High-density Airborne Laser Scanning Research Articles

Assessing Forest Traversability for Autonomous Mobile Systems Using Close-Range Airborne Laser Scanning

Advances in sensor technology and computing performance has brought us into an era of digital forestry where a forest environment can be digitally replicated. At the same time, an increasing interest in the use of unmanned vehicles and other autonomous mobile systems (AMSs) in forest mapping and operations has emerged. However, a forest is an unstructured and rather complex environment for AMSs to operate in, and usually some kind of a priori information of traversability is required. The aim of this study was to assess forest traversability for AMSs using high-density airborne laser scanning (ALS) point clouds. It was assumed that such point clouds acquired from a helicopter flying at a low altitude can be used to characterise vegetation obstacles affecting forest traversability. A voxel-based vegetation occupancy analysis was carried out with the aim to detect open space to define traversable three-dimensional space. The experimental setup included seven sample plots (32×32 m) representing diverse boreal forest structures. Terrestrial laser scanning (TLS) was used for obtaining reference for vegetation occupancy. Comparison between ALS and TLS revealed an overall accuracy of 0.85–0.94 with a recall of 0.78–0.91 and a precision of 0.62–0.74 for ALS-based voxel classification for vegetation occupancy depending on forest structure. This implies that up to 91% of voxels assigned a classification »occupied« based on the TLS could be correctly classified using the ALS, while up to 74% of voxels assigned a classification »occupied« using the ALS were occupied based on the TLS. Density of low vegetation accounted for 83% of the variation in accuracy and precision. The feasibility of vegetation occupancy information to be used by an AMS for navigation was also demonstrated. It was assumed that the ALS data convey as sufficient information of AMS path planning as does the TLS data. The experiments showed that out of 1393 randomly generated paths based on empty space detected by the ALS, 72% were considered feasible when validated with the TLS data. The success rate in path planning varied from 0.54 to 0.92 between the sample plots and was seemingly affected by vegetation density that accounted for 53% of variation in success rate. Altogether, the demonstrated possibility to predefine forest traversability using remote sensing will support the use of AMSs in forestry.

Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning

This paper presents the development and application of a deep learning-based approach for semi-automated detection of tar production kilns using new Finnish high-density Airborne Laser Scanning (ALS) data in the boreal taiga forest zone. The historical significance of tar production, an important livelihood for centuries, has had extensive environmental and ecological impacts, particularly in the thinly inhabited northern and eastern parts of Finland. Despite being one of the most widespread archaeological features in the country, tar kilns have received relatively little attention until recently. The authors employed a Convolutional Neural Networks (CNN) U-Net-based algorithm to detect these features from the ALS data, which proved to be more accurate, faster, and capable of covering systematically larger spatial areas than human actors. It also produces more consistent, replicable, and ethically sustainable results. This semi-automated approach enabled the efficient location of a vast number of previously unknown archaeological features, significantly increasing the number of tar kilns in each study area compared to the previous situation. This has implications also for the cultural resource management in Finland. The authors’ findings have influenced the preparation of the renewal of the Finnish Antiquities Act, raising concerns about the perceived impacts on cultural heritage management and land use sectors due to the projected tenfold increase in archaeological site detection using deep learning algorithms. The use of environmental remote sensing data may provide a means of examining the long-term cultural and ecological impacts of tar production in greater detail. Our pilot studies suggest that artificial intelligence and deep learning techniques have the potential to revolutionize archaeological research and cultural resource management in Finland, offering promising avenues for future exploration.

Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery

Abstract In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape‐level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large‐scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~5 pulses/m2) in conjunction with unrectified colour‐infrared (CIR) aerial imagery. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS‐derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from eight stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves and dead trees by linear discriminant analysis. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation using only ALS‐derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation respectively. Our findings imply that wall‐to‐wall large‐scale high density ALS data can be used to detect retention trees rather accurately—even larger dead trees—and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost‐effective option for large area mapping of retention trees in countries with such data available.

Generating Douglas-fir Breeding Value Estimates Using Airborne Laser Scanning Derived Height and Crown Metrics.

