Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe

Ecological Engineering 100 (2017) 219–230

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Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng

Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe Olga Brovkina a,∗ , Jan Novotny a , Emil Cienciala b , Frantisek Zemek a , Radek Russ b a Remote Sensing Department, Global Change Research Institute, Academy of Sciences of the Czech Republic, v.v.i., Brno, Belidla 986/4a, 60300, Czech Republic b IFER – Institute of Forest Ecosystem Research, Cs. armady 655, Jilove u Prahy, 254 01, Czech Republic

a r t i c l e

i n f o

Article history: Received 22 February 2016 Received in revised form 5 December 2016 Accepted 8 December 2016 Keywords: Biomass estimation Spruce Beech Airborne remote sensing Tree level Plot level

a b s t r a c t The study presents three methods for estimation of forest aboveground biomass (AGB) at tree and plot levels using different categories of airborne data. The first method estimates AGB from high spatial resolution hyperspectral (HS) data. The second method estimates AGB from airborne laser scanning data. The third method explores the synergy between hyperspectral and LiDAR data to estimate AGB. The results are compared with AGB estimated from field measurements. The results demonstrate that: 1) The biomass estimation from the HS data showed a good correlation with field biomass values for spruce, beech and mixture of these species at tree and plot levels, but also the highest uncertainties in comparison with the other two methods; 2) The biomass estimation from the LiDAR data had a strong correlation with field biomass values for spruce for tree level and a good correlation for spruce, beech and mixture of these species for plot level; 3) The biomass estimation from fused HS and LiDAR data showed the best results for tree and plot levels for the study sites. This study expands on previous research assessing the applicability of HS, LiDAR and fused datasets for AGB assessment. It proves the efficiency of using fused HS and LiDAR data and suggests the use of HS-based methods for biomass assessment when laser scanning data are not available. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Accelerating development of remote sensing techniques and easier access to data generated by these tools creates a high potential for their application in many fields, such as agriculture, environmental monitoring, forestry, etc. Here, we will focus on an application of airborne hyperspectral (HS) and laser scanning (LiDAR) data for forest aboveground biomass (AGB) estimation. Forest AGB plays an important role in carbon storage assessment and our current understanding of the carbon cycle (Gower et al., 2001; Kindermann et al., 2008; Zhang et al., 2014). Traditional approaches to field measurements, i.e. forest inventories, are the most accurate ways for collecting biomass data. However, data collection by these methods is time-consuming, expensive and limited to accessible areas. Such issues can be addressed through the assessment of AGB from airborne HS and/or LiDAR data (Nelson et al., 2004; Latifi et al., 2012; Thenkabail et al., 2012). Gleason and Im (2011) presented summary statistics based on papers published

∗ Corresponding author. E-mail address: [email protected] (O. Brovkina). http://dx.doi.org/10.1016/j.ecoleng.2016.12.004 0925-8574/© 2016 Elsevier B.V. All rights reserved.

after the year 2000 with a focus on AGB estimation. They found that 10% of the papers used airborne hyperspectral data alone, the application of airborne LiDAR data (both full waveform LiDAR and discrete-return LiDAR) accounted for 37%, and a combination of both types of sensors was used in 13% of the papers. Multispectral sensors or radar systems were used in the rest of the studies −40%. LiDAR-based airborne instruments provide a direct means of estimating forest characteristics: tree heights (Popescu and Wynne 2004; Falkowski et al., 2006; Luther et al., 2014), canopy density (Munukata et al., 2010), and tree species classification (Hollaus et al., 2009; Heinzel and Koch 2011; Khameneh 2013). The forest characteristics from LiDAR allow calculation of AGB based on allometric equations (Koch 2010; Kankare et al., 2013; Luther et al., 2014). This means that the common approach is to estimate tree heights from LiDAR first, then wood volume is modelled, and finally, wood volume is recalculated to AGB. Biomass estimation from airborne LiDAR data in a boreal Canadian forest was obtained with R2 = 0.92 (Mora et al., 2013). AGB from LiDAR data showed a high correlation (R2 = 0.99) with field-measured forest characteristics in a mixed fir and oak forest (Gonzalez et al., 2010). The use of HS data alone has been validated for biomass applications in recent years. For example, HS data have been used to

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Table 1 Selected recent studies on aboveground biomass estimation at tree and plot levels based on airborne HS data, LiDAR data or fusion of these data. Category

Data used

Forest site

Method

Accuracy

Reference

Tree level AGB estimation

LiDAR

Tree height extraction, field allometry

R2 = 0.71 nRMSE = 26.4%

Kankare et al. (2013)

LiDAR

Scots pine and Norway spruce, small presence of deciduous Pine and deciduous

Model with DBH as a predictor

Zhao et al. (2009)

LiDAR

Pine

LiDAR and HS

Mixed conifer and deciduous forest

R2 from 0.80 to 0.88 RMSE from 138 to 237 Mg/ha R2 = 0.88 RMSE = 162 Mg/ha R2 from 0.51 to 0.61 PRESS RMSE from 12% to 14%

LiDAR

Scots pine, Norway spruce

LiDAR

Douglas fir, western hemlock, black spruce Douglas fir, western hemlock Scots pine, European beech Moist evergreen forest

