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Detecting and estimating attributes for single trees using laser scanner. Photogramm J Finl

  • January 1999
Authors:
Juha Hyyppä at Finnish Geospatial Research Institute at National Land Survey
Juha Hyyppä
  • Finnish Geospatial Research Institute at National Land Survey
M. Inkinen
M. Inkinen
M. Inkinen
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The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
The Photogrammetric Journal of Finland, Vol. 16, No. 2, 1999
... The developments in remote sensing technology and need for more precise forest information have driven a shift from area-level forest inventories (Naesset 1997b(Naesset , 1997a(Naesset , 2002 to estimating attributes of individual trees (Hyyppä and Inkinen 1999). Laser scanning has proven useful for estimating such attributes, as it provides detailed three-dimensional (3D) information of the target. ...
... ALS has been used for forest inventory (see, e.g., Hyyppä et al. 2008), change detection (Gobakken and Naesset 2004;Yu et al. 2006;Vastaranta et al. 2012), biodiversity assessment (Goetz et al. 2007;Clawges et al. 2008;Kim et al. 2009;Müller and Brandl 2009), and various other forestrelated applications. Generally, ALS-based forest monitoring can be divided into two approaches: the area-based approach (Naesset 2002), and the single-tree approach (Hyyppä and Inkinen 1999). The former is based on observing the relationship between field-measured forest attributes and ALS-derived metrics, whereas the latter is based on directly measuring attributes of individual trees from the point cloud. ...
... ITD is a widely studied topic, as it serves as the basis for most single tree level forest inventory applications. Such applications include tree species classification (Dalponte et al. 2014;Amiri et al. 2019), forest mensuration (Hyyppä and Inkinen 1999;Popescu et al. 2003) ,and standing dead tree detection (Yao et al. 2012;Casas et al. 2016). ALS-based ITD can roughly be divided into two main categories depending on whether individual trees are extracted from a canopy height model (CHM) derived from the point cloud (see, e.g., Hyyppä et al. 2001;Persson et al. 2002;Dalponte and Coomes 2016) or from the point cloud directly (Li et al. 2012;Lu et al. 2014). ...
Detecting individual dead trees using airborne laser scanning
Article
Full-text available
  • Jan 2023
The global crises – climate change and biodiversity loss – have created a need for precise and wide-scale information of forests. Airborne laser scanning (ALS) provides a means for collecting such information, as it enables mapping large areas efficiently with a resolution sufficient for object-level information extraction. Deadwood is an important component of the forest environment, as it stores carbon and provides a habitat for a wide variety of species. Mapping deadwood provides information about the valuable areas regarding biodiversity, which can be used in, e.g., conservation and restoration planning. The aim of this thesis was to develop automated methodology for detecting individual fallen and standing dead trees from ALS data. Studies I and II presented a line detection based method for detecting fallen trees and evaluated its performance on a moderate-density ALS dataset (point density approx. 15 points/m2) and a high point density unmanned aerial vehicle borne laser scanning (ULS) dataset (point density approx. 285 points/m2). In addition, the studies inspected the dataset, methodology, and forest structure related factors affecting the performance of the method. The studies found that the length and diameter of fallen trees significantly impact their detection probability, and that the majority of large fallen trees can be identified from ALS data automatically. Furthermore, study I found that the amount and type of undergrowth and ground vegetation, as well as the size of surrounding living trees determine how accurately fallen trees can be mapped from ALS data. Moreover, study II found that increasing the point density of the laser scanning dataset does not automatically improve the performance of fallen tree detection, unless the methodology is adjusted to consider the increase in noise and detail in the point cloud. Study III inspected the feasibility of high-density discrete return ULS data for mapping individual standing dead trees. The individual tree detection method developed in the study was based on a three-step process consisting of individual tree segmentation, feature extraction, and machine learning based classification. The study found that, while some of the large standing dead trees could be identified from the ULS dataset, basing detection on discrete return data and the geometrical properties of trees did not suffice for acquiring applicable deadwood information. Thus, spectral information acquired with multispectral laser scanners or aerial imaging, or full-waveform laser scanning is necessary for detecting individual standing dead trees with a sufficient accuracy. The findings of this thesis contribute to the existing deadwood detection methodology and improve the understanding of factors to take into account when utilizing ALS for detecting dead trees at a single-tree-level. Although remotely sensed deadwood mapping is still far from a resolved topic, these contributions are a step towards operationalizing remotely sensed biodiversity monitoring.
