Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data

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1 Forest Science and Technology ISSN: (Print) (Online) Journal homepage: Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data Taejin Park, Woo-Kyun Lee, Jong-Yeol Lee, Woo-Hyuk Byun, Doo-Ahn Kwak, Guishan Cui, Moon-Il Kim, Raesun Jung, Eko Pujiono, Suhyun Oh, Jungyeon Byun, Kijun Nam, Hyun-Kook Cho, Jung-Su Lee, Dong-Jun Chung & Sung-Ho Kim To cite this article: Taejin Park, Woo-Kyun Lee, Jong-Yeol Lee, Woo-Hyuk Byun, Doo-Ahn Kwak, Guishan Cui, Moon-Il Kim, Raesun Jung, Eko Pujiono, Suhyun Oh, Jungyeon Byun, Kijun Nam, Hyun-Kook Cho, Jung-Su Lee, Dong-Jun Chung & Sung-Ho Kim (2012) Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data, Forest Science and Technology, 8:2, 89-98, DOI: / To link to this article: Copyright Taylor and Francis Group, LLC Published online: 26 Apr Submit your article to this journal Article views: 182 View related articles Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at Download by: [ ] Date: 04 December 2017, At: 07:56

2 Forest Science and Technology Vol. 8, No. 2, June 2012, Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data Taejin Park a, Woo-Kyun Lee a *, Jong-Yeol Lee a, Woo-Hyuk Byun a, Doo-Ahn Kwak b, Guishan Cui a, Moon-Il Kim a, Raesun Jung a, Eko Pujiono a, Suhyun Oh c, Jungyeon Byun a, Kijun Nam a, Hyun-Kook Cho d, Jung-Su Lee e, Dong-Jun Chung f and Sung-Ho Kim d a Division of Environmental Science and Ecological Engineering, Korea University, Seoul , Republic of Korea; b Environmental GIS/RS Centre, Korea University, Seoul , Republic of Korea; c Department of Climate Environment, Graduate School of Life & Environmental Sciences, Korea University, Seoul , Republic of Korea; d Division of Forest Resources Information, Korea Forest Research Institute, Seoul , Republic of Korea; e Department of Forest Management, College of Forest Environmental Science, Kangwon National University, Chuncheon , Republic of Korea; f National Forest Resource Inventory Center, National Forest Cooperatives Federation, Seoul , Republic of Korea (Received 7 February 2012; Accepted 2 March 2012) The importance of estimating forest volume has been emphasized by increasing interest on carbon sequestration and storage which can be converted from volume estimates. With importance of forest volume, there are growing needs for developing efficient and unbiased estimation methods for forest volume using reliable data sources such as the National Forest Inventory (NFI) and supplementary information. Therefore, this study aimed to develop a forest plot volume model using selected explanatory variables from each data type (only Forest Type Map (FTM), only airborne LiDAR and both datasets), and verify the developed models with forest plot volumes in 60 test plots with the help of the NFI dataset. In linear regression modeling, three variables (LiDAR height sum, age, and crown density class) except diameter class were selected as explanatory independent variables. These variables generated the four forest plot volume models by combining the variables of each data type. To select an optimal forest plot volume model, a statistical comparing process was performed between four models. In verification, Model no. 3 constructed by both FTM and airborne LiDAR was selected as an optimal forest plot volume model through comparing root mean square error (RMSE) and coefficient of determination (R 2 ). The selected best performance model can predict the plot volume derived from NFI with RMSE and R 2 at (m 3 ) and 0.48, respectively. Keywords: airborne LiDAR; forest plot volume; Forest Type Map; linear regression analysis; National Forest Inventory Introduction In recent years, global researchers have been trying to study the carbon circulation mechanism for quantifying carbon storage and sequestration with increasing concern of climate change issues (Sellers et al. 1997; Running et al. 2000). The terrestrial ecosystem, where many organisms including humans live, has been recognized as an essential intermediary of carbon circulation. To understand about the role of the terrestrial ecosystem, it is necessary to investigate the relationships between soil, vegetation, and atmosphere (Chapin et al. 2002). Because climate change can alter the carbon storage in the terrestrial ecosystem, thereafter that change can affect the carbon balance in atmospheric environment and climate conditions (Bachelet et al. 2001; Cao et al. 2003, 2005). Especially, forest ecosystems, one of the most important and biggest parts in the terrestrial ecosystem for controlling the cycle of materials, should be assessed regarding carbon storage and sequestration to understand carbon circulation (Chapin et al. 2002). In addition, every country has strongly required research related with forest carbon storage and sequestration due to the convention on climate change agreement and Kyoto protocol implementation (Park et al. 2011b). Accordingly, many countries including South Korea have constructed a National Forest Inventory (NFI) system to provide fundamental information about quantifying forest volume and biomass for understanding forest carbon storage and sequestration (Kim et al. 2010a). It can be used to estimate national forest volume using plot measurements and developed volume equations (KFRI 2008; Son et al. 2008). Although the forest volume derived from NFI measurements is precisely estimated by field surveys, there are difficulties in predicting spatial distributions of forest volume in uninvestigated areas (Chunget al. 2009). Existing forest statistics aiming to predict forest volume in uninvestigated areas have practical limitations to provide spatial information due to uncertainty and *Corresponding author. leewk@korea.ac.kr ISSN print/issn online Ó 2012 Korean Forest Society