Plot level AGB estimation

HS HS HS Fusion of LiDAR and HS Fusion of LiDAR and HS

Scots pine, European beech Moist evergreen forest

Fusion of LIDAR and HS

Mixed deciduous forest

estimate grassland biomass directly (Psomas et al., 2011), to provide precise species classification (Martin et al., 1998; Buddenbaum et al., 2005; Leckie et al., 2005; Cho et al., 2007; Pena et al., 2013) and to estimate tree crown diameter (Gougeon 1995; Leckie et al., 2003; Bunting and Lucas, 2006). Goodenough et al. (2008) created a carbon map for a Canadian forest site based on biomass estimated from airborne hyperspectral data. They demonstrated that biomass obtained from 4 m spatial resolution data agreed with ground measurements (R2 = 0.90). Schlerf (2006) stated that biomass cannot be retrieved from HS data (7 m spatial resolution), because there is a poor relationship between stem biomass and vegetation indices. There have been a few studies that have attempted to improve forest biomass by combining LiDAR data with HS imagery (see Table 1). A fusion of airborne HS and LiDAR data was used in the studies of Latifi et al. (2012) and Laurin et al. (2014) for the AGB estimation in German forest stands and West Africa rainforests, respectively. The findings of Laurin et al. (2014) showed that the integration of HS bands (R2 = 0.70) improved the model based on LiDAR data alone (R2 = 0.64) for AGB estimation in tropical regions. On the contrary, LiDAR-based predictions were not notably improved by contributing HS features in biomass assessment for coniferous species at a temperate forest site in Germany (rRMSE = 49% for LiDAR-based model, rRMSE = 48% for data fusionbased model) (Latifi et al., 2012). Most of the recent studies explore biomass estimation either at tree level or plot level (Table 1). Despite the considerable number of studies that use LiDAR data, HS data or a fusion of both in AGB estimation, there is no recommendation as to which data and method should be used at tree or plot levels for AGB estimation. The objectives of this study are as follows: 1) to estimate forest AGB from the airborne HS data, LiDAR data and fusion of HS and LiDAR data; and 2) to examine which method is more suitable at tree level and plot level in assessment of forest AGB.

Model with DBH as a predictor Relationship between measured characteristics and RS data metrics Model with statistical LiDAR features as predictors Analysis of canopy structure indices Model with HS features Model with HS bands and VIs as predictors Regression analysis with HS bands and Vis Model based on LiDAR and HS metrics Model based on LiDAR and HS metrics Species form HS, height from LiDAR, allometric equations

Popescu and Wynne (2004) Anderson et al. (2008)

R2 = 0.71 nRMSE = 24.9%

Kankare et al. (2013)

R2 = 0.84, standard error is 7.5% R2 from 0.82 to 0.92

Lefsky et al. (2002)

nRMSE from 40% to 60% R2 = 0.36 RMSE = 116.8 Mg/ha nRMSE from 35% to 45% R2 = 0.70 RMSE = 64.3 Mg/ha R2 = 0.90 RSE = 11.8 Mg/ha

Goodenough et al. (2008) Latifi et al. (2012) Laurin et al. (2014) Latifi et al. (2012) Laurin et al. (2014) Lucas et al. (2008)

2. Methods and procedures 2.1. Study sites Our study sites are located in the Moravian-Silesian Beskydy Mountains, Czech Republic (Fig. 1), which is a part of the flysch zone of the Western Carpathians in a region of geologically young mountains. In terms of composition and tectonic structure the sites are characterized by multiple rhythmic alternation of claystone, siltstone, sandstone and conglomerate. The prevailing soil types are medium and typical podzols. The first site, Bily Kriz (BK; 18◦ 54 E, 49◦ 50 N, at an altitude of 750–950 m a.s.l.), is characterised by managed forest stands of monoculture Norway spruce (Picea abies, L.) aged 25–90 years, along with an admixture of broadleaved trees (22.7%) with a mean age of 72 years, mostly represented by European beech (Fagus sylvatica, L.). Less abundant species include goat willow (Salix caprea, L.), silver birch (Betula pendula, L.) and individually occurring silver fir (Abies alba, L.) trees. The second site, Tesinske Beskydy (TB; 18◦ 47 E, 49◦ 37 N, an altitude of 500–900 m a.s.l.), is also covered by managed forest stands with Norway spruce (Picea abies, L.) and European beech (Fagus sylvatica, L.) as the dominant tree species. The site also has a scattered admixture of Scots pine (Pinus sylvestris, L.), silver fir (Abies alba, L.), European larch (Larix decidua, L.) and ash (Fraxinus excelsior, L.) (Michalko 1986). These two sites have similar growing conditions (similar climatic conditions, soil type and geology, altitude, close geographical location) and the same prevailing species (Norway spruce and European beech). Tree level AGB was estimated in the BK region. Plot level AGB was estimated in the TB region. 2.2. Data collection 2.2.1. Airborne data The HS data (65 bands within the spectral range of 400–1000 nm) were acquired in September 2013 by the AISA Eagle

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Fig. 1. Study area (white borders). Bily Kriz (BK) site is on the left, Tesinske Beskydy (TB) site is on the right. Field forest plots are marked with white circles (BK) and white spots (TB).

Table 2 Characteristics of airborne data from the study sites. Hyperspectral data

BK site TB site

LiDAR data

spectral range, nm

number of bands

spatial resolution, m

point cloud density, point/m2

400–1000

65

0.4 1.0

50 1

sensor at a resolution of 0.4 m at the BK site and in June 2015 by the CASI sensor at a resolution of 1 m at the TB site (Table 2). The image pre-processing included radiometric, atmospheric and geometric corrections. The radiometric correction of the HS images was performed using CaliGeo 4.6.4 (Spacim) software and ENVI 4.4. The atmospheric correction was done in ATCOR4 6.0 (ReSe Applications Schlaepfer) and the georectification was done in PARGE 3.2. LiDAR data were acquired during the vegetation period of 2013 using a Riegl LMS-Q680i scanner with a point cloud density over 50 points per square meter for the BK site and a point cloud density over 1 point per square meter for the TB site. The data were preprocessed by the vendor, Geodis holding company (www.geodis. cz). It encompassed full-waveform decomposition and georeferencing from RiProcess software package (by Riegl) and an export in LAS format after strip adjustment.

Airborne data from the BK site were used for tree and plot level AGB estimations. Airborne data from the TB site were used for the plot level AGB estimation. Ortophoto RGB image (TopGis, s.r.o.) was used for visual interpretation of canopy density at BK and TB site.

