Most of Japanese young plantation forests have reached an age when thinning is needed, but they have not yet been cut, and stand density is often too high. An economical method is needed for selecting stands that should be cut. We are developing a method of stand density estimation using aerial photography. In previous work, we devised a method for high resolution color aerial-photographs. It divides images into a series of color bands, and estimates stand densities by the brightness in a combination of different band images. Ground truthing is needed to improve precision. In our earlier work, we used a set of 22 plots for ground truthing. Here, we present ground truthing of mono-color aerial photographs obtained for a growing stock investigation project in Ehime prefecture, Japan. The project is to estimate the CO_2 sink potential for all forests in the prefecture. Vegetation profiles are measured by airborne laser altimetry assisted by a consecutive series of ground truth surveys. Performance of mono-color aerial photography method was rather poor, indicating that manual contrast adjustment should be improved.

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The forest road design process includes extensive field investigations and dynamic real-time decision-making processes to create the best forest road design. These processes do require significant effort, and require the most experienced personnel to affect the best outcome. However, such "front end efforts" pay huge dividends in estimating construction costs, as well as benefits of the improved design in the out years in both utility of use and maintenance of the constructed road. One of the major faults of past techniques was the low reproducibility of geographical features, and the resultant impacts that subsequently were encountered during the design and construction phases of the road projects. In this research, in order to improve the above-mentioned facts, the forest road design technique was developed using the LiDAR (Light Detection and Ranging) data that presented more accurate geographical features. The forest roads constructed before and after the LiDAR measurement were surveyed. Elevations on the cross-sections of the forest road constructed before LiDAR measurement were compared with those from 1m grid DEM made from LiDAR data and 10m grid DEM made from 1/5,000 topographic map. The mean square error between actual measurements and 1m grid DEM was 1.12m. On the other hand, the mean square error between actual measurements and 10m grid DEM was 6.02m. Earthwork-volumes estimated using the actual measurement of the forest road constructed after LiDAR measurement and by the program using 1m grid DEM were 3,596.48m^3 and 3,641.51m^3 respectively, while the earthwork volume using 10m grid DEM was 10,637.6m^3. Ground surfaces produced by LiDAR data represented actual ground surfaces accurately and the results of the forest road design using LiDAR data were similar to the actual forest road. The goal of the efforts demonstrated that using LiDAR enhanced the opportunities for planners to examine alternative designs to improve ease of design, cost of construction, and maintainability.

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The Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) is the first spaceborne lidar instrument for routine global observation of the Earth. GLAS records a vertical profile of the returned laser energy from a footprint of approximate 70m diameter. The GLAS waveform data (GLA01) and the Land/Canopy Elevation product (GLA14) provide information on vegetation spatial structure. In this study the use of the GLAS data for forest structural parameters retrieval was evaluated using airborne LVIS (NASA's Laser Vegetation Imaging Sensor) data and field measurements. The tree height indices from airborne large-footprint lidars such as LVIS have been successfully used for estimation of forest structural parameters in many studies. The tree height indices, based on lidar return energy quartiles from GLAS data were compared to similar tree height indices derived from LVIS data within the GLAS footprints. The results show that the tree height indices derived from the GLAS and LVIS waveforms were highly correlated. Our analysis showed that tree height and biomass obtained from field measurements can be predicted from GLAS data. Comparisons of the near-repeat-pass GLAS data acquired in Fall of 2003 (L2A), Fall of 2004 (L3A), and early Summer of 2005 (L3C) and 2006 (L3F) show that the surface elevations from GLAS were consistent. When the mean distance between corresponding points from two 4.5km orbits (260 GLAS shots from L2A and L3F) was 82.6m, the R^2 of the elevations from these two orbits was 0.997, with a RMSE of 4.1m. The top tree heights from the near-repeat-pass GLAS orbits show significant differences, probably due to the heterogeneity of the forests.