3 90 T. Park et al. variation under various forest conditions. Therefore, estimating and predicting forest volume in uninvestigated areas has required more reliable and accurate alternative ways (Chung et al. 2009; Yim et al. 2009). To overcome this limitation of estimating the forest volume based on NFI measurements, various interpolation methods using supplementary information were applied to estimate the spatial distribution of forest volume (Yim et al. 2009). Especially, remotely sensed data, which can be easily processed and used for inaccessible broad areas, are globally adopted to predict forest volumes in uninvestigated areas (Chung et al. 2009; Yim et al. 2009). Among remotely sensed data, a Forest Type Map (FTM) based on aerial photographs is usually appraised as efficient supplemental information for large areas, because FTMs are constructed under identical standards on a national scale (Kim et al. 2010a). However, an FTM can have limitations for improvement of prediction accuracy due to categorical data composition. Furthermore, FTM-based interpolation is also insufficient as it does not consider forest cover change and vertical forest structure. Laser surveying techniques, considered as a new and non-destructive approach, have been applied to forest areas for easily acquiring three-dimensional information (Means et al. 1999; Leckie et al. 2003; Kim 2007; Kwak et al. 2007, 2010; Næsset 2007; Park et al. 2011a). Early LiDAR application in estimation of forest volume and biomass was established by individual tree identification, in which the elementary process is to extract individual tree height and diameter at breast height (DBH) from crown width (Popescu et al. 2003; Chen et al. 2007; Kim et al. 2010b; Kwak et al. 2010). Thereafter, the stand volume and biomass have been estimated using height distributional approaches to avoid the complications of the individual tree approach such as difficulties in considering spatial variation of forest structure (Nelson et al. 1988; Harding et al. 2001; Parker et al. 2001; Drake et al. 2002; Park et al. 2011a). According to many studies using the LiDAR vertical profile approach, this approach can be effective for estimating forest volume at plot and stand level. This study was aimed to explore the feasibility of FTM and airborne LiDAR data (independent variables) for estimating forest volume with NFImeasured plot volume (dependent variable) through developing the forest plot volume model. The models were separately developed by three data types such as only airborne LiDAR, only FTM, and both datasets. Developed forest plot volume models were verified with a plot volume of 60 independent test plots. Furthermore, we tried to identify which model and data type was more appropriate for forest plot volume estimation. Due to the scheme of the NFI surveying system that was designated to circular plots, FTM and airborne LiDAR data was adequately transformed to plot level. Accordingly, forest volume level resulting from this study was plot volume and we have used a specific terminology, forest plot volume, for expressing the forest volume throughout this paper. Material Study area The study areas were located in Yangpyeong City of Kyeonggi Province (Datum: Geodetic Reference System 1980 (GRS80); coordinates: the upper left E, N and lower right E, N), central South Korea (Figure 1). The area is approximately 878 km 2 and situated from 160 to 1157 m asl. The forest area in the study site was dominated by steep hills, and composed of Pinus koraiensis, Larix leptolepis, Pinus densiflora, Pinus rigida and Quercus spp. In the forest area, only 114 plots (55 sample plots and 60 test plots) were selected from NFI data for developing and verifying the forest plot volume model. Airborne LiDAR data An Optech ALTM 3070 (a discrete LiDAR system) was used for acquisition of the LiDAR data, with the flight dates from 11 April to 28 May The study Figure 1. Location of study area presented by mosaic aerial photograph taken from April to May 2009.

