Forestry An International Journal of Forest Research

Forestry An International Journal of Forest Research Forestry 2016; 89, 159 169, doi:10.1093/forestry/cpv048 Advance Access publication 13 December 2015 Characterising forest structure using combinations of airborne laser scanning data, RapidEye satellite imagery and environmental variables Jonathan P. Dash 1 *, Michael S. Watt 2, Santosh Bhandari 3 and Pete Watt 3 1 Scion, PO Box 3020, Rotorua, New Zealand 2 Scion, PO Box 29237, Fendalton, Christchurch, New Zealand 3 Indufor Asia-Pacific Ltd, PO Box 105039, Auckland, New Zealand *Corresponding author. E-mail: jonathan.dash@scionresearch.com Received 29 July 2015 The objective of this study was to compare the utility of combinations of data from airborne laser scanning (ALS), RapidEye satellite imagery and auxiliary environmental data to predict stand structure in a plantation forest. Both parametric and non-parametric modelling techniques that could simultaneously predict a multivariate response were employed and found to produce predictions with similar levels of accuracy. Response variables were derived from 463 field measurement plots that were used during model development; a further 60 randomly selected plots were set aside for validation of model performance. Candidate predictor variables were extracted from the ALS data, satellite data and auxiliary environmental data, and the variables with the greatest explanatory power were used to create six separate models based on combinations of the data sources. Model validation showed that models using RapidEye data only were the least precise and that adding auxiliary environmental data only led to a moderate improvement in model precision. The model precision observed was similar to those reported previously from studies using satellite data to predict stand structure. Models developed using data from ALS were by far the most precise and adding information from satellite data or auxiliary environmental data led to negligible improvement in the prediction of stand structure. Although the outputs of both model types were similar, the practical efficiencies of using the non-parametric approach make it appealing to meet the demands of managers of industrial plantation forest managers. Introduction A detailed knowledge of forest structure provides a basis for understanding important ecosystem functions such as carbon sequestration, hydrological cycling, timber resource provision and habitat availability. At national, or regional, scales, this information is used to guide policy and land-use decisions. Since their origins in Scandinavia in the early twentieth century, sample-based national forest inventories have long provided this information (Tomppo et al., 2010). For operational forest managers, stand-wise forest inventory information supports planning of silvicultural and harvesting operations, underpins forest valuations and marketing and informs decisions about recreation and biodiversity management (Husch et al., 2003). At both national and stand-wise scales, forest inventory relies on field measurements and statistical theory to provide a means for inference about the population of interest. Since the advent of the application of aerial photography to forest inventory in the 1950s (McRoberts et al., 2010) remote sensing systems have played an important role in forest inventory. Auxiliary data from remote sensing have been shown to offer substantially improved precision compared with conventional measurement approaches (Næsset, 2002, 2004; Naesset, 2007; Dash et al., 2015). Furthermore, it also provides a mechanism for deriving forest attribute maps that are valuable to forest managers. Since the launch of the LandSat-1 satellite in 1972, researchers have sought to take advantage of the data collected by spaceborne sensors to improve estimates of forest structure. Subsequent satellite deployments with upgraded capabilities have provided improved image resolution, increased temporal frequency of return to a given point on the Earth s land surface and greater spectral coverage (McRoberts et al., 2010). Numerous studies have shown that multispectral satellite data can be utilized to model and map important forest attributes across forest landscapes (Donoghue and Watt, 2006; Kayitakire et al., 2006; McRoberts and Tomppo, 2007; Ozdemir, 2008; Shamsoddini et al., 2013). High-resolution optical satellite imagery is amongst the most useful forms of remote sensing for predicting forest structure (Shamsoddini et al., 2013). Very high-resolution systems such as the Worldview satellites remain prohibitively expensive for regular forest management assessments and the lower resolution of cost-effective options such as LandSat limits their utility as forest management tools. With moderate acquisition costs, high resolution, and regular return frequency, the RapidEye satellite system potentially provides a viable solution for forest assessments if the utility of the system can be verified. # Institute of Chartered Foresters, 2015. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. 159

Forestry Airborne laser scanning (ALS) is an active remote-sensing technology that can be used to provide detail on the structure of forest canopies using the light detection and ranging (Lidar) technique. Since the early applications of ALS to forest inventory (Nelson et al., 1984; Maclean and Krabill, 1986; Nilsson, 1996), the technology has been widely used to spatially quantify variation in tree height and forest structure at various resolutions and in many forest types (Holmgren et al., 2003; Maltamo et al., 2006; Naesset, 2007; Packalén et al., 2011; Stone et al., 2011; Watt and Watt, 2013). The use of ALS surveys has become common practice for stand-wise forest inventories in several countries (Næsset et al., 2004) and forms a component of numerous national forest inventories (Stephens et al., 2012). Forest managers often have access to a variety of spatial surfaces detailing environmental data across their resource. This information is commonly provided by government agencies and is available at little or no cost. Consequently, there is considerable interest in determining whether information from these surfaces can improve the prediction of stand structure when used in combination