Applications of TLS and ALS in Evaluating Forest Ecosystem Services: A Southern Carpathians Case Study

Abstract

Forests play an important role in biodiversity conservation, being one of the main providers of ecosystem services, according to the Economics of Ecosystems and Biodiversity. The functions and ecosystem services provided by forests are various concerning the natural capital and the socio-economic systems. Past decades of remote-sensing advances make it possible to address a large set of variables, including both biophysical parameters and ecological indicators, that characterize forest ecosystems and their capacity to supply services. This research aims to identify and implement existing methods that can be used for evaluating ecosystem services by employing airborne and terrestrial stationary laser scanning on plots from the Southern Carpathian mountains. Moreover, this paper discusses the adaptation of field-based approaches for evaluating ecological indicators to automated processing techniques based on airborne and terrestrial stationary laser scanning (ALS and TLS). Forest ecosystem functions, such as provisioning, regulation, and support, and the overall forest condition were assessed through the measurement and analysis of stand-based biomass characteristics (e.g., trees’ heights, wood volume), horizontal structure indices (e.g., canopy cover), and recruitment-mortality processes as well as overall health status assessment (e.g., dead trees identification, deadwood volume). The paper, through the implementation of the above-mentioned analyses, facilitates the development of a complex multi-source monitoring approach as a potential solution for assessing ecosystem services provided by the forest, as well as a basis for further monetization approaches.

1. Introduction

Forest is playing a crucial role in biological diversity, local welfare, the balance of carbon emissions, and the global economy [1,2,3]. In the context of climate change, the understanding of forest ecosystem processes’ importance is essential in assuring sustainable management and economic development [4]. Toward this purpose, forest monitoring was established as the main tool for studying the dynamics of forest structure and functioning and its response to anthropogenic influences [3,5]. The necessity of this tool is highlighted by decisional factors’ requirements and forest governance [6]. Due to the high complexity of the forest dynamics, a high amount of warranted information is needed in the characterization process.

The primary mechanism of forest monitoring in assuring the data integration is developing forest inventories focused on parameters related to the main dendrometric characteristics of trees (e.g., diameter at breast height (DBH), height-DBH ratio, crown width). Besides these variables, the monitoring also has to take into consideration information regarding the climate (temperature and precipitations) and pollution (atmospheric depositions). However, it is a well-known fact that the traditional forest inventory can be expensive, time-consuming, and requires a large amount of qualified personnel [3]. Moreover, forest inventory is limited to statistically established sample plots, resulting in a weaker representativity at larger scales [7,8,9].

To overcome the mentioned limitations, alternative solutions and measuring methodologies were sought in the remote-sensing field. In the past decades, remote-sensing systems have evolved, ensuring a large variety of applications [10]. As expected, the remote-sensing portfolio already contains several techniques addressing forest ecology and management [11]. From their beginning, remote-sensing systems were mostly equivalated to satellite imagery. New instruments of interest here, airborne laser scanning, unmanned aerial vehicles, digital photography systems, and terrestrial laser scanners, have more recently captured the researchers’ attention, gradually gaining visibility through a large number of scientific studies.

Land cover analysis [12,13], biomass estimation [14,15,16,17], hazard identification [18,19,20], structure assessment [21,22,23,24,25,26], and ecological indicators are just some of the most frequent applications of remote sensing in forestry. The major advantages of remote sensing are related to its capability of capturing a large amount of data and the possibility of revisiting in relatively short periods, as well as the plurality of the associated analyses [24].

A keen interest in remote sensing was shown toward biophysical parameters, such as DBH, tree height, volume, and implicitly biomass. The majority of these parameters were initially computed employing regression models, with input data derived from crown projections and height measurements from passive sensors [27,28,29], calibrated with ground samples. New technologies, as is the case of terrestrial laser scanning, propose different approaches for estimating tree characteristics. These provide a more direct method that involves point cloud classification, tree segmentation, and stem reconstruction [30,31,32]. Besides the biophysical parameters, active remote-sensing systems are used to describe stands’ structure through indirect analyses of the number of trees, canopy stratification, and trees distribution. As described in the work of [24,33,34], airborne and terrestrial laser scanning represent optimal solutions in describing forest stands through structural indicators based on point cloud processing.