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... Naesset 2002;White et al. 2013). In addition to attributes describing forest areas, ALS metrics can also be utilized for predicting attributes for single trees (Hyyppä and Inkinen 1999). ALS data used for detecting single tree crowns has typically been denser than data used for describing forest areas. ...
... The necessary stages are detecting the trees, extracting the features describing each tree, and finally, estimation of tree attributes (e.g. Hyyppä and Inkinen 1999;Persson et al. 2002). In studies I-III ALS-based height models were used in detecting the trees, tree-level features were extracted from the ALS point clouds, and tree attributes were estimated either by using either a field reference with non-parametric methods or existing allometric models. ...
... CHM typically underestimates the height of the canopy (e.g. Hyyppä and Inkinen 1999). The underestimation results from overestimation of the DTM, but also from underestimation of the DSM. ...
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... Structure and growth measurements assessed using airborne scanning light detection and ranging (LiDAR), commonly referred to as airborne laser scanning (ALS), can help fill these knowledge gaps by providing three-dimensional data at multiple spatiotemporal scales. ALS has been widely demonstrated to provide accurate measurements of many forest structure attributes, including tree height and canopy cover, across large spatial extents (Naesset, 1997;Hyyppa and Inkinen, 1999;Lefsky et al., 1999;Smith et al., 2009;Sibona et al., 2016;. Studies that use direct tree height measurements have shown that high pulse density ALS (>8 ppm) can estimate tree height with lower error and bias than indirect field measurements Ganz et al., 2019;Wang et al., 2019). ...
... This is an important missing link as this information could help calibrate modeled post-fire tree growth in fire effects and earth system models and provide improvements to how fire effects are integrated within forestry growth and yield models (Steady et al., 2019). There is a welldocumented history of accurately measuring individual tree height growth over time using multitemporal ALS datasets (Hyyppa and Inkinen, 1999;Yu et al., 2004;Ma et al., 2018;Zhao et al., 2018). Using active fire observations and individual tree measurements from multitemporal ALS data has been proposed to objectively quantify fire impacts on individual trees , but to date has not been assessed. ...
Integrating active fire behavior observations and multitemporal airborne laser scanning data to quantify fire impacts on tree growth: A pilot study in mature Pinus ponderosa stands
Article
Methods that integrate pre-, active-, and post-fire measurements to quantify fire effects across multiple spatial scales are needed to improve our understanding of ecological effects following fire and for informing natural resource management decisions that rely on post-fire growth and yield estimates. Given growth and yield modeling systems require tree level measurements to parameterize diameter and height distributions, effective datasets require both tree and stand level characterization. However, most stand-to-landscape scale fire effects studies use optical multispectral data (e.g., 30 m spatial resolution Landsat data) which are too coarse to quantify tree-level effects and is limited in its ability to quantify changes in forest structure. Most studies also fail to integrate active fire behavior observations, such as heat flux, limiting their ability to identify mechanisms of tree injury and mortality and/or predict fire effects. Combining active fire observations and structural measurements derived from multitemporal airborne laser scanning (ALS) data has been proposed to quantify fire effects on tree structure and growth but has yet to be tested. In this pilot study, we used a combination of fire behavior and heat flux metrics, including Fire Radiative Power per unit area (FRP: W m − 2) and Fire Radiative Energy per unit area (FRE: J m − 2), along with multitemporal field and ALS measurements to quantify fire intensity impacts on mature tree height growth. Prescribed fires were conducted in 2014 in thinned and unthinned mature Pinus ponderosa stands and plot-scale fire behavior and heat flux metrics were quantified using standard videography methods and tower-mounted infrared radiometers. Tree height growth was quantified using multitemporal field and ALS data and included pre-fire measurements and post-fire measurements up to eight years post-fire. Results show that trees exposed to the surface fire treatments had height growth that was less than unburned trees. The results also show that height growth 5-8 years post-fire is reduced in trees exposed to greater fire intensities, in terms of maximum FRP per unit area and rate of spread. There was no significant relationship between height growth and other fire behavior metrics (FRE per unit area, average flame length, flame residence time), although height growth decreased with greater FRE per unit area and increased with greater flame residence time. These findings, taken together with similar sapling-, mature tree-and landscape-scale studies, suggest that an integrated active-fire behavior and ALS-data approach may provide a quantitative, scalable method for assessing fire effects on tree structure and growth.
