Abstract Airborne Laser Scanning Data Research Articles

Assessing the potential of synthetic and ex situ airborne laser scanning and ground plot data to train forest biomass models

Abstract Airborne laser scanning data are increasingly used to predict forest biomass over large areas. Biomass information cannot be derived directly from airborne laser scanning data; therefore, field measurements of forest plots are required to build regression models. We tested whether simulated laser scanning data of virtual forest plots could be used to train biomass models and thereby reduce the amount of field measurements required. We compared the performance of models that were trained with (i) simulated data only, (ii) a combination of simulated and real data, (iii) real data collected from different study sites, and (iv) real data collected from the same study site the model was applied to. We additionally investigated whether using a subset of the simulated data instead of using all simulated data improved model performance. The best matching subset of the simulated data was sampled by selecting the simulated forest plot with the highest correlation of the return height distribution profile for each real forest plot. For comparison, a randomly selected subset was evaluated. Models were tested on four forest sites located in Poland, the Czech Republic, and Canada. Model performance was assessed by root mean squared error (RMSE), squared Pearson correlation coefficient (r$^{2}$), and mean error (ME) of observed and predicted biomass. We found that models trained solely with simulated data did not achieve the accuracy of models trained with real data (RMSE increase of 52–122 %, r$^{2}$ decrease of 4–18 %). However, model performance improved when only a subset of the simulated data was used (RMSE increase of 21–118 %, r$^{2}$ decrease of 5–14 % compared to the real data model), albeit differences in model performance when using the best matching subset compared to using a randomly selected subset were small. Using simulated data for model training always resulted in a strong underprediction of biomass. Extending sparse real training datasets with simulated data decreased RMSE and increased r$^{2}$, as long as no more than 12–346 real training samples were available, depending on the study site. For three of the four study sites, models trained with real data collected from other sites outperformed models trained with simulated data and RMSE and r$^{2}$ were similar to models trained with data from the respective sites. Our results indicate that simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available.

Generating synthetic laser scanning data of forests by combining forest inventory information, a tree point cloud database and an open-source laser scanning simulator

Abstract Airborne laser scanning (ALS) data are routinely used to estimate and map structure-related forest inventory variables. The further development, refinement and evaluation of methods to derive forest inventory variables from ALS data require extensive datasets of forest stand information on an individual tree-level and corresponding ALS data. A cost-efficient method to obtain such datasets is the combination of virtual forest stands with a laser scanning simulator. We present an approach to simulate ALS data of forest stands by combining forest inventory information, a tree point cloud database and the laser scanning simulation framework HELIOS++. ALS data of six 1-ha plots were simulated and compared to real ALS data of these plots. The synthetic 3D representations of the forest stands were composed of real laser scanning point clouds of individual trees that were acquired by an uncrewed aerial vehicle (UAV), and, for comparison, simplified tree models with cylindrical stems and spheroidal crowns. The simulated ALS point clouds of the six plots were compared with the real point clouds based on canopy cover, height distribution of returns and several other point cloud metrics. In addition, the performance of biomass models trained using these synthetic data was evaluated. The comparison revealed that, in general, both the real tree models and the simplified tree models can be used to generate synthetic data. The results differed for the different study sites and depending on whether all returns or only first returns were considered. The measure of canopy cover was better represented by the data of the simplified tree models, whereas the height distribution of the returns was – for most of the study sites – better represented by the real tree model data. Training biomass models with metrics derived from the real tree model data led to an overestimation of biomass, while using metrics of the simplified tree model data resulted in an underestimation of biomass. Still, the accuracy of models trained with simulated data was only slightly lower compared to models trained with real ALS data. Our results suggest that the presented approach can be used to generate ALS data that are sufficiently realistic for many applications. The synthetic data may be used to develop new or refine existing ALS-based forest inventory methods, to systematically investigate the relationship between point cloud metrics and forest inventory variables and to analyse how this relationship is affected by laser scanning acquisition settings and field reference data characteristics.

