes 400 kaukokartoituskohteen sironnan suuntariippuvuuden mittaamiseen. Näillä mittauksilla kerätty datakirjasto on yksi maailman suurimmista ja kattavimmistaan lajissaan. FIGIFIGO-mittauksia on hyödynnetty useissa tutkimuksissa esim. satelliitti havaintojen ja kaukokartoitus sensoreiden lennonaikaisessa kalibroinnissa ja validoinnissa, sekä ilmakuvauksen heijastuskerroinreferenssikohteiden kehittämisessä. Mittaustulokset on myös julkaistu tieteellisissä julkaisuissa laajentaen yleistä ymmärrystä kaukokartoituskohteiden valonsironnasta. Kaksi jälkimmäistä mittalaitetta ovat prototyyppejä joilla on testattu ja demonstroitu uutta tekniikkaa jota ei ole aiemmin hyödynnetty kaukokartoituksessa tällä tavoin. Molemmat mittauskonseptit tuottivat lupaavia tuloksia mahdollistaen uudentyyppisten mittausten tekemisen. Saadut tulokset antavat ymmärtää että mittauskonseptien kehittämistä kannattaa jatkaa ja on todennäköistä että tämän kaltaiset mittausmenetelmät tulevat jo lähitulevaisuudessa leviämään laajempaan käyttöön kaukokartoituksessa.
  • Poorazimy, Maryam; Ronoud, Ghasem; Yu, Xiaowei; Luoma, Ville; Hyyppä, Juha; Saarinen, Ninni; Kankare, Ville; Vastaranta, Mikko (MDPI AG, 2022)
    Remote Sensing
    The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 × 32 m. The ALS crown metrics of width (WD), projection area (A2D), volume (V), and surface area (A3D) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Δ), especially for the 3D crown metrics of V and A3D, with Cohen’s D values of 1.09–1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (rΔ) and showed statistically significant differences with Norway spruce and birch in terms of rΔWD, rΔA2D, rΔV, and rΔA3D at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in rΔWD, rΔV, and rΔA3D (p-value < 0.0001). However, the amount of rΔ variability that could be explained by the species was only 2–5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales.
  • 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).
  • Drag, Lukas; Burner, Ryan C.; Stephan, Jorg G.; Birkemoe, Tone; Doerfler, Inken; Gossner, Martin M.; Magdon, Paul; Ovaskainen, Otso; Potterf, Maria; Schall, Peter; Snäll, Tord; Sverdrup-Thygeson, Anne; Weisser, Wolfgang; Mueller, Joerg (2023)
    Climate, topography and the 3D structure of forests are major drivers affecting local species communities. However, little is known about how the specific functional traits of saproxylic (wood-living) beetles, involved in the recycling of wood, might be affected by those environmental characteristics. Here, we combine ecological and morphological traits available for saproxylic beetles and airborne laser scanning (ALS) data in Bayesian trait-based joint species distribution models to study how traits drive the distributions of more than 230 species in temperate forests of Europe. We found that elevation (as a proxy for temperature and precipitation) and the proportion of conifers played important roles in species occurrences while variables related to habitat heterogeneity and forest complexity were less relevant. Furthermore, we showed that local communities were shaped by environmental variation primarily through their ecological traits whereas morphological traits were involved only marginally. As predicted, ecological traits influenced species' responses to forest structure, and to other environmental variation, with canopy niche, wood decay niche and host preference as the most important ecological traits. Conversely, no links between morphological traits and environmental characteristics were observed. Both models, however, revealed strong phylogenetic signal in species' response to environmental characteristics. These findings imply that alterations of climate and tree species composition have the potential to alter saproxylic beetle communities in temperate forests. Additionally, ecological traits help explain species' responses to environmental characteristics and thus should prove useful in predicting their responses to future change. It remains challenging, however, to link simple morphological traits to species' complex ecological niches. Read the free Plain Language Summary for this article on the Journal blog.
  • 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.
  • Luoma, Ville (Finnish Society of Forest Science, 2022)
    Dissertationes Forestales
    Forests are dynamic ecosystems that are constantly changing. The most common natural reasons for change in forests are the growth and death of trees, as well as the damage occurring to them. Tree growth appears as an increment of its structural dimensions, such as stem diameter, height, and crown volume, which all affect the structure of a tree. Repeated measurements of tree characteristics enable observations of the respective increments indicating tree growth. According to current knowledge, the tree growth process follows the priority theory, where trees aim to achieve sufficient lightning conditions for the tree crown through primary growth, whereas increment in diameter results from the secondary growth. Tree growth is known to have an effect on the carbon sequestration potential of trees as well as on the quality of timber. To improve the understanding of the underlying cause–effect relations driving tree growth, methods to quantify structural changes in trees and forests are needed. The use of terrestrial laser scanning (TLS) has emerged during the recent decade as an effective tool to determine attributes of individual trees. However, the capacity of TLS point cloud-based methods to measure tree growth remains unexplored. This thesis aimed at developing new methods to measure tree growth in boreal forest conditions by utilizing two-date TLS point clouds. The point clouds were also used to investigate how trees allocate their growth and how the stem form of trees develops, to deepen the understanding of tree growth processes under different conditions and over the life cycle of a tree. The capability of the developed methods was examined during a five- to nine-year monitoring period with two separate datasets consisting of 1315 trees in total. Study I demonstrated the feasibility of TLS point clouds for measuring tree growth in boreal forests. In studies II and III, an automated point cloud-based method was further developed and tested for measuring tree growth. The used method could detect trees from two-date point clouds, with the detected trees representing 84.5% of total basal area. In general, statistically significant changes in the examined attributes, such as diameter at breast height, tree height, stem volume, and logwood volume, were detected during the monitoring periods. Tree growth and stem volume allocation seemed to be more similar for trees growing in similar structural conditions. The findings obtained in this thesis demonstrate the capabilities of repeatedly acquired TLS point clouds to be used for measuring the growth of trees and for characterizing the structural changes in forests. This thesis showed that TLS point cloud-based methods can be used for enhancing the knowledge of how trees allocate their growth, and thus help discover the underlying reasons for processes driving changes in forests, which could generate benefits for ecological or silvicultural applications where information on tree growth and forest structural changes is needed.
  • 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)