Remote sensing has been increasingly used to assist forest inventory. Airborne Laser Scanning (ALS) systems can accurately estimate tree height in forests, and are being combined with more traditional optical images that provide further details about the horizontal structure of forests. To predict forest attributes two main techniques are applied to process ALS data: the Area Based Approach (ABA), and the Individual Tree Detection (ITD). The first part of this study was focused on the effectiveness of integrating ALS data and aerial imagery to estimate the wood volume in
Intensively managed forest plantations have an important role, both nationally and internationally, due to their ability to efficiently produce large quantities of biomass. This biomass can be utilized in forms suitable for bioenergy or, after processing into cellulosederivatives, in materials such as paper. The myriad uses of fibers derived from intensively managed plantation forests can aid in the reduction of anthropogenic pressures on natural forests, while simultaneously generating millions of jobs (
In Brazil, 4.4 million jobs are generated directly and indirectly by forest plantations, and the total reforested area exceeds 7.6 million hectares (
In order to manage the growth and yield of these plantations, annual forest inventories are required. In general, the traditional approaches to conducting plantation inventories follow the precepts of sampling theory, which is based on ground measurements collected from simple random sampling plots. The forest attributes of interest are then inferred for an entire stand based on observations collected at each sample plot (
Both active and passive remote sensors have been presented as feasible alternatives for estimating forest variables. High spatial resolution optical data is useful at providing spectral information on species and condition (
To estimate volume in
The ABA approach is most suited to the estimation of a number of crucial forest stand attributes over large areas (
In this case, the ITD approach is the most appropriate to estimate individual tree characteristics. However, algorithms normally used in the ITD approach detect, on average, 75% of trees (
According to
The merging of ALS and optical remote sensing data can assist in the accurate estimation of forest attributes, with algorithms based on height or brightness (
In this study, we use ABA and ITD to estimate stand volume in
The study was performed in a forest plantation owned by FIBRIA S.A. company, located in São Luiz do Paraitinga in São Paulo State, Brazil (23° 33′ S, 45° 34′ W and 23° 30′ S, 45° 31′ W 
The forest stands in the study area were planted in December 2006 and harvested between April and May 2013, following a standard 6.5 year rotation period. All harvested wood was taken to a mill. The trees were planted in rows with fixed tree spacing (2 × 3 m), resulting in a density of 1667 stems per hectare.
Reference data were based on field measurements collected at 48 circular plots (r = 11.28 m, area of 400 m²) between April and May of 2013 prior to harvesting. In each plot, all diameter at breast height (DBH) were recorded, and the height of 10 trees were measured. Heights for all remaining trees in the plot were then estimated by applying the
After harvesting, all wood arriving at the mill was weighed. The wood density was obtained according to Archimedes’ principle and, therefore, the total volume of wood at the mill could be defined by weighttovolume (WV) relationships obtained in the density measurement process. A complete description can be found in
ALS data were acquired by a RIEGL LMS  Q680I^{®} system (Riegl Gmbh, Horn, Austria) simultaneously on 15 January 2012 with aerial photographs under freecloud conditions. The average ALS point density was 5 pulses per m^{2}. An orthophoto was generated from aerial images with a pixel size of 0.15 cm. The 4 spectral bands were blue (429514 nm), green (514600 nm), red (600676 nm) and nearinfrared (695831 nm).
Initially we separated ALS ground returns from the vegetation returns, to generate a 15 cm resolution DTM and DSM, which is the minimum size resolution based on point density (
We extracted LiDAR metrics from the ALS data (
The first step was the estimation of wood volume using the ABA. Three different methods were applied, depending on the input data source. In the first approach, only variables derived from ALS data were used, in the second approach only variables derived from the imagery were used, and in the final one we combined both sets of variables.
One of the most important considerations when working with a large number of predictor variables in a multiple linear regression is variable selection. To that end, we applied the Best Subset Selection (BSS), which matches an appropriate model to each model size (
The total volume of the forest stands was obtained by the mean ABA estimation multiplied by the total area.
The kfold estimate of RMSE is computed by averaging the values obtained by
where
For the second step we used high resolution multispectral images for detecting individual treetops, applying the local maximum filtering method, where the central location of the crown is assigned the highest spectral value (
To generate the final ITD product, all five intermediate outputs were used. First, the layer with the highest number of detected trees, with their locations visually corrected, was taken as the basis for ITD. From this layer, we removed all the treetops in a 0.7 m buffer around each tree to avoid multiple peaks from single tree. Treetops detected in the second layer, outside this buffer were added creating a new layer of additional ITD points. This layer was then taken as the new basis for ITD. This procedure was repeated until all layers had contributed to the generation of a single ITD layer.
Based on the final ITD layer, stem counts were aggregated into 5 × 5 m grid cells, according to ABA. Following this, the number of trees in each grid cell was identified by local maxima derived from the ITD approach, resulting in a graphical representation of the number of trees per hectare for the entire study area.
The mean difference (MD) detection of the ITD approach was done by comparing the number of detected trees and the number of observed trees in the inventory plots. The difference between the two was considered the response variable, while the ALS and image variables were considered the predictor variables, leading to the modeling of the ITD mean difference, and its prediction for the entire study area. In our case this MD was modeled with 3 variables, maximum height, slope, and ITD detection, as explanatory variables. These variables were selected by applying the Best Subset Selection method and crossvalidation, as described earlier. The MD detection model was applied to the entire study area, representing the MD per hectare, to correct the initial estimates of tree count per pixel.
In order to generate the volume per tree in each 5 × 5 m cell, the previouslyestimated stand volume for the entire study area (m^{3} ha^{1}), obtained by the integration of ALS data and highresolution imagery, was divided by the number of trees per hectare detected by the ITD approach.