Regarding this matter, the literature offers a rich variety of active remote-sensing-based forest variables, from foliage indices [24,35,36] (leaf area index—LAI, gap probability—p_gap) to trees spatial distribution [37,38] (mostly distance and angles between trees, but also the position itself for marginal trees detection, sampling plot edge effect mitigation, etc.). Satellite imagery also proposes indicators related to the status of forest stand health [39,40,41,42], an aspect that will not be detailed here since passive remote sensing does not make the subject of our study.

Disregarding the plethora of variables and its promising evolution, passive remote-sensing technology still demands innovative approaches to address the requirements of ecological relevant indicators [11]. The constant need for ground measurement calibration represents the main disadvantage of most passive remote-sensing systems. Furthermore, the applications based on regression models can lead to important errors due to potentially incorrect assumptions regarding the relationship between forest characteristics [43,44].

In the ecological research field, active remote-sensing data are increasingly being used. Quantifying forest ecosystems information from indices based on active remote-sensing highlights the need for further analysis and adaptation. The processing and uptake of these data are necessary for linking the indicators to the capacity of forest ecosystems to provide benefits. These benefits materialize in what we call ecosystem services and represent the ecosystems’ benefits, processes, and assets for providing human well-being [45].

In the field of research, the relationship between ecology and economy has been attributed with great importance, a fact that is corroborated by the very nature of ecosystem services. This has made it possible to develop the concept of natural capital on an environmental basis [46] and led to the idea of value, from a monetary point of view, of the ecosystem services and goods [47]. The need to exploit the benefits of ecosystems derives from their contribution to the human economy [48,49] and their expression in services and commercial goods [50,51].

Nowadays, there is a multitude of methods for evaluating and monetizing services, most of them being subjective. The methods are based on human preferences or physical costs upon which ecosystem services can be integrated [46]. The established methods are based on damaged cost avoided, replacement cost, market price, productivity cost, hedonic pricing, benefits transfer, and contingent evaluation method [51,52,53].

Despite the difficulties encountered in the process of applying ecosystem evaluation methods, they have an essential role in communicating the value of nature to the decisional factors and policymakers [54]. In this regard, there is an absolute need for objective ecological indicators that can provide information about ecosystem health status and structure.

This paper intends to identify and test several methods and variables applicable to airborne and terrestrial stationary laser scanning to quantify the capacity of the forest ecosystem in providing benefits. The identification of suitable ecosystem services will be performed according to The Economics of Ecosystem and Biodiversity (TEEB) classification [51,55]. Alongside, Millennium Ecosystem Assessment classification (MEA) and Common International Classification of Ecosystem Services (CICES), TEEB represents one of the widely known ecosystem services classification networks. The latter is a global campaign aiming at raising awareness regarding biodiversity’s economic benefits and the rising costs of ecosystem degradation. The final purpose of this initiative is to analyze and explain in a mainstream approach the importance of taking action [56]. This classification was adopted because it corresponds faithfully to the functions attributed to the studied stands according to the Romanian forest legislation. The majority of ecosystem functions will be analyzed in relation to the existing indicators, as well as other variables adapted to active remote-sensing sampling. The paper does not intend to calibrate or to validate existing methodologies but to showcase a minimal set of indicators computed through active remote-sensing methods that can offer sufficient information about the ecosystems’ capacity to provide services. Furthermore, as mentioned above, the paper aims only at information obtained through the use of ALS and stationary TLS measurements, excluding any other potential data based on satellite imagery or other passive remote-sensing technologies.

2. Materials and Methods

2.1. Study Site

To analyze the identified methods and variables, ten stands were considered in the current study, each of them being designed as a one-hectare rectangular plot with three 15 m-radius circular subplots within them.

The ten one-hectare plots are located in two different areas of the Southern Carpathian mountains, thus covering three of the most representative tree species of Romania. These are sessile oak (Quercus petraea) and beech (Fagus sylvatica) in the hill region and Norway spruce (Picea abies) in the mountainous region (Figure 1).

Both deciduous and coniferous forest plots were considered in the process of assessing the applicability of the studied methods as well as in the evaluation of the different structural characteristics of the plots. Therefore, the plots were chosen in relation to species, age, and applied silvicultural interventions (

Table 1. Sample plots characteristics.