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Wind is the most important abiotic hazard for forestry in Britain. Most forests have been established in upland areas in locations that are commonly affected by high winds and poor soil conditions. Strong winds cause significant loss of timber every year in Great Britain and have profound effects in wood quality though increases in the proportion of compressed wood, poor stem straightness, repeated loss of leaders and important alterations in the relationship between height and diameter. ForestGALES (Geographical Analysis of the Losses and Effects of Storms in Forestry) is a process-based model that provides a better understanding of the variability in the wind forest climatology, an estimation of the critical wind speed to cause wind damage and the return period for that damage to occur. At present, ForestGALES is currently being linked to ArcGIS and LiDAR data has been evaluated to estimate the effects of stand structure in the probability of wind damage. To do so, the model has been adapted to operate with tree lists generated by LiDAR. In this context, three canopy delineation algorithms have been tested in connection to existing allometric relationships. TreeVAW (POPESCU, 2006), TreesVIS (WEINACKER et al., 2004) and ITC (GOUGEON, 2005). The results provided a valid method for evaluating the effects of stand variability on wind damage and the effectiveness of Airborne Laser Scanning for monitoring forest structure and its effects on wind stability.

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The aim of this study is to examine the performance of a numerical ellipsoid modeling methodology to estimate tree structural characteristics in mixed forest using airborne Light Detection And Ranging (LiDAR) data along with airborne Digital Matrix Camera (DMC) image. In three-dimensional numerical analysis using points cloud of LiDAR data, ellipsoid model has the potential to simultaneously estimate tree top position, diameter and shape of individual tree crown. A Japanese cedar plantation with randomly mixed pine trees was chosen in this study as this type of forest, which is typical of Japanese cedar plantation in Japan. We developed a methodology consisting of both tree species classification and estimation of characteristics of tree structure with the followings steps: (1) classification of area of cedar and pine trees in the mixed plantation by using ortho-DMC image, (2) estimation of number of trees and estimation of tree top location in horizontal plane by standard ellipsoid model for each species, derived from Crown Height Model (CHM) and based on random selections of points clouds on each of the classified areas, (3) estimation of tree top height and realistic shape of individual tree by using a truncated cone shape model and LiDAR points cloud in respective classified areas. The study area is a cedar plantation forest in Northern Japan. LiDAR measurements with a density of 14.65 pulses/m^2 and DMC imagery with a spatial resolution of 10cm are used in this study For validation, ground truth data of tree species, geographic tree position and tree height were measured at the study site. The developed methodology could correctly identify a total of 73 out of 89 cedar trees in the areas classified as cedar, and 12 out of 29 pine trees in areas classified as pine. Validation of estimated tree height resulted in coefficient of determination (R^2) of 0.72 and 0.78 for pine and cedar respectively. This study indicates that fitting the ellipsoid model and the truncated cone shape model to LiDAR points cloud is able to simultaneously estimate tree top position, crown shape and diameter of individual tree crown.

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The line-intercept sampling (LIS) method has found important applications in such areas as forest and wildlife, ecological and biological sciences, and crop and agriculture fields. LIS is a sampling technique to make observations along line transects in order to make inferences of area properties. The placement of transects can be chosen in many different manners, i.e., randomly or systematically. The motivation of this study is to use LIS to infer regional information of forestry biophysical parameters based on the linear transects measurements of a profiling LiDAR system. However, there is no optimum method to properly derive a reasonable measure to the uncertainty of LIS estimates. As such, the study first developed a theoretical framework to describe the LIS estimation in two settings, one with fixed landscape configuration, and another with random configuration. The subsequent simulation of transect observation is realized for two categorical maps: the artificial one simulated by SIMMAP, and the real one classified from Landsat ETM+ multispectral imagery. The simulated samples were used to test four estimators. The methodology employed in this study provides a good starting point for practically implementing the quantification of variance estimates with LIS.