with remotely sensed data. Little research has compared the precision of models predicting stand structure created by combining data from satellite imagery or ALS with auxiliary environmental data. Given the high cost of acquiring ALS data, it is important to determine how the precision of models created using satellite imagery and auxiliary environmental data compare to the precision of those that use ALS data. This is particularly relevant given the ongoing rapid expansion in the availabilityof satellite datawith the advent of commissioning of the Sentinel satellites and a multitude of low-cost commercial satellites. With this in mind, it is timely that a comprehensive comparison using combinations of ALS and satellite data supplemented with auxiliary environmental data should be undertaken. In many cases, forest managers are interested in predicting stand yields that are multivariate and which must be additive and consistent. For example, in New Zealand s plantation forests, variables of interest are typically numerous log-product volumes that must sum to the total stand volume. If each variable is modelled separately this can lead to unnecessary complexity and inconsistency. Previous research has shown that non-parametric techniques provide a suitable framework for estimating multivariate yields in a single step in industrial plantations (Hudak et al., 2008; Dash et al., 2015). Consideration must also be given to parametric methods, such as seemingly unrelated regression (SUR), which also have the capability of simultaneously estimating several dependent variables (Bollandsås et al., 2013b). We are unaware of any previous studies that have compared parametric and non-parametric methods for estimating multivariate stand attributes based on combinations of satellite imagery and ALS data supplemented by auxiliary environmental data. The objective of this study was to investigate the utility of combinations of data from ALS, high-resolution satellite imagery and environmental surfaces for predicting stand structure. The focus on data from the RapidEye satellites is particularly significant as this technology represents a cost-effective means of acquiring data at an appropriate resolution over large areas. However, the utility of these data as a useful input into models that can estimate forest structure has not yet been rigorously tested. Furthermore, it was hypothesized that the auxiliary environmental data contained additional information on variables that are important to the prediction of stand structure that cannot be detected directly by the available ALS or satellite imaging. Methods Study location Data for this study were measured in Kaingaroa forest, which is located in the North Island of New Zealand (Figure. 1). Kaingaroa is New Zealand s largest contiguous plantation covering around 180 000 ha. The majority of the forest occupies the pumice plateau region of the Central North Island and has generally flat topography. The northern part of the forest is characterized by rolling hills and areas of steeper terrain. The terrain gradually slopes upwards towards the forest s southern extent leading to a notable gradient in productivity. The forest soils are classified as Orthic Pumice belonging to the Kaingaroa series (Hewitt, 1993) with those in the north of the forest derived from Tarawera ash. The study was restricted to stands of Pinus radiata, which cover 92 per cent of the total forested area. Response variables Grid sampling was used to install ground plots throughout the study forest. Plots were located at the intersections of a sampling grid that had a randomized start point and orientation. The sampling unit was a slope adjusted Figure 1 Map showing the location of the plots used for fitting and validation of the tested models. 160

Characterising forest structure using combinations of airborne laser scanning data 0.06-ha bounded, circular field plot. A survey grade global positioning system was used to fix the centre of each plot; this fix was differentially corrected using local base stations. Within all plots, the diameter at breast height (dbh) was measured for all trees. Tree height was measured for a subset of suitable plot trees that were selected from across the dbh range. These measurements were used to fit a regression model between dbh and height for each plot; this was then used to estimate heights of trees where height was not measured. Tree measurements were used to derive a set of stand attributes that were used as response variables during modelling. The response variables were a commonly used set of forest attributes, used for forest valuation and planning and regularly estimated from forest inventories. Total stem volume (m 3 ha 21 ) was calculated as the sum of the volumes of all tree stems within each measurement plot, stem volume was calculated from measurements of dbh and total tree height using a volume and taper equation supplied by the forest manager. Basal area (m 2 ha 21 ) is the sum of the stem cross-sectional area at breast height. Mean top height (m) was estimated as the average height of the primary leaders of the 100 largest trees per hectare, where largest is defined in terms of the tree s dbh. Stand density (sph) is the number of stems per hectare with a presence at breast height (1.4 m). RapidEye imagery from 16 to 28 January 2014 was acquired over the study area. The images were delivered as 16-bit digital numbers, and subsequent processing was undertaken using ENVI 4.7 image processing software. Top-of-atmosphere reflectance values were calculated from each band of the RapidEye imagery, and a total of 94 candidate predictor variables were extracted. Various vegetation indices (Table 1) were calculated from the reflectance values. These metrics were calculated using four different window sizes 3 3, 5 5, 15 15 and 25 25 pixels. The algorithm used applied a consistent displacement of 1 pixel and direction of 1358. Seven vegetation indices were computed from the RapidEye data. These indices and the equations used to produce them are provided in Table 1. The textural attributes, developed by Haralick et al. (1973), have been commonly utilized in remote sensing research. We used the most relevant grey-level co-occurrence matrix (GLCM) textural attributes for