2.2.2. Field data Field data were collected in the summer of 2013. At both sites, the sampling plots were circular with an area of 500 m2 (radius of 12.62 m). At the BK site, the plots were distributed across the site area with a distance of 200 m from each other. At the TB site, the plots were scattered across the area with a distance of about 1 km, sampling stands aged between 25–65 years (Fig. 2). At each plot, two concentric circles were used to conduct measurements of all trees with diameter at breast height (DBH) from 7 cm (inner plot circle with a radius of 3 m) and from 12 cm (across entire plot) out

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a = 59.567 and b = 30.472 (R2 = 0.60, n = 447). Since the number of beech trees at the study site available for measuring height and DBH was low, we also used field measurements from the CzechTerra landscape inventory program (Cienciala et al., 2016) in order to obtain a statistically more representative dataset. 2.4. Methods for AGB estimation based on hyperspectral data Two methods were used for estimating AGB based on hyperspectral data, one for tree level AGB estimation and one for plot level AGB estimation (Fig. 2). 2.4.1. Method for AGB estimation at tree level from HS data The method for AGB estimation at tree level from HS data includes: 1) development of a species composition map using supervised classification of HS data; 2) crown segmentation; 3) tree height estimation using crown diameter; and 4) AGB estimation using allometric equations. Finally, we compared estimated tree level biomass with field-surveyed tree level biomass. Fig. 2. Framework of AGB estimation based on hyperspectral data.

of all trees above 7 cm DBH. Tree height was measured for all trees unless there were more than 10 trees per species and plot. In those instances, the height for the remaining trees was estimated using locally derived species-specific height functions with DBH as an independent variable based on the measured tree samples. The data were collected using the Field-Map technology (www.fieldmap.cz), including an electronic caliper for breast height measurements and laser rangefinder for tree height and tree crown attributes. The BK site included 25 sampling plots (19 spruce, 2 beech and 4 mixed – spruce and beech). The TB site included 68 sampling plots (39 spruce, 14 beech and 15 mixed – spruce and beech). 2.3. Allometry for AGB estimation We used allometric Eq. (1)–(4) to estimate tree level AGB for prevailing species, Norway spruce and European beech (Wirth et al., 2004; Wutzler et al., 2008). These equations were derived from field measurements carried out on trees grown in stand conditions similar to those in our study area. Plot level AGB values were calculated using the sum of tree level AGB values weighted by the representative tree number per plot. AGBspruce = AGBneedles + AGBbranches + AGBdrybranches + AGBstem ,(1) where each tree AGB category was calculated (Wirth et al., 2004): AGB = a ∗ b ∗ exp (c ∗ ln (DBH) − d)

(2)

where a = 1.0849, b = 1.0226, c = 1.9162, d = −3.19632 for AGBneedles ; a = 1.1332, b = 1.0103, c = 2.2552, d = −3.96201 for AGBbranches ; b = 1.1107, c = 2.04823, d = −3.09062 for a = 1.1146, AGBdrybranches ; and a = 1.0142, b = 1.0238, c = 2.44277, d = −2.50602 for AGBstem . To retrieve the DBH (cm) for spruce trees, we used a parameterized model based on measured tree heights in the sample plots (R2 = 0.84, n = 257): DBH = −b/log

 H − 1.3  a

(3)

where a = 64.188, b = 30.446, and H is the height [m] of spruce trees. We used equation (4) to estimate AGB for beech (Wutzler et al., 2008): AGBbeech = 0.0551 ∗ DBH2.11 ∗ H0.589

(4)

where H is the height [m] of beech trees. DBH model parameters were also derived for beech from equation (3), resulting in

2.4.1.1. Map of species composition. A map of species composition derived from a HS image was used for tree level and plot level AGB estimation, and for AGB estimation through the combination of HS and LiDAR data. Supervised classification by Mahalanobis Distance method (Richards 1999) was applied. The procedures began with the formation of forest type classes for the classification as regions of interest (ROI) in the image. All pixels were classified into ROI classes. The BK region included species classes (“spruce”, “beech”, “birch”, “silver fir”), “shadows” and “artificial objects” (roads and buildings). The TB region included species classes (“spruce”, “beech”, “pine”, “silver fir”), “shadows” and “artificial objects”. All class probabilities were equal. The problem of isolated pixels was solved by sieving the classes, using a blob grouping procedure with the minimum number of pixels contained in a class group set to 12 and the number of neighbouring pixels set to 4. The clump classes procedure was applied, using morphological operator of the size 3 × 3 pixels, in order to cluster adjacent similar classified areas together. The accuracy of the classification result was estimated by calculation of a confusion matrix. 2.4.1.2. Crown segmentation procedure. A tree crown segmentation procedure was needed for the further derivation of individual tree parameters. We used a local maxima approach for tree detection and seeded region growing for the delineation phase of the segmentation procedure. Positions of trees were searched as local maxima of brightness, using a sliding window of a size that varied adaptively. Crown segments were then grown, using stopping conditions based on an expected crown size and a brightness difference (see Novotny´ et al., 2011 for more details on the segmentation procedure). We used data from the near infrared (NIR) spectral region as an input layer in the segmentation procedure because it has the maximum vegetation reflectance and a principal relationship to the structural properties of the canopy in this region (Thenkabail et al., 2012). A sum of 20 spectral bands (from 0.791 nm to 0.971 nm) was calculated for every pixel. The resulting crown segments were saved as a shape file and tree crown diameters were calculated with a geographic information system (GIS). 2.4.1.3. Tree height estimation. The tree height values were estimated using the allometric equations that relate tree height and tree crown diameter (Pretzsch 2009): H = 11.5 ∗ CD−10.5, for Norway spruce (Picea abies, L.), (R 2 = 0.79)

(5)

O. Brovkina et al. / Ecological Engineering 100 (2017) 219–230 H = 19.3 ∗ 1n(CD)−0.9, for European beech (Fagus silvatica, L.) (R 2 = 0.76)

223

(6)

where H is the height of tree and CD is the crown diameter of tree. 2.4.1.4. AGB estimation. AGB at tree level from HS data was estimated using allometric Eqs. (1)–(4). 2.4.2. Method for AGB estimation at plot level from HS data The method for estimating AGB at plot level from HS data consisted of: 1) creation of a map of species composition using a supervised classification of HS data; 2) estimation of the average plot crown diameter; 3) tree height estimation using crown diameter; 4) estimation of canopy density and tree number of plot, and 5) AGB estimation at plot level using allometric equation. Finally, we compared the estimated plot level biomass with field-surveyed plot level biomass. 2.4.2.1. Estimation of average crown diameters of a plot. The method for estimating average crown diameters was based on granulometry, which is a field that principally deals with determining the size distribution of particles in an image (Gonzalez and Woods 2002; Proisy et al., 2007). The average CD-method consisted of two steps. The first step was called window binarization, and the second step was the grain-size algorithm. Window binarization used a spatial filter percentile, which converts “grey” brightness to binary categories of brightness/darkness. If the value was less than that of the pixel brightness, corresponding with a percentile, then the pixel was assigned the value zero (“black”), otherwise the value was one (“white”). The second step was the heuristic algorithm for crown diameter calculation. This algorithm scans the black-andwhite image (from the first step) and for every pixel centre in a moving window of fixed size the average diameter of white spots was calculated. The average crown diameter d˜ in the window was computed as 4S d˜ = P

