Remote Sensing of Environment

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1 Remote Sensing of Environment 114 (2010) Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data Mariano García a,, David Riaño b,c, Emilio Chuvieco a, F. Mark Danson d a Department of Geography, University of Alcalá, Alcalá de Henares, Madrid, Spain b Institute of Economics and Geography, Spanish ational Research Council (CSIC), Albasanz Madrid, Spain c Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, 250-, The Barn, One Shields Avenue, Davis, CA , USA d Centre for Environmental Systems Research, School of Environment and Life Sciences, University of Salford, Manchester M5 4WT, UK article info abstract Article history: Received 8 June 2009 Received in revised form 25 ovember 2009 Accepted 28 ovember 2009 Keywords: LiDAR Intensity Biomass fractions Carbon content Biomass fractions (total aboveground, branches and foliage) were estimated from a small footprint discretereturn LiDAR system in an unmanaged Mediterranean forest in central Spain. Several biomass estimation models based on LiDAR height, intensity or height combined with intensity data were explored. Raw intensity data were normalized to a standard range in order to remove the range dependence of the intensity signal. In general terms, intensity-based models provided more accurate predictions of the biomass fractions. Height models selected were mainly based on a percentile of the height distribution. Intensity models selected included variables that consider the percentage of the intensity accumulated at different height percentiles, which implicitly take into account the height distribution. The general models derived considering all species together were based on height combined with intensity data. These models yielded R 2 values greater than 0.58 for the different biomass fractions considered and RMSE values of 28.89, and 1.51 Mg ha 1 for aboveground, branch and foliage biomass, respectively. Results greatly improved for species-specific models using the main species present in each plot, with R 2 values greater than 0.85, 0.70 and 0.90 for black pine, Spanish juniper and Holm oak, respectively, and with lower RMSE for the biomass fractions. Reductions in LiDAR point density had only a small effect on the results obtained, except for those models based on a variation of the Canopy Reflection Sum, which was weighted by the mean point density. Based on the species-specific equations derived, Holm oak dominated plots showed the highest average carbon contained by aboveground biomass and branch biomass and Mg ha 1 respectively, while for foliage biomass carbon, Spanish juniper showed the highest average value (3.04 Mg ha 1 ) Elsevier Inc. All rights reserved. 1. Introduction Forest ecosystems play a key role in the global carbon cycle, since carbon is exchanged naturally between forests and the atmosphere through photosynthesis, respiration, decomposition and combustion (IPCC, 2000). At a global scale, forest ecosystems hold approximately 80% of the carbon contained in the aboveground vegetation biomass and 40% of the carbon contained in roots, litter and soils (Dixon et al., 1993). Biomass carbon pools act as a sink for atmospheric CO 2 and, in the Mediterranean region, carbon sequestration by forests ranges between 0.01 and 1.08tC ha 1 annually (Croitoru & Merlo, 2005). In Spain, approximately 19% of total annual carbon emissions are sequestered by forests (Montero et al., 2005). Forests also have a net emission of CO 2 to the atmosphere due to land use change associated with anthropogenic activities. Forest fires are also a critical factor in Corresponding author. Tel.: ; fax: addresses: mariano.garcia@uah.es (M. García), david.riano@cchs.csic.es (D. Riaño). carbon budgets, due to the impact of fires on vegetation disturbance and succession, and greenhouse gas emissions (Chuvieco, 2008; Goetz et al., 2006). Most countries do not have inventory data expressed directly in terms of biomass, and use instead conversion and/or expansion factors applied to the growing stock data to estimate biomass (Marklund & Schoene, 2006). Therefore, methods are required to provide accurate estimates of forest biomass and its changes to increase our understanding of the role of forests in the carbon cycle, for greenhouse gas inventories, terrestrial carbon accounting (Muukkonen & Heiskanen, 2007), a sustainable forest management (FAO, 2006), and to estimate fuel loading (Finney, 1998). Estimation of biomass fractions is also important to perform appropriate silvicultural treatments, which can help to mitigate CO 2 emissions or to reduce fuel load, and because the residence time of carbon is different for each biomass fraction (Montero et al., 2005). Conventional methods for biomass estimation are based on field measurements and, although these methods are more direct, they are generally limited in terms of spatial and temporal samplings since they require time-consuming destructive sampling. Remote sensing provides the /$ see front matter 2009 Elsevier Inc. All rights reserved. doi: /j.rse

2 M. García et al. / Remote Sensing of Environment 114 (2010) only method for generating detailed and spatially explicit information on forest biomass, given its potential to provide information at a wide range of spatial and temporal scales; nevertheless, appropriate methods to extract relevant data need to be developed. Many studies have been conducted using passive optical data to estimate vegetation biomass with different degrees of success based on the correlation between biomass and the spectral response of vegetation cover (Foody et al., 2003; Leboeuf et al., 2007; Meng et al., 2007; Muukkonen & Heiskanen, 2007). A traditional approach is to relate vegetation indices, mainly the DVI, to ground-measured biomass data through a statistical model (Meng et al., 2007; Tan et al., 2007). Foody et al. (2003) used neural networks to estimate biomass in three different tropical regions and compared this approach to vegetation indices or multiple regression using all optical bands of Landsat TM observing that the neural network approach provided the strongest relationships between predicted and observed biomass values. A common problem found with passive sensors in densely vegetated areas however, is that they can saturate at high biomass levels, resulting in a lack of sensitivity in the remotely sensed data when estimating aboveground biomass higher than 100 Mg ha 1 (Cohen & Spies, 1992). Synthetic Aperture Radar (SAR) data has been shown to be sensitive to biomass levels at higher values than passive sensors, although asymptotic relationships have also been reported as described in elson et al. (2007). Light Detection and Ranging (LiDAR) technology, has shown great potential to estimate several biophysical and structural properties over a wide range of forest types (Lefsky et al., 1999; Morsdorf et al., 2008; aesset, 2004; Riaño et al., 2004) given the capability of LiDAR sensors to provide 3-D information of vegetation structure. Large footprint full-waveform systems have been shown to provide accurate estimates of aboveground biomass in tropical and deciduous forests over a wide range of conditions, with relationships that remained non-asymptotic with biomass values up to 450 Mg ha 1 (Drake et al., 2002; Lefsky et al., 1999). These studies implicitly use the LiDAR intensity, which is related to the amount of energy reflected back to the sensor, to derive