remote sensing applications as described in previous research (Baraldi and Parmiggiani, 1995; Solberg, 1999; Lu, 2005; Tuominen and Pekkarinen, 2005; Kayitakire et al., 2006). These included the mean (ME), variance (VAR), SD, contrast (CON), angular second moment (ASM), entropy (ENT), homogeneity (HOM), energy (EN), correlation (COR) and dissimilarity (DISS). All textural metrics were calculated using the glcm package in R (Zvoleff, 2014). Candidate predictor variables ALS data The ALS survey was undertaken between the 23 January and 6 March 2014 using an Optech Pegasus scanner to collect a discrete, small footprint, dataset. Data collection was characterized by a pulse rate frequency of 100 khz, a maximum scan angle of 128 off nadir, and a swath overlap of 25 per cent. These settings yielded a dataset with a footprint size of 0.25 m and a mean pulse density of 11.46 points m 22. Returns were classified into ground and non-ground returns automatically using the Terra- Scan module of the TerraSolid software product. Classification accuracy was improved by subsequent manual inspection and reclassification where required. Metrics extracted from the ALS data included height percentiles (P5ht, P10ht, P20ht,..., P99ht, m), the mean (Hmean, m) and maximum height (Hmax, m), several metrics describing return height distribution through the canopy (skewness, coefficient of variation, standard deviation (SD) and kurtosis) and measures of canopy density, and pulse penetration, such as the percentage of returns reaching within 0.5 m of the ground (Pzero, %) and the percentage returns above 0.5 m (Pcover, %). A total of 62 candidate predictor variables were extracted from the ALS data. Rapideye imagery The RapidEye satellite system is a constellation of five satellites carrying identical sensors, all of which were launched at the end of 2008 (RapidEye AG, 2011). Data are collected in five spectral bands, which include Band 1 blue (440 510 nm), Band 2 green (520 590 nm), Band 3 red (630 685 nm), Band 4 red edge (690 730 nm) and Band 5 near-infrared (NIR) (760 850 nm) and delivered at an effective spatial resolution of 5 m. The suppliers provided a level 3A product that had undergone a range of pre-processing steps including the application of radiometric, sensor and geometric corrections. The delivered product was aligned to a cartographic map projection with the default geometric correction based on ground control points derived from DigitalGlobe 2-m satellite imagery and the New Zealand 25-m Digital Elevation Model. The intention of the orthocorrection process was to remove distortions inherent in the imagery; this ensured that the delivered data conformed to a map projection and were corrected for terrain displacement. Auxiliary environmental data Environmental data were extracted from spatial surfaces that included primary and secondary terrain attributes (Palmer, 2008), land classification according to climate and soil type (Leathwick et al., 2003) and monthly and annualclimate data(mitchell, 1991; Leathwicket al., 2002). Noteworthyenvironmental variables used in the analyses included mean annual and monthlyair temperature, relative humidity, solar radiation, vapour pressure deficit and rainfall. A spatial soil water balance model developed for P. radiata (Palmeret al., 2009) wasused to determine mean annualand seasonal root-zone water storage (W) for all plot locations. Fractional available root-zone water storage was determined from these data and the maximum available root-zone water storage, W max,asw/w max. The C:N ratio which provides a useful index of nitrogen mineralization (Watt and Palmer, 2012) was used to represent soil fertility. A total of 86 candidate predictor variables were extracted from the auxiliary environmental data. Analyses From the full dataset of 493 field plots, 60 plots were randomlyselected and used for model validation. The remaining 433 plots were used for the model fitting process. Separate models were created from the following combinations of predictor variables: (1) RapidEye metrics, (2) RapidEye metrics and auxiliary environmental data, (3) Lidar metrics, (4) Lidar metrics in combination with auxiliary environmental data and (5) Lidar metrics in Table 1 Vegetation indices used within the modelling Index Equation Normalized difference (NIR 2 Red)/(NIR + Red) vegetation index (NDVI) Enhanced vegetation index (EVI) 2.5((NIR 2 Red)/(NIR + 6Red 2 7.5Blue + 1)) Red edge ratio (RE) Green red edge/red Simple ratio (SR) NIR/red Green ratio (GR) NIR/green Vegetation index (VI) Green/red Brightness (Blue + green + red + red edge + NIR)/5 161

Forestry combination with RapidEye metrics, and (6) Lidar metrics in combination with auxiliary environmental data and RapidEye metrics. Although stand age was available in the study dataset and is known to be a useful predictor of stand structure, this was excluded from the set of candidate predictor variables for this study. This meant that the results are applicable to situations where stand age is unknown. The six different models were created using both parametric and non-parametric modelling methods. Using the mixed procedure within SAS (SAS Institute, Inc., 2008), we tested for autocorrelation using the likelihood ratio test (Littell et al.,2006). Spatial autocorrelation was found to be insignificant within the dataset probably because the plots were located a sufficient distance apart. Consequently, all presented models do not include the effects of spatial autocorrelation. Model precision was compared for both parametric and non-parametric models using data from the validation dataset. The coefficient of determination (R 2 ) and root mean square error (RMSE) were determined manually using actual and predicted values extracted from each model. The relative root mean square error (RRMSE) was calculated for eachvariable bydividing the RMSE by the mean prediction value and expressing this value as a percentage. Model bias was assessed through the mean relative error for each observation in the validation dataset defined as the measured value predicted value divided by the mean value and expressed as a percentage. Relative, rather than absolute, values were used so that variables with different units could be easily compared. Bias was also assessed through