4 area was measured at an altitude of 1000 m, with a sampling density of approximately 3*5 points per square meter, with radiometric resolution, scan frequency, and scan width of 12 bits, 70 Hz and +258, respectively. For accurate analysis, we used LiDAR returns within +108 scan angle as overlapped areas were eliminated by MicroStation and TerraSolid programs to reduce the scan angle affecting mean tree height and stem volume estimations (Næsset 2007). Prior to extraction of parameters from airborne LiDAR data, every airborne LiDAR return was classified by the automatic procedure within the TerraScam program. The LiDAR returns were classified into two groups including ground and above-ground returns. Ground returns are determined to be reflected on the ground within the plots, and canopy returns include every return except ground returns. National Forest Inventory (NFI) data In South Korea, 5th NFI has been conducted on only forest area (as of 2011, approximately 6.4 million ha) from The scheme of the survey was the systematic sampling with an interval of 4km6 4 km. In addition, there are four circular sample plots by cluster sampling in one intersection of grid line by 4 km 6 4 km. The size per sample site was 0.08 ha with m radius (Figure 2). In the sample sites, the tree species, age, height, and DBH were measured based on individual trees. The coordinate of plot, elevation, slope and aspect were also acquired at the center of each plot. As of 2011, the inventory has been performed, and study area was surveyed in In this study, every plot in Yangpyeong city was selected for 55 sample and 60 test sites. The coordinate was measured on the center plot using the GPS Pathfinder Pro XR, manufactured by Trimble to geo-match with NFI plot, LiDAR, and FTM data. Forest Science and Technology 91 Forest Type Map (FTM) data The FTM includes tree species (19 types), diameter class (four classes), age class (six classes), and crown density class (three classes) (Table 1). It has been constructed every 5 years in relation to the forest inventory project. In this study, 4th FTM, prepared from 2001 to 2005, was used for extracting forest stand characteristics. Among the attributes of FTM, tree species was excluded and only diameter, age, and crown density class were employed. The reason for excluding tree species in this study is an inconsistency of species information between NFI and FTM. The inconsistency of dominant tree species between NFI and FTM was caused by only different surveying and recording level of each dataset. It meant that NFI species surveying system was performed by individual tree level, thereafter, its information was converted to stand level through integrating individual tree species distribution within target plot (Chung et al. 2009; Kim et al. 2010a). Therefore, individually surveyed species information of NFI had large species information, whereas stand level had only coniferous, deciduous, and mixed forest area. On the other hand, the species information of the FTM was determined in stand level by interpreting aerial photographs based on customized method and surveying statistically sampled regions. The recorded whole species in the FTM were classified into 16 species (Kim et al. 2010a). Due to the difficulty caused by unmatchable species information, we decided to exclude species information. Method This study was separately performed to develop the forest plot volume model using various independent variables derived from different datasets. The independent variable were extracted by classifying data type into only airborne LiDAR, only FTM, and both dataseta (airborne LiDAR and FTM). The NFI- Figure 2. National Forest Inventory distribution (a) and scheme of each sub-plot (b, c) in South Korea (Kwak et al. 2011).