variables that were subsequently used to estimate biomass. Discrete-return systems have also been used to successfully estimate aboveground biomass at individual tree level and up to stand levels (Lim & Treitz, 2004; æsset & Gobakken, 2008; Bortolot & Wynne, 2005; Popescu, 2007). These systems have been used either alone, or in combination with passive optical or RaDAR data (Hyde et al., 2007; Lucas et al., 2008). Regardless of the type of LiDAR system used however, estimation of biomass is generally based on regression equations relating vegetation biomass to LiDAR-derived variables. Some studies have also shown the potential of LiDAR systems to estimate forest carbon content using a quantile-based approach (Patenaude et al., 2004) or by combining LiDAR data and an Ecosystem Demography (ED) model (Thomas et al., 2008). A few studies have estimated the biomass fractions (Lim & Treitz, 2004; Lucas et al., 2008) and in most previous studies the relationships found to estimate vegetation biomass were based on height-related or canopy cover variables derived from the distribution of pulses rather than the intensity of the returns recorded by the system. The intensity recorded by small footprint systems has been shown to be useful for planimetric offset correction (Maas, 2002; Vosselman, 2002; García et al., 2009), tree species classification (Donoghue et al., 2007; Holmgren & Persson, 2004; Korpela et al., 2008; Moffiet et al., 2005), fractional cover (Hopkinson & Chasmer, 2009; Zhao & Popescu, 2009) or lava flow identification (Mazzarini et al., 2007), but its application to the estimation of structural and biophysical characteristics of forest stands has been more limited. Hall et al. (2005) estimated some forest stand structural characteristics in a ponderosa pine forest, including total aboveground and foliage biomass, based on small footprint LiDAR. Though some of the models evaluated by Hall et al. (2005) used intensity metrics as predictor variables, none of the regression models finally selected was based on the intensity of the returns. This study aims to estimate the carbon contained in the vegetation biomass fractions of a Mediterranean forest using height and intensity data from a discrete-return LiDAR system. The specific goals were: (1) To find a general equation to estimate aboveground biomass, and the vegetation biomass fractions; (2) To evaluate the effects of species composition and to obtain species-specific equations; (3) To evaluate the usefulness of intensity data recorded by small footprint systems to estimate biomass; (4) To assess the effect of point density on the estimation of biomass fractions. 2. Methods 2.1. Study area and dataset This study was carried out in the atural Park of the Alto Tajo in Guadalajara, in central Spain (UL: ; W; LR: ; W) (Fig. 1). The area has rough topography with a mean elevation of 1200 m, and a range of 895 to 1403 m, and dominated by pine forests (Pinus nigra Arn.; Pinus sylvestris L.; and Pinus Pinaster Ait.). Other species present in the Park are Spanish juniper (Juniperus thurifera L.), and oaks (Quercus faginea Lam.; Quercus ilex L.; and Quercus pyrenaica Willd.) ( Contenidos/espaciosnaturales/PDF/alto%tajo%20-%20informacion.pdf). The study area was flown twice at the end of the spring period in 2006 (May 16th and June 3rd), by the United Kingdom atural Environment Research Council (ERC) Airborne Research and Survey Facility. The LiDAR system used was an Optech-ALTM3033, with a laser pulse rate of 33 khz. The mean flying heights were 750 m and 775 m above ground level for the first and second flights respectively, with a maximum scan angle of ±12, and a beam divergence of 0.2 mrad resulting in a footprint diameter at nadir of approximately 18 cm. The mean point density for each flight line was approximately 1.5points m 2. At each date, three strips were flown in a orth South direction, without overlap and the total area covered was about 382 km 2. The data provided by the ERC included X, Y, Z coordinates and intensities of first and last returns. Data from both flights were used together after verification and adjustment of a small relative spatial offset between dates (García et al., 2009) resulting in an effective increase in the point density. After integrating both datasets, the point density over the areas corresponding to field plots ranged from 1.5 to 4.5 points m Field-based biomass fractions estimation Field data were recorded during July and August 2006 and August circular plots with a radius of 10 m were placed within several stands distributed along the three orth South flight lines. The stands were representative of the main species present in the area covered by the LiDAR data, namely black pine (P. nigra Arn.), Spanish juniper (Juniper thurifera L.), and Holm oak (Q. ilex L.). Plot selection was done using as reference an Airborne Thematic Mapper (ATM) image, which was acquired simultaneously with the LiDAR data, and the study site was visited to assure the accessibility to the field plots. In addition, since the flight strips did not overlap, intensity images were generated from the cloud points and used to ensure that the field plots were covered by the LiDAR data. To determine the coordinates of each plot, reference points were placed in open areas (e.g. crops, pastures, etc.) based on differential GPS positioning using an Ashtech/ Z-Surveyor system and a geodetic vertex located within the study area as a permanent station. From these reference points, a total station was used to locate the centre of each plot by standard surveying methods. This was necessary because of the effect of dense crown cover on the accuracy of GPS positions, which may be a significant error source in forest areas (aesset, 2001). Thus, after considering the error propagation of the GPS and the total station methods, all plots were located with an accuracy better than 15 cm, which was

3 818 M. García et al. / Remote Sensing of Environment 114 (2010) Fig. 1. Location of the study area. Enlarged figure shows the LiDAR data (gray) over an ATM image (R: IR, G: red, B: green) of the atural Park of the Alto Tajo. considered adequate given the nominal accuracy of the LiDAR data (50 cm in X,Y) and the plot-level scale of the study. In each plot, tree species was recorded and the diameter at breast height (DBH) was callipered for each tree with a DBH 10 cm. In addition, following a systematic sampling strategy, four individual trees were selected, one in each direction (orth, South, East and West), and their height, crown diameter and height of the first living branch were measured using a fibreglass tape and a hypsometer. Species-specific allometric equations were applied to estimate total aboveground biomass, biomass of branches and foliage biomass. Montero et al. (2005) developed a non-linear model based on DBH to estimate biomass fractions for the main trees species in Spain. They separated branches in three groups according to their diameter (d): d 2cm, 2cmbd 7cm, d7 cm. In this study, after estimating the biomass of branches in accordance with their diameter, the three groups were merged into a single class to provide a single estimate for branches. In the case of the black pine, Montero et al. (2005) did not provide equations to estimate foliage biomass, and hence the equations used in the Ecological and Forestry Inventory of Cataluña were applied (Burriel et al., ). The total aboveground biomass resulting from adding the values