1. Introduction
Trees are among the most important plants for the terrestrial biosphere [
1,
2]. For improving our understanding of tree populations at varying scales, improvements in methodologies and technologies are needed for characterizing individual trees and forests. This methodological knowledge gap is also listed among the most important ecological research topics according to Sutherland et al. [
3]. Remote and close-range sensing techniques provide the state-of-the-art in mapping and characterizing trees and forests [
4,
5]. Laser scanning is an active remote sensing technique recording three-dimensional (3D) environment providing billions of 3D points. It has been the main driving force behind the development of characterization of trees in the last two decades [
6,
7,
8]. An alternative to laser scanning are image matching approaches [
9,
10,
11,
12] that can also be used to derive dense point clouds with high geometric accuracies. Dense point clouds acquired with aircrafts or from unmanned aerial vehicles (UAVs) using laser scanning or photogrammetric image matching can be used to detect single trees [
13], reconstruct 3D crown structures [
14], derive height and stem dimensions [
15], and predict tree attributes such as species, biomass and stem volume [
16,
17,
18,
19].
However, due to the incomplete tree detection and inaccurate tree characterization, some of the tree attributes (e.g., stem diameters) have been challenging to reliably obtain with remote and close-range sensing techniques [
20,
21]. In varying forest conditions, only part of trees have been detected from point clouds collected above a canopy, as suppressed trees have most often been occluded [
20,
22,
23]. The use of the whole point cloud information, instead of canopy height model (CHM)-based techniques (see, e.g., [
24]) has improved the detection of suppressed trees [
6] but a robust solution is still missing. A greater part of the trees can be detected from terrestrial point clouds acquired below a canopy [
4,
7]. Terrestrial laser scanning (TLS) provides a detailed 3D representation of surrounding forest structures, enabling an automated characterization of trees and stands [
4,
25,
26]. Compared with conventional forest inventory methods, such as the use of clinometers for measuring height and calipers or measurement tapes for measuring stem diameters [
27], the use of TLS point clouds enable non-destructive approaches to estimate stem profile and volume [
28,
29,
30] and to characterize a branching structure of trees [
31,
32] which can further improve the modelling of tree biomass [
33,
34]. However, the close-range hemispherical scanning geometry often limits the ability of TLS techniques to comprehensively characterize upper parts of a forest canopy [
4,
21,
35]. Therefore, several meters of error in TLS-based tree height estimates are common [
21]. Furthermore, errors in tree height estimates lead to erroneous estimates for stem volume and mean tree height at plot level. In addition, occlusion due to dense undergrowth vegetation, branches and other trees, hinder the automatic detection of all trees (e.g., [
21]).
To overcome the challenges with detecting suppressed trees, characterizing an upper canopy, and occlusion, terrestrial and aerial point cloud data could be combined for an improved forest characterization [
21,
36,
37,
38]. While better capturing the upper canopy structure for more reliable tree height measurement, the multisensorial approach could also enable improved tree detection due to the different measurement geometries of the used sensors. Theoretically, a more complete set of forest observations should lead to improved estimates for a forest structure. In recent years, the use of UAVs has become an attainable option for small-scale forest monitoring [
18,
39,
40,
41]. A detailed 3D information on a forest canopy structure can be acquired even by a consumer-grade UAV equipped with an RGB camera [
42] and subsequent point cloud extraction using image processing techniques such as Structure-from-Motion photogrammetry [
43]. Low-cost UAV imaging systems could provide complementary information for TLS-based forest monitoring at an affordable price [
37]. In previous studies, UAV photogrammetry has been successfully integrated with point clouds from terrestrial photogrammetry [
38] and laser scanning [
37]. However, the benefits of using the combination of TLS and aerial point cloud data instead of using TLS data alone in characterizing varying forest structures has not previously been examined.