(7)

where S is an area of spots and P is a common perimeter. S is equal to the number of white points, P is equal to the number of black points with at least one neighbouring white point. It is clear that for ˜ We also tested cases a single circle S = r 2 , P = 2r, 4S/P = 2r = d. for discrete geometry and found that Eq. (7) works appropriately for more complicated cases such as groups of intersected tree crowns. Moreover, using Eq. (7) allowed the evaluation of crown diameter without determining the crown boundary, which leads to fast and simple algorithm implementation. The method of calculating the size of the granules for the binary images determined the output image of the average diameter of the spots in a specified-size window. Each pixel of the output image was assigned to the average diameter of the spot as defined in (7). The result of this automatic processing was represented in an image comprising the crown diameter values in pixels. For each forest plot from the field measurements, the crown diameter was assessed as a mean value of all diameters of the respective plot (see Brovkina et al., 2015 for more details). 2.4.2.2. Estimation of average tree height of a plot. The average tree height values were estimated using the allometric Eqs. (5) and (6). 2.4.2.3. Canopy density estimation. Forest canopy density (CanDen) was defined as the proportion between forest floor cover resulting from the downward projection of the tree crowns, expressed as a percentage. The result of

Fig. 3. Framework of AGB estimation based on LiDAR data.

the window binarization procedure from the previous step (estimation of average tree height for plot) was used to estimate the forest canopy density for each forest plot. We used simple division: CanDen = White pix/Total area

(8)

where Total area is an area of the plot, and White Pix is an area of tree (i.e. white) pixels in the plot. The results were compared with visual interpretation of high resolution orthophoto image. 2.4.2.4. AGB estimation. AGB for each plot was estimated based on the model: AGBplot = Numberoftrees ∗ AGBtree

, (9)

where Numberoftrees is the number of trees in each plot derived from the canopy density estimation, and AGBtree is the tree biomass calculated according to tree species based on allometric Eqs. (1)–(4) from 2.3. 2.5. Methods for AGB estimation based on LiDAR data Methods for AGB estimation based on LiDAR data included tree level AGB estimation and plot level AGB estimation (Fig. 3). 2.5.1. Method for AGB estimation at tree level from LiDAR data The method for AGB estimation at tree level from LiDAR data included: 1) development of a map of species composition; 2) individual tree crown detection, 3) tree height estimation, and 4) AGB estimation using allometric equations. Finally, we compared estimated tree level biomass with field-surveyed tree biomass estimation. 2.5.1.1. Map of species composition. We used the following full-waveform properties recommended by Heinzel and Koch (2011) for mid-European temperate forest: mean intensity at the top-of-the-canopy level, mean echo width at the top-of-the-canopy level and the mean number of echoes per pulse. The three-dimensional point cloud was projected onto a two-dimensional raster which allowed a strong reduction of data volume while still considering the information from each reflection. The interpretation of the variables from recent studies showed that the most important basic full-waveform parameter is the intensity of the reflection (Heinzel and Koch 2011; Lim et al., 2003). Standard deviation was used to characterize the texture from the intensity of raster. The final raster represented four properties (three waveform properties and texture). We classified coniferous and broadleaf forest from the prepared raster using the Mahalanobis Distance method of supervised classification. The field data and ground truth observations were used to create the training samples. All pixels were classified into species classes (“coniferous young”, “coniferous mature”, “broadleaf”) and “artificial objects” (roads and buildings). The problem of isolated

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pixels was solved by sieving the classes, using a blob grouping procedure with the minimum number of pixels contained in a class group set to 8 and the number of neighbouring pixels set to 4. The clump classes procedure was applied, using a morphological operator of the size 3 × 3 pixels, in order to cluster adjacent similar classified areas together. The accuracy of the classification result was estimated by calculation of a confusion matrix. 2.5.1.2. Individual tree detection procedure and tree height estimation. Firstly, we transformed the LiDAR point cloud into a canopy height model (CHM), which is a subtraction of a digital terrain model (DTM) from a digital surface model (DSM). In our case, ground points in the cloud were classified using lastools scripts (http:// www.cs.unc.edu/∼isenburg/lastools/) with the settings for a natural environment. The DTM was interpolated using a Delaunay triangulation of ground points. DSM was interpolated using a Delaunay triangulation of the highest points. We chose the common pixel size of 0.4 m for both models for the LiDAR and HS data fusion (next section). Secondly, positions of individual trees were detected in the CHM as local maxima of height. We smoothed the CHM with a Gaussian low pass filter and then searched local max´ ima with a sliding window of a size that varied adaptively (Novotny, 2014). We saved the coordinates and height of each local maximum to a point shape file for a subsequent GIS analysis. 2.5.1.3. AGB estimation. AGB at tree level from LiDAR data was estimated using allometric Eq. (1)–(4) from 2.3. 2.5.2. Method for AGB estimation at plot level from LiDAR data To estimate plot level AGB for the BK site we summed the total AGB from LiDAR data for all trees in each forest plot. To estimate plot level AGB for the TB site we generated a model with metrics derived from LiDAR data: 1) canopy density, 2) canopy height, and 3) intensity. Because the density of 1 point/m2 (TB site) was insufficient to separate species, we used only spruce plots for the model. The spruce plots (n = 39) were split in calibration (n = 19) and validation (n = 20) sets. The model was validated based on AGB for plots estimated from field data. 2.5.2.1. Canopy density estimation. Canopy density (crown cover) is the ratio of vegetation to ground as seen from the air. Firstly, the separation of LiDAR point cloud was done to split ground multipoint and aboveground points. Secondly, a separation procedure of the amount of aboveground points and the amount of total points was applied. We obtained the canopy density from point cloud data by taking the ratio of first returns higher than 1.37 m (standard for DBH measure) to all first returns. The results fell between 0–1, where 0 represented no canopy and 1 very dense canopy. The results were compared with visual interpretation of high resolution orthophoto image. 2.5.2.2. Canopy height estimation. Canopy height was estimated from LiDAR data as follows. The first return points and bare earth points were generated and the difference between these two point datasets was determined. The difference results represented, over forest, the canopy height. The accuracy of LiDAR canopy height was estimated by comparison with field measurements of height of trees at each plot. 2.5.2.3. AGB model at plot level from LiDAR data. To estimate plot level AGB for the BK site we summed the total AGB from LiDAR data for all trees in each forest plot. To estimate plot level AGB for TB site we generated a model with metrics derived from LiDAR data: 1) canopy density, and 2) canopy