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Data from the Geoscience Laser Altimeter System (GLAS) aboard the Ice Cloud and Land Elevation Satellite (ICESat) were used to explore the potential of satellite LiDAR for the estimation of forest parameters such as vegetation height and stemwood volume. This was carried out for the Forest of Dean, Gloucestershire, UK, a semi-ancient, highly mixed, temperate forest. Previous research suggests use of Waveform Extent (the difference of alternate model fit Signal Begin and Signal End) and a Terrain Index (maximum minus minimum elevations from a 7×7 matrix, 10m resolution DTM) to provide the most robust estimate of maximum canopy height. These waveform-based maximum vegetation height estimations were used to investigate the potential of satellite LiDAR for the estimation of stemwood volume for the tallest species within each footprint. Relationships were established with predictions of stemwood volume calculated from Forestry Commission yield models. These equations succeeded in explaining 68% of variance with 88.7m^3/ha RMSE for coniferous species and R^2 of 0.65 with 68.2m^3/ha RMSE for broadleaf species. The ability of satellite LiDAR waveforms to account for stemwood volume within mixed composition stands was also investigated. Area under the waveform canopy return, maximum canopy height, dominant canopy height and height of cumulative energy percentiles were considered. The height of the 90^<th> percentile of cumulative energy was found to best represent the weighted stemwood volume of heterogeneous stands producing R^2 of 0.57, 92.3m^3/ha RMSE and R^2 of 0.59, 67.5m^3/ha RMSE for stands dominated by coniferous and broadleaf species respectively. The results of this local study indicate the potential for similar methods to be applied to regional or national scales.

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With an objective of evaluating forest habitability for wildlife in terms of forest structure, wildlife abundance was observed using automated infrared sensor cameras while forest structure of the habitat was quantified with airborne laser profiling in three study areas of 400ha each set up around Mt. Ishizuchi-san, Mt. Myoujin-ga-mori, and Mt. Takanawa-san in Ehime Prefecture, Japan. Two parameters derived from airborne laser profiling, i.e. mean and standard deviation of standing timber stock in each study area, were used as structural indexes of forests, while the richness of fauna was quantified as the number of inhabitant species and their frequency of being captured by the automated camera. Of the four possible combinations of the forest and faunal parameters, only the one between the photographic capture frequency and standard deviation of standing timber stock revealed strong negative correlation. Thus it was reasoned that the variability in timber stock has resulted from altitude variability within a given study area which tends to be more pronounced in higher and more remote areas, leading to a conclusion that what is really correlated with wildlife abundance is the proximity to the human domain.

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Airborne Light Detection and Ranging (LiDAR) is a useful tool for scaling up physiological processes from an individual tree to the landscape level, because it can measure parameters that are related to canopy structure. The objectives of this study were (1) to calculate the canopy and stand parameters that characterize the heterogeneity of beech canopies and stands from LiDAR data and (2) to examine the applicability of these parameters by comparing them with field census measurements. A total of eight census plots with various stand structures were set up in beech forests on the Appi plateau (Iwate Pref., northern Japan) and on Mt. Naeba (Niigata Pref., central Japan). LiDAR data was used to calculate several parameters for describing canopy structure: gap ratio; mean canopy height; standard deviation (SD) and coefficients of variance (CV) of canopy height models (CHMs); canopy and stand surface area derived from the CHMs and digital surface models. The gap ratios and CVs of the CHMs were closely related to basal area (BA), and it may be possible to use them to quantify this variable when factors such as low gap ratio and topographic condition are considered. The CV tended to increase with the gap ratio. In contrast, canopy surface area was not strongly related to the canopy structure parameters. Consequently, the gap ratio and the CVs of the CHMs are the preferable parameters for representing structural properties of beech stands. Further analyses are needed to understand uncertainties in the relationships (inter alia) between, gap sizes, canopy height and individual tree size.

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In this study, we tried to estimate the stand structure of deciduous broad-leaved forest and mixed forest using multi-temporal LiDAR data, and it was compared with field survey result and photo interpretation result. As a result, there is the consistency in LiDAR data obtained the same period and the reproducibility of the DSM is high. In deciduous broad-leaved forest, the amount of changes in the DSM around the defoliation allows us to understand the stand structure such as the covering situation of sub tree and shrub and floor plant. In mixed forest, the multi-temporal LiDAR data is effective for the evergreen tree/deciduous tree classification.