obtained for each biomass component was slightly higher than the value retrieved by applying the allometric equation derived to estimate total aboveground biomass. In order to avoid this overestimation, Montero et al. (2005) proposed correction of the estimated biomass fractions by multiplying them by the percentage of the total biomass that each component (foliage, branches, etc.) represented. Finally, for each plot the total aboveground biomass, the biomass of branches and foliage biomass, were obtained by summing the values for the individual trees LiDAR data processing LiDAR points were classified into ground and non-ground (vegetation) returns with a morphological filter. Starting from the lowest points, which were assumed to be ground returns, the algorithm builds an initial triangulated irregular network (TI), and the routine iteratively adds new points that satisfy certain distance and angle thresholds. These thresholds determine how close a point must be to a triangle plane to be added to the new TI (Soininen, 2005). A digital terrain model (DTM) was created from the ground points by applying a spline interpolation method. The height above the ground of each vegetation point was computed as the difference between the Z coordinate of the point, and the Z value of the DTM at the same X, Y position: h i = Z i Z int erpolated Both first and last return data corresponding to each field plot were clipped from the LiDAR dataset and several variables were derived from the height distribution of canopy returns (Table 1). To classify canopy returns, and separate them from the rest of non-ground points, a threshold was applied according to the height of the first living branch measured in the field. For a few of young black pine and Spanish juniper dominated plots the height to the first living branch was close to zero. Thus, in order to avoid ground returns being included, a 30 cm threshold was applied to separate them from canopy returns. Though different authors have selected different height thresholds to avoid confusion with ground points (Maltamo et al., 2005), the 0.3 m threshold was considered sufficient after visual inspection of those plots. The 25th, 50th, 75th, 90th and 99th percentiles and mean canopy height were obtained for each plot. Height quantiles have shown to be suitable to estimate foliage and above ground biomass (Lim & Treitz, 2004) inotherforesttypessuchasuneven-aged,maturetoovermature hardwood forest, as well as to estimate carbon content (Patenaude et al., 2004). Since LiDAR beams exhibit differential penetration into forest canopies, information on canopy structure can be inferred. Hence, several distribution measurements related to the canopy structure were ð1þ

4 M. García et al. / Remote Sensing of Environment 114 (2010) Table 1 Metrics derived from the height distribution of LiDAR data. Height metrics Label Characteristics a 25th percentile P_25h Variables related to biomass based on 50th percentile P_50h the relationship of biomass with height 75th percentile P_75h 90th percentile P_90h 99th percentile P_99h Mean height Mean_h Standard deviation Std_h Characterizes the canopy structure based on the distribution of returns Canopy length CL_h Provides a measure of the distribution of canopy material Kurtosis Kurt_h Provides a summary of the canopy Skewness Skew_h structure, based on the shape of the height distribution Coefficient of variation CV_h Summarizes the relative dispersion of the height data within the canopy 99th percentile 50th percentile Dif_99_50_h Characterizes the distribution of biomass within the canopy 99th percentile 25th Dif_99_25_h percentile 90th percentile 50th Dif_90_50_h percentile 90th percentile 25th Dif_90_25_h percentile Proportion of canopy hits CanHits_h Provides a measure of the canopy cover a See text for more detail. generated in this study, namely canopy length (maximum minus minimum height of canopy returns) and standard deviation of heights. In addition, the skewness, kurtosis and coefficient of variation of the height distribution were computed, which provide a summary of the canopy structure based on the vertical distribution of the heights of the laser returns (Donoghue et al., 2007; Jensen et al., 2008). Since the penetration depth is affected by the arrangement of the leaf and woody material within the canopy, Jensen et al. (2008) used percentile differences as an indication of the biomass distribution within the canopy. Following this approach, the following percentile differences were computed from the canopy returns to describe the biomass distribution: 99th 50th, 99th 25th, 90th 50th and 90th 25th. Finally, the proportion of canopy hits, defined as the ratio of canopy returns to all returns, was computed to represent the canopy cover (Morsdorf et al., 2006; Solberg et al., 2006). This variable, expressed as a percentage, was found by Riaño et al. (2004) to be the most accurate estimator of leaf area index (LAI) and canopy cover in two contrasting forests in central Spain Intensity data In addition to the X, Y and Z triplets, LiDAR systems are able to record the intensity of the returns, which is a measure of the amount of energy reflected back to the sensor. The intensity recorded by LiDAR systems is a function of many variables such as laser power, incidence angle, target reflectivity and area, atmospheric absorption and the range (sensortarget distance) (Coren & Sterzai, 2006). A special form of the radar equation was developed by Wagner et al. (2006) to describe the intensity of the return of a laser pulse, neglecting atmospheric transmittance and introducing the cross-section of the scatterer, which is a function of the illuminated area and the reflectance of the target (see Wagner et al. (2006) for more details). In order to compare intensity values between different scans, regions or flights, it is therefore necessary to calibrate the intensity data. The simplest method to correct intensity consists of normalization of the range to a user-defined standard range: I = I* R2 R 2 S ð2þ where I is the normalized intensity, I is the raw intensity value, R is the range (sensor-target distance) and R s is the standard range (e.g m). This method, applied in several studies (Starek et al., 2006; Donoghue et al., 2007), eliminates the effect of path length variations on the intensity recorded by the system, providing values equivalent to the intensity that would have been recorded if all points were at the same range. Höfle and Pfeifer (2007) developed a data-driven correction, which empirically estimated the parameters of a global correction function by least-squares adjustment, accounting for all range-dependent influences. This approach required homogeneous areas flown at different altitudes to derive the parameters of the global function and was not considered here. Other more sophisticated correction models have been presented by Coren and Sterzai (2006) and Höfle and Pfeifer (2007) which take into account not only the spreading loss, but the effect of the LiDAR incidence angle, atmospheric effects and even the effect of the pulse repetition rate of the emitted energy on the intensity. However, since the application of these methods requires data that were not available for this study, normalization to a standard range was applied, using the minimum height of the study area. The range and scan angle were not available for each LiDAR point, therefore the range of each point was approximated as the