Fig. 4. Framework of AGB estimation based on a fusion of hyperspectral and LiDAR data.

height. Because the density of 1 point/m2 (TB site) was insufficient to separate species, we used only spruce plots for the model. The spruce plots (n = 39) were split in calibration (n = 19) and validation (n = 20) sets. The model was validated based on AGB for plots estimated from field data. 2.6. Methods for AGB estimation based on a fusion of HS and LiDAR data Methods for AGB estimation based on a fusion of HS and LiDAR data included tree level AGB estimation and plot level AGB estimation (Fig. 4). 2.6.1. Method for AGB estimation at tree level from the fusion of HS and LiDAR data The method for AGB estimation at tree level using the fusion of HS and LiDAR data included: 1) development of a map of species composition from HS data, 2) individual tree crown detection from LiDAR data, 3) tree height estimation from LiDAR data, and 4) AGB estimation using allometric equations. Finally, we compared the estimated tree level biomass with field-survey tree level biomass. 2.6.2. Method for AGB estimation at plot level from the fusion of HS and LiDAR data The method for AGB estimation at plot level from the fusion of HS and LiDAR data consisted of: 1) creation of a map of species composition from HS data; 2) applying the AGB model at plot level from LiDAR data to spruce, beech and mixed forest classes. Finally, we compared the estimated plot level biomass with field-surveyed plot level biomass. 2.7. Statistical methods A confusion matrix was calculated to compare the species classification result with ground truth information. The overall accuracy was calculated by summing the number of pixels classified correctly and dividing it by the total number of pixels. The Kappa coefficient (Rosenfield and Fitzpatrick-Lins, 1986) was used to compare the specific class differences between the classifications. We used the coefficient of determination, R2 , to indicate how well the tree level AGB and the plot level AGB values from HS and LiDAR data fit the respective results from field data. Root mean squared error (RMSE) was calculated for the quantitative assessment of AGB values from the study methods. The coefficient of variation of the RMSE, CVRMSE , was useful for the non-dimensional comparison of RMSE normalized to the mean of the observed data.

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Table 3 Comparison between field AGB estimates (x) and modelled AGB estimates (y) for tree level (BK site) and plot level (BK and TB sites). Method, Species

R2

Equation

CVRMSE [%]

Tree level AGB, BK site AGB HS spruce AGB HS beech AGB HS all AGB LiDAR spruce AGB LIDAR beech AGB LIDAR all AGB fusion spruce AGB fusion beech AGB fusion all

0.54 0.61 0.56 0.85 0.78 0.81 0.87 0.81 0.85

y = 0.82x + 0.01 y = 0.80x + 0.09 y = 0.81x + 0.03 y = 0.92 x − 0.02 y = 0.75x + 0.03 y = 0.91 x − 0.01 y = 1.10 x − 0.01 y = 0.77x + 0.05 y = 0.90 x − 0.06

45 54 51 28 48 33 22 41 28

Plot level AGB, sum of tree level biomass values for each plot, BK site y = 0.74 x − 3.62 0.56 48 AGB HS all AGB LiDAR all y = 1.02 x − 0.44 0.78 27 AGB fusion all 0.84 y = 1.21 x − 1.57 24 Plot level AGB, BK site AGB HS spruce AGB LiDAR spruce AGB fusion spruce

0.62 0.67 0.74

y = 1.31 x − 1.82 y = 0.71x + 2.07 y = 0.76x+ 1.18

52 37 31

Plot level AGB, TB site AGB HS spruce AGB HS beech AGB HS mixed AGB HS all AGB LiDAR spruce AGB fusion spruce AGB fusion beech AGB fusion mixed

0.59 0.68 0.67 0.65 0.65 0.65 0.78 0.75

y = 0.69x + 5.00 y = 0.73x + 27.44 y = 0.73x + 26 y = 0.71x + 14.20 y = 0.84x + 34.11 y = 0.84x + 34.11 y = 0.57x + 56.07 y = 0.69x + 41.85

61 50 40 56 26 26 31 29

3. Results The results obtained for AGB estimation based on HS data, LiDAR data and synergy of both categories of data at tree level (BK site) and plot level (BK site and TB site) are summarized in Table 3. The biomass estimation from fused hyperspectral and LiDAR data showed the best results for tree and plot levels for our study sites for all species classes. LiDAR data alone had a strong correlation with field biomass values for spruce class and good correlation for beech and mixed classes. Uncertainties for AGB from HS were higher, than for AGB from LiDAR and AGB from fused data. However, HS data alone showed a good correlation with field biomass values for tree and plot levels and can be used independently for AGB estimation at the study sites. Fragments of the AGB maps are given in Figs. 5–7 . The accuracy of tree species classification from HS and LiDAR data was estimated by comparing the classification result with ground truth ROIs information via a confusion matrix (Table 4). The vegetation map from HS data for the BK site contained four forest classes and demonstrated an overall accuracy of 86% and Kappa coefficient of 0.78. The result from HS data for the TB site demonstrated an overall accuracy of 87% and Kappa coefficient of 0.82. The result from LiDAR data demonstrated an overall accuracy of 81% and Kappa coefficient of 0.72. The final vegetation map from LiDAR data contained “coniferous” and “broadleaf” pixels, where the “coniferous” class was obtained by merging “coniferous young” and “coniferous mature” classes. The density of 1 point/m2 (TB site) was insufficient to perform a separation of species. Crown diameters from the segmentation procedure at tree level (BK site) and average crown diameters from the granulometry procedure at plot level (TB site) fitted well with crown diameters estimated from field data, with R2 = 0.61 and R2 = 0.79, respectively (Fig. 8). The accuracy of LiDAR input – the canopy height model – was estimated by comparing the height of trees in the LiDAR data with