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Forest stand variables (mean height, stand volume, and mean diameter breast height (DBH), tree density) were estimated in evergreen and deciduous broad leaved forest stand using LiDAR. LiDAR data were acquired along 12km and 28km transects with 1 pulse per square meter and small foot print (20cm). We set plots in evergreen and deciduous broad leaved forest stands from small to large on the transect and measured forest stand variables (mean DBH: 3.4-41.2cm; mean H: 3.1-17.4m; V: 25.1-854m^3; N: 295-9,507ha^<-1>; n=18). Laser pulses of digital canopy height model were extracted in each plot and LiDAR indexes were calculated: average, maximum, 90, 75, 50, 25 percentiles, standard deviation, and coefficient of variation. A linear regression analysis was performed between LiDAR indexes and forest stand variables. Mean height had the highest relationship with the LiDAR index 75 percentile (r^2=0.79); stand volume with the LiDAR index average (r^2=0.79), mean DBH with the LiDAR index 75 percentile (r^2=0.56), and tree density with LiDAR index 75 percentile (r^2=0.52). These results showed that low density LiDAR was useful for forest stand variable and would be useful for update and modification of forest base map and forest register.

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Airborne laser profiling of mainland Ehime Prefecture, Japan was conducted to develop an entirely new method of land-cover classification, partly in preparation for the post-Kyoto national carbon budget accounting, and partly for correction of the existing government land-use statistics, which should constitute the very basis of the impending national carbon budget accounting of the Kyoto Protocol. The altimetry data was obtained by using NASA's Portable Airborne Laser System (PALS) along 23 parallel flight lines 4km apart from each other covering the whole mainland portion of the prefecture. Based on the resulting surface profile representing topography and structures on the ground with some reference to laser return intensity and nadir video images, land cover along the flight line was classified into "forest", "farmland", "residential and urban", and "others" using PALSA (PALS Analyzer), a software developed for this particular purpose. This line evaluation was then developed into area statistics by simply multiplying by the distance between the flight lines, i.e. 4km. The resultant land-cover estimates not only differed from the existing government statistics as much as the latter does within itself, but also helped to identify the causes and sources of discrepancy quantitatively. Thus it was concluded that coordinated use of this new method with the existing system of land-use statistics would improve the overall credibility of the land-use/land-cover statistics of the prefecture and the nation.

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This study investigated the effects of laser-sampling density on individual-tree detection and tree height estimation changing the sampling density by overlapping three flight data in a mountainous coniferous forest. The LiDAR system used in this study was mounted on a fixed-wing aircraft. The study area was closed-canopy, middle-aged Japanese cedar (Cryptomeria japonica) plantation in Japan. We prepared three sets of single flight data (3.2 points/m^2), three sets of double-overlapping data (6.5 points/m^2) consisted of two single flight data, and one set of triple-overlapping data (9.7 points/m^2) consisted of three single flight data within this study plot. Namely, a total of seven datasets were used in the analysis. The numbers of detected same trees among same laser-sampling density datasets were different and increased with the increase of the density. The detection rate of same trees among all datasets was approximately 55%, and the detected trees belonged to dominant and co-dominant trees within the plot. In all datasets, we found that if a given field tree has relatively lower treetop-elevation and smaller crown radius than that of the nearest field tree, and these trees are close to each other, the lower tree is difficult to detect in mountainous coniferous forest. But the number of detected small trees between 10m and 18m height increased with the increase of laser-sampling density. LiDAR-derived median and mean tree heights were slightly greater than that of field measured tree height in this study site. Although there were significant differences between field measured and LiDAR-derived tree heights for all datasets (p<0.01), the difference between maximum and minimum RMSE for tree height estimates was only 0.17m and the maximum RMSE was 1.02m. All results of this study indicate that although greater laser-sampling density data can provide information of more varying tree size, 3 or 4 points/m^2 of laser-sampling density data would provide accurate individual-tree detection of upper-storey trees and tree height estimates, given as RMSE, is approximately 1m in middle-aged Japanese cedar forests in mountainous areas.

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The timber stock of mainland Ehime prefecture was estimated using airborne laser profiling data. Our provisional analysis revealed that: 1) at 5,435km^2, the laser estimate of land area obtained as a simple product of flight path length and its 4km width was fairly consistent with the figure of 5,455km^2 by the Geographical Survey Institute, considered the most reliable of the government statistics; 2) on the other hand, at 176 million m^3 for the entire prefecture our estimate of standing timber stock turned out to be twice as much as the government figure of 87 million m^3; 3) judging from the precision in land-area estimation and results from other research, our estimate is considered more likely to represent the actual timber stock than the government figure; 4) thus airborne laser altimetry would provide more accurate national forest carbon budget for the Kyoto Protocol than does the existing national forest inventory; 5) at the present density of laser profiling transects, 4km apart from each other, however, no reasonable accuracy is expected at the municipality level.