difference between the average altitude of the flight and the altitude of each point. Due to the small scan angle (±12 ), considering the altitude difference instead of the actual range should not cause large errors. To assess the effect of the range normalization performed, the median intensity value of several circular plots with a radius of 2 m was extracted over different asphalt roads present along the three flight strips. The roads sampled presented the same type of asphalt, could be considered invariant in terms of reflectance, and were located at different altitudes ranging from 1068 m to m. As for the effects of the incidence angle on the intensity, several studies have shown that, for small angles (up to 15 ), this effect can be neglected (Coren & Sterzai, 2006; Kukko et al., 2008). To verify this assumption, an asphalt road that crossed one flight strip from east to west was used. Once corrected for the effect of the path length variation, any residual trend in the intensity values along the road would be associated with the effect of the scan angle, given the across-track direction of the road and because other factors (atmospheric conditions and emitted energy) could be considered constant, since the same instrument was used, and the atmospheric conditions were similar for both flights. The median normalized intensity value of each sampled plot was plotted to verify that the data had no trend. The Optech-ALTM3033 system used in this study operates in the near infrared region at a wavelength of 1064 nm. This region of the electromagnetic spectrum is particularly suitable for vegetation studies due to the high spectral reflectance of vegetation at this wavelength given that the reflectance of leaves is generally higher than woody material due to multiple internal scattering within leaves, although the magnitude of the difference will vary between species. One important aspect of the intensity of the returns reflected from canopy elements is the fact that the laser pulse is reflected by objects at different depths within the canopy giving rise to multiple peaks in the return waveform. Therefore, variation of the intensity of the returns from forest canopies is associated with variations in the distribution and density of canopy elements within the beam, the reflectivity of the foliage elements, and the algorithm used to interrogate the return waveform (Moffiet et al., 2005). At a plot level there are structural features at both coarse scale (e.g., canopy openness) and fine scale (e.g., vegetation elements) that contribute to the average and standard deviation of the LiDAR intensity for the plot and smooth the effects of multiple target reflections. Although many studies using intensity have relied on the use of first echoes only (Donoghue et al., 2007; Korpela et al., 2008) other authors have shown the utility of using all returns at the plot level (Holmgren & Persson, 2004; Hopkinson & Chasmer, 2009; Solberg,

5 820 M. García et al. / Remote Sensing of Environment 114 (2010) ). Hopkinson and Chasmer (2009) found that fractional cover was better estimated using the intensity of all returns than using the commonly used echo-count methods. Although these authors proposed a method to correct the intensity of the returns due to transmission loss, this correction had little effect on the results. For this study, first and last returns were used together, since last returns represent a deeper penetration of the beam into the canopy and can provide valuable information of the canopy components, and even on the lower canopy layers or understory vegetation. Although it is not possible to directly determine the reflectance of objects with LiDAR data, it is expected that the intensity of the returns will be sensitive to different components and hence will provide additional information to allow estimation of different biomass fractions, given their partial dependence on the reflectance of the object within the beam. After normalization of the intensity values to a standard range, several variables were derived for each plot (Table 2). In the same way as for the height distributions, the mean intensity value and several percentiles of the intensity values were obtained from the canopy returns (25th, 50th, 75th, 90th and 99th). These percentiles were expected to be sensitive to the different canopy materials (biomass fractions) given the different spectral response of leaf and woody materials in the IR region. To capture intensity-related structural characteristics, the following variables were derived: kurtosis, range, skewness, coefficient of variation and standard deviation of intensity. Canopy cover was represented as a function of the reflectance of the different canopy elements by computing the proportion of the intensity of canopy hits, that is, the sum of canopy intensity divided by the sum of the intensity of all returns for a given plot. This variable has been found to be strongly related to LAI and canopy gap fraction in a orwegian pine forest (Solberg, 2008) and across different forest ecozones in Canada (Hopkinson and Chasmer, 2009). Since this variable is defined using ground returns it can be affected by the differences in reflectance between canopy and ground, and therefore, it should be corrected for this effect (Lefsky et al., 1999; Means et al., 1999; Morsdorf et al., 2006). However, the correction factor to be applied is site dependent and since for the study area used in this research it was not determined, no such correction was applied. Table 2 Metrics derived from the normalized intensity of the LiDAR data. Intensity metrics Label Characteristics a 25th percentile intensity P_25i Captures the difference in reflectivity 50th percentile intensity P_50i of the different canopy components 75th percentile intensity P_75i 90th percentile intensity P_90i 99th percentile intensity P_99i Mean intensity Mean_i Standard deviation intensity Std_i Characterizes the canopy structure Coefficient of variation CV_i based on the intensity of the returns Range of intensities Range_i Skewness Skew_i Characterizes the canopy structure Kurtosis Kurt_i based on the shape of the distribution of intensity values Proportion of canopy hits CanHits_i Provides a measure of the canopy cover % of intensity of the height percentile 25 % of intensity of the height %Int_P25 %Int_P50 Provides a measure of the biomass distribution within the canopy. These variables implicitly consider the height percentile 50 % of intensity of the height %Int_P75 percentile 75 % of intensity of the height %Int_P90 percentile 90 % of intensity of the height %Int_P99 percentile 99 Canopy reflection sum CRS Provides a measure of canopy closure Density weighted canopy reflection sum DWCRS Provides a measure of canopy closure normalizing the different pulse density for each plot to the mean density of the flight a See text for more detail. A set of variables were proposed that take into account the percentage of the total intensity accumulated in each of the height percentiles considered. These metrics, percentage of intensity of the ith-height percentile, were expected to describe how the biomass is distributed within the canopy, based on the intensity accumulated at different height percentiles. It should be noted that these variables implicitly consider the height distribution of the returns since they are defined based on the ith-height percentiles. Means et al. (1999), using the full-waveform SLICER system, derived