Progeny test trials in British Columbia are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of parent trees and their offspring. Accurate and timely collection of phenotypic data is critical for estimating BVs with confidence. Airborne Laser Scanning (ALS) data have been used to measure tree height and structure across a wide range of species, ages and environments globally. Here, we analyzed a Coastal Douglas-fir [Pseudotsuga menziesii var. menziesii (Mirb.)] progeny test trial located in British Columbia, Canada, using individual tree high-density Airborne Laser Scanning (ALS) metrics and traditional ground-based phenotypic observations. Narrow-sense heritability, genetic correlations, and BVs were estimated using pedigree-based single and multi-trait linear models for 43 traits. Comparisons of genetic parameter estimates between ALS metrics and traditional ground-based measures and single- and multi-trait models were conducted based on the accuracy and precision of the estimates. BVs were estimated for two ALS models (ALSCAN and ALSACC) representing two model-building approaches and compared to a baseline model using field-measured traits. The ALSCAN model used metrics reflecting aspects of vertical distribution of biomass within trees, while ALSACC represented the most statistically accurate model. We report that the accuracy of both the ALSCAN (0.8239) and ALSACC (0.8254) model-derived BVs for mature tree height is a suitable proxy for ground-based mature tree height BVs (0.8316). Given the cost efficiency of ALS, forest geneticists should explore this technology as a viable tool to increase breeding programs’ overall efficiency and cost savings.

UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV-borne and airborne imagery to those derived from low- (1–2 pts/m2) and high-density (ca. 20 pts/m2) ALS-scanning for the precise local-scale modelling of woody vegetation structures (the number and height of trees/shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100% for UAV-based models; however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV-based models compared to 95.8% for the high-density ALS model. The percentage mean absolute errors (%MAE) of shrub/tree heights derived from UAV data ranged between 12.2 and 23.7%, and AImg height accuracy was relatively lower (%MAE: 21.4–47.4). Combining UAV-borne or AImg-based digital surface models (DSM) with ALS-based digital terrain models (DTMs) significantly improved the nDSM height accuracy (%MAE: 9.4–13.5 and 12.2–25.0, respectively) but failed to significantly improve the detection of the number of individual shrubs/trees. The height accuracy and detection success using low- or high-density ALS did not differ. Therefore, we conclude that UAV-borne imagery has the potential to replace custom ALS in specific local-scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS-based digital terrain models can further improve the success of the use of such data.

A Crown Quantization-Based Approach to Tree-Species Classification Using High-Density Airborne Laser Scanning Data

Crown features derived from high-density airborne laser scanning (ALS) data have proven to be effective for forest species classification at the individual tree level. Most of the general state-of-the-art (SoA) techniques rely on coarse-level crown features extracted from ALS data and under-utilize both the spatial and the spectral information available in the point clouds, Moreover, they are designed on the expected properties of the specific analyzed forest. We present a novel species classification approach, based on quantization of the entire 3-D tree crown into smaller elementary crown volumes (ECVs) that effectively captures the spatial distribution of filled (i.e., stem, branch, and foliage) and empty volumes of crowns. In the first step, a data-driven process dynamically tests and compares three quantization strategies to tailor the definition of the ECV to the forest type (e.g., conifer and deciduous forest). In the second step, for each ECV, a histogram vector is made up of features representing the light detection and ranging (LiDAR) point distribution and intensity to model the internal and the external local crown characteristics. Then, tree histogram feature vectors are obtained by stacking all the ECV histogram feature vectors. Finally, classification is performed by a support vector machine (SVM) classifier using the histogram intersection kernel. All experiments were performed on three high-density (50–200 points/m2) ALS data sets of deciduous, conifer, and mixed (i.e., both deciduous and conifer) trees. The higher classification accuracy of the proposed method over the SoA one proves its ability to better capture the crown characteristics of individual trees, including species-specific traits.

Comparison of TanDEM-X InSAR data and high-density ALS for the prediction of forest inventory attributes in plantation forests with steep terrain