Fig. 5. Map of forest aboveground biomass (AGB) developed by tree level method from hyperspectral data, where the two colours symbolize species (BK site).

heights measured in the field, both at tree level at the BK site and plot level at the TB site (Fig. 9). The estimated forest canopy density for each forest plot ranged from 0.6 to 0.9 (based on HS data) and from 0.7 to 0.9 (based on LiDAR data). Generally, the forest canopy density derived from airborne data was in agreement with the visual interpretation of orthophoto image (R2 = 0.69 for HS data and R2 = 0.78 for LiDAR data). The model for estimation plot level biomass from LiDAR data used two LiDAR metrics – tree height and canopy density of plot for TB sites. The model equation is presented in (10), where CanDen is canopy density for plot, H is the mean height of trees for plot (R2 = 0.60, n = 19 (number of validation plots)):

AGB = 1.89 × CanDen × H 1.552

(10)

4. Discussion Considering the complexity of forest ecosystem biomass estimation and the inherent uncertainty associated with it, we can state that the reported results based on airborne data can be used in practical forestry. Further in our discussion, we mainly focus on our coefficient of determination (R2 ) and coefficient of variation (CVRMSE ) results from all models in comparison with similar recent studies.

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fication accuracy of 75% with the classification of 11 forest cover types based on the airborne HS data. In comparison, species classification accuracies were 72%, 60%, and 40% for hemlock (Tsuga heterophylla (Raf.) Sarg.), balsam (Abies balsamea, L.), and redcedar (Thuja plicata, Donn ex D. Don) trees from airborne HS scanner data of 0.7 m resolution (Leckie et al., 2005). The tree crown diameters were calculated using HS data and a segmentation procedure, which fitted well crown diameters from field data (R2 = 0.61) (Fig. 8a). Compared to similar previous studies employing the crown diameter estimation, the accuracies varied within imagery and forest site characteristics. Bunting and Lucas (2006) obtained an accuracy of 88% for tree crown delineation from monospecies stands (white cypress pine (Callitris glaucophylla, L.) based on HS data. Leckie et al. (2003) demonstrated a tree-fortree isolation accuracy of 50–60% in coniferous stands with various crown closure conditions at the site. Gougeon (1995) determined an accuracy of 81% of the crown size estimation using 8-band multispectral data in coniferous plantations.

Fig. 6. Map of forest aboveground biomass (AGB) developed by tree level method from LiDAR data, where circles define trees, the colours symbolize species and AGB amount (BK site).

4.1. AGB estimation based on HS data 4.1.1. AGB estimation for tree level from HS data Comparison of biomass estimates from HS data and from field data at tree level yields a coefficient of determination of R2 ranging from 0.54 to 0.61 (CVRMSE from 45% to 54%), depending on the tree species analysed (Table 3). Uncertainties in biomass estimates here could follow mostly from the direct segmentation of tree crown method as discussed in section 2.4.1. HS tree species classification, as a part of biomass assessment at tree level, also contributed to potential errors in our biomass estimates. Recent studies reported that a direct estimation of AGB based only on HS data is not likely (Koch, 2010) and HS bands have a limited predictive power when used alone for AGB estimation (Schlerf, 2006). The authors were unable to find studies related to AGB estimation based on HS data only at tree level (Table 1). Therefore, our method of AGB estimation from HS data can be seen as a solution to expand current approaches to AGB estimation. The classification of the HS data for the BK site separated four tree species classes: “spruce”, “beech”, “silver fir” and “birch” (overall accuracy of 86% and Kappa coefficient of 0.78) (Table 4). The relatively low (58.6%) classification accuracy of “birch” is caused by the partial presence of “birch” pixels in the “beech” class. This can be explained by the similar spectral features of birch (Betula pendula, L.) with spectral features of beech (Fagus silvatica, L.) in 400–900 nm spectral region for this area at the beginning of September. However, our result confirms recent studies showing that airborne HS data can be used in species classification with high accuracy. For example, Buddenbaum et al. (2005) reported the classification accuracy (Kappa) of 0.74 in coniferous forest stands from the airborne HS data. Martin et al. (1998) achieved the overall classi-

4.1.2. AGB estimation at plot level from HS data The biomass estimation from HS data and biomass obtained from field data at plot level yield a coefficient of determination of R2 from 0.56 to 0.68 (CVRMSE from 24% to 61%) depending on the analysed tree species (Table 3). Goodenough et al. (2008) showed that AGB from AVIRIS HS data agreed with ground measurements of biomass with an R2 = 0.9 in a Canadian forest. Laurin et al. (2014) estimated AGB in a tropical forest with coefficient of determination of R2 = 0.36 using airborne HS data. A map of species composition derived from HS data was used to derive plot level AGB from the HS data (TB site) and AGB from the synergy of HS and LiDAR data (TB sites). The producer accuracies of classes from the TB site “spruce”, “beech” and “silver fir” were slightly higher than the producer accuracies of the corresponding classes from the BK site. We assume that spatial resolution of 1 m (TB site) is more appropriate for forest species classification than spatial resolution of 0.4 m (BK site). This assumption is supported by results of Pena et al. (2013). The authors tested forest species classification with HS data of 0.3 m and 2.4 m and found that spatial resolution of 2.4 m was the most appropriate to represent the spatial variability of the tree species. The average crown diameters for plot level fitted well the crown diameters from field data (R2 = 0.79) (Fig. 8b). We noted, that the accuracy of granulometry procedure (CVRMSE = 12%) for average crown diameter at the TB site was slightly higher than the accuracy of crown segmentation procedure (CVRMSE = 14%) for crown diameters at the BK site. The granulometry method for the average plot level crown diameter estimation avoids the crown delineation procedure (and significant errors from the procedure) that could explain the accuracy difference of the two approaches. The forest canopy density calculated from the HS data for each forest plot was compared with the canopy density calculated from LiDAR data for TB site. We found that the canopy density from the HS data had a relatively weak correlation with the canopy density from LiDAR data (R2 = 0.56) – when compared on a plot level. In more detail, densities from HS range from 0.6 to 0.9, while the real densities (from visual interpretation) range from 0.8 to 0.9 for the plots at the TB site. Munukata et al. (2010) showed a high correlation (r = 0.89) between the estimation (LiDAR data) and observation (field data) of the canopy density in a coniferous forest. 4.2. AGB estimation based on LiDAR data 4.2.1. AGB estimation at tree level from LiDAR data The biomass estimation based on LiDAR data and biomass obtained from field data at tree level yield a coefficient of determination R2 that varies from 0.78 to 0.85 (CVRMSE from 28% to 48%)

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227

Fig. 7. Map of forest aboveground biomass developed by plot level method from the fusion of hyperspectral and LiDAR data (TB site).