The objective of this study is to investigate the advantages of combining photogrammetric UAV and TLS point clouds (i.e., multisensorial close-range sensing approach) to improve the accuracy of detecting trees, measuring tree height, and estimating forest structural attributes on 27 sample plots located in managed boreal forests. We hypothesize that the differing measurement geometries between TLS and UAV point cloud data will lead to a more complete characterization of individual trees and therefore, to a more complete characterization of the vertical and horizontal structures of the plots. We assess the performance of the multisensorial approach in the vertical and horizontal forest characterization by using the most common forest structural attributes. Number of trees per hectare (TPH), basal area (G), and basal-area weighted mean diameter (Dg) are used as measures for characterizing the horizontal structure whereas basal-area weighted mean height (Hg) is a measure describing the vertical structure of the plots. Mean stem volume (Vmean) is a forest structural attribute that is affected by both the vertical and the horizontal forest structure. We compare the performance of the multisensorial approach (TLS+UAV) with the performance obtained with mere TLS point clouds to assess whether it is beneficial to complement TLS with dense aerial point clouds.
3. Results
There were no differences in the tree detection accuracy between the use of only TLS data and multisensorial point clouds (
Table 2,
Figure 4). Out of the total number of 2102 Scots pine trees, 2076 (98.8%) were automatically detected with both approaches. The stem volume of the detected trees accounted for 99.5% of the stem volume of all the Scots pine trees. On average, the tree height was underestimated by 0.33 m (1.7%) and the RMSE in the tree height measurements was 1.47 m (7.4%) with the multisensorial approach. The accuracy was slightly decreased when the measurements were only based on the TLS data, as the tree height was underestimated by 0.65 m (3.3%) with an RMSE of 1.64 m (8.3%). In TPH, G and D
g, there were no differences in the bias or the RMSE when these attributes characterizing the forest horizontal structure were derived from the TLS or the multisensorial point clouds (
Table 2). TPH, G and D
g were all slightly underestimated (1%–2.5%) whereas the RMSEs for TPH, G, and D
g were 4.8%, 3.3% and 1.5%, respectively. The estimation accuracies of H
g and V
mean, on the other hand, differed between the TLS-only and the multisensorial approach. In H
g, the bias decreased from −0.75 m (−3.6%) to −0.45 m (0.58%) and the RMSE from 0.88 m (4.3%) to 0.58 m (2.8%) when the multisensorial approach was used instead of only TLS data. In V
mean the multisensorial approach provided a slightly lower RMSE (12.81% vs. 14.55% from TLS-only) compared to the TLS, but the estimates included more bias (4.97% vs. 0.82%).
4. Discussion
We investigated how much the forest characterization capacity can be improved in managed Scots pine forests if TLS point clouds are complemented with photogrammetric point clouds acquired from above a canopy using an UAV (i.e., multisensorial close-range sensing approach). We hypothesized that the different measurement geometries between the TLS and the UAV point cloud data would lead to a more complete characterization of single trees and therefore to a more complete characterization of the vertical and the horizontal forest structure. We assessed the performance of the vertical and horizontal characterization by using the most common forest structural attributes, in which TPH, G, and D
g represented the horizontal variation of the Scot pine plots, whereas H
g described their vertical structure, and V
mean was affected by both the vertical and the horizontal structure. The results supported our hypothesis, as the forest structural attributes directly related to the vertical forest structure were more accurately estimated with the multisensorial point clouds (
Table 2). Additional benefits from the photogrammetric UAV point clouds were seen in the tree segmentation stage of the data processing. However, the multisensorial close-range sensing approach did not considerably improve the characterization of the horizontal forest structure compared to the use of only the TLS point clouds. This finding was somewhat unexpected, as the different measurement geometries were assumed to also lead to an improved tree detection as different trees were anticipated to be occluded in the TLS and the UAV data. Though, it should be noted that the sample plots located in managed Scots pine stands and already the TLS-based tree detection rate was better than has been reported in most of the studies in boreal forests [
21,
52]. In more complex forests, use of multisensorial data should theoretically improve the tree detection rate, and therefore the estimation of TPH, G, D
g as well as V
mean.