examination of plots between predicted and measured values. Surfaces of model predictions were visually assessed to check that predictions were logical and consistent with expected patterns. Seemingly unrelated regression modelling Multiple linear regressions using the ordinary least squares (OLS) technique have been widely used to predict multiple stand attributes from remotely sensed data. Using this method, model parameters foreach attribute are estimated independentlyof otherattributes. However, this method is statistically inefficient if there are strong correlations between the error terms of the respective regression models, which are often observed for models of forest standattributes(næssetet al., 2005). Underthesecircumstances, itispossible to take advantage of these correlated errors through estimating model parameters using a simultaneous system of equations. The SUR technique has been proven to provide a gain in parameter estimation efficiency when the error terms of equations in the system are correlated (Zellner, 1962). All parametric analyses were undertaken using R (R Development Core Team, 2014) with SUR undertaken using the systemfit package (Henningsen and Hamann, 2007). Initial variable selection was undertaken using OLS via the following procedure. For each model variables were introduced sequentially starting with the variable that exhibited the strongest correlation, this continued until further additions were not significant, or did not substantially improve model precision. Variable selection was undertaken manually, one variable at a time, and plots of residuals were examined prior tovariable addition to ensure that the variable was included in the model using the least biased functional form. Once all variables were identified, the SUR procedure was used to examine the significance of the variables. Variables were only retained in the final SUR models if these were significant at P ¼ 0.05. k-nn modelling The non-parametric k-nearest neighbour (k-nn) modelling method was used to impute stand attributes based on the detail in the fitting, or reference, dataset. Under the nearest neighbour estimation approach variables of interest (Y) are imputed for target elements, commonly pixels covering an area of interest, where Y has not been measured. Imputation of Y is based on auxiliary variables (X) that are known for all elements in the population (N) and are correlated with Y. A subset of the elements in N have paired observations of both X and Y and are referred to as the reference dataset. Imputation for a target element is estimated as a function of k Y values in the reference dataset that have X values that are closest, using a measure of statistical proximity, to the X values of the target element (Magnussen and Tomppo, 2014). The k-nn models were developed using the yaimpute package (Crookston and Finley, 2008) of the R statistical software (R Development Core Team, 2014). The k-nn models were developed using the random forest algorithm (Breiman, 2001) to define the statistical distance between target and reference observations in covariate space. Under the random forest approach, observations are considered similar if they tend to converge in the same terminal node in a suitably constructed collection of classification and regression trees (Breiman, 2001; Liaw and Wiener, 2012). In this context, the metric used to define statistical distance is calculated as one minus the proportion of trees where a target observation is in the same terminal node as a reference observation (Crookston and Finley, 2008). For each imputation, k was varied and the value of k that minimized the RMSE within the validation dataset was selected for use in the predictive models. The imputed value (Y) for a given target was calculated using the distance weighted average of the k nearest reference observations. Results All four response variables showed considerable variation in both the fitting and validation dataset (Table 2). The fitting dataset covered the full range of values for all four response variables, and the distributions of both fitting and validation datasets were examined and found to be similar. Model comparison When averaged across both model types, the models developed using only RapidEye data had the highest RRMSE with a mean value of 39.0 (range 27.18 51.04 per cent). Mean top height was predicted with the lowest RRMSE (mean 27.2 per cent), and total standing volume had the highest RRMSE value (mean 50.1 per cent). RapidEye variables that were used for predicting stand attributes included the ratios brightness and vegetation index (VI) along with several texture metrics (contrast, entropy and homogeneity) in the blue, red and near-infrared bands. Compared with models with only RapidEye data, the inclusion of auxiliary environmental data with RapidEye data resulted in a modest reduction in RRMSE of 1.6 per cent over all dependent variables (37.4 vs. 39.0 per cent). Predictions of mean top height were found to show the least bias (mean relative error 4.1 per cent) and predictions of total stem volume were the most biased (mean relative error 10.3 per cent) for the models constructed with RapidEye and auxiliary environmental data. When combined with RapidEye data, the most important environmental variables for predicting stand attributes were clearskyshortwave radiation duringwinter(topographicallyadjusted), profile curvature and mean maximum air temperature in spring. Models developed using Lidar metrics as predictors performed considerably better than models developed using RapidEye data (mean reduction in RRMSE 19.29 per cent) were substantially less biased (mean reduction in relative error 2.22 per cent). Stand attribute models fitted using only Lidar metrics were precise and relatively unbiased (mean RRMSE ¼ 19.44 per cent, mean R 2 ¼ 0.85, mean relative error ¼ 2.38 per cent). The most useful Lidar metrics for predicting stand attributes included the 90th and 70th height percentile (P90 and P70), the height of the mean return (H mean ), the SD of return heights and a density metric. Including auxiliary environmental data in combination with Lidar metrics led to negligible improvement in the coefficient of 162