Sample Plot	Species	Age [Years]	Silvicultural Interventions	Forest Districts
SGT	Sessile oak	190	Progressive	Mihăești
SGTM	Sessile oak	190	Without interventions	Mihăești
SFR	Beech	40	Thinning	Mihăești
SFRM	Beech	40	Without interventions	Mihăești
SFT	Beech	120	Progressive	Mihăești
SFTM	Beech	120	Without interventions	Mușetești
SMR	Norway spruce	50	Thinning	Mușetești
SMRM	Norway spruce	50	Without interventions	Mușetești
SMT	Norway spruce	150	Progressive	Mușetești
SMTM	Norway spruce	150	Without interventions	Mușetești

).

2.2. Conventional Field Data Collection

In order to ensure control over the LiDAR data sets, a classical inventory was also carried out in the plots. Field measurements included DBH, tree height, crown height, crown width, and position of each tree (XYZ coordinates) and targeted all the trees with a DBH equal to or greater than 6 cm. To acquire these variables, an integrated GIS field software and electronic mapping and dendrometrics sensors [57] for recording tree positions and canopy characteristics were used.

2.3. Terrestrial Laser Scanner Data

In each 15 m circular subplot, five terrestrial scans were performed accordingly to a cardinal point sampling scheme to compensate for the shadowing (Figure 2a). The scanning process was achieved with a phase shift terrestrial laser scanner [58]. The resulting point clouds were characterized by 8 µs per scan point and over 44 million points per 360° sweep.

Regarding the TLS pre-processing methods for classification and segmentation of the point cloud prior to obtaining the stems and the foliage, an approach proposed by Pascu et al. was followed [24,30,32,59] (Figure 2b).

2.4. Airborne Laser Scanner Data

The airborne LiDAR data for the one-hectare plots were collected through the use of a full-wave airborne laser scanner [60]. The discrete points extraction was conducted by the provider of the data sets, according to the standard processing procedure. Following processing, an average point density of 6 points/m² was reached (Figure 3).

Further analyses, such as the ground-non-ground classification, were performed using filtering algorithms by means of dedicated software [61], as shown in the work of [62]. The digital terrain model (DTM) was generated through an inverse distance-weighting interpolation, which ensured a 1 × 1 m spatial resolution. The DTM was further used as support in the computation of several parameters (e.g., tree height, canopy height).

2.5. Ecosystem Services Identification and Evaluation

The literature proposes an entire series of ecosystem functions and services assessment methods (monetary, non-monetary, and integrated methods). In this research, the interest was to gather reliable information needed in applying those evaluation methods. The ecosystem services identification is presented according to the ecosystem functions stated by TEEB, and the paper intends to cover the majority of the functions.

3. Results

3.1. Provisioning Services

Wood products are one of the most prominent resources provided by forest ecosystems [63], being a direct economic benefit that can be easily assessed from a monetary point of view. In literature, wood products, equated to above-ground biomass, are an important variable that can be estimated through remote-sensing techniques. Between the implementation of biophysical parameters relationships [64,65] to allometric models and direct measurements [30,66,67,68], the above-ground biomass estimation gained impressive interest in research due to the associated accuracy.

In our study, the applied methodology was the one proposed by Pascu et al. in the work of [30]. Therefore, the above-ground estimation implied the use of stand volume derived from number of trees, DBH, and tree height (Figure 4). Even though other studies [69,70] show volume underestimation when based on terrestrial laser scanning data, this was due to low stand heterogeneity. The accuracy presented by Pascu et al. in what concerns the number of trees and DBH is more than satisfactory (errors under 5%). Moreover, the use of terrestrial laser scanning proved to be an adequate approach for the above-ground volume computation [71].

Height values computed through this active remote-sensing technology show biases and errors, also highlighted by several research papers [30,69,70,72,73]. To overcome this limitation, compensations were applied based on airborne laser scanning.

For computing above-ground tree volume, the logarithmic regression equation (Equation (1)) described in the work of [74] was used:

$$\log v=a_0+a_1\log d+a_{2}lo{g}^{2}d+a_3\log h+a_{4}lo{g}^{2}h$$

(1)

where:

d—tree diameter at breast height;

v—tree volume;

h—tree height;

a₀, a₁, a₂, a₃, a₄—species-specific regression coefficients.

The above-ground volumes for each plot are presented in

Table 2. Above-ground volume and mean stand characteristics.