5 92 T. Park et al. Table 1. Attribute information of Forest Type Map. Attribute Code Class Symbol Description DBH Class 1 Young class 0 A stand that has more than 50% of crown closure of trees which of diameter at breast height(dbh) is below 6cm 2 Small DBH tree 1 A stand that has more than 50% of crown closure of trees which of DBH is below 6 16cm 3 Medium DBH tree 2 A stand that has more than 50% of crown closure of trees which of DBH is below 18 28cm 4 Large DBH tree 3 A stand that has more than 50% of crown closure of trees which of DBH is below 30cm Age Class 1 I age class I A stand that has more than 50% of crown closure of 1 10 year aged trees 2 II age class II A stand that has more than 50% of crown closure of year aged trees 3 III age class III A stand that has more than 50% of crown closure of year aged trees 4 IV age class IV A stand that has more than 50% of crown closure of year aged trees 5 V age class V A stand that has more than 50% of crown closure of year aged trees 6 VI age class VI A stand that has more than 50% of crown closure of more than 51 year aged trees Crown Density A Low A stand that has less than 50% of crown closure B Middle A stand that has more than 51% and less than 70% of crown closure C High A stand that has more than 71% of crown closure measured plot volume was used as a dependent variable in model development. In this study, preprocessed and geo-matched independent variables were employed for regression modeling of the NFI-measured forest plot volume, using the airborne LiDAR and FTM data (Figure 3). Data preprocessing Prior to the data preprocessing procedure, we firstly consider data coordinate systems of each dataset. To extract information from each dataset within the same target plot, geo-matching procedure was preferentially performed for the corresponding geographical location of each dataset. In this study, each datum of three dataset was converted to GRS80 datum using ArcGIS projection transformation tool. After geo-matching procedure, forest plot volume derived from each dataset should be reanalyzed by considering data construction systems. In the case of NFI-measured forest plot volume, it was calculated by allometric functions based on density, height, and DBH of individual trees within a plot area. However, NFI-measured forest plot volume was represented by volume per ha unit, which meant that derivations from NFI needed to convert to volume per actual forested area. Also, other variables from airborne LiDAR and FTM should be considered for forested area within a fixed plot area, because the process for NFI-measured forest plot volume included forest density factor which can provide forest coverage and number of trees information. Therefore, this study tried to consider ratio of areas, which is proportion of covered or uncovered area by trees, through classifying forest and non-forest area using airborne LiDAR height profiles under the same standard height used to classify canopy and ground returns. The LiDAR data should be normalized for each return to retain the real height information related to the return hierarchy (van Aardt et al. 2006). To normalize all returns, a Digital Terrain Model (DTM) using only ground returns was firstly generated. Thereafter, the heights of all returns upon canopy were subtracted from the DTM heights. Finally, we used the canopy LiDAR returns except understory layer of which height was almost lower than 2.5 m. In addition, only forested area was extracted by classified LiDAR returns (Figure 4b). To construct explanatory variable, we calculated LiDAR height sum inner NFI plot according to research of Kwak et al. (2011). Actually, we could extract uncountable variables from that data within target plot. However, every extracted variable couldn t explain volumetric structure of forest. As a result of a study by Kwak et al. (2011), their research showed that LiDAR height sum information inner NFI plot, which is a way of non-parametric approach, can be an explanatory variable for explaining NFI-measured forest plot volume. Therefore, this study used LiDAR height sum variable with avoiding variable selection procedure in airborne LiDAR data. The forest plot volume derived from NFI measurements was calculated and recorded in volume per ha

6 Forest Science and Technology 93 Figure 3. Flowchart for estimation of forest plot volume using NFI, FTM, and airborne LiDAR data. unit. Therefore, classified forested area using LiDAR returns was applied to recalculate an actual forest volume per forested area (Figure 4a). The age and diameter class of FTM can be adopted for regression analysis due to characteristics of each attribute. However, crown density class should be converted from categorical data to continuous data through replacing with median value based on each definition of class. In addition, each attribute were also normalized by forested area, thereafter, attribute determination of target plot was depending on the ratio of each attributes occupation area (Figure 4c, d, e). Through data preprocessing, four applicable independent variables, such as height sum from airborne LiDAR, age, diameter, and crown density class from FTM, were extracted and adopted to regression modeling. To identify which data type is more significant to estimate forest plot volume, we separately prepared the independent variables by three types (Table 2). Forest plot volume model development To develop the forest plot volume model, we adopted simple and multiple linear regression analysis according to number of variable in each data type. In the case of Type 2 using only airborne LiDAR data, simple linear regression analysis was used to explain the NFImeasured plot volume. Data type 1 and 3, which used only FTM and both dataset, respectively, were regressed under multiple linear regression analysis. Prior to develop the forest plot volume model by multiple regression modeling, independent variables should be assessed to identify multi-collinearity using Variance Inflation Factor (VIF) (O Brien 2007) and Pearson s correlation coefficients (van Aardt et al. 2006). Because the multi-collinearity can cause undermined the statistical significance of an independent variable and increased standard error of aregression coefficient (Allen 1997). Therefore, the variables, which have a VIF below 10 and Pearson s correlation coefficients below 0.5, were only used to develop forest plot volume model for avoiding problems of