Characterising forest structure using combinations of airborne laser scanning data Table 2 Variation in plot attributes, Rapid Eye reflectance, ratios and texture, key environmental variables and LiDAR metrics for the fitting (n ¼ 431) and validation datasets (n ¼ 60) Variable Fitting dataset Validation dataset Mean Range Mean Range Stand age (year) 16.6 2.84 37.8 17.8 3.89 32.8 Total stem volume (m 3 ha 21 ) 335.2 0.8 1012.1 396.2 8.8 929.9 Basal area (m 2 ha 21 ) 33.3 0.2 76.4 37.2 3.2 79.6 Top height (m) 24.6 2.5 46.2 27.1 4.9 43.5 Stand density (sph) 539.1 83.3 1650 501.3 133.4 1366.7 RapidEye spectral Blue (band 1) 0.0736 0.058 0.122 0.0719 0.059 0.114 Green (band 2) 0.0031 6 10 24 0.024 0.0029 8 10 24 0.013 Red (band 3) 0.0373 0.024 0.123 0.0349 0.026 0.111 Red edge (band 4) 0.0787 0.061 0.149 0.0768 0.058 0.136 Near infrared (band 5) 0.235 0.156 0.366 0.235 0.180 0.329 RapidEye ratios EVI 0.546 0.226 0.817 0.551 0.276 0.753 NDVI 0.726 0.284 0.846 0.737 0.340 0.842 RE 0.0065 0.002 0.045 0.0065 0.002 0.029 SR 6.81 1.78 12.0 7.17 2.03 11.7 GR 98.9 10.9 336 103 15.8 279 VI 0.0814 0.020 0.324 0.0826 0.025 0.314 Brightness 0.0855 0.066 0.134 0.0843 0.071 0.117 RapidEye texture Band 1 VAR-25 8.23 3.84 20.5 7.79 4.03 15.4 Band 2 ENT-25 0.253 0 1.61 0.208 0 1.02 Band 4 ASM-25 0.856 0.263 1.00 0.849 0.428 1.00 Band 4 SD-25 2.09 1.94 3.83 2.04 1.94 3.00 Band 4 ME-25 0.0670 0.063 0.118 0.0654 0.063 0.097 Band 5 CON-15 0.242 0.002 1.14 0.264 0.002 0.697 Band 5 COR-15 0.438 20.029 0.924 0.417 20.017 0.908 Band 5 ENT-15 1.12 0 3.00 1.14 0 2.43 Band 5 ME-25 0.180 0.130 0.251 0.182 0.146 0.228 Environmental variables Av. air temp. ( o C) 11.2 9.27 14.1 11.2 9.86 13.7 Av. spr. air temp(8c) 10.5 8.47 13.0 10.6 9.01 12.6 Av. rad. (MJ m 2 day 21 ) 19.2 12.1 21.1 19.2 13.8 21.3 Av. spr. rad. (MJ m 2 day 21 ) 23.9 15.1 25.8 23.9 18.4 26.0 Ann. Rainfall (mm) 1420 1119 2216 1444 1158 2144 Slope (degrees) 5.24 0 31.9 6.50 0 20.6 Lidar metrics H05 (m) 0.35 0.015 6.81 0.41 0.027 3.95 H50 (m) 14.0 0.16 33.8 15.6 0.258 30.7 H80 (m) 18.2 0.37 39.8 20.0 1.60 37.6 H95 (m) 21.1 0.79 43.4 22.9 2.54 41.4 Elev LCV (m) 0.34 0.15 0.63 0.33 0.17 0.63 Int LCV (m) 0.47 0.098 0.56 0.49 0.31 0.56 For textural measures, variables that were included within the models are shown. Following the texture measures, the window sizes of either 15 15 or 25 25 pixels are denoted, respectively, by 15 and 25. determination (R 2 ¼ 0.86) and only small reductions in the RRMSE (mean reduction in RRMSE ¼ 1.1 per cent) and relative error (mean reduction ¼ 0.27 per cent). When used in combination with Lidar, average annual solar radiation was the only environmental metric selected for inclusion in the model. Similarly, combining RapidEye data with Lidar metrics provided little improvement over models that used Lidar metrics alone. There was no substantial improvement in the coefficient of determination, RRMSE or relative 163

Forestry mean error when compared with models using Lidar alone. Models using a combination of Lidar, auxiliary environmental data and RapidEye data had similar levels of model precision to models using Lidar and environmental data only. Using all three data sources, the mean model coefficient of determination was 0.86, mean RRMSE was 18.26 per cent and mean relative error was 1.95 per cent. Lidar-based models were far more precise than models constructed from RapidEye data for all stand attributes. Predictions of mean top height were extremely precise for models that included Lidar in the prediction dataset (mean R 2 ¼ 0.99, mean RRMSE ¼ 3.93 per cent), and these models exhibited little apparent bias (mean relative error ¼ 0.44 per cent). Predictions of mean top height were considerably less precise for models using RapidEye data without Lidar data (mean R 2 ¼ 0.53, mean RRMSE ¼ 26.61 per cent). A similar trend was evident for basal area (Figure 2), where the mean R 2 increased from 0.42 to 0.77 and mean RRMSE decreased from 33.45 to 21.06 per cent, when Lidar metrics were included as predictors. The inclusion of Lidar metrics in the predictive models also had a marked impact on the precision of models for total stem volume (increase in mean R 2 ¼ 0.46 and mean reduction in RRMSE ¼ 26.8 per cent) and stand density (increase in mean R 2 ¼ 0.35 and mean increase in RRMSE ¼ 16.34 per cent). Both k-nn and SUR were found to be suitable techniques for simultaneously predicting stand attributes using remotely sensed data. Although the precision of the non-parametric and parametric approaches was verysimilar all but two of the non-parametric models were more precise than their parametric equivalents (Figure 2). Expressed as a percentage of the mean RRMSE of the non-parametric models, the mean RRMSE of the parametric models was on average 99 per cent and ranged between 96 and 102 per cent. Parametric modelling yielded a more precise model when constructed with a combination of RapidEye and environmental data and the final model constructed using all available data sources. Non-parametric models were more biased than parametric models for all of the constructed models with mean relative prediction errors of 3.99 and 1.98 per cent, respectively. The disparity was greatest for models constructed using RapidEye data alone where models developed using k-nn had a mean relative error of 6.6 per cent compared with 2.6 per cent for models developed using SUR. Mean prediction error for non-parametric (2.6 per cent) and parametric models (1.7 per cent) was much closer for the models constructed using all data sources. Plots of the predictions against measured values for final models (Figure 3) indicate some evidence of under prediction at larger values for total stem volume (Figure 3b,f) and stand density (Figure 3d,h). The prediction of mean top height (Figure 3a,e) is excellent, and an under prediction of basal area at larger values is evident in both model types. This bias is slightly more pronounced for the nonparametric(figure 3f) than the parametric model(figure 3b) output. The mean value of k selected for use was 10. Selected values of k ranged between 26 for the model constructed using only RapidEye data to 3 for the model constructed using only Lidar data. Discussion The results of this study show that, as expected, Lidar metrics were the most useful predictors of stand attributes of the data sources investigated. Combining auxiliary