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... The models are then used to predict the corresponding attributes over a grid tessellating the inventory area. Alternatively, the individual tree crown (ITC) approach has been proposed to provide treelevel information for tree crown segments delineated from ALS data (Brandtberg 1999;Hyyppä 1999). In the ITC approach, tree crowns of individual trees are segmented from ALS data and tree attributes are then predicted from the ALS data for each segment (Dalponte et al. 2018). ...
Imputing stem frequency distributions using harvester and airborne laser scanner data: a comparison of inventory approaches
Article
Full-text available
Stem frequency distributions provide useful information for pre-harvest planning. We compared four inventory approaches for imputing stem frequency distributions using harvester data as reference data and predictor variables computed from airborne laser scanner (ALS) data. We imputed distributions and stand mean values of stem diameter, tree height, volume, and sawn wood volume using the k-nearest neighbor technique. We compared the inventory approaches: (1) individual tree crown (ITC), semi-ITC, area-based (ABA) and enhanced ABA (EABA). We assessed the accuracies of imputed distributions using a variant of the Reynold’s error index, obtaining the best mean accuracies of 0.13, 0.13, 0.10 and 0.10 for distributions of stem diameter, tree height, volume and sawn wood volume, respectively. Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. The forest attribute, inventory approach, stand size and the laser pulse density had significant effects on the accuracies of imputed frequency distributions, however the ALS delay and percentage of deciduous trees did not. This study highlights the utility of harvester and ALS data for imputing stem frequency distributions in pre-harvest inventories.
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... An alternative method to describe stand density using LiDAR technology is the individual tree detection (ITD) method. This consists of the identification of each individual in the population, using information on the shape and dimensions of the crown to identify and locate each tree (Hyyppa 1999). The success of this method depends upon the quality of the LiDAR point cloud and the algorithm implemented for tree identification (Wallace et al. 2014). ...
Estimation of stand density using aerial LiDAR information: Integrating the area-based-approach and individual-tree-detection methods in plantations of Pinus radiata
Article
Full-text available
  • Aug 2023
A mixed approach was applied using aerial LiDAR information to estimate the stand density in a Pinus radiata plantation. The methodsused individual tree detection (ITD) information to improve stand density estimates from the approach-based area (ABA) method.Method 1, which corresponds to the traditional ABA estimation in a linear mode, obtained a RMSE = 23.6 % and a AIC = 840.9,where the LiDAR metrics used were in the 95 % percentile and the ratio between first returns over 1.3 m (COV). Method 2, whichcorresponds to an Individual Tree Detection (ITD) algorithm configured with a search window of 3 meters and a height defined by the50th percentile, resulted in a RMSE = 49%. The mixed method 3 used the number of trees detected in method 2 as an additional metricin the ABA method, generating RMSE = 20.9 % and a AIC = 822.1. Method 4 was defined as mixed with error, which incorporated thenumber of trees estimated using the ITD method as another predictor variable, generating a RMSE = 21.3 % and a AIC = 835.2. Themethod with the best performance was 3, reducing 2.7 percentage points with respect to the RMSE of method 1 (traditional ABA). Theintegration of the ABA and ITD methods improved estimations of stand density, and also achieved better representation of the spatialvariability of the number of trees at complete stand level. (PDF) Estimation of stand density using aerial LiDAR information: Integrating the area-based-approach and individual-tree-detection methods in plantations of Pinus radiata. Available from: https://www.researchgate.net/publication/373239318_Estimation_of_stand_density_using_aerial_LiDAR_information_Integrating_the_area-based-approach_and_individual-tree-detection_methods_in_plantations_of_Pinus_radiata
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... The prerequisite for GNSS navigation is that data of the structure of the forest will be available, including the coordinates of the position of individual trees. We are currently seeing a growing number of studies dealing with the use of LIDAR for mapping the structure of forest vegetation, e.g., Hyyppä and Inkinen [16], Persson et al. [17], and Peuhkurinen et al. [18]. Navigation of an off-road vehicle using GNSS methods in a forest depends both on the accuracy of mapping the positions of individual trees and on the quality of the navigation itself. ...
GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
Article
Full-text available
  • May 2023
One of the basic possibilities of orientation in forest stands is the use of global navigation satellite systems (GNSS). Today, these systems are used for pedestrian orientation and also for off-road vehicle navigation. This article presents the results of research aimed at measuring the quality of GNSS signal in different types of coniferous and deciduous vegetation for the purpose of optimizing the navigation of off-road vehicles. To determine the structure (density) of the forest stand, tachymetry was chosen as the reference method. The Trimble Geo 7X cm edition device with Tornado for 7X antenna devices using real time VRS (virtual reference station) method was used to measure GNSS signal quality. This article presents the results of recorded numbers of GNSS satellites (GPS, GLONASS, Galileo and BeiDou) during the driving of a terrain vehicle in two different forest locations. Significant presented results include the deviations of vehicle positions determined by GNSS from tachymetrically precisely measured and marked routes along which the vehicle was moving. The authors of the article focused on the accuracy of determining the position of the vehicle using GNNS, as the most commonly used device for off-road vehicle navigation. The measurement results confirmed the assumption that the accuracy of positioning was better in deciduous forest than in coniferous (spruce) or mixed vegetation. This research was purposefully focused on the possibilities of navigation of military vehicles, but the achieved results can also be applied to the navigation of forestry, rescue and other types of off-road vehicles.
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... In general, two approaches are used to determine forest characteristics from airborne laser scanning data. In the rst approach, the determination of tree and stand characteristics is based on Individual Tree Detection (ITD; Hyyppä and Inkinen 1999). The result is a vector layer containing the parameters of the tree crowns with their additional features. ...
Use of remote sensing data to assess the dynamics of selected characteristics of Białowieża Forest stands in 2015-2019
Chapter
Full-text available
  • Apr 2022
This chapter is, in a sense, a synthesis and summary of the remote sensing analyses carried out during the project. The remote sensing data obtained in the following years of the project made it possible to determine the direction of changes in the stands of the Białowieża Forest. Based on the remote sensing data obtained, a number of stand characteristics were determined, which were then followed by an in-depth analysis of changes in species composition and stand structure. The natural processes observed include: changes in the spruce stands (including the decline of spruce caused by the bark beetle outbreak), the appearance of natural regeneration, the formation of gaps, the enlargement or overgrowth of existing gaps, as well as changes in the proportion of individual species and structural changes in the stands. By using several time series of different remote sensing data, a comprehensive picture of the changes in forest stands and their dynamics in the Białowieża Forest (BF) was obtained. This chapter presents the main results of the analyses carried out using a combination of remote sensing data from different sources (LIDAR, satellite images, aerial photographs) and ground-based measurements. The obtained results are not only of scientific value, but can also be a valuable source of information for the administrative institutions managing the Białowieża Forest area.
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Virtual forests: a review on emerging questions in the use and application of 3D data in forestry
Article
Full-text available
  • Jun 2023
Digital 3D technologies are emerging methods for recording and visualizing forests. Therefore, it is not surprising that these technologies have seen many applications and developments in recent years. In this study, we conducted a comprehensive review of existing 3D technologies within the context of forestry and how they interact with users and stakeholders. We present a summary of the requirements, visualization, and application of virtual forests. This includes an overview of state-of-the-art 3D reconstruction and visualization tools, which have seen a major increase in interest in the past few years, as evidenced by a preliminary analysis on research keywords. Based on the reviewed studies, we present the current trend and emerging questions, as well as challenges in the field of virtual forests. Further, we discuss the identified trends and challenges related to data acquisition, along with existing and potential future interactions between the 3D data and more specific demands from the forestry sector. We conclude that the use of digital 3D data in forestry is on the rise and that such novel methods show great potential and merit further attention.