7 94 T. Park et al. Figure 4. Data preparation and construction of each dataset with investigated forested area. Geographical locations of NFI plots (a), LiDAR return distribution within each sub-plot by forested area (b), Age, Crown density and Diameter class distribution, respectively (c, d, e). Table 2. type. Type No. Listed applicable independent variables by data Data type Applicable independent variables Type 1 FTM Age class Diameter class Crown density class Type 2 Airborne Height sum Type 3 LiDAR FTM & Airborne LiDAR Age class Diameter class Crown density class Height sum In the process for developing forest plot volume model, combinations of employed variables developed various number of different models by number of variables and data type. Generally, different regression models with various combinations of selected variables and data type can be assessed by their R 2, adjusted R 2, Root Mean Square Error (RMSE), Sum of Square Error (SSE), Akaike s Information Criterion (AIC), Mallow s Cp, and Bayesian Information Criterion (BIC) values (SAS 2006). In this study, we fully assessed regressed forest plot volume models using various statistics, such as R 2,adjustedR 2, RMSE. Also, every developed model equations were evaluated by applying these models to predict plot volumes of 60 independent test plots. multi-collinearity (Park et al. 2011a). In this study, independent variable combination derived from only FTM and both dataset should be assessed by two statistical indicator of multi-collinearity. After selection of explanatory variables, simple and multiple linear regression analyses were performed using combinations of selected variables (Equation (1)). y ¼ a þ b 1 x 1 þ b 2 x 2 þ b 3 x 3 þ ::: þ b n x n þ e ð1þ where, y is the plot volume measured in the field from NFI, x 1, x 2, x 3,..., x n are the selected independent variables, a, b 1, b 2, b 3,..., b n are the regression parameters, and e is the residuals. In the case of simple linear regression analysis, Equation (1) was downsized to one selected independent variable and one regression parameter. Results and discussion Preprocessed NFI-measured plot volume, Height sum derived from airborne LiDAR, and attributes of FTM were adequately applied to simple and multiple linear regression analysis by number of explanatory variables. Every constructed variables inner NFI plots were distributed with NFI-measured plot volume as Figure 5. The Height sum variable from airborne LiDAR data had a continuously proportional relationship with NFI measurement. On the other hand, other variables derived from FTM including Age, Diameter, and Crown density class had indistinct trends when plot volume increase, however, these variables partly explained about plot conditions related with plot volume. As a result of selecting explanatory variables in the case of multivariate analyses such as Type 1

8 Forest Science and Technology 95 Figure 5. Relationships between NFI-derived forest plot volume and independent variables. and 3, three explanatory variables were selected: Height sum, Age and Crown density class with low multi-collinearity due to the VIF of approximately 1.0 and Pearson s correlation coefficients below 0.5 (Table 3). From the result, Diameter class was eliminated due their relatively higher coefficients with Age class than 0.5 in model developments of Type 1 and 3 (Table 4). In Type 2 analysis, the LiDAR-Height sum variable was only used to regress to the forest plot volume model. Each independent variables of Type 1 and 2 developed candidates of forest plot volume model, respectively (Model no. 1 and 2). The selected independent variables in Type 3 also generated two candidate models by combining the three reduced independent variables (Model no. 3 and 4). Table 5 shows the model development statistics of each model. Model no. 3 from FTM and airborne LiDAR showed the best statistical performance in terms of the highest R 2 and lowest RMSE. Whereas, the Model no.1 from FTM showed the worst statistical Table 3. Results of correlation coefficients for selecting explanatory variable. Correlation coefficient (p-value) Height sum Age class Diameter class Crown density class Height sum Age class Diameter class Crown density class performance. However, in case of the adjusted R 2, that of Model no. 4 was higher than that of Model no. 3. The adjusted R 2 was generally used to assess the determination of multivariate regression models. Therefore, we required further evaluation methods. In this study, we verified these models using 60