environmental data or RapidEye data with Lidar metrics led to negligible improvements on models developed with Lidar alone. The precision of models developed using RapidEye alone were moderately precise and combining auxiliary environmental data with RapidEye led to modest improvements in model precision. The utility of data from ALS for predicting stand attributes is well-known and widely accepted within forestry. There are numerous examples describing the use of ALS for predicting stand attributes such as merchantable volume, stand height and biomass (Næsset, 2002; Bollandsås et al., 2013a). The use of metrics derived from high-resolution satellite imagery for predicting stand attributes has been well studied (Tomppo and Katila, 1991; Tomppo et al., 1999). A comparative study (Hyyppä et al., 2000) using various imagery sources including aerial photography, SPOTpan, SPOT XS and LandSat TM found that, in most cases, the accuracy of stand variable retrieval was higher for image types with higher spatial resolutions. Wallner et al. (2014) used metrics derived from RapidEye data, with a 5 m resolution, to model stand structural attributes and reported R 2 values for stand density (0.4), basal area (0.58) and stand volume (0.63) that were consistent with those found in the current study (Figure 2). The WorldView-2 satellite provides imagery at a 2 m spatial resolution. Previous research in P. radiata plantations in Australia (Shamsoddini et al., 2013) found a very strong relationship between texture metrics and stand height (R 2 ¼ 0.9) and stand density (R 2 ¼ 0.85) and moderate relationships with basal area (R 2 ¼ 0.53) and stand volume (R 2 ¼ 0.56) in a cross-validation based on the 61 plots also used for model fitting. Using the same sensor, a study conducted in a dry land Aleppo pine (P. halepensis Mill.) plantation (Ozdemir and Karnieli, 2011) reported similar results to the current study for stand density (R 2 ¼ 0.38, RMSE ¼ 110 sph) and basal area (R 2 ¼ 0.54, RMSE 1.79 m 2 ha 21 ). Previous studies have shown that image saturation, occurring post-canopy closure, limits the utility of optical data as an auxiliary variable for predicting stand parameters. This is particularly apparent for very high productivity plantations such as the subject of this study (Tomppo et al., 1999). It is worth noting that including stand age as a predictor with the metrics derived from satellite data would be likely to considerably improve model performance. These benefits are available to managers of industrial plantations where the planting date is recorded in stand record systems. Using imagery from IKONOS-2 (1 m spatial resolution) collected over a European plantation, Kayitakire et al. (2006) reported precise predictions of stand height (R 2 ¼ 0.76) and stand density (R 2 ¼ 0.82) but somewhat less precise predictions of basal area (R 2 ¼ 0.35). Our results add to the evidence that supports the utility of satellite imagery for predicting stand attributes. Prediction accuracy does appear to improve as spatial resolution increases, although this effect is likely confounded by other factors such as forest type and age, the size and quality of the datasets used for model fitting and the rigour of the validation comparison. Although RapidEye data have a coarser spatial resolution than WorldView or IKONOS, our results suggest that RapidEye data has reasonable utility for estimating stand structure. Coupled with the low cost of the imagery and rapid return frequency of the sensor, RapidEye represents an attractive proposition for forest managers. Combining Lidar data with RapidEye data provided no tangible improvement in model performance compared with models using Lidar metrics alone. This finding is consistent with previous work (Saarela et al., 2015) that showed that combining Landsat data with aerial Lidar resulted in no improvement compared with models created using only LiDAR data. Similar results have been 164

Characterising forest structure using combinations of airborne laser scanning data Figure 2 Variation in coefficient of determination (a, d), RMSE as a percentage of the mean (b, e) and mean error (ME) as a percentage of the mean for models created using SUR (a, b, c) and k-nearest neighbour (d, e, f). Results for four stand dimensions are shown that include tree height (open circles), basal area (open squares), total stem volume (filled triangles) and stand density (filled circles). For reference, a dashed line is drawn through the origin on c and f. observed in other studies that have combined ALS data with airborne hyperspectral data to assess stand structure (Latifi et al., 2012). These findings might be different if the objective of the modelling was prediction of forest type, land cover or species classification where the spectral information in the RapidEye imagery may be more useful. Given these results, and the relative expense of Lidar and RapidEye imagery, it may be prudent to use the two data sources in combination by examining relationships between components of each sensor to effectively expand the Lidar coverage as in Ahmed et al. (2014). Alternatively using a point in time Lidar dataset to fix a digital terrain model (DTM) and then using periodic satellite imagery acquisitions to update canopy height may be an accurate and cost-effective option for monitoring stand structure and development. Alternative techniques including using radar and using photogrammetry based on satellite data to derive 3D structure (Maack et al., 2015) provide a potentially valuable alternative when a DTM is available. Combining auxiliary environmental data with either remote sensing data source led to moderate improvements in model prediction accuracy. Previous studies have found environmental data to be useful for prediction of forest productivity (Watt et al., 2010; Sabatia and Burkhart, 2014) but our results suggest that its 165

Forestry Figure 3 Relationship between predicted and measured (a, e) mean top height, (b, f) total stem volume, (c, g) basal area and (d, h) stand density. Panels on the right represent predictions made by SUR while those on the left show predictions from k-nearest neighbour surfaces. The predictions are based on models constructed using variables from all data. The 1:1 line is shown on all panels as a dashed line. 166