Table 4 Results of tree species classification from airborne data. Data

Classes

Producer accuracy%

Overall accuracy%

Kappa coefficient

HS 0.4 m (BK site)

Spruce Beech Silver fir Birch

97.7 94.2 76.5 58.6

86

0.78

HS 1 m (TB site)

Spruce Beech Silver fir Pine

98.1 95.1 77.3 69.2

87

0.82

LiDAR 50 points/m2 (BK site)

Coniferous young Coniferous mature Broadleaf

86.1 81 72.7

81

0.72

depending on the tree species analysed (Table 3). Uncertainties in biomass estimates at tree level could follow mostly from uncertainties in the tree species classification based on LiDAR data, which

classified trees to generalized groups of “coniferous” and “deciduous” without splitting into various species of spruce, beech, fir and birch. Although silver birch (Betula pendula, L.) and individu-

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Fig. 8. Comparison between crown diameters estimated from hyperspectral data (CD HS) and crown diameters from field data (CD field) at tree level from BK site (a) and plot level from TB site (b).

Fig. 9. Comparison between tree heights derived from laser scanning data (H LiDAR) and tree heights measured in the field (H field) for tree level from BK site (a) and plot level from TB site (b).

ally occurring silver fir (Abies alba, L.) play a minor role, the ways to improve the classification result needs to be a topic for further research. Despite this fact, the accuracy of the suggested method based on LiDAR data only is higher than the accuracy of the HS method only. On the other hand, our AGB estimation results from LiDAR data display lower R2 than in previous studies. For example, high accuracy of biomass estimation was obtained with R2 = 0.92 in a boreal Canadian forest (Mora et al., 2013) and with R2 = 0.99 in a mixed fir and oak forest (Gonzalez et al., 2010). The classification of the raster distinguished two forest classes: “coniferous” and “broadleaf” with the overall accuracy of 81% and Kappa coefficient of 0.72 (Table 4). High classification accuracy was achieved by incorporating spatial variation of intensity when grouping species into coniferous and broadleaved tree groups. This is in agreement with the findings of Heinzel and Koch (2011) in the case of grouping species into coniferous and broadleaved trees (overall accuracy of 91%). Ørka et al. (2007) achieved 74% overall accuracy in classifying coniferous and broadleaved trees. However, our classification from LiDAR data was not an improvement over our classification from HS data in the study, where the map of four tree species was created. To improve the species classifica-

tion from LiDAR data, i.e. separation of “beech” and “birch” pixels within the broadleaved group and “spruce” and “fir” pixels within the coniferous group, we recommend exploring other features such as the shape of crown in future research. Hollaus et al. (2009) achieved 83% accuracy in determining the species of spruce, larch and beech trees using the geometric shape information from full waveform airborne laser scanning data. Khameneh (2013) developed an approach for identification of three-dimensional shapes (cone, sphere and cylinder) of three species (spruce, pine and birch). The overall obtained accuracy in species classification was 77%, and this percentage increased when only coniferous and deciduous types of classification were used (84% accuracy). The tree heights at tree level derived from the laser scanning data were close to the field measurements (R2 = 0.99) with a small offset (Fig. 9a). Popescu and Wynne (2004) found a stronger agreement between LiDAR and field measurements in a pine (Pinus sylvestric L.) forest (R2 = 0.99). In addition, Falkowski et al. (2006) indicated that the tree height estimates from airborne LiDAR data fit well with field measures of tree height (R2 = 0.94) in a mixed coniferous forest. Luther et al. (2014) predicted the average height of black spruce (Picea mariana, L.) with R2 = 0.86 from airborne

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laser scanner data. The offset of linear fit equation (Fig. 9a) follows our expectation because laser scanning tends to underestimate the height of trees. 4.2.2. AGB estimation at plot level from LiDAR data Two LiDAR metrics were used in the biomass model for plot level − average tree height for plot and canopy density for plot. Based on the model, the plot level biomass estimation from LiDAR data indicated an R2 of 0.65 and a CVRMSE of 26% for the TB site and an R2 of 0.78 and a CVRMSE of 27% for the BK site with biomass obtained from field data. Lefsky et al. (2002) predicted AGB at plot level in a coniferous forest with higher R2 = 0.84 and much lower standard error of 7.5%. They used canopy structure indices derived from LiDAR data for modelling. Uncertainties in our biomass estimates at plot level could be mostly caused by the small input data set for the biomass model. Uncertainties in biomass estimates at plot level for the BK site (sum of tree level biomass values for each plot) could follow from forest species classification from LiDAR data (discussed in 4.2.1). The plot level biomass estimates based on the sum of tree biomass values for each plot had a higher R2 and less residual variance than plot level biomass estimates based on model building (see plot level AGB estimates for the BK site, Table 3). Our results are within the range of those reported in Finish broad mixed forest stands where airborne LiDAR data were used for Norway spruce plot level biomass estimation (R2 = 0.68) (Kankare et al., 2013). It should be mentioned that the number of plots was much higher in Kankare et al. (2013) – 254 plots compared to 68 (TB site) and 25 (BK site) plots used in our study. Latifi et al. (2012) achieved relative errors of 32%–58% for coniferous and 32%–45% for total plot level biomass based on LiDAR metrics only. 4.3. AGB estimation based on a fusion of HS and LiDAR data 4.3.1. AGB estimation at tree level from the fusion of HS and LiDAR data The biomass estimation for tree level derived from the fusion of HS and LiDAR data showed the best results for the BK site (Table 3). This is mainly due to the complementary information content of the data, e.g. tree species from HS data and tree height from LiDAR data. We obtained the highest R2 between estimated AGB from airborne data and AGB from field data (R2 from 0.81 to 0.87) and relatively lower CVRMSE values (CVRMSE from 22% to 41%). The multisensor approach with HS and LiDAR data, in general, improved the wood volume estimation and consequently the biomass assessment. Findings of Laurin et al. (2014) showed that the integration of HS bands improved the original LiDAR-based model from R2 = 0.64 to 0.70 for AGB estimation in a tropical forest. Anderson et al. (2008) reported about 8–9% increase of biomass explained by the use of the integrated data in comparison to either HS (AVIRIS) or LiDAR metrics applied singly in a north-eastern North America deciduous and coniferous forest. LiDAR-based prediction of AGB was not improved by HS integration in coniferous species forest site in Germany (Latifi et al., 2012). 4.3.2. AGB estimation at plot level from the fusion of HS and LiDAR data The fusion of HS and LiDAR data showed the best results for AGB estimation at plot level for the BK and TB sites (Table 3). We obtained the highest R2 between estimated AGB for plot from airborne data and AGB from field data (R2 from 0.65 to 0.84) and relatively lower CVRMSE values (CVRMSE from 24% to 31%). Popescu and Wynne (2004) combined LiDAR and multispectral airborne data to estimate the plot level AGB in mixed deciduous and pine forests. They found the maximum R2 values for AGB of 0.32 for deciduous and 0.82 for pines with respective RMSEs of 44 t/ha and 29 t/ha. Latifi et al. (2012) achieved relative errors of 30%–55% for conifer-