Plot	V [m3 ha−1]	dm [cm]	hm [m]	vm [m3]
SGT	444.1	21.91	20.7	0.97
SGTM	646.4	24.15	22.36	0.90
SFR	434.6	17.81	21.9	0.37
SFRM	509.8	18.25	26.26	0.48
SFT	457.2	24.83	18.9	1.07
SFTM	622.3	25.45	19.4	1.05
SMR	345.7	17.3	17.8	0.28
SMRM	420.1	17.45	15.8	0.29
SMT	409.5	29.29	21.6	0.90
SMTM	558.3	33.01	26.6	0.93

V—stand volume; d_m—stand mean diameter; h_m—stand mean height; v_m—tree mean volume.

. When compared to the field measurements based on the same methodology (Equation (1)), the errors are between 4.6% and 13.3%.

Considering the biophysical parameters, differences in mean stand volume can be observed between the plots where silvicultural interventions were applied and those without interventions. The reduced volume, specific to the young forest stands and to those targeted by interventions, confirms the viability of the methods and results and makes it possible to compare them in terms of wood product provisioning.

3.2. Regulating Services

At the moment, the ecosystem services specific to regulating functions represent a great challenge in the evaluating processes [54]. This function includes services for air quality regulation, moderation of extreme events, erosion prevention, and carbon sequestration [51,55,75]. In the context of evaluating the related services, specific indicators were developed in the field of ecological research.

The assessment methods tend to use indirect measurements and quantify the relationship between different variables. Tree canopy cover, canopy structure indices (e.g., leaf area index), and trees distribution are the most used parameters in the majority of the evaluating approaches [76,77,78].

3.2.1. Structural Indices

As previously mentioned, forest structure characteristics and biodiversity are the main sources of information for the assessment of ecosystem services. To establish the capacity of ecosystems in supplying regulating services, indices such as Clark-Evans nearest neighbor index (CE), uniform angle index (UAI), and relative dominance diameter index were computed at the subplot level.

Clark-Evans nearest neighbor index (CE) describes the horizontal trees distribution by using the mean distance between a reference tree and the nearest neighbors and the mean distance defined by a Poison distribution [79]. CE can range from 0, when the stand is characterized by tree clustering, to 2.1491 [79] in the case of regular distribution.

Uniform angle index (UAI) describes the uniform distribution of the nearest neighboring trees in relation to the reference tree [38]. The method is based on the angles between trees, compared to a uniform dispersion angle of 72° (Equation (2)). The interpretation of these values is made according to the confidence interval of 0.475–0.517 [38], describing a random distribution.

$$UAI=\frac{1}{n}{\displaystyle \sum }_{i=1}^{n}UA{I}_i=\frac{1}{4n}{\displaystyle \sum }_{i=1}^n{\displaystyle \sum }_{j=1}^4{z}_{ij}$$

(2)

where:

n—number of reference trees

z_ij—angle coefficients in relation to the reference (72°), 1 if <72°, 0 if > 72°

UAI_i—uniform angle index

The relative dominance diameter index (IDR) is defined as the ratio between the number of trees with a diameter greater than the reference tree. The value of this indicator reaches values in the range (0–1) and is interpreted in relation to five default thresholds. Thus, in relation to the number of trees with a diameter larger than the reference, the indicator falls into the following categories: shade tolerant, dominated, co-dominant, dominant, predominant. These categories correspond to the Kraft classes, a method used for validating the obtained values. The variable considered in the evaluation of the dominancy indicator may be substituted by other tree characteristics such as height or species.

In the structural indices computation process, the edge effect was removed in order to ensure accurate results. This was performed by selecting only the trees within an inner buffer, defining an area smaller than that of the circular subplots (Figure 5).

The interpretation of these indices made it possible to identify the supplied services and the level to which they could be quantified. CE values greater than 1 suggested that the studied subplots were characterized by a more uniform horizontal structure. An exception was identified in the SFTM-3 subplot, which was characterized by a mean value of 0.4. This could be explained by the smaller number of trees clustered together and by the fact that this circle is crossed by a forest harvesting road. Based on the calculated t-values for the CE, according to the work of [80], the subplots that overpass 1.96 can be described as having a regular distribution (

Table 3. Horizontal structure indices in the 15 m-radius subplot.