Characterising forest structure using combinations of airborne laser scanning data practicality for predicting stand-level structural attributes is limited. This is probably because the study stands are intensively managed and so their structure is substantially altered through intervention. It is possible that in natural forests, or less intensively managed stands, the environmental data would be more useful. Using stand age to modify the relationship between stand attributes and environmental data would also likely greatly improve the performance of this data source. Both k-nn and SUR were successfully used to predict multivariate stand attributes. The precision of models produced by the parametric and non-parametric approaches was very similar. This result is consistent with a comparison of SUR with the most similar neighbour-modelling technique that found the accuracy of those two approaches to be of a similar magnitude when predicting diameter distributions and basal area (Bollandsås et al., 2013b). Several studies have found that non-parametric methods outperform parametric methods in the prediction of stand attributes (Packalén and Maltamo, 2006; Maltamo et al., 2009; Zhou et al., 2011). However, this is not true of all instances (Mora et al., 2013), and the most effective technique is likely to be defined by the properties of the particular study forest or landscape (Pierce et al., 2009). Predictions from the non-parametric models were marginally more biased than those from the parametric models. This bias was most apparent in the models constructed using RapidEye data; these were also the least precise models and had the highest k value selected. The reason for this is not known although variation in the prediction quality of parametric and nonparametric models has been assigned to a number of factors including the linearity of the underlying model and the balance of the dataset (Haara and Kangas, 2012). The observed bias may have been attributable to the use of the random forest algorithm for assigning neighbours. Random forest has been shown to be a highly efficient prediction method particularly when the number of predictor variable is large and interactions or correlations amongst the predictors are complex and numerous (Strobl et al., 2008). Random forest has been widely and successfully used in forest management with numerous studies reporting its predictive accuracy (Hudak et al., 2008; Vauhkonen et al., 2010; Dash et al., 2015). Despite this, there are several examples of prediction bias associated with the use of random forest (Breidenbach et al., 2010; Latifi and Koch, 2012; Temesgen and Hoef, 2014). At the very least models developed using random forests should be checked for bias and forest managers should be advised to enter into their use cautiously lest they are exposed to biased predictions that lead to poor decision-making. The value of k selected also has a considerable effect on model performance and can be a source of apparent bias. In this study, it was noted that the best performing models, such as those using ALS data, resulted in the selection of smaller k values than models with lower precision such as those fitted with RapidEye or auxiliary environmental data. Larger values of k result in model predictions that tend towards the mean of the reference dataset resulting in an under prediction for higher values and an over prediction at smaller values. This finding indicates that models where a large value of k is selected must be used with caution although visual assessment of maps produced from models fitted using RapidEye data indicated that prediction was broadly consistent with models fitted using ALS data over large areas. Regardless of the minutiae of the modelling approach, nearest neighbour techniques have several practical advantages over parametric approaches that no doubt contribute to their growing popularity. In conclusion, this research has shown that ALS isthe mostvaluable data source of those trialled for predicting stand attributes. Including auxiliary environmental data or RapidEye data only improved these models by a negligible amount. In the absence of ALS, RapidEye imagery supplemented with auxiliary environmental data provides a cost-effective alternative that can offer moderate prediction accuracy. Both the parametric and nonparametric modelling techniques investigated in this study provided comparable results. Acknowledgements The authors gratefully acknowledge the diligent and professional contribution of all field teams involved. Duncan Harrison of Scion is acknowledged for producing Figure 1. Reviews of this paper by John Moore, Gary Kerr and the anonymous reviewers provided useful feedback and have improved the quality of this document. Conflict of interest statement None declared. Funding This research was supported by the Growing Confidence in Forestry s Future programme, which is jointly funded by the New Zealand Ministry of Business, Innovation and Employment and the New Zealand Forest Growers Levy Trust. We are grateful to Timberlands Ltd. for supplying the plot information and Lidar dataset. References Ahmed, O.S., Franklin, S.E. and Wulder, M.A. 2014 Integration of lidar and Landsat data to estimate forest canopy cover in coastal British Columbia. Photogramm. Eng. Remote Sensing 80, 953 962. Baraldi, A. and Parmiggiani, F. 1995 An investigation of the textural characteristics associated with gray-level cooccurrence matrix statistical parameters. IEEE Transact. Geosci. Remote Sens. 33, 293 304. Bollandsås, O.M., Gregoire, T.G., Næsset, E. and Øyen, B. 2013a Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data. Stat. Methods Appl. 22, 113 129. Bollandsås, O.M., Maltamo, M., Gobakken, T. and Næsset, E. 2013b Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest. Forestry 86, 493 501. Breidenbach, J., Nothdurft, A. and Kändler, G. 2010 Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. Eur. J. Forest Res. 129, 833 846. Breiman, L. 2001 Random forests. Machine Learn. 45, 5 32. Crookston, N.L. and Finley, A.O. 2008 yaimpute: An R package for knn imputation. J. Stat. Soft. 23, 1 16. Dash, J.P., Marshall, H.M. and Rawley, B. 2015 Methods for estimating multivariate stand yields and errors using k-nn and aerial laser scanning. Forestry. 88, 237 247. 167