Forest inventory based on canopy height model derived from airborne laser scanning data

Abstract Airborne laser scanning (ALS) has emerged as a remote sensing technology capable of providing data suitable for deriving all types of elevation models. A canopy height model (CHM), which represents absolute height of objects above the ground in metres (e.g., trees), is the one most commonly used within the forest inventory. The aim of this study was to assess the accuracy of forest inventory performed for forest unit covered 17,583 ha (Slovakia, Central Europe) using the CHM derived from ALS data. This objective also included demonstrating the applicability of freely available data and software. Specifically, ALS data acquired during regular airborne survey, QGIS software, and packages for R environment were used for purpose of this study. A total of 180 testing plots (5.6 ha) were used for accuracy assessment. The differences between CHM-predicted and ground-observed forest stand attributes reached a relative root mean square error at 10.9%, 23.1%, and 34.5% for the mean height, mean diameter, and volume, respectively. Moreover, all predictions were unbiased (p-value < 0.05) and the strength of the relationships between CHM-predicted and ground-observed forest stand attributes were relative high (R2 = 0.7 – 0.8).

Classification of high-voltage power line structures in low density ALS data acquired over broad non-urban areas.

Airborne laser scanning (ALS) has gained importance over recent decades for multiple uses related to the cartography of landscapes. Processing ALS data over large areas for forest resource estimation and ecological assessments requires efficient algorithms to filter out some points from the raw data and remove human-made structures that would otherwise be mistaken for natural objects. In this paper, we describe an algorithm developed for the segmentation and cleaning of electrical network facilities in low density (2.5 to 13 points/m2) ALS point clouds. The algorithm was designed to identify transmission towers, conductor wires and earth wires from high-voltage power lines in natural landscapes. The method is based on two priors i.e. (1) the availability of a map of the high-voltage power lines across the area of interest and (2) knowledge of the type of transmission towers that hold the conductors along a given power line. It was tested on a network totalling 200 km of wires supported by 415 transmission towers with diverse topographies and topologies with an accuracy of 98.6%. This work will help further the automated detection capacity of power line structures, which had previously been limited to high density point clouds in small, urbanised areas. The method is open-source and available online.

Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies

Abstract Airborne laser scanning (ALS) technology delivers large amount of data collected from airborne level. These data are used for many different applications in forestry, civil engineering, environmental studies and others. To acquire the best possible results from the data, accuracy analysis is a necessary part of data processing chain. Therefore, considering the increasing interest worldwide in the use of laser scanning data, improving the quality control (QC) tools is a crucial pursuit. This study underlines the possible error sources, summarises the existing QC knowledge for ALS data and proposes an optimised QC procedure. The procedure was implemented in selected applications and evaluated for three different environments, namely, forests, rural areas and croplands. The proposed solution is almost fully automatic outside from the module that supports the operator in the classification examination. The workflow is scalable and can be expanded with new modules that enhance the functionality. The presented procedures can save up to 30 min of manual checks for every 1 km2 area.

Verification and Updating of the Database of Topographic Objects with Geometric Information About Buildings by Means of Airborne Laser Scanning Data

Abstract Airborne laser scanning data (ALS) are used mainly for creation of precise digital elevation models. However, it appears that the informative potential stored in ALS data can be also used for updating spatial databases, including the Database of Topographic Objects (BDOT10k). Typically, geometric representations of buildings in the BDOT10k are equal to their entities in the Land and Property Register (EGiB). In this study ALS is considered as supporting data source. The thresholding method of original ALS data with the use of the alpha shape algorithm, proposed in this paper, allows for extraction of points that represent horizontal cross section of building walls, leading to creation of vector, geometric models of buildings that can be then used for updating the BDOT10k. This method gives also the possibility of an easy verification of up-to-dateness of both the BDOT10k and the district EGiB databases within geometric information about buildings. For verification of the proposed methodology there have been used the classified ALS data acquired with a density of 4 points/m2. The accuracy assessment of the identified building outlines has been carried out by their comparison to the corresponding EGiB objects. The RMSE values for 78 buildings are from a few to tens of centimeters and the average value is about 0,5 m. At the same time for several objects there have been revealed huge geometric discrepancies. Further analyses have shown that these discrepancies could be resulted from incorrect representations of buildings in the EGiB database.