The best fitting models obtained by crossvalidation contained 4 parameters, including the intercept, for all the three approaches tested (
Across the whole study area, the mean estimated volume per hectare based on the ABA method was 319.9 m^{3}, totaling 44.466.1 m^{3}. When this result was compared with the volume measured at the mill (considered the true volume by the company), we found a difference of 2.98%, while when comparing the forest inventory data with the mill volume, it overestimated by 3.29%. In addition, it was observed that the combination of both ALS and imagery data could provide more information regarding the occurrence of drought and canopy gaps in the forest.
Overall, using the ABA method 151.048 trees were detected in the study area, with an average of 994 trees ha^{1}, while the average number of trees per hectare estimated by the forest inventory was 1708 trees ha^{1}. Therefore, approximately 42% of the trees were not detected using the above approach, reinforcing the need to correct the remotely sensed tree count by modeling the tree count mean difference (MD).
After identifying the predictive mean difference model, we performed a linear adjustment using the least squares method. The model selected used three variables: maximum height (
The average number of trees per hectare detected by the conversion of ITD into ABA (semiITD) for the study area after mean difference (MD) correction was 1707.7, while the inventory data provided a figure of 1708.3 trees per hectare. Therefore, the mean volume per tree estimated from study data was 0.188 m^{3}, while the mean volume of an individual tree derived from forest inventory was 0.195 m^{3}.
Accurate stand attribute estimates are critical for effective forest plantation management, partly due to their markedly shorter rotation ages and faster growth rates relative to natural forests (
In this research, we used two approaches to estimate stand volume in
The model applied in the volumetric prediction used two variables derived from aerial imagery, the near infrared band (
Despite the use of highresolution imagery, the second approach (which used the aerial image variables) showed the poorest performance among the three approaches tested (RMSE = 8.45%).
Our results show that use of information from a single remote sensing technology can meet the predictive needs of plantation managers, so long as expectations of accuracy and precision are attained accordingly. However, our results also highlight that the integration of data from multiple remote sensing technologies, such as the ALS and highresolution passive imagery data used here, can lead to more accurate results than the use of a single technology.
This improvement in accuracy is achievable even in situations where the difference in time between data acquisition (2012) and field measurement (2013) is up to one year, as is the case in this study. Because ALS data acquisition can be costly, the field measurementremote sensing hybrid approach can be of remarkable benefit to forest managers. The integration of data from different sources with different acquisition dates can increase the useful life of forest attribute data collected by any means, potentially decreasing the frequency of acquisition and therefore reducing costs.
While ABA provided estimates of total wood volume for the entire study area, individual tree delineation, detected from passive image data, allowed us to downscale the estimates to the individual tree level. Images were used for tree detection instead of ALS data because the trees were planted at a relatively high density (3 × 2 m) resulting in small canopy sizes. Their small size made it challenging for crowns to intercept a sufficient number of LiDAR points for crown extent to be accurately defined. Therefore, the performance of ALS was poorer when detecting individual trees compared to imagebased approaches.
Despite improvements over ALS data, we still found the accuracy of single tree detection from images to be unsatisfactory (58% correctly detected trees) and so a correction factor was introduced. This correction, based primarily on slope, allowed us to increase the accuracy of single tree detection and achieve unbiased individual tree volume estimates. Slope proved to be a key variable to include in the modeling of tree detection error. With increasing slope more trees were located in the same projected area, leading to an aggregation of the canopies and an increase in the maximum tree height of the inventory plots. Increased maximum tree height, therefore, showed a higher correlation with the standard deviation (
In general, the correction procedure applied to our tree counting process performed well, showing good agreement with existing tree count estimates and leading to individual tree volumes with negligible mean difference and low RMSE (12.66%). Our corrected estimates of tree count were greater than uncorrected values identified from the imagebased detection by 713.7 trees per hectare, very close to extant estimates based on the forest inventory. The final estimates of individual tree volume after mean difference correction were 0.188 m^{3} per tree, 3.5% less than the inventory estimate (
We demonstrated that a traditional ITD approach can be modified by using ABA, in spite of losing the exact location of each tree, but enabling to obtain the desired tree density information (
Accurate information about wood volume at both the stand and individual tree levels is required to support the effective management of forest plantations. In this study we enhanced the traditional areabased approach using individual tree detection. The integration of geometrical and spectral information provided by ALS and aerial imagery, respectively, allows for the reliable predictions of volume at both spatial scales. Moreover, remote sensing data integration results in lower mean difference and RMSE. Our approach allowed for the estimation of individual tree volume with negligible bias.
The authors would like to acknowledge the Brazilian Research Foundation, CNPq, and the National Institute for Space Research (INPE) for supporting this research, FIBRIA S/A cellulose company for supplying data from their plantation, and the Integrated Remote Sensing Studio (IRSS) at the Faculty of Forestry, University of British Columbia, Canada, for its assistance, support and friendship. David Williams provided some editorial assistance.
The study area near São Luíz do Paraitinga (São Paulo, Brazil), including the 6.5 yearold
Scatterplot of the observed and predicted values of stand volume for each of the approaches analyzed in this study. (1) ALS data; (2) aerial images; (3) both datasets.
Maps of ABAderived predictions for stand volume (top left), corrected number of trees (top right) and mean tree volume (bottom).
Descriptive statistics of the reference plots (n=48) for the basal area (G), diameter at breast height (DBH), total height (HT), dominant height (H_{DOM}) and cellulose volume without bark (V).
Variable  Min  Max  Mean  SD 