a metric called the Canopy Reflection Sum (CRS) to describe the canopy closure, which was found to be the most accurate predictor of foliage biomass in a coniferous forest in Oregon, USA. This variable was defined as the sum of the portion of the waveform return reflected from the canopy (Means et al., 1999). Using a discrete-return system Hall et al. (2005) defined the CRS as the sum of the intensity of all returns divided by the area of the sampled site to compensate for the different sizes of their plots. Since the field plots used in this study had the same size, the canopy reflection sum was simply defined as the sum of the corrected intensity of all canopy returns. Still, given that the point density of the field plots varied between 1.5 points m 2 and 4.5 points m 2, a larger CRS could result from a larger number of points for a given plot. Therefore, to take into account the influence of the different point density along the flight lines on the estimation of the CRS, a correction was applied to be used with discrete-return system. The correction factor, or weight, was calculated as the mean point density of the whole data set divided by the density at a given plot, and it was applied to the canopy reflection sum to provide the Density Weighted Canopy Reflection Sum (DWCRS): DWCRS = n I T i =1 P dflight d plot where: n I : Sum of the normalized intensity of canopy reflections. i =1 d flight : Mean point density for the whole data set. d plot : Point density of the plot considered Statistical analyses Statistical analyses were carried out separately for height and intensity metrics and for height and intensity metrics together derived from the LiDAR dataset. First, a general model was developed to estimate biomass for the whole study area that included all species. In order to identify an appropriate model and select the variables with the greatest explanatory power from the LiDAR-derived metrics, a stepwise regression was initially carried out, in which the selection of explanatory variables is automatically performed. This method initially includes the independent variable showing the highest R 2 with the dependent variable. Additional variables are included into the model based on an F-test, under the assumption of normality of the variables. Since it is an iterative process, the variables were previously normalized using logarithmic or inverse transformations where necessary. The Variance Inflation Factor (VIF) was also investigated to identify the existence of collinearity in the selected models. Subsequently, to provide a more robust estimation of coefficients of the selected parameters, a Jackknife technique was subsequently applied, following a similar approach used by Lucas et al. (2008). The Jackknife systematically omits one observation at a time, and performs n-estimations of the parameters using n 1 observations. Finally the mean value of the n-estimations was selected as the definitive parameter, and a measure of dispersion, the standard deviation, was used to assess the uncertainty of parameter estimation. This method also provides information on the influence of each observation on the model. ð3þ

6 M. García et al. / Remote Sensing of Environment 114 (2010) Table 3 Measures used for model performance evaluation. Accuracy measurement Definition Characteristics vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Root mean square error O (RMSE) ðp i O i Þ 2 i : Observed value. u t P i =1 i : Model-predicted value. : umber of observations. Mean absolute difference (MAE) Systematic root mean square error (RMSE S ) Unsystematic root mean square error (RMSE U ) Index of agreement (d) jp i O i j i =1 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u t ðˆp i O i Þ 2 i =1 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u t ðp i ˆP i Þ 2 i = ðp i O iþ 2 i =1 5 ðjp 0 i j + j O0 i jþ2 i =1 0 d 1 MAE and RMSE are similar measures; however MAE is less sensitive to extreme values. Pî: Estimated value based on the ordinary least-squares regression, Pî =a+bo i. Explains how much of RMSE is systematic, and assess the model errors that are predictable. It should approach to zero. Explains how much of RMSE is unsystematic, and therefore is not predictable mathematically. It should approach to RMSE P i =P i O O i =O i O O : is the mean observed value d: is a descriptive measure which can be widely applied to make cross-comparisons between models. In order to assess the effect of the species composition, speciesspecific LiDAR equations were developed according to the dominant species in the plot. A species was considered to be dominant if its basal area represented more than the 60% of the total basal area of the plot. Following this criterion, 19 out of 45 plots were classified as black pine, 11 were considered Spanish juniper, and 7 were Holm oak. The remaining 9 plots were considered a mixture of species. The 60% threshold was applied in an attempt to increase the number of samples for Spanish juniper and Holm oak; however, for most of the plots the dominant species presented basal areas greater than 75%. Subsequently, stepwise regression was applied to define the speciesspecific model, and a Jackknife analysis applied as before. Following the approach described above, the three strongest models were selected for each species considering: 1) height metrics alone, 2) intensity metrics, and 3) height combined with intensity metrics. In many cases, there was only a small difference between the strongest models in terms of R 2.AccordingtoWillmott (1982), ther 2 value can often lead to misleading conclusions when comparing the performance of several models, as the coefficient of determination is often unrelated to the sizes of the difference between observed and predicted values. Several researchers have proposed to use an array of complementary measures to evaluate model performance since no single index can completely describe it (Fox, 1981; Willmott, 1982). Thus, a set of measures proposed by Willmott (1982) were used to evaluate and select the most appropriate models(table 3). These measures evaluate the mean difference between observed and predicted values (mean absolute error (MAE) and root mean square error (RMSE)), the relative error in model estimates (index of agreement (d)), the systematic portion of the RMSE (RMSE S ) which should approach zero, and the unsystematic portion of the RMSE (RMSE U ) which should approach the RMSE. The measures used to assess the performance of the models were obtained after back-transformation of the variables. Although some authors indicate the need to correct for the bias introduced by log transformations (Beauchamp & Olson, 1973; Sprugel, 1983), others have shown that the bias is generally small (Pastor & bockheim, 1981; Turner & Long, 1975) and application of the correction factor does not improve the estimations of biomass. Therefore, for this study no correction factor was applied Effects of point density on biomass estimation Point density has been shown to be even more important than footprint size or flight altitude, in determining certain forest variables such as crown area and volume (Goodwin et al., 2006). To assess the effect of point density on the estimation of aboveground biomass and biomass fractions, the estimates from the LiDAR data acquired on May Fig. 2. Variation in LiDAR intensity with the sensor-object distance (range), before (filled circles) and after range dependency removal (empty circles).