Satellite interferometric synthetic aperture radar (InSAR) is emerging as a viable low-cost alternative method to airborne laser scanning (ALS) for forest inventory though little research has examined its efficacy for plantation forests located in temperate regions on steep terrain. InSAR and ALS data were collected from Geraldine Forest which is located on rolling to very steep topography in the southeast of New Zealand. These data were combined with an extensive set of plot measurements from which mean top height (H), basal area (G), stem density (N), and total stem volume (TSV) were calculated. InSAR and ALS-based Random Forest models of each variable were developed and compared.Using the ALS data as a reference, the mean RMSE of the InSAR DSM and DTM surfaces were, respectively, 4.58 and 8.09 m and these errors increased to mean values of, respectively, 6.02 and 10.17 m for slopes of 40–50°.ALS-based models were substantially more precise than those developed from InSAR for H (R2 = 0.86 vs. 0.60; RMSE% = 5.47 vs. 10.8%), G (R2 = 0.56 vs. 0.32; RMSE% = 21.5 vs. 30.4%), N (R2 = 0.47 vs. 0.09; RMSE% = 32.3 vs. 43.2%), and TSV (R2 = 0.70 vs. 0.41; RMSE% = 19.4 vs. 30.7%). The base metrics (i.e. ALS height and canopy cover variables) accounted for most of the variance in the ALS models with addition of further metrics providing ≤ 1% reduction in the RMSE%. Base metrics (i.e. InSAR observables) also accounted for most of the variation in InSAR models. Addition of metrics from a mixed Canopy Height Model (CHM), derived from InSAR Digital Surface Model (DSM) and ALS Digital Terrain Model (DTM), resulted in reductions in RMSE% of 3.1–5.4% for H, G, and TSV models with addition of textural metrics providing further reductions of 0.2–0.3% for H and G models. Addition of metrics from the radar CHM, derived from the InSAR DTM and DSM, and texture metrics reduced the RMSE% of the base model for N by 2.3% and 0.5%, respectively.The results were generated using a SAR image pair with a height of ambiguity (HOA) that was higher than ideal, which reduced the sensitivity of results to changes in terrain. Despite this limitation, and the steep slopes throughout the forest, the InSAR models described here had comparable precision to developed InSAR models for key inventory metrics from previous studies.

Characterizing variations in growth characteristics between Douglas-fir with different genetic gain levels using airborne laser scanning

In British Columbia, Canada, Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] selective breeding is used to develop genetically improved regeneration stock. To evaluate realized performance of improved stock, breeders take frequent measurements in genetic gain trials to determine whether yield gains are being met. Currently, variables collected focus on individual tree yield attributes; however, information generated from progeny test trials may not directly reflect plantation performance. Realized yield trials help to bridge the gap between progeny test estimated gain and plantation setting performance. High density airborne laser scanning (ALS) has the potential to identify variables that could improve the selection and validation process. We utilized ALS collected from an unmanned aerial system to assess performance of genetic improvement across three genetic levels and three stand spacings. ALS derived metrics were used to test three hypotheses: (1) tree height is correlated with the level of genetic superiority, (2) tree crown shape varies across genetic level, and (3) tree crown density is associated with genetic level. Random forest algorithms were used to identify candidate ALS metrics. To account for interaction effects, a two-way analysis of variance was conducted for each metric, followed by a post-hoc test to investigate significant differences between genetic level and spacing. The scale and shape parameters of Weibull probability density functions, vertical complexity index, and the fraction of euphotic voxels were found to be important metrics. Results show that genetically superior trees are typically taller, with higher, shorter and denser crowns. In addition, variation across genetic level may be indicative of greater phenotypic plasticity, as superior trees possess the ability to respond to tighter stand spacing.

Comparison of high-density LiDAR and satellite photogrammetry for forest inventory