The tree detection from TLS point clouds is greatly dependent on the comprehensiveness of a point cloud. A grid of 10 m between the scan positions produced uniform data for a detailed characterization of even tropical trees [
56]. It was reported in [
57] that the highest tree detection rate of 82% was obtained with seven scan locations at the vertices of a hexagon together with a center scan in temperate forests. The RMSE of tree height estimates varied between 2.8 and 4.7 m (13%–30%) in [
21], which is considerably higher than the results obtained with the TLS data only here (i.e., RMSE of 1.6 m). It should be noted, however, that the scan design was different between these two studies (i.e., five scan locations in [
21] and eight in our study) together with the fact that the plots in [
21] had a more complex tree species composition compared to the pure Scots pine plots in this study. The improvements for the estimates of TPH, G, D
g, H
g, and V
mean between [
52] and our study is also noticeable, indicating the effect of a forest structure but also a scan design in a reliable characterization of the forest structure. Automatic and manual measurements of tree height from 3D point clouds acquired with a laser sensor mounted on an UAV showed an RMSE of ≥10% and a bias of ~3% [
58]. Although the inclusion of the UAV point clouds provided only slightly lower RMSE (2.8%) and bias (−2.2%) for H
g compared to the TLS data only (4.3% and −3.6%, respectively), here, the RMSE especially was considerably lower than reported by [
58]. Although there was a difference between the UAV sensors, the difference in heterogeneity in a forest structure is assumed to be the main reason for the differences between these two studies.
A forest structure can be characterized by using field inventories [
27], close-range sensing [
4,
27,
56] or remote sensing [
5,
59]. In field inventories, typically calipers are used for dbh and clinometers for the tree height measurements. If tree positions are mapped, a global navigation satellite system and a tacheometer are needed. Field inventories can be time-consuming, require skillful personnel, and measurements are prone to human errors. Nevertheless, field inventories provide reliable information from trees, but from a limited number of tree attributes (such as species, dbh and height). Still, forest field inventories are most often considered as a reference for other forest characterization methods [
27]. Technologies such as airborne laser scanning and digital stereo interpretation of aerial imagery (e.g., [
60]) provide the state-of-the-art in characterizing forests based on remote sensing techniques as large forested areas and landscapes can be characterized with these techniques. Generally, direct observations from the traditional forest inventory attributes are not attainable with these techniques but instead are derived from observations of heights of a canopy, height variations, canopy covers, and spectral properties of sample units [
60]. Thus, reference information from the attributes of interest are required as prediction models between the attributes of interest and the remote sensing observations are generated. Another limitation relates to the amount of detected trees; typically, only dominant trees or trees contributing to a canopy surface can be identified [
20,
22,
23]. Close-range sensing technologies include, but are not limited to, TLS and UAVs [
4,
61,
62] and they can be used in characterizing single forest stands or small forested landscapes [
61]. Although multiple TLS scans are required from each forest stand to capture its structure. Time consumption on the site with TLS is slightly less than in treewise field inventories with calipers and clinometers. However, TLS provides a more complete description of a forest structure, especially below a crown base height [
32], and the measurements are objective. In dense forest stands, the occlusion may limit the number of observations that are received from stems and crowns which decreases the forest characterization capacity [
4,
59]. In general, the number of scans that are required for the automatic detection of each tree [
23,
52], tree species recognition [
63], and systematic underestimation of tree height [
21] are the major bottlenecks when mere TLS is used for characterizing forests. UAV photogrammetry has the same limitations as airborne remote sensing technologies, in particular, their unsuitability for direct measurement of single tree attributes. However, UAVs offer flexibility to data acquisition and lower flying costs over small areas than aircrafts [
64]. Considering the strengths and weaknesses of the above alternatives for characterizing forest at single tree level, it seems beneficial to combine TLS and UAV data. This statement is also supported by our findings. Compared to the use of TLS data only, the multisensorial approach was able to improve the characterization of the forest structure. In our multisensorial approach, the TLS data was complemented with UAV photogrammetry, but a similar outcome is expected with any comparable photogrammetric or laser scanning point cloud collected from manned or unmanned platforms. The advantage of the UAV photogrammetric approach is that the system cost is low, and its operation is highly automated. Thus, it does not considerably increase the costs or complexity of TLS campaigns, where an operator is in the field anyway. It should be noted that we did not test the use of UAV data alone. However, based on the existing knowledge, it is known that by using similar sensors that were used here, a complete tree detection and tree stem characterization is still challenging [
17,
65].
We aimed for developing methodologies and technologies that can be used to characterize single trees, forest plots and, even further, to improve our understanding on tree populations at varying scales. Instead of calipers and clinometers, researchers and forest organizations are more and more interested in using close-range remote sensing sensors for characterizing forests. Based on our study, it is beneficial to combine point clouds that are collected below and above a canopy using TLS and UAV for characterizing the horizontal and vertical structure of managed forests.