The development of laser/radar ranging measurements without scanning and proper attitude control for forest inventory in 1970s–1990s [1
] promoted the application of laser measurement in forestry and led to the rapid adaptation of airborne laser scanning (ALS) in forest inventory. At first, ALS was applied in determining forest terrain elevations [12
]. This was immediately followed by standwise mean height and volume estimation [14
], based on the data collected via ranging measurements, and very soon ALS was applied to inventorying, focusing on individual trees [17
] with the advent of rapid image processing, tree species classification [21
] and the measurement of tree growth and detection of harvested trees [23
] based on bi-temporal data sets. Over 10 years, the extraction of forest variables has been divided into two categories: area-based inventories and inventories based on individual trees or groups of trees. Concurrently with these developments, laser scanning has increasingly provided the core data set for mapping authorities. The point density of laser scanning has increased constantly. In addition to being used in forest inventory, ALS data from forested areas is used for purposes such as flight obstacle mapping, power line mapping, virtual city visualization and mapping, and telecommunication planning.
In the Nordic countries the retrieval of stand characteristics (e.g., mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume), which are needed in forest management planning, is currently being replaced by ALS-based inventory methodologies. As regards operational forest inventories, the two-stage procedure using ALS data and field plots, i.e.
, area-based approach (ABA, [24
]), has become common and a reference for other inventory methodologies. The foremost advantages of the state-of-the-art ABA, when compared to traditional standwise field inventory (SWFI), are greater precision in the prediction of forest variables [25
], sampling-based estimation of forest variables with the possibility to calculate accuracy statistics, and (at least in principle) ALS-based inventory is not dependent on stand boundaries. Moreover, current ALS data acquisition and processing costs are less than those of traditional SWFI methods.
The ALS-based forest inventory methodology based on individual tree detection (ITD) has been widely studied recently, but is not widely used in practice, due to assumed problems related to tree detection under various forest conditions [26
]. Other problems related to the practical use of ITD include the need for higher ALS point density, which adds to the costs and the amount of data that would need to be stored, as well as inadequate tree species identification accuracy. The assumed main advantage of ITD would be that it provides true stem distribution series, enabling better predictions of timber assortments. Stem distributions are predicted in the ABA, causing inaccuracy in timber assortment estimates and forest value [29
]. Another advantage of ITD is the reduced amount of expensive fieldwork compared to that needed when applying the ABA approach.
The results obtained for individual tree extraction have varied significantly from study to study. Percentage of correctly delineated trees has ranged from 40% to 93%, [17
]. It was not known how much of this variation was caused by the methods and how much by forest conditions, until the international benchmarking study “Tree Extraction” (2005–2008) was carried out. In order to test the tree extraction methods using the same data sets, the European Spatial Data Research Organization (EuroSDR) and the International Society for Photogrammetry and Remote Sensing (ISPRS) initiated the “Tree Extraction” project to evaluate the quality, accuracy, and feasibility of automated tree extraction methods based on airborne laser scanner data and digital aerial images. The project was hosted by the Finnish Geodetic Institute (FGI). Twelve partners from USA, Canada, Norway, Sweden, Finland, Germany, Austria, Switzerland, Italy, Poland and Taiwan participated in the test included in the “Tree Extraction” project. The partners were requested to extract trees using the given ALS and image datasets. Another objective of the study was to find out how the point density impacts on individual tree extraction. The results were published in the project’s final report [27
]. The report sets out the accuracy of tree extraction per partner, but it does not present a more detailed analysis of the results. The final report showed that the extraction method is the main factor affecting achieved accuracy. When the laser point density increased from 2 points to 8 points per m2
, the improvement in crown delineation accuracy was marginal.
Tree detection accuracy results from heterogeneous forests are presented in Pitkänen et al.
] where the detection accuracy was only 40% (70% for dominant trees). Yu et al.
] presented an accuracy of 69% for tree detection under various forest conditions (different forest densities, ages, site types and tree species). Heinzel et al.
] introduced an approach that classifies crown size in advance and uses this information as prior knowledge for single-tree extraction. Crown size is classified from aerial color infrared image texture with an improved grey-scale granulometry followed by a crown size adapted watershed segmentation of single trees. The accuracy varies between 64% and 88%. Vauhkonen et al.
] tested several algorithms under different types of forests; Eucalyptus plantation in Brazil, coniferous and deciduous plots in Germany and mainly coniferous plots in Norway and in Sweden. The tree detection rate varied between 54% and 86%. These results are on a completely different scale from those obtained by Peuhkurinen et al.
], where ITD was carried out in two marked stands (density ∼465 stems per ha). The number of harvestable trees was underestimated by only <3%, but this result may include some commission errors, i.e.
, a single tree is segmented into several segments, thus increasing the number of detected trees. Falkowski et al.
] showed that across a full range of canopy conditions in a mixed-species, structurally diverse conifer forest in northern Idaho, United States, the effect of the canopy cover density is significant for tree detection accuracy. Vastaranta et al.
] combined automated ITD and visual interpretation to acquire reference data for ABA. They assumed that additional visual interpretation would significantly enhance the accuracy of the derived plot-level forest variables and provide superior results when used to train the ABA, in contrast to mere automated ITD. Visual interpretation improved the accuracy of ITD validated at plot-level as RMSE of stem volume decreased from 32.1% to 28.6%. However, there was no improvement in ABA predictions.