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Influence of phenology on waveform features in deciduous and coniferous trees in airborne LiDAR
Article
Full-text available
Information on forest structure is vital for sustainable forest management. Currently, airborne LiDAR remote sensing has been well established as an effective tool to characterize the structure of canopies and forest inventory variables. Radiometry and geometry are highly intertwined in LiDAR remote sensing of forest vegetation and phenology influences the geometric-optical properties of deciduous and evergreen trees causing seasonal variation in LiDAR observations. This variation may be considered as a nuisance or exploited in for example tree species identification. Airborne LiDAR data are also influenced by sensor functioning, acquisition settings, scan geometry and the atmosphere. Reliable estimation of subtle phenological effects calls for data in which the impact of the external factors is minimal. We experimented with such data and explored LIDAR waveforms (WFs) in boreal trees in winter, early summer and late summer. Our objectives were to i) assess the match of the multitemporal LiDAR data for observing true changes in vegetation; ii) quantify the influence of phenology in deciduous and evergreen trees; iii) study the effect of varying scan zenith angle (SZA) and canopy age on WF features in different phenostates; iv) assess the temporal feature correlation in individual living and dead standing trees. A WF-recording pulsed LiDAR sensor unit operating at the wavelength of 1550 nm was used in repeated acquisitions. WF attributes such as energy, peak amplitude and echo width were derived for each pulse and were localized vertically to crown, understory and ground components. Silver and downy birch, black alder, European aspen, Siberian larch, Scots pine, Norway spruce and dead standing spruce formed our strata. Results showed that phenology caused more variation in WF features of deciduous trees compared to evergreen conifers. Deciduous trees displayed substantial between-species variation that was linked with differences in branching pattern, leaf orientation and bark reflectance. Pine displayed a possible winter-early summer anomaly in canopy backscattering that may be linked with changes in foliage clumping or with the role of stamens in early summer trees. Trees displayed positive temporal correlation in WF features and correlations were the strongest in evergreen and deciduous conifers and decreased with time. SZA had minor influence on WF features whereas age exercised a strong effect on many features with parallel variation between species and phenostates. Structural changes following death, i.e. ‘aging’ changed the geometric WF features of dead standing trees. Our results provide new insights for enhancing tree species identification by using WF LiDAR and for LiDAR time-series analysis of vegetation.
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INDIVIDUALIZAÇÃO DE ÁRVORES EM AMBIENTE FLORESTAL NATIVO UTILIZANDO MÉTODOS DE SEGMENTAÇÃO EM MODELOS DIGITAIS PRODUZIDOS A PARTIR DA TECNOLOGIA LIDAR
Article
Full-text available
  • Aug 2013
O sistema de perfilamento a LASER aerotransportado, baseado na tecnologia Light Detection and Ranging (LIDAR) é uma técnica de sensoriamento remoto ativo que tem demonstrado bons resultados na mensuração da estrutura vertical e horizontal em ambientes florestais, auxiliando o entendimento dos ecossistemas florestais, produzindo informações com alta confiabilidade sobre variáveis chaves no estudo de parâmetros biofísicos da vegetação. Contudo, a maioria dos estudos se concentra em florestas plantadas para fins comerciais ou em tipos florestais mais homogêneos, são poucos os estudos em florestas tropicais, caracterizadas pela heterogeneidade de espécies e formas. Este estudo foi realizado em uma unidade de conservação urbana formada por vegetação típica de matas semidecíduas e de cerrado. A pesquisa propõe uma metodologia para individualização de árvores em ambiente florestal nativo por meio de modelos digitais produzidos a partir da tecnologia LIDAR. Os dados utilizados foram adquiridos por meio de um sistema de perfilamento a LASER Aerotransportado modelo Leica ALS 50. Foi testado um procedimento de segmentação por crescimento de regiões em modelos digitais com os valores de altura das árvores. Foram demarcadas parcelas em campo, sendo então georreferenciadas todas as árvores dominantes contidas dentro de cada parcela. Algumas árvores, usadas como amostras de validação, tiveram suas áreas de copas mensuradas em campo, permitindo a avaliação da eficácia do procedimento de segmentação por meio de análises estatísticas de correlação e regressão.
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