Plots	Subplot	Nref	CE	t-Value *	W
SMTM	1	17	1.715	1.19	0.456
	2	11	1.829	2.19	0.432
	3	7	1.689	2.59	0.393
SGTM	1	20	1.558	3.12	0.563
	2	19	1.558	3.14	0.526
	3	25	1.825	2.68	0.510
SFTM	1	31	1.074	0.56	0.547
	2	37	1.272	1.59	0.574
	3	20	0.405	-8.75	0.55
SFRM	1	64	1.423	1.09	0.553
	2	55	1.283	0.91	0.515
	3	37	1.166	0.97	0.5
SMRM	1	92	1.269	0.4	0.532
	2	106	1.171	0.21	0.709
	3	87	1.025	0.04	0.548
SMT	1	46	1.751	3.17	0.531
	2	55	1.604	1.95	0.524
	3	38	1.429	2.41	0.561
SGT	1	39	1.321	2.7	0.545
	2	38	1.556	2.13	0.59
	3	35	1.575	3.65	0.558
SFT	1	26	1.551	4.46	0.635
	2	42	1.781	3.77	0.642
	3	36	1.549	3.34	0.643
SFR	1	71	1.309	0.68	0.715
	2	99	1.866	1.16	0.707
	3	76	1.628	0.95	0.725
SMR	1	102	1.301	0.38	0.719
	2	131	1.244	0.21	0.722
	3	147	1.252	0.37	0.723

N_ref—number of reference trees, * t—CE value.

).

When uniform angle index values were analyzed, differences between plots could be observed, thus detecting structural differences between the corresponding stands. The uniform angle index values ranged between 0.393 and 0.725, values covering the entire interpretation interval. Within the old sessile oak stand, without interventions (SGTM), the corresponding subplots reached values equivalent to a rather random distribution. This was the case with the 2.3 (0.510) subplot, reaching values quite different than its counterpart, subplots 2.1 and 2.2, characterized by a clustered structure (Table 2). In the case of the Norway spruce (SMTM), the reached values defined a uniform structure, while the young beech stand with interventions (SFR) was characterized by a clustered structure in all subplots.

Also, from this analysis, the difference between the plots with interventions and those without could be observed. The plots covered with silvicultural treatments tend to describe more clustered structures, an effect caused by the increased distance between trees after harvesting.

Figure 6 facilitates the interpretation of the structure and conditions similarity within a plot. As stated before, discrepancies appeared in SFTM for the CE index and in SMRM for the uniform angle index. The latter was a consequence of a windthrow event that had affected the SMRM-3 subplot.

Analyses of relative dominance diameter index described the vertical stand structure at the subplot level. A similarity could be observed between old Norway spruce (SMTM) subplots (Figure 7), indicating a uniform structure within the stand and a uniform tree distribution between classes. The sessile oak stand is characterized by a lower degree of heterogeneity and more unevenness between classes.

3.2.2. Carbon Storage

Carbon is stocked in forest stands in the following five pools: above and below-ground living biomass, soil, litter, and deadwood [81,82]. Apart from the variables used for the above-ground volume, carbon stock evaluation (

Table 4. Carbon stock required variables.

Plot	V [m3 ha−1]	D 1 [kg m−3]	R 1	BEF 2	CF 3	Carbon Stock [tC·ha−1]
SGT	444.1	584	0.22	1.4	0.48	151.88
SGTM	646.4	584	0.22	1.4	0.48	221.06
SFR	434.6	545	0.19	1.4	0.46	129.66
SFRM	509.8	545	0.19	1.4	0.46	152.09
SFT	457.2	545	0.19	1.4	0.46	136.40
SFTM	622.3	545	0.19	1.4	0.46	185.65
SMR	345.7	353	0.2	1.3	0.51	74.68
SMRM	420.1	353	0.2	1.3	0.51	90.76
SMT	409.5	353	0.2	1.3	0.51	88.47
SMTM	558.3	353	0.2	1.3	0.51	120.61

¹ [83] ² [84] ³ [85].

) required another set of parameters, namely the theoretical number of trees per hectare, wood density, root-to-shoots ratio, and biomass expansion factor (Equation (3)). These were retrieved from specific yield tables and international guides [83,84].

$$C_{stock}={\displaystyle \sum }V\ast D\ast \left(1+R\right)\ast BEF\ast CF$$

(3)

where:

$C_{stock}$—carbon stock [tC]

V—tree volume [m³]

D—wood density [t/m³]

R—root-to-shoot ratio

BEF—biomass expansion factor

CF—carbon fraction

Due to the methodology for the above-ground volume, for the sessile oak and beech species, the biomass expansion factor (BEF) was omitted, as the regression equation for volume already took into consideration the branches’ volume. Including BEF would have led to biased results.