G (m^{2} ha^{1})  27.08  39.16  33.96  2.75 
DBH (cm)  13.80  17.34  16.00  0.65 
HT (m)  20.82  27.93  24.89  1.70 
H_{DOM} (m)  25.20  33.27  29.79  2.06 
V (m^{3} ha^{1})  223.19  391.99  331.16  37.27 
List of the variables selected for model inclusion in each of the three approaches adopted in this study.
Data  Category  ALS metrics  Acronym 

ALS  Height  Minimum height  h_{min} 
Maximum height  h_{max}  
Mean height  h_{mean}  
Variance of height  h_{var}  
Standard deviation of height  h_{sd}  
Coefficient of variation of height  h_{cv}  
Mode of height  h_{mode}  
Kurtosis of height  h_{k}  
Height percentiles: 1^{st}, 5^{th}, 10^{th}, 20^{th}, 25^{th}, 30^{th}, 40^{th}, 50^{th}, 60^{th}, 70^{th}, 75^{th}, 80^{th}, 90^{th}, 95^{th}, 99^{th}  h_{p01}, h_{p05}, h_{p10}, h_{p20}, h_{p25}, h_{p30}, h_{p40}, h_{p50}, h_{p60}, h_{p70}, h_{p75}, h_{p80}, h_{p90}, h_{p95}, h_{p99}  
Cover  Number of first returns above mean  C_{ab mean}  
Number of returns above 2 m  C_{ab2m}  
Percentage of canopy  C_{%}  
Topography  Slope (°) 


Mean altitude  
Aerial Image  Spectral Properties  Band 1  blue 

Band 2  green 


Band 3  red 


Band 4  near infrared 


Simple ratio between bands B1/B2, B1/B3, B1/B4, B2/B3, B2/B4, B3/B4 


Normalized Difference Vegetation Index 

Fitting statistics of the best regression models used to estimate the total tree volume in the investigated area with three different approaches. (MD): Mean difference; (RMSE): root mean squared error; (Adj): Adjusted. For the acronym of variables included in the models, see
Approach  Regression Model Form  AdjR²  MD  MD %  RMSE  RMSE %  pvalue(ttest) 

(1) ALS data  585.17 + 22.48·( 
0.65  0.032  0.001  22.79  6.84  0.993 
(2) Aerial images  585.17 + 7.859·( 
0.48  0.64  0.19  28.17  8.45  0.883 
(3) Both datasets  625.16 + 27.74·( 
0.79  0.06  0.018  17.43  5.23  0.983 