Point cloud data derived from stereo satellite imagery has the potential to provide large-scale forest inventory assessment but these methods are known to include higher error than airborne laser scanning (ALS). This study compares the accuracy of forest inventory attributes estimated from high-density ALS (21.1 pulses m−2) point cloud data (PCD) and PCD derived from photogrammetric methods applied to stereo satellite imagery obtained over a Pinus radiata D. Don plantation forest in New Zealand. The statistical and textural properties of the canopy height models (CHMs) derived from each point cloud were included alongside standard PCD metrics as a means of improving the accuracy of predictions for key forest inventory attributes. For mean top height (a measure of dominant height in a stand), ALS data produced better estimates (R2 = 0.88; RMSE = 1.7 m) than those obtained from satellite data (R2 = 0.81; RMSE = 2.1 m). This was attributable to a general over-estimation of canopy heights in the satellite PCD. ALS models produced poor estimates of stand density (R2 = 0.48; RMSE = 112.1 stems ha−1), as did the satellite PCD models (R2 = 0.42; RMSE = 118.4 stems ha−1). ALS models produced accurate estimates of basal area (R2 = 0.58; RMSE = 12 m2 ha−1), total stem volume (R2 = 0.72; RMSE = 107.5 m3 ha−1), and total recoverable volume (R2 = 0.74; RMSE = 92.9 m3 ha−1). These values differed little from the estimates of basal area (R2 = 0.57; RMSE = 12.2 m2 ha−1), total stem volume (R2 = 0.70; RMSE = 112.6 m3 ha−1), and total recoverable volume (R2 = 0.73; RMSE = 96 m3 ha−1) obtained from satellite PCD models. The statistical and textural metrics computed from the CHMs were important variables in all of the models derived from both satellite and ALS PCD, nearly always outranking the standard PCD metrics in measures of importance. For the satellite PCD models, the CHM-derived metrics were nearly exclusively identified as important variables. These results clearly show that point cloud data obtained from stereo satellite imagery are useful for prediction of forest inventory attributes in intensively managed forests on steeper terrain. Furthermore, these data offer forest managers the benefit of obtaining both inventory data and high-resolution multispectral imagery from a single product.

Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests

The development of new approaches to individual tree crown delineation for forest inventory and management is an important area of ongoing research. The increasing availability of high density ALS (Airborne Laser Scanning) point clouds offers the opportunity to segment the individual tree crowns and deduce their geometric properties with a high level of accuracy. Top-down segmentation methods such as normalized cut are established approaches for delineation of single trees in ALS point clouds. However, overlapping crowns and branches of nearby trees frequently cause over- and under-segmentation due to the difficulty of defining a single criterion for stopping the partitioning process. In this work, we investigate an adaptive stopping criterion based on the visual appearance of trees within the point clouds. We focus on coniferous trees due to their well-defined crown shapes in comparison to deciduous trees. This approach is based on modeling the coniferous tree crowns with elliptic paraboloids to infer whether a given 3D scene contains exactly one or more than one tree. For each processed scene, candidate tree peaks are generated from local maxima found within the point cloud. Next, paraboloids are fitted at the peaks using a random sample consensus procedure and classified based on their geometric properties. The decision to stop or continue partitioning is determined by finding a set of non-overlapping paraboloids. Experiments were performed on three plots from the Bavarian Forest National Park in Germany. Based on validation data from the field inventory, results show that our approach improves the segmentation quality by up to 10% across plots with different properties, such as average tree height and density. This indicates that the new adaptive stopping criterion for normalized cut segmentation is capable of delineating tree crowns more accurately than a static stopping criterion based on a constant Ncut threshold value.

DETECTION OF SINGLE TREE STEMS IN FORESTED AREAS FROM HIGH DENSITY ALS POINT CLOUDS USING 3D SHAPE DESCRIPTORS

Abstract. Airborne Laser Scanning (ALS) is a widespread method for forest mapping and management purposes. While common ALS techniques provide valuable information about the forest canopy and intermediate layers, the point density near the ground may be poor due to dense overstory conditions. The current study highlights a new method for detecting stems of single trees in 3D point clouds obtained from high density ALS with a density of 300 points/m2. Compared to standard ALS data, due to lower flight height (150–200 m) this elevated point density leads to more laser reflections from tree stems. In this work, we propose a three-tiered method which works on the point, segment and object levels. First, for each point we calculate the likelihood that it belongs to a tree stem, derived from the radiometric and geometric features of its neighboring points. In the next step, we construct short stem segments based on high-probability stem points, and classify the segments by considering the distribution of points around them as well as their spatial orientation, which encodes the prior knowledge that trees are mainly vertically aligned due to gravity. Finally, we apply hierarchical clustering on the positively classified segments to obtain point sets corresponding to single stems, and perform ℓ1-based orthogonal distance regression to robustly fit lines through each stem point set. The ℓ1-based method is less sensitive to outliers compared to the least square approaches. From the fitted lines, the planimetric tree positions can then be derived. Experiments were performed on two plots from the Hochficht forest in Oberösterreich region located in Austria.We marked a total of 196 reference stems in the point clouds of both plots by visual interpretation. The evaluation of the automatically detected stems showed a classification precision of 0.86 and 0.85, respectively for Plot 1 and 2, with recall values of 0.7 and 0.67.