Vastaranta et al.
] investigated ITD error sources, and their effects on forest management planning calculations. The investigated error sources were detection of trees, errors in tree height prediction and errors in tree diameter prediction. The effects of these errors were analyzed with Monte Carlo simulations. The results showed that the foremost error source in ITD is in tree detection.
This paper includes further analyses of the results obtained in the “Tree Extraction” project [27
]. Four additional methods were added for comparison, namely (1) Local maxima (LM) finding, (2) Multi-scale Laplacian of Gaussian, (3) Minimum curvature-based tree detection and (4) LM finding with varying window size. The LM finding method is a relatively easy and fast implementation, ready for operational use, and advanced methods should provide better results. Additionally, the results are analyzed for various tree heights, and omission and commission errors were included. Finally, more conclusions are drawn from the experiment’s results.
Section 2 describes the data sets used in the study. Section 3 provides a brief description of the methods used by the partners and of the methods used for the evaluation. Section 4 presents the results for the evaluated parameters and discussion. The accuracy of tree height and location determination, crown delineation and the number of extracted trees are analyzed. Key conclusions are given in Section 5.
The results confirmed that the extraction method is the main factor impacting achieved accuracy, as proposed by Kaartinen and Hyyppä [27
], and that laser point density has less impact on individual tree detection.
Depending on the application, the criteria for tree detection and extraction can be different. For example, the methods that are most suitable for dominant tree detection should be used in flight obstacle mapping, power line monitoring, and telecommunication planning. In this evaluation, the best methods for dominant tree detection were FGI_MCV, FGI_LOCM, Norway, and Metla, and FOI if the pulse density is close to 8 points per m2
. They were also better than manual processing. For forest inventory, the method should be such that it is suitable of recording accurately the stand DBH distribution. Since DBHs are calculated for each individual tree based on height, tree species and crown size, the foremost criterion is correct height distribution. Allometric models describing the relationships between tree crown size, height, and DBH are highly sensitive to errors in the input data. Automated measurement results of tree crown size, in particular, tend to be prone to errors. Thus, the estimation of DBH on the basis of tree height and crown size results in a fairly significant degree of uncertainty. Nearest-neighbor methods applicable to single-tree interpretation are, therefore, currently under development [23
]. The extracted data, acquired from detected trees, need to be calibrated with the ground truth, but it is vital that the method reveals as correctly as possible the number of dominant and suppressed trees with a small number of commission errors. This is an aspect where significant work has yet to be undertaken. Extracted individual trees can also be used in a simple way for improving area-based estimates with significantly improved accuracy and without using any calibrations [52
], and thus individual tree extraction techniques are currently also important from the practical forestry point of view.
Based on the results of the present experiment, an approach using the high detection rate of FGI_MCV for all tree sizes (but further processing the suppressed tree data at the point level, as is done in the Zürich approach) is a solution deserving of more attention. Full waveform technology is also expected to improve individual tree detection, especially in the case of suppressed trees, as waveform analysis can be used to produce denser point clouds within the crowns. Additionally, a simple method based on LM finding (proposed, e.g., in Hyyppä et al.
]) has turned out to be one of the best techniques applied in this experiment, and thanks to its simple implementation, it is especially feasible for commercial production.
In the tree cluster approach (TCA), the first phase is to segment CHM, as is done in many ITD approaches. In the second phase, accurately located field trees are linked to the corresponding segments [53
]. Contrary to the ITD model, it is not assumed that a single segment represents a single tree. In the TCA, all the field trees located within the segments’ area are linked to corresponding segments. Thus, segments may include none, one, two or even more trees. This solves the tree detection problem in practice and the bias of the estimated area-level volume and basal-area is reduced.
Individual tree extraction is perhaps one of the few applications where automation provides higher quality than does manual processing. Vastaranta et al.
] tried to improve the automatically detected trees with manual processing when acquiring reference for area-based inventory, but the accuracy of area-based predictions could not be improved. Several of the methods in the present experiment were superior to manual processing in dealing with dominant, co-dominant, and suppressed tree storeys. This also means that manually processed tree maps based on airborne laser surveys cannot be used as reference for developing automatic algorithms for tree detection, although this has been done previously. Inventories based on individual trees require reference data of individual trees collected in the field by some other means. Calibration is needed to reduce the underestimation of tree height and calibration of the basal area and stem volume (e.g., [57
In general, and as was to be expected, the taller a tree is, the better location accuracy is, as could be expected. Tree height accuracy, after high outliers had been deleted, was better than 0.5 m in all tree height classes when using the leading methods in this experiment, and this is a significant result, even though the number of the lowest level trees was small.
Solutions based on individual trees can be applied even with point densities of 2 points per m2
or lower (e.g., [57
]), but the optimum point density is most probably dependent on tree size and stand density of the forest. When dealing with sapling stands, a point density of 10 points per m2
or higher is expected to increase the accuracy of the extraction results.
This international benchmarking experiment also demonstrated that the quality of one method versus other methods cannot be verified without testing the methods in the same forest conditions, since the effect of variability of forest conditions is believed to have a high impact on the achieved accuracy. This is evident when the results achieved in this study are compared to those reported in existing literature.