The obtained carbon stock values ranged between 74.68 tC·ha⁻¹ (273.82 tCO₂·ha⁻¹) in the case of the young Norway spruce plot covered with silvicultural intervention and 221.06 tC·ha⁻¹ (810.55 tCO₂·ha⁻¹) in the case of the old sessile oak plot. The upper values of the storage capacity interval of the studied plots are in accordance with those stated in the work of [86]. The lower values are a consequence of age and species characteristics (wood density, root-to-shoot ratio, and carbon fraction).

3.2.3. Foliage Indices

Active remote-sensing technology advances allowed for the development of multiple applications addressing the canopy structure, crown dynamics, and phenology [21,24,29,87,88,89,90,91]. These applications based on active remote-sensing data are a powerful tool in the decisional process associated with forestry and ecology sectors. From the variety of indices computed through remote sensing, in the research field, the leaf area index (LAI) is the most commonly used. Furthermore, along with the LAI, an important role in improving the canopy description is held by leaf area density (LAD), which offers detailed information regarding the stand vertical structure. Leaf area index estimation as the ratio between leaves (single-faced) area and area of the studied plot, was measured over time through various indirect methods (orbital sensors, hemispherical photography, and light intensity attenuation) [92,93,94], and still require improvement in what concerns the stability and robustness of their results. Alternatively, airborne laser scanning, despite its limitations related to penetration capability, has promising results in forestry indices and parameters computation, including those above-mentioned [70,95].

In this study, LAI and LAD were estimated through the MacArthur and Horn equation [96] developed on the principle of the Beer–Lambert law [97,98] and following methodologies proposed in other related research papers [95,99,100,101,102]. Thus, to each voxel from the processed point cloud (voxel—5 × 5 × 1 m), the following proposed equation was applied [102]:

$$LA{D}_{i-1,i}=\ln \left(\frac{S_e}{S_t}\right)\frac{1}{k\mathsf{\Delta }z}$$

(4)

where:

$S_e$—number of pulses entering the voxel;

$S_t$—number of pulses exiting the voxel;

k—Beer–Lambert law extinction coefficient;

z—voxel height (1 m).

From the variety of estimated indices resulting when applying derivatives of the above-mentioned methodology, of most interest to our study were the total LAI values, the height of the mean LAD, and standard deviation corresponding to each voxel (cell of a three-dimensional grid) column taken into consideration.

As shown in the case of the IDR, sessile oak (SGTM) is characterized by an uneven structure, a fact also illustrated in the LAI and LAD values. In the northwest part of the plot, the higher density of smaller trees impacted the LAI and height of the mean LAD, reaching values in the range 1–3, respectively, 5–10 m.

As expected, the Norway spruce plot is characterized by smaller standard deviation values, suggesting a constant horizontal structure throughout the plot (Figure 8). In the case of the sessile oak plot, the standard deviation trend highlights a generation individualization through higher variation within the upper levels of the canopy.

3.3. Supporting Services

In the majority of the research papers, this function is not a self-contained one. Millennium Ecosystem Assessment classification (MEA) [103] presents the support function as integration between provisioning, regulating, and cultural functions, quantifying benefits that ensure the rest of the services. In the Common International Classification of Ecosystem Services (CICES), the support function is not promoted as one and is considered an underlying structure that provides indirect outputs [104,105].

Understory biomass has an important ecological significance in forest ecosystem stability and in assessing the relationships between wildlife and their habitat. Despite the low proportion in above-ground volume, the understory biomass represents a tool for the researchers in evaluating the food provisioning and the quality of the environment [106,107,108,109,110,111,112,113,114].

The understory biomass computation implies a complex and expensive forest inventory due to multiple variables that should be taken into consideration. Active remote-sensing applications that aim at assessing understory biomass were proposed. Terrestrial and airborne laser scanning data were analyzed in order to estimate the understory, following [106,112,115].

This study addressed the methodology proposed by the authors of [116] that aims to predict the presence of shrub layers from aerial-based point clouds. In the mentioned thesis, two indices were computed: (a) undergrowth return fraction and (b) undergrowth cover density. For our case, of most interest was the undergrowth return fraction, expressed as the ratio between the number of points in the 0.5–5 m range and the total number of points (Figure 9).