Geometric Refinement of ALS-Data Derived Building Models Using Monoscopic Aerial Images

Airborne laser scanning (ALS) has proven to be a strong basis for 3D building reconstruction. While ALS data allows for a highly automated processing workflow, a major drawback is often in the point spacing. As a consequence, the precision of roof plane and ridge line parameters is usually significantly better than the precision of gutter lines. To cope with this problem, the paper presents an approach for geometric refinement of building models reconstructed from ALS data using monoscopic aerial images. The core idea of the proposed modeling method is to obtain refined roof edges by intersecting roof planes accurately and reliably extracted from 3D point clouds with viewing planes assigned with building edges detected in a high resolution aerial image. In order to minimize ambiguities that may arise during the integration of modeling cues, the ALS data is used as the master providing initial information about building shape and topology. We evaluate the performance of our algorithm by comparing the results of 3D reconstruction executed using only laser scanning data and reconstruction enhanced by image information. The assessment performed within a framework of the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark shows an increase in the final quality indicator up to 8.7%.

Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland

Airborne laser scanning (ALS) based stand level forest inventory has been used in Finland and other Nordic countries for several years. In the Russian Federation, ALS is not extensively used for forest inventory purposes, despite a long history of research into the use of lasers for forest measurement that dates back to the 1970s. Furthermore, there is also no generally accepted ALS-based methodology that meets the official inventory requirements of the Russian Federation. In this paper, a method developed for Finnish forest conditions is applied to ALS-based forest inventory in the Perm region of Russia. Sparse Bayesian regression is used with ALS data, SPOT satellite images and field reference data to estimate five forest parameters for three species groups (pine, spruce, deciduous): total mean volume, basal area, mean tree diameter, mean tree height, and number of stems per hectare. Parameter estimates are validated at both the plot level and stand level, and the validation results are compared to results published for three Finnish test areas. Overall, relative root mean square errors (RMSE) were higher for forest parameters in the Perm region than for the Finnish sites at both the plot and stand level. At the stand level, relative RMSE generally decreased with increasing stand size and was lower when considered overall than for individual species groups.

ALS-based wood volume models of forest stands and comparison with forest inventory data

Abstract Airborne laser scanning (ALS) based standing wood volume models were analysed in two contrasting test sites with different forest types in Estonia. In Aegviidu test site main tree species are Scots pine and Norway spruce and Laeva test site is mainly dominated by deciduous species. ALS data measurements were carried out for Aegviidu in 2008 and for Laeva in 2013. Approximately 450 sample plots were established additionally to the forest inventory dataset in both test sites. Exclusive to the sample plots, 46 stands were measured in 2012 in Aegviidu for stand level model. The sample plot-based model standard error in Aegviidu was Se = 59.8 m3/ha (22%) and in Laeva Se = 69.2 m3/ha (29%). The stand-level model based on 46 measured stands from Aegviidu had Se = 38.4 m3/ha. Based on the models a cross-validation between the two test sites was carried out and systematic differences between the two test sites were found. The reasons are related to differences in optical properties of trees, crown shapes, flight configuration and canopy cover even though the sample plot based models included ALS-based canopy cover variable. The ALS-based wood volume estimate was also compared to forest inventory (FI) data and systematically larger estimates compared to FI dataset in both test sites were found. This average systematic error increased substantially (by 100 m3/ha) for stands with volume over 250 m3/ha. It was also detected that a model developed on small point clouds drawn for sample plots may produce systematic errors when applied to stand-level point clouds.

Predicting stand-thinning maturity from airborne laser scanning data

Airborne laser scanning (ALS) has been used in recent years to acquire accurate remote-sensing material for carrying out practical forest inventories. Still, much of the information needed in forest management planning must be collected in the field. For example, forest management proposals are often determined in the field by an expert. In the present study, statistical features extracted from ALS data were used in logistic regression models and in nonparametric k-MSN estimation to predict the thinning maturity of stands. The research material consisted of 381 treewise measured circular plots in young and advanced thinning stands from the vicinity of Evo, in southern Finland. Timing of thinning was determined in the field by an expert and coded as a binary variable. Models were developed (1) to locate stands that will reach thinning maturity within the next 10-year period and (2) for stands in which thinning should be done immediately. For comparison purposes, logistic regression models were formulated from accurately field-measured stand characteristics. Logistic regression models based on ALS features predicted the thinning maturity with a classification accuracy of 79% (1) and 83% (2). The respective percentages were 66% and 83% with models based on field-measured stand characteristics and 70% and 86% with k-MSN. The study showed that ALS data can be used to predict stand-thinning maturity in a practical way.