7 822 M. García et al. / Remote Sensing of Environment 114 (2010) Fig. 3. Variation in LiDAR intensity over an asphalt road crossing one of the flight strips from east to west. 16th 2006 were compared to the estimates derived from both flights together. To facilitate the comparison, the previously selected models from using data from both flights were applied to the variables derived from data for a single flight. The measures proposed by Willmott (1982) were used to assess whether the reduction of point density weakened the performance of the models developed to estimate biomass fractions Carbon stocks estimation A biomass to carbon fraction was applied to convert biomass values to carbon content. This fraction varies between 45% and 53% depending on species, however a default value of 50% is commonly applied (Marklund & Schoene, 2006). Montero et al. (2005) provided species-specific parameters which were applied in this study to estimate the carbon content. Thus, the carbon content for black pine was 50.9% of the total biomass, whereas for Spanish juniper and Holm oak the carbon content was estimated to be 47.5% of the total biomass. To provide plot-level estimation of carbon content the LiDAR-derived biomass values for each plot were multiplied by the carbon fraction parameter of each species considered, and then weighted by the basal area of the species in the plot. 3. Results 3.1. Intensity normalization Fig. 2 shows the effect of range on the intensity data. An obvious trend with range is observed before the normalization of the data (filled circles) that is clearly reduced after range normalization (empty circles). Given the rough terrain of the study area the variation of the sensor-object distance constituted an important source of intensity variation. Prior to the normalization of the data to a standard range (minimum height), the intensity values were correlated to range (r= 0.68; p-value: 0.000), which was significantly reduced (r=0.38; p-value=0.009) after eliminating the range dependency. The remaining correlation between range and intensity after normalization of the data may be a consequence of the approximations made in the process, for example using the mean flying height when we know that this was probably not constant along a flight line. Fig. 3 shows the variation of intensity over an asphalt road in an across-track direction to that of the flight. After normalizing the data to a standard range, variations in intensity in either side of the midpoint would be related to the scan angle but it can be seen that after normalization to a standard range, there is no apparent spatial trend in the intensity data Empirical models Table 4a shows the explanatory variables selected for each general model and the coefficient of determination obtained for the 45 plots using height, intensity, and height combined with intensity metrics. It also shows the VIF used to assess the existence of multicollinearity of the models. Although there is no unanimity regarding what values of VIF indicate the existence of collinearity between the explanatory variables, it is commonly accepted that values above 10 are indicative of multicollinearity. It is observed that the selected models did not present collinearity problems. The models derived from height and intensity variables together provided the best models in terms of variance explained, with R 2 values of 0.67; 0.67 and 0.60 for aboveground biomass (AGB), branch biomass (BB) and foliage biomass (FB) respectively. For the models based on the height distributions, a percentile variable was selected for all biomass fractions, whereas for foliage biomass the proportion of canopy hits, which is a measure of canopy cover, was also selected. Using intensity, the variables selected were related to the canopy cover as well as to the intensity accumulated at different height percentiles, that is, variables that were related to the distribution of biomass within the canopy. For foliage biomass, the highest intensity percentile was also selected. The results obtained applying species-specific models(table 4b) showed higher coefficients of determination. In general terms, the height-related models were based on one explanatory variable, a quantile of the height distribution, while more complex models, based on two or three independent variables, were obtained from intensity or the combined used of height and intensity. All intensity models, except that for the foliage biomass of Holm oak, selected a variable related to the intensity accumulated at a given height percentile. For black pine, intensity-related models showed higher coefficient of determination (R 2 AGB=0.91; R 2 BB=0.89; R 2 FB=0.89) than those using height only, and explained slightly more variance than the models derived using height combined with intensity variables. Models derived from height distribution data were based on the mean canopy height and a percentile of the height distribution, whereas for the intensity models, variables related to canopy closure, canopy structure and the intensity accumulated at the lowest height percentile were selected. The combined models selected the mean canopy height and a percentile of the height distribution and an intensity variable related to the distribution of canopy biomass (%IntP25). In the case of aboveground biomass for Spanish juniper, the same independent variable (%IntP75) was selected when using intensity data alone or height combined with intensity data. %Int_P75 was selected as an explanatory variable in the intensity model for aboveground biomass, branch biomass and, for the foliage biomass