The old Norway spruce plot, in comparison with the sessile oak, is characterized by a sparse distribution of the shrub layer of lower intensities, with no understory clusters identified. In the case of the sessile oak plot, a central area with a high density of understory vegetation could be observed, mirrored in the northern part, by the lower values of the canopy height model. Overall, the sessile oak plot recorded a value of 0.20, which according to the work of [116], is indicative of a medium-to-high shrub cover intensity (Figure 10).

The majority of the rest of the plots have a low-to-minim shrub cover, covering 6% to 10%. An issue identified through field observations was in the case of SMRM. The plot is characterized by a high shrub coverage, but due to the lower age and high tree density, the laser beams could not penetrate the canopy layer. This resulted in a small number of points near the ground and an underestimation of the shrub layer. This shortcoming can be compensated by using TLS data to complete the ALS points cloud.

3.4. Structure Analysis for Cultural Services Assessment

The cultural services are the most problematic in what concerns the evaluation processes. The services provided by the one-hectare plots are not traded on the market, and therefore the methods of valuation applied tend to be more subjective. In addition, the evaluation of the forest ecosystem’s capacity in providing these benefits is a challenging one due to public preferences and the number of variables involved.

The forest structure indices computed under the regulating function section and part of the health status information can be used in quantifying the human preferences regarding the ideal distribution and biodiversity. Tree clusters, number of trees, sparse distribution, higher canopy density, light penetration, visibility, understory volume, and snags volume can all be indirectly assessed through tree distribution characteristics and mortality analysis.

The snags identification and mortality characteristics were analyzed based on airborne laser scanning data according to the work of [117] methodology (Figure 11).

After processing, the point clouds were classified into four classes, namely live trees, small snags, live crown edge snags, and higher canopy snags. Due to the small proportion of dead trees in the studied plots, not all classes were well represented. Moreover, following the analysis, none of the snag classes were identified in the Norway spruce plots, apart from sparse, unrepresentative small snags in the understory. By way of comparison, the sessile oak plot presents a higher proportion corresponding to the live crown edge snags class.

A crucial role is attributed to the higher canopy snags class, which makes possible the identification of dead treetops. A higher proportion of snags would have allowed for the evaluation of a ratio between deadwood and the above-ground biomass. This information could have then been used in the carbon sequestration estimation or the mortality rate of the forest stand.

4. Discussion

Forest ecosystems are characterized by various structures and complex processes defined by a plethora of intra- and inter-plot relationships. The assessment of all services provided by these ecosystems’ characteristics is still a challenging subject for the research field [118]. Therefore, this study aimed to highlight some of the most important and quantifiable services employing the latest applications of active remote-sensing technology [119].

Taking advantage of the terrestrial stationary laser scanning, the obtained values for above-ground volume, at tree and stand level, was within the characteristic tolerances [30]. Moreover, we compensated the height-specific bias caused by the terrestrial laser scanner’s inability to penetrate dense canopies, a well know feature relevant to Romanian forests [24], by deriving a canopy height model from aerial laser scanning.

Compared to the rest of the functions, provisioning could be evaluated most straightforward [76,120]. By only knowing the above-ground volume and market price, this service could be monetized. To better understand this service, a technical approach can further be used by classifying the wood in relation to the type of final product or the quality of the timber, information that can be extracted from management plans.

The assessment of carbon sequestration is a more complex process, and it partially uses wood volume calculations. The results are influenced by multiple biophysical parameters (wood density, the ratio between above and below-ground biomass, or the biomass expansion factor) [84]. All these parameters depend on the species composition, a feature that cannot be presently assessed on a large scale by means of close-range active remote sensing [121,122]. Furthermore, as described in the IPCC Guideline [84,123], forest ecosystems stock a large amount of carbon not only in living biomass, so other pools (soil and dead organic matter) should also be taken into consideration for a real carbon emission/removals analysis. On the other hand, assumptions are made even within the country-level estimation. Pools as soil, deadwood, or litter are considered to be neutral in the carbon emission and removals balance. Therefore, only considering the living biomass can represent a viable solution for carbon sequestration and stock assessment.