Forestry Donoghue, D.N.M. and Watt, P.J. 2006 Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests. Int. J. Remote Sens. 27, 2161 2175. Haara, A. and Kangas, A. 2012 Comparing K nearest neighbours methods and Linear regression-is there reason to select one Over the other? Math. Comput. Forestry Natural Res. Sci. 4, 50 65. Haralick, R.M., Dinstein, I. and Shanmugam, K. 1973 Textural features for image classification. IEEE Trans. Syst. Man Cybernetics SMC-3, 610 621. Henningsen, A. and Hamann, J.D. 2007 systemfit: a package for estimating systems of simultaneous equations in R. J. Stat. Soft. 23, 1 40. Hewitt, A.E. 1993 New Zealand soil classification. In Ecosystem Services in New Zealand - Conditions and Trends. J.R. Dymond (ed.). Manaaki Whenua Press, Lincoln, New Zealand. Holmgren, J., Nilsson, M. and Olsson, H. 2003 Estimation of tree height and stem volume on plots using airborne laser scanning. For. Sci. 49, 419 428. Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E. and Falkowski, M.J. 2008 Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sens. Environ. 112, 2232 2245. Husch, B., Beers, T.W. and Kershaw, J.A. Jr. 2003 Forest Mensuration. Wiley. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S. and Zhu, Y.H. 2000 Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. For. Ecol. Manage. 128, 109 120. Kayitakire, F., Hamel, C. and Defourny, P. 2006 Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens. Environ. 102, 390 401. Latifi, H. and Koch, B. 2012 Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands. Int. J. Remote Sens. 33, 6668 6694. Latifi, H., Fassnacht, F. and Koch, B. 2012 Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sens. Environ. 121, 10 25. Leathwick, J., Morgan, F., Wilson, G., Rutledge, D., McLeod, M. and Johnston, K. 2002 Land Environments of New Zealand: A Technical Guide. Ministry for the Environment, Wellington, and Manaaki Whenua Landcare Research, 184 pp. Leathwick, J., Wilson, G., Rutledge, D., Wardle, P., Morgan, F., Johnston, K. et al. 2003 Land Environments of New Zealand. Ministry for the Environment, Wellington, and Manaaki Whenua Landcare Research. Liaw, A. and Wiener, M. 2012 Breiman and Cutler s random forests for classification and regression. R Package Version 4.6 7. Littell, R.C., Milliken, G.A., Stroup, W.W., Wolfinger, R.D. and Schabenberger, O. 2006 SAS for Mixed Models. SAS Institute. Lu, D. 2005 Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int. J. Remote Sens. 26, 2509 2525. Maack, J., Kattenborn, T., Fassnacht, F.E., Enßle, F., Hernández, J., Corvalán, P. and Koch, B. 2015 Modeling forest biomass using very-high-resolution data - combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images. Eur. J. Remote Sens. 48, 245 261. Maclean, G.A. and Krabill, W.B. 1986 Gross-merchantable timber volume estimation using an airborne lidar system. Canadian J. Remote Sens. 12, 7 18. Magnussen, S. and Tomppo, E. 2014 The k-nearest neighbor technique with local linear regression. Scand. J. For. Res. 29, 120 131. Maltamo, M., Eerikäinen, K., Packalén, P. and Hyyppä, J. 2006 Estimation of stem volume using laser scanning-based canopy height metrics. Forestry 79, 217 229. Maltamo, M., Peuhkurinen, J., Malinen, J., Vauhkonen, J., Packalén, P. and Tokola, T. 2009 Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data. Silva Fennica 43, 507 521. McRoberts, R.E. and Tomppo, E. 2007 Remote sensing support for national forest inventories. Remote Sens. Environ. 110, 412 419. McRoberts, R.E., Cohen, W.B., Erik, N., Stehman, S.V. and Tomppo, E.O. 2010 Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scand. J. For. Res. 25, 340 367. Mitchell, N.D. 1991 The derivation of climate surfaces for New Zealand, and their application to the bioclimatic analysis of the distribution of kauri (Agathis australis). J. R. Soc. N. Z. 21, 13 24. Mora, B., Wulder, M.A., White, J.C. and Hobart, G. 2013 Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LIDAR. Remote Sens. 5, 2308 2326. Næsset, E. 2002 Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 80, 88 99. Næsset, E. 2004 Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand. J. For. Res. 19, 164 179. Naesset, E. 2007 Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scand. J. For. Res. 22, 433 442. Næsset, E., Gobakken, T., Holmgren, J., Hyyppä, H., Hyyppä, J., Maltamo, M. et al. 2004 Laser scanning of forest resources: The nordic experience. Scand. J. For. Res. 19, 482 499. Næsset, E., Bollandsås, O.M. and Gobakken, T. 2005 Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data. Remote Sens. Environ. 94, 541 553. Nelson, R., Krabill, W. and MacLean, G. 1984 Determining forest canopy characteristics using airborne laser data. Remote Sens. Environ. 15, 201 212. Nilsson, M. 1996 Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens. Environ. 56, 1 7. Ozdemir, I. 2008 Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. Int. J. Remote Sens. 29, 5643 5655. Ozdemir, I. and Karnieli, A. 2011 Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel. International Journal of Appl. Earth Observ. Geoinform. 13, 701 710. Packalén, P. and Maltamo, M. 2006 Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. For. Sci. 52, 611 622. Packalén, P., Mehtätalo, L. and Maltamo, M. 2011 ALS-based estimation of plot volume and site index in a eucalyptus plantation with a nonlinear mixed-effect model that accounts for the clone effect. Ann. Forest Sci. 68, 1085 1092. Palmer, D.J. 2008 Development of national extent terrain attributes (TANZ), soil water balance surfaces (SWatBal), and environmental surfaces, and their application for spatial modelling of Pinus radiata productivity across New Zealand. PhD thesis, University of Waikato, Hamilton, New Zealand. 410 pp. Palmer, D.J., Watt, M.S., Hock, B.K. and Lowe, D.J. 2009 A dynamic framework for spatial modelling Pinus radiata soil water balance (SWatBal) across New Zealand. Scion Bull. 234, 93. Pierce, K.B., Ohmann, J.L., Wimberly, M.C., Gregory, M.J. and Fried, J.S. 2009 Mapping wildland fuels and forest structure for land management: A comparison of nearest neighbor imputation and other methods. Canadian J. Forest Res. 39, 1901 1916. R Development Core Team. 2014 R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/. 168