8 M. García et al. / Remote Sensing of Environment 114 (2010) Table 4 Predictor variables selected for each model obtained to estimate biomass from LiDAR data. Species Estimated variable LiDAR Predictor variables selected and VIF b R 2 dataset a a) General models All (n=45) AGB a 1/P50_h 0.58 b %Int_P50 (1.06); CV_i (1.01); 0.59 CanHits_i (1.05) c 1/P50h (1.03); %Int_P25 (1.03) 0.67 BB a 1/P25_h 0.51 b %Int_P75 (1.08); %Int_P99 (1.08); 0.48 CanHits_i (1.00) c 1/P25h (1.22); P75_i (1.15); 0.67 %Int_P99 (1.08) FB a 1/P90_h (1.08); exp(canhits_h) (1.08) 0.38 b P99i (1.01); CanHits_i (1.00); 0.58 %IntP90 (1.01) c P99i (1.04); %IntP50 (1.47); exp(canhits_h) (1.07); %IntP90 (1.44) 0.60 b) Species-specific models. A species was classified as dominant if its basal area was 60% of the total basal area of the plot Black pine AGB a 1/mean_h 0.73 (n=19) b CV_i (1.60); %Int_P25 (1.48); 0.91 DWCRS (1.45) c 1/mean_h (1.00); %Int_P25 (1.00) 0.88 BB a 1/P90_h 0.69 b CV_i (1.60); %Int_P25 (1.48); 0.89 DWCRS (1.45) c 1/P90_h (1.00); %Int_P25 (1.00) 0.86 FB a 1/mean_h 0.76 b CV_i (1.60); %Int_P25 (1.48); 0.89 DWCRS (1.45) c 1/mean_h (1.00); %Int_P25 (1.00) 0.87 Spanish AGB a 1/P25_h 0.58 juniper b %Int_P (n=11) c %Int_P BB a 1/P25_h (1.39); (1/ Dif_99_50_h) (1.39) 0.76 b %Int_P c %Int_P75 (1.21); Sqrt(CV_h) (1.21) 0.85 FB a 1/P25_h 0.66 b %Int_P75 (1.26); P99_i (1.26) 0.86 c %Int_P75 (1.56); P99_i (1.30); Sqrt(CV_h) (1.24) 0.96 Holm oak (n=7) a b AGB a 1/P99_h (1.29); exp(canhits_h) (1.29) 0.93 b DWCRS (1.00); %Int_P90 (1.00) 0.98 c 1/P99_h (1.02); %Int_P75 (1.02) 0.96 BB a 1/P99_h 0.79 b DWCRS (1.00); %Int_P90 (1.00) 0.98 c 1/P99_h (1.02); %Int_P75 (1.02) 0.96 FB a exp(canhits_h); 1/P99_h 0.99 b DWCRS (4.12); Range_i (4.12) 0.96 c DWCRS (1.57); Ln(CL_h) (1.57) 0.96 a: Height ; b: Intensity, c: Height and Intensity. In brackets VIF, presented only for multiple models. model, it was selected in combination with a percentile of the intensity. Regarding the model based on the height data, a percentile of the height (1/P25_h) distribution was selected as an explanatory variable; however, the model to estimate branch biomass also selected a variable related to the distribution of biomass within the canopy (1/Dif_99_50_h). The models derived by the combined use of height and intensity variables explained more variance than the models using height and intensity separately, with R 2 values of 0.72 for aboveground, 0.85 for branch biomass, and up to 0.96 for foliage biomass. The results for Holm oak must be treated cautiously given the very small sample size, although they do provide some information on the potential variables to explain the variance. The models derived to estimate aboveground biomass and foliage biomass from height data, used a percentile of the height distribution and a measure of the canopy cover as explanatory variables, though they were introduced into the model in a different order (the weight of the variables changed from one model to another). The model obtained to estimate branch biomass from height data was based on the 99th percentile of the height distribution alone. As for the intensity-derived models, the same explanatory variables were selected to estimate aboveground and branch biomass, both based upon the DWCRS and the intensity accumulated at the 90th percentile of the height distribution. The DWCRS was also selected along with the range of intensities as predictor variables to estimate foliage biomass. Intensity models showed the highest explanation of variance when used to estimate aboveground and branch biomass, whereas to estimate foliage biomass the height model showed the highest R 2 value. Table 5 presents the results of the set of measures proposed by Willmott (1982) to assess the performance of the different models. General models to estimate aboveground biomass performed similarly; nevertheless the model combining height and intensity variables provided the lowest errors and highest index of agreement. In the case of branch biomass, the combined model showed the lowest errors and the value of the index of agreement was much higher than the index agreement obtained by height or intensity models alone. In contrast, for the foliage biomass estimation, the model using intensity data alone showed a small increase in performance compared to the model combining height and intensity variables. It should be noted that for all general models using height or intensity-derived variables alone, the RMSE S was higher than the RMSE U, while models using height combined with intensity variables overcame this problem with the exception of the branch biomass fraction model. Table 5 Model performance measures proposed by Willmott (1982). Species Estimated variable LiDAR MAE b RMSE b b RMSE S dataset a RMSE U b All (n=45) AGB a b c BB a b c FB a b c Black pine (n=19) Spanish juniper (n=11) Holm oak (n=7) a b AGB a b c BB a b c FB a b c AGB a b c The same model as for Intensity was obtained BB a b c FB a b c AGB a b c BB a b c FB a b c a: Height ; b: Intensity, c: Height and Intensity. Derived after back-transformation of variables. d b

9 824 M. García et al. / Remote Sensing of Environment 114 (2010) For black pine, intensity models showed higher accuracy than height models, particularly for aboveground biomass estimation, and performed slightly better than models based on the combined used of height and intensity data. The magnitude of the errors for the intensity model was almost half those of the height model, and the index of agreement was 14% higher. There was almost no difference between the intensity model obtained to estimate foliage biomass and the combined model. In the case of Spanish juniper, the model derived to estimate aboveground biomass using intensity data showed stronger results than the height model, with an increment of 7% in the index of agreement, and a reduction in the systematic RMSE to half that of the height model. Regarding the branch biomass and foliage biomass estimation, the integrated use of height and intensity provided the highest performance, though differences were small. Finally for Holm oak dominated plots, intensity models to estimate aboveground biomass were slightly better than models combining height and intensity data, but performed