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ous and 35%–45% for total plot level biomass based on both HS and LiDAR metrics. The addition of HS data to LiDAR at BK site resulted in an increase of R2 values from 0.78 to 0.84 for all plots and from 0.67 to 0.72 for spruce plots. The addition of HS data to LiDAR at TB site resulted in a possible estimation of AGB for spruce, beech and mixed forest separately and also in an increase of R2 values from 0.65 to 0.78. 5. Conclusion The study presents three methods for AGB estimation: on the basis of either airborne HS data, LiDAR data, or the fusion of HS and LiDAR data both at tree and plot levels. Our findings prove the efficiency of using the synergy of HS and LiDAR data, which was an improvement over the biomass estimation from HS or LiDAR data alone, both at tree level and plot level. Results also show that high resolution HS data alone could be used for AGB estimates when LiDAR data are not available. In future work the authors plan to expand the study to test the robustness of the methods over a more heterogeneous forest area and improve the intermediate steps of the HS-based crown delineation procedure and LiDARbased species classification. Acknowledgements This research was supported by the Ministry of Education, Youth and Sports of CR within the National Sustainability Program I (NPU I) (grant number LO1415) and COST project No. OCO9001. References Ørka, H.O., Næsset, E., Bollansås, O.M., 2007. Utilizing airborne laser intensity for tree species classification. ISPRS Workshop on Laser Scanning and SilviLaser 36, 300–304. Anderson, J.E., Plourde, L.C., Martin, M.E., Braswell, B.H., Smith, M.L., Dubayah, R.O., Hofton, M.A., Blair, J.B., 2008. Integrating waveform LiDAR with hyperspectral imagery for inventory of a northern temperate forest. Remote Sens. Environ. 112, 1856–1870. Brovkina, O., Latypov, I., Cienciala, E., 2015. Estimating of average tree crown size using high-resolution airborne data. J. Appl. Remote Sens. 9 (1), http://dx.doi. org/10.1117/1.JRS.9.096053. Buddenbaum, H., Schlerf, M., Hill, J., 2005. Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. Int. J. Remote Sens. 26 (24), 5453–5465. Bunting, P., Lucas, R., 2006. The delineation of tree crowns in Australian mixed species forests using hyperspectral compact airborne spectrographic imager (CASI) data. Remote Sens. Environ. 101, 230–248. Cho, M.A., Skidmorea, A., Corsi, F., van Wieren, S.E., Sobhana, I., 2007. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl Earth Obs. Geoinform. 9 (4), 414–424. ˇ ˇ epánek, ˚ cková, H., Altman, J., Kopáˇcek, J., Hunová, ˚ Cienciala, E., Russ, R., Santr uˇ I., Stˇ P., Oulehle, F., Tumajer, J., Ståhl, G., 2016. Discerning environmental factors affecting current tree growth in Central Europe. Sci. Total Environ. 573, 541–554, http://dx.doi.org/10.1016/j.scitotenv.2016.08.115. Falkowski, M.J., Smith, A.M.S., Hudak, A.T., Gessler, P.E., Vierling, L.A., 2006. Automated estimation of individual conifer tree height and crown diameter via two-dimantional spatial wavelet analysis of lidar data. Can. J. Remote Sens. 32 (2), 153–161. Gleason, C.J., Im, J., 2011. A review of remote sensing of forest biomass and biofuel: options for small-area applications. GISci. Remote Sens. 48 (2), 141–170. Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing, 2nd ed. Pearson Education, inc, publishing as prentice hall. Gonzalez, P., Asner, G.P., Battles, J.J., Lefsky, M.A., Kristen, M.W., Palace, M., 2010. Forest carbon densities and uncertainties from LiDAR, QuickBird and field measurements in California. Remote Sens. Environ. 114, 1561–1575. Goodenough, D.G., Niemann, K.O., Dyk, A., Hobart, G., Gordon, P., Loisel, M., Chen, H., 2008. Comparison of AVIRIS and AISA airborne hyperspectral sensing for above-ground forest carbon mapping. Geosci. Remote Sens. Symp. Gougeon, F.A., 1995. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can. J. Remote Sens. Spec. Issue Aer. Opt. Remote Sens. 21, 274–288. Gower, S.T., Krankina, R.J., Olson, R.J., Apps, M., Linder, S., Wang, C., 2001. NET primary production and carbon allocation patterns of boreal forest ecosystems. Ecol. Appl. 11 (5), http://dx.doi.org/10.1890/10510761(2001)011[1395:NPPACA]2.0.CO;2.

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Glossary AGB: Forest above ground biomass DBH: Diameter at breast height CanDen: Canopy density CD: Crown diameters HS data: Hyperspectral data LiDAR: Light detection and ranging