The utility of structural indices was highlighted in a long list of studies [24,119,124,125,126], and the indirect quantification made by means of these indices could be considered a proper method for evaluating forest ecosystem capacity to provide services. Forest structure represents a valuable source of information, and relating structure characteristics to specific services is the approach used in this study.

Soil and water regulation services assured by the forest ecosystem are quantifiable through trees distribution, LAI, LAD, and canopy projection [127,128]. The distances, the angles, and the relationship between different individuals define the rate of success of the forest to ensure the regulation function. Indices such as uniform angle index, Clark-Evans nearest neighbor index, and relative dominance diameter index can describe the ideal structure, which prevents gaps or corridors from occurring. The resulting values, corresponding to the studied plots, describe a uniform tree distribution. According to the uniform angle index values, the Norway spruce plot has a better capacity to assure the regulation function if we consider the ideal structure being a random one.

When analyzing the CE, the obtained values tend to reach the upper half of the range, close to a perfectly regular hexagonal distribution (2.1491). There are some dissimilarities between CE and UAI regarding, in particular, the sessile oak plot due to the high number of trees in the sampled area. However, of great importance in soil and water regulation services is the fact that none of the plots is characterized by clustered trees that would facilitate soil erosion and low water retention.

Air regulation function was evaluated in this study through the use of LAI and LAD [129]. The capacity of the forest ecosystem to provide these services is directly proportional to LAI values. For the studied plots, LAI values are within the 0–6 range, with a considerable proportion in the upper classes throughout the entire old Norway spruce plot. Due to lower values in the canopy height model and implicit smaller crown volumes, the sessile oak recorded lower intensities in the northern part of the plot.

The support function assessment was evaluated based on the shrub cover, indicating the capacity of the studied forests to provide various species’ habitat requirements. Important differences were observed between the studied areas. The sessile oak plot (SGTM) recorded a larger area covered by understory vegetation. This analysis can also provide additional information on the forest structure, information that can be used in further biodiversity assessments.

The results regarding mortality have not allowed any further analyses regarding the dead matter stratification. However, it offered enough information to assess the overall health status of the forest ecosystem [117,123,130]. By following the presented structural indices, as well as the ones addressing foliage, while simultaneously considering the human preferences toward the ideal forest structure, a suitable evaluation of the cultural services could be deployed.

Given the results corresponding to our studied plots, the lack of clustered trees, large gaps, and overall canopy structure, an appropriate scale for referencing forest ecosystems’ capability to provide cultural benefits could have also been developed.

5. Conclusions

In order to emphasize and maximize the ecological, social, and economic benefits of forests, suitable assessment methods are required. Active remote-sensing technology, with the proven advantages and characteristic limitations, can represent the foundation for multiple approaches aiming to quantify the capacity of the forest ecosystem to provide services. This study highlighted the possibility of using two different active remote-sensing data sets and several techniques to assess the main ecosystem functions according to TEEB classification.

To estimate the key biophysical parameters of a tree, terrestrial laser scanning point clouds proved to be a viable solution. The processing of this data source led to errors associated with DBH of below 1 cm [30] at the subplot level when analyzing the mean tree. Precisions associated with tree coordinates are comparable to those obtained through the electronic field mapping system. Using the TLS-based variables as well as the airborne laser scanning data, the provisioning function, particularly wood products, was evaluated. This was performed by means of above-ground volume, characterized by errors smaller than 6%.

Combining the terrestrial and aerial laser scans, the evaluation of regulating function was also possible. The indices computed by processing the above-mentioned data sources proved to be a suitable basis for acquiring the forest’s horizontal structure and the distribution of trees. CE, UAI, and IDR implementations through active remote-sensing approaches can represent the link between ecosystem services and human preferences, but also the qualitative parameters for assessing the degree of ensuring certain services, namely soil stability, air quality, and water regulation.

Challenges still exist in applying active remote-sensing techniques due to the complex ecosystems’ intra- and inter-plot relationships. However, the development of tools to address the environmental assessment requirements is encouraged by the stakeholders and decisional factors. Thus, it can be stated that active remote-sensing applications have a significant role in forestry, a role that translates to an overall improvement of human well-being.

Therefore, implementing the described methodologies highlighted the necessity of developing custom reference scales relevant in the assessment processes of the relative capacity of forest ecosystems to supply benefits. To achieve this, the study should be extended to address further stands of different structures, species compositions, and microclimates.