An application-oriented automated approach for co-registration of forest inventory and airborne laser scanning data

Airborne laser scanning (ALS) data can be used for downscaling point-based forest inventory (FI) measurements to obtain spatially distributed estimates of forest parameters at a more detailed, local scale. Such downscaling algorithms usually consist of a direct coupling between selected FI parameters and ALS data collected at the field sampling locations. Thus, precise co-registration between FI and ALS data is an essential preprocessing step to obtain accurate predictive relationships. This paper presents a new, automated co-registration approach that searches iteratively for the best match between an ALS-based canopy height model and the tree positions and heights measured during the FI. While the basic principle of the algorithm applies to various types of FI sampling configurations, the co-registration approach was developed specifically to take into account the tree selection criterion posed by angle count sampling. The angle count sampling method only includes trees that at a given distance from the sample plot centre have a minimum required diameter at breast height (DBH). This tree selection criterion leads to maximum plot radii and number of inventoried trees that strongly vary from sample plot to sample plot. In the automated co-registration procedure, several criteria (e.g. the occurrence of more than one spatial cluster of minimum residuals and a predominance of deciduous trees in a sample plot) were used to detect possible uncertain solutions and to reduce post-processing efforts by an image operator. Model calibration and validation were based on national forest inventory (NFI) and ALS data from the Austrian federal state of Vorarlberg. Transferability and robustness of the approach was verified using an independent local FI. The results show that 68% of the NFI sample plots and 74% of the local FI plots could be automatically co-registered to a location at a distance of less than 5.0 m from the reference location. The maximum difference of 5.0 m used for marking a solution as correct was based on the relatively small influence that deviations of up to this value have on ALS-based predictions of biophysical forest variables at a stand level. The quality flagging criteria adopted were very successful in identifying uncertain solutions; only one out of 153 co-registered sample plots with a deviation from the reference data set greater than 5.0 m was not identified as uncertain. Applying the automatically co-registered sample plots in calibration of a growing stock model provided estimates that were clearly superior to those obtained with the original plot positions and even slightly outperformed those based on manual co-registration. As the algorithm developed will be part of an operational processing chain for Austrian NFI data, it has a high practical relevance.

Identification of Scandinavian Commercial Species of Individual Trees from Airborne Laser Scanning Data Using Alpha Shape Metrics

Abstract Airborne laser scanning (ALS) data are not usually considered to be very informative with respect to tree species, and this information is often obtained by combining such data with spectral image material. The aim here was to test the ability of variables derived solely from ALS data to describe the crown shape and structure characteristics required for tree species discrimination. For that purpose, we constructed tree crown approximations from the three-dimensional return data by applying a computational geometry approach, the alpha shape concept, and developed metrics for describing them. We examined the ability of these metrics to classify Scandinavian commercial species (pine, spruce, and deciduous trees) by means of linear discriminant analysis and compared the alpha shape metrics with groups of ALS-based height, density, intensity, and two-dimensional texture variables. For evaluating the classification accuracy, we used a test data set composed of 92 dominant or codominant trees detected and delineated manually from ALS data with a density of approximately 40 returns m−2. The alpha shape metrics proved capable of discriminating between all three species classes evaluated, and several height distribution and texture variables were found to discriminate between the coniferous tree species. An overall accuracy of approximately 95% and a κ coefficient of 0.90 was achieved using a combination of the variables. Because this initial application of the alpha shape metrics was carried out using a test data set on only such trees that are clearly detectable from remotely sensed data, further research is required to apply the approach presented here within stands with a continuous canopy. Furthermore, as the tree observations considered here were highly biased toward mature coniferous trees, experiments using more representative data sets are needed to generalize the result obtained.