COMPARISON OF HIGH AND LOW DENSITY AIRBORNE LIDAR DATA FOR FOREST ROAD QUALITY ASSESSMENT

Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.

COMPARISON OF HIGH AND LOW DENSITY AIRBORNE LIDAR DATA FOR FOREST ROAD QUALITY ASSESSMENT

Abstract. Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.

Burned forest characterization at single-tree level with airborne laser scanning for assessing wildlife habitat

Abstract Abundance, size, and spatial distribution of standing dead trees (snags), are key indicators of forest biodiversity and ecosystem health. These metrics represent critical habitat components for various wildlife species of conservation concern, including the Black-backed Woodpecker (Picoides arcticus), which is strongly associated with recently burned conifer forest. We assessed the potential of Airborne Laser Scanning (ALS) to detect and characterize conifer snags and identify Black-backed Woodpecker habitat using previously derived empirical thresholds of conifer snag basal area. Over the footprint of the Rim Fire, a megafire that extended (~ 104,000 ha) through a heterogeneous mosaic of conifer forests, oak woodlands, and meadows in the Sierra Nevada mountains of California, we identified conifer snags and estimated their basal area from single-tree ALS-derived metrics using Gaussian processes in four major steps. First, individual trees were mapped using the Watershed Segmentation algorithm, resulting in 87% detection of trees with stem diameter larger than 30 cm. Second, the snag/live classification model identified snags with an overall accuracy of 91.8%, using the coefficient of variation of height and intensity together with maximum intensity and fractional cover as the most relevant metrics. Third, the conifer/hardwood snag classification model utilizing the maximum height, median height, minimum intensity, and area metrics separated snag forest types with an overall accuracy of 84.8%. Finally, a Gaussian process regression model reliably estimated conifer snag stem diameter (R2 = 0.81) using height and crown area, thus significantly outperforming regionally calibrated conifer-specific allometric equations. As a result, ~ 80% of the snag basal area have been mapped. Optimal and potential habitat for Black-backed Woodpecker comprise 53.7 km2 and 58.4 km2, respectively, representing 5.1 and 5.6% of the footprint of the Rim Fire. Our study illustrates the utility of high-density ALS data for characterizing recently burned forests, which, in conjunction with information about the habitat needs of particular snag-dependent wildlife species, can be used to assess habitat characteristics, and thus contribute greatly to forest management and biodiversity conservation.

Forest road quality control using ALS data

Good road conditions are necessary for the smooth transportation of forest machines and products. High-density airborne laser scanning data were used here to determine the quality of road surfaces and ditching systems. Forest roads in Kiihtelysvaara, Finland, were assessed in August 2013. Eight categories (structural condition, seasonal damage, drying, bridges, surface wear, visibility, coppicing, and flatness) have been inventoried and divided into three quality classes: poor, satisfactory, and good. The topographic position index, standardize elevation index, and hydrology tools were used on digital elevation models with different resolutions to test which categories could be derived. The road surface quality was most clearly related to surface wearing and flatness, and the topographic position index described the road surface best at resolutions of 0.20 m and 0.25 m; however, the standardized elevation index was superior at a 0.50 m resolution. The ditching system plays an important role in the drying of roads, and the hydrological tools and land facet analysis were most suitable for identifying the location of ditches and assessing their quality at 0.20 m and 0.25 m resolutions, respectively. The road surface was classified in all resolutions at least 66% correctly, whereas the ditches were classified in all resolutions at least 60% correctly. The results confirm that airborne laser scanning data can be used for obtaining quality information on forest roads.

Modeling Tree Characteristics of Individual Black Pine (Pinus nigra Arn.) Trees for Use in Remote Sensing-Based Inventory

The main aim was to develop models for predicting diameter at breast height (DBH), merchantable tree volume (V), and aboveground biomass (AGB) of individual black pine (Pinus nigra Arn.) trees grown in Sub-Mediterranean Croatian pure even-aged forests, which will be suitable for remote sensing based forest inventories. In total, eight variables obtained from field measurement, existing database, and digital terrain model were candidates for independent variables in regression analysis. DBH, V, and AGB were modeled as linear function of each of the independent variables, and all possible linear combinations thereof. Goodness of fit of every model was then evaluated using R2 statistic. Comparison between selected models showed that the variability of all dependent variables are explained best by models which include both crown diameter and tree height as independent variables with coefficients of determination of 0.83, 0.89, 0.82 for DBH, V, and AGB, respectively. Consequently, these models may be recommended as the most suited for DBH, V and AGB estimation of black pine trees grown in pure Sub-Mediterranean forest stands using high-resolution aerial images or high-density airborne laser scanning data. This assumption should be further validated by conducting remote sensing inventory and comparing the obtained results with field measurement results.