significantly better than the height model. Errors for the intensity model were half the magnitude of the errors obtained with the height model. For branch biomass estimation, models derived from intensity also provided the highest accuracy. In the case of foliage biomass very similar results were obtained for all models, although the model obtained from height data presented the lowest errors. It should be noted that after applying species-specific models based on intensity or the combined used of height and intensity variables, the systematic RMSE was lower than the unsystematic RMSE; however, this proportionality of systematic and unsystematic parts of the RMSE was not always fulfilled by height models. Therefore, since a good model should show a systematic portion much lower than the unsystematic, intensity and combined models performed better than height models. Table 6 presents the final models selected after consideration of the previous model performance measures. It also presents the standard deviation of the parameters derived, which indicate the uncertainty of the estimation of the parameters. Fig. 4 presents the scatterplots of observed biomass fractions versus predicted values obtained with the best regression models derived from the LiDAR dataset. It can be observed that the general Table 6 Models selected to estimate biomass fractions from LiDAR data. Species Model All Ln(AGB) = (1/p50h) (%intP25) Param stand dev: ±0.05 ±0.10 ±0.002 Ln(BB) = (1/p25h) (P75_i) (%intP99) Param std dev: ± ± ±0.001 ±0.043 Ln(FB) = (P99i) (CanHits_i) (%IntP90) Param std dev: ±0.55 ±0.001 ±0.058 ±0.007 Black pine Ln(AGB) = (C.V._i) (%intP25) (DWCRS) Param std dev: ±0.219 ±0.292 ±0.004 ±0.030 Ln(BB) = (C.V._i) (%intP25) (DWCRS) Param std dev: ±0.249 ± ±0.004 ±0.036 Ln(FB) = (C.V_i) (%intP25) (DWCRS ) Param std dev: ±0.094 ±0.166 ±0.002 ±0.013 Spanish Ln(AGB) = (%intP75) juniper Param std dev: ±1.017 ±0.014 Ln(BB) = (%IntP75) (sqrt(CV_h)) Param std dev: ±0.923 ±0.014 ±0.568 Ln(FB) = (%IntP75) (P99i) (sqrt(CV_h)) Param std dev: ±0.787 ±0.012 ±0.001 ±0.154 Holm Ln(AGB) = (DWCRS) 0.55(%IntP90) oak Param std dev: ±4.823 ±0.026 ±0.055 Ln(BB) = (DWCRS) (%intP90) Param std dev : ±5.329 ±0.028 ±0.061 Ln(FB) = (exp(CanHits_h)) (1/p99h) Param std dev: ±0.218 ±0.074 ±0.626 model derived using data from all field plots, provided good estimations for the different biomass fractions considered. In all cases the intercept was not significantly different from zero (p-value0.05) and the slope of the observed versus predicted values regression line was not significantly different from one (p-value0.05) except for branch biomass (p-value=0.0256). When considering the species-specific equations, the regression lines of observed versus predicted values provided lines closer to the 1:1 line than the general models. For all species and biomass fractions considered, the slope and intercept were not significantly different from one and zero respectively (p-value0.05). For the Holm oak plots, high R 2 values may be the result of one particular plot presenting very high biomass values compared to the rest of the plots, significantly increasing the total range of biomass values. When eliminating that plot, R 2 values remained very high (above 0.95) for all biomass fractions Effect of point density on biomass estimation To test the effects of reduction of the point density the data analysis was repeated using only the first flight data (May 16th 2006), resulting in a point density for each plot between 0.97 points m 2 and 2.1 points m 2. Table 7 shows the performance of the models after reducing the point density. Very similar results were obtained when considering the general model for all species; with variations of R 2 values smaller than 5% and very similar error values compared to the values obtained using the whole dataset. Examination of species-specific models showed that results for black pine were weaker if point density was reduced. The coefficient of variation and the index of agreement were up to 20% lower than those obtained using both data sets, and the errors significantly increased, up to three times. In the case of Spanish juniper the determination coefficient improved up to 9%, but the index of agreement and the errors remained nearly the same. Estimations of aboveground and branch biomass fractions for the Holm oak plots were much less accurate when the reduced point density dataset was used, with R 2 values reduced by more than a 30% and errors much larger Carbon stocks estimation Table 8 presents the mean, maximum and minimum carbon contained in the vegetation biomass and its different fractions for the study area. The carbon contained in the aboveground biomass, as derived from the biomass estimated by the general model, ranged from 7.69 and Mg ha -1, with a mean value of Mg ha 1. The mean carbon content for branch biomass was Mg ha 1, ranging between 2.98 and Mg ha 1 and for foliage biomass the estimated carbon content varied between 0.75 and 4.85 Mg ha 1 with a mean value of 1.78 Mg ha 1. Considering the carbon content of each species estimated accordingly to their species-specific equations, Holm oak dominated plots presented the highest mean value for the carbon contained in the aboveground biomass (44.66 Mg ha 1 )followedby black pine (30.82 Mg ha 1 ) and Spanish juniper (27.26 Mg ha 1 ). This pattern was also observed for the branch biomass carbon content with mean values of Mg ha 1, Mg ha 1 and Mg ha 1 for Holm oak, Spanish juniper and black pine respectively. Plots dominated by Spanish juniper gave the highest mean value of carbon contained by foliage biomass (3.04 Mg ha 1 ), followed by black pine dominated plots (1.63 Mg ha 1 ) and plots where the dominant species was Holm oak (1.37 Mg ha 1 ). 4. Discussion 4.1. Intensity normalization ormalization of the intensity data to a standard range removed the effect of range variation along the flight strips caused by variable