9 96 T. Park et al. independent test plots to select an optimal forest plot volume model. The regression models generated via each data type were verified practically with 60 independent test plots. When the Age and Crown density class variables extracted from only FTM (Model no. 1) were used, validation results by observed and predicted data showed that R 2 and RMSE value of Model no. 1 were 0.31 and m 3, respectively (Figure 6a). On the other hand, the R 2 and RMSE of Model no. 2, which was developed by extracting variable from only LiDAR height profile, were evaluated to relatively higher than Model no. 1 (Figure 6b). As a result of the verification using test plots, the coefficient of determinations of Model no. 3 and 4 were 0.48 and 0.47 and RMSE were m 3 and m 3, respectively (Figure 6c and d). In overall, the Model no. 3 has the best statistical performance in the validation with 60 independent test dataset. Variables extracted from LiDAR data showed satisfactory P-value in every candidate models including that variable. It implied that LiDAR height sum variable effectively explain forest plot volume with high statistical significance. When compared with other variables such as Age and Crown density class derived Table 4. type. Type No. from FTM, variable from airborne LiDAR is more explanatory independent variable. Because LiDAR has direct forest structure data acquisition system based on three-dimensional range finder techniques for demonstrating forest plot volume (Drake et al. 2002; van Aardt et al. 2006; Chen et al. 2007; Kwak et al. 2011). However, FTM was indirectly generated by image interpretation of two-dimensional aerial photographs. Each variable from interpreted aerial photograph is limited to estimate forest plot volume which can be explained in three dimensions. Although attributes derived from FTM showed low statistical significance, these variables take a role in improving the accuracies of forest volume model when comparing the results (whether including FTM-derived variables or not) (Figure 6). As a whole, the Type 3 using variables extracted from both data source (airborne LiDAR and FTM) was the most appropriate informant for forest plot volume estimation among developed every model from three data types. Furthermore, we can put forward that LiDAR height profile can be adopted for developing forest plot volume model and attributes of FTM can be used as supplementary information in that work. Results of selection of explanatory independent variables and developed candidate of forest plot volume model by data Data type Selected explanatory independent variables Developed candidate of forest plot volume Model (variable combination) Type 1 FTM Age class Crown density class Model no.1 (Age and Crown density class) Type 2 Airborne LiDAR Height sum Model no.2 (Height sum) Type 3 FTM & Airborne LiDAR Age class Crown density class LiDAR height sum Model no.3 (Age, Crown density class and Height sum) Model no.4 (Crown density class and Height sum) Table 5. Results of simple and multiple linear regression analysis for developing forest plot volume model. Model No. (Type No.) F-value P 4 F Model Fitting t-test RMSE Adjusted (m 3 ) R 2 R 2 Parameter Estimate Std. Error t-value P 4 jtj Model no Intercept (Type 1.) Age class Crown density class Model no Intercept (Type 2.) Height sum Model no Intercept (Type 3.) Height sum Age class Crown density class Model no Intercept (Type 3.) Height sum Crown density class

10 Forest Science and Technology 97 Figure 6. Verification results between observed plot volume and predicted plot volumes by different four models. In this study, some limitations occurred in data construction. Firstly, NFI measurement was surveyed from 2006 to 2010, while FTM was prepared from 2001 to 2005 with different time ranges. Also, LiDAR data acquisition was performed in It can negatively influence to match dataset between forest plot volume derived from NFI and other sources. Overcoming this limitation should be done with dataset of same time range for improving the accuracy of forest plot volume prediction. Secondly, stand species information was excluded for connectivity of forest plot volume from NFI measurement, which was composed with only three forest types in stand level, with FTM. To improve these dataset, we need to extract stand species from NFI data at individual tree level in further study through species integration process. In addition, this study was performed to predict forest volume at plot scale, although the airborne LiDAR can provide more detailed height information of forest structures with high spatial resolution. After finely identifying the relationship of LiDAR height profile with forest volume at grid scale (51 m) with using attributes of FTM as supplementary information, we can predict the spatial distribution of forest volume at grid scale. Conclusion The objective of this study was to develop forest plot volume model using NFI, airborne LiDAR and FTM data. After applying simple and multiple linear regression analysis to extracted variables, the result of developed forest plot volume model showed a positive aspect of application of LiDAR data in expanding NFI measurement at plot level. The selected best performance model, optimal forest plot volume model combined FTM with airborne LiDAR, can predict the plot volume derived from NFI with RMSE and R 2 at (m3) and 0.48, respectively. In further study, we try to overcome some limitations of this study and to develop an efficient method for predicting forest volume at grid scale through identifying the relationship of LiDAR height profile with using attributes of FTM as supplementary information. In developing a way for estimating forest volume on a national scale, the

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