Automatic Detection of Small Single Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning

A large proportion of Norway’s land area is occupied by the forest-tundra ecotone. The vegetation of this temperature-sensitive ecosystem between mountain forest and the alpine zone is expected to be highly affected by climate change and effective monitoring techniques are required. For the detection of such small pioneer trees, airborne laser scanning (ALS) has been proposed as a useful tool employing laser height data. The objective of this study was to assess the capability of an unsupervised classification for automated monitoring programs of small individual trees using high-density ALS data. Field and ALS data were collected along a 1500 km long transect stretching from northern to southern Norway. Different laser and tree height thresholds were tested in various combinations within an unsupervised classification of tree and nontree raster cells employing different cell sizes. Suitable initial cell sizes for the exclusion of large treeless areas as well as an optimal cell size for tree cell detection were determined. High rates of successful tree cell detection involved high levels of commission error at lower laser height thresholds, however, exceeding the 20 cm laser height threshold, the rates of commission error decreased substantially with a still satisfying rate of successful tree cell detection.

Cross-Correlation of Diameter Measures for the Co-Registration of Forest Inventory Plots with Airborne Laser Scanning Data

Continuous maps of forest parameters can be derived from airborne laser scanning (ALS) remote sensing data. A prediction model is calibrated between local point cloud statistics and forest parameters measured on field plots. Unfortunately, inaccurate positioning of field measures lead to a bad matching of forest measures with remote sensing data. The potential of using tree diameter and position measures in cross-correlation with ALS data to improve co-registration is evaluated. The influence of the correction on ALS models is assessed by comparing the accuracy of basal area prediction models calibrated or validated with or without the corrected positions. In a coniferous, uneven-aged forest with high density ALS data and low positioning precision, the algorithm co-registers 91% of plots within two meters from the operator location when at least the five largest trees are used in the analysis. The new coordinates slightly improve the prediction models and allow a better estimation of their accuracy. In a forest with various stand structures and species, lower ALS density and differential Global Navigation Satellite System measurements, position correction turns out to have only a limited impact on prediction models.

Detection of harvested trees in forests from repeated high density airborne laser scanning

Abstract. Identification of harvested and fallen trees is a prerequisite for the detection and measurement of changes in forests. This paper presents a three step approach to monitor harvested and fallen trees based on direct comparison of repeated high density airborne LIDAR data. In a first step differences between data sets are obtained from a point to point comparison, such that the data can be reduced to the deviating points only. Secondly, the resulting points are clustered into spatially connected regions using region growing. Finally, individual trees are extracted from the clusters by analysing their relative proximity and by analysing geometric properties of points in the clusters. Two data sets, acquired at a four year interval and covering a forest with mainly deciduous trees, are compared. First results show that most points relating to a change can be extracted and that clustering of these with region growing enables us to efficiently separate harvested and fallen trees from the remaining trees. Grouped harvested trees could not be separated using the region growing approach due to touching crowns. Segmentation of these using spectral clustering however identified individual regions well, but the results depend mainly on the pre-defined number of clusters. Crowns of grouped trees can be therefore separated if the number of trees is known.

Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures

The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using laser height and intensity data. The main objective of the present study was to assess a possible improvement in the performance of classifying tree and nontree laser echoes from high-density ALS data. The data were collected along a 1000 km long transect stretching from southern to northern Norway. Different geostatistical and statistical measures derived from laser height and intensity values were used to extent and potentially improve more simple models ignoring the spatial context. Generalised linear models (GLM) and support vector machines (SVM) were employed as classification methods. Total accuracies and Cohen’s kappa coefficients were calculated and compared to those of simpler models from a previous study. For both classification methods, all models revealed total accuracies similar to the results of the simpler models. Concerning classification performance, however, the comparison of the kappa coefficients indicated a significant improvement for some models both using GLM and SVM, with classification accuracies >94%.