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The distribution of snow depth in a mountainous watershed located in northern Gunma Prefecture, Japan was measured by applying an airborne laser scanner on three different occasions: an autumn period with no snow, a mid winter period with maximum snow cover, and a late winter period with a maximum rate of snowmelt. Depth of snow was estimated as the difference in elevation between the snow and the ground surface. Snow depth at the opening of the meteorological station adjacent to the outlet of the watershed was compared with automatic readings of the ultrasonic distance sensor equipped on the ground. Discrepancies between the airborne and the ground-based snow depths were within an acceptable range for both periods, while slightly larger discrepancies were observed between the values during the late winter period. Snow appeared to be deeper with increasing elevation in most of the watershed, except the highest part where it became shallower. Decrease in snow depth from the mid to the late winter periods was larger in the lower part of the watershed, suggesting higher rates of snowmelt. Overall, the airborne laser scanner could measure snow depth at the flat opening quite accurately and was useful to capture spatial and temporal patterns of snow depth over the watershed. However, it appeared that snow depth could be calculated to be negative in some cases, typically when influenced by lodging of dense vegetation such as bamboo grasses due to the weight of snow. While these results are encouraging, further research on the measurement of snow depth on steep slopes and/or under tree canopies is recommended.

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Since United Nations Conference on Environment and Development, the world community has formally recognized that forests have a crucial role to play mitigating global warming and it is necessary to evaluate their role through repeatable, verifiable, and transparent scientific data analyses. The Kyoto Protocol and a subsequent document, the Intergovernmental Panel on Climate Change (IPCC) Special Report and Good Practice Guidance for Land Use, Land Use Changes and Forestry, recommended establishing a scientifically neutral method to evaluate and monitor forest land changes and forest biomass dynamics with international standards. Satellite remote sensing has been identified as one tool that can be used to measure forest area, rates of change in land use, location of forest activities, etc. Also satellite data has many advantages that are not only transparent and verifiable but also cost effective, including periodic data acquisition that is internationally available. When negotiators decided the modality of the forest inventory scheme of Kyoto Protocol, they relate it to the imaged characteristics of satellite remote sensing data. But the utility of data from satellites has some difficulties in estimating growing stock changes, and in distinguishing some type of forest activities, such as thinning. In this context LiDAR has a potentiality to provide measures for estimating carbon stock changes, greenhouse gas emissions, and removals associated with forest lands under UNFCCC and Kyoto Protocol. The definition of forest under Kyoto Protocol requires the minimum threshold of forest area, tree crown density, and tree height to be determined within specific ranges. Satellite data have not work well to separate forests according to such a precise threshold. However, LiDAR will be able to provide enough information to judge whether stands will be able to satisfy the definition of a forest. This report discusses the advantages of LiDAR from the view point of the inventory scheme under Kyoto Protocol.

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Airborne scanning LiDAR (Light Detection and Ranging) data has significant potential to update, audit, calibrate, and validate operational stand-level forest inventories by providing information on canopy height, vertical structure, and ground elevation. However, using LiDAR data as an operational data source in a sampling context requires repeatable and consistent attribute estimation (i.e. height), from data collected over several acquisition flight lines. We examined the consistency of LiDAR height estimates obtained from the Scanning LiDAR Imager of Canopies by Echo Recovery (SLICER) instrument over Jack pine (Pinus banksiana, var. Lamb) and black spruce (Picea mariana, var. Mill.) forest stands in central Saskatchewan, Canada. Two analyses were undertaken: first, estimated tree heights derived from pairs of LiDAR returns, acquired from multiple flight lines and within 9m of a single LiDAR footprint, were compared to assess the consistency of height estimates (point stability); secondly, height estimates from multiple flight lines within individual forest inventory polygons were compared to assess the consistency of within-polygon estimates of tree height (polygon stability). The point stability analysis indicated that over all forest classes estimates of height were consistent, with 94% of LiDAR returns (n=15,896) having a pair-wise height difference within ±5m. On a polygon basis, both between- and within-flight line standard deviations were considered. Results indicated that the within-polygon variability in estimated tree heights was captured by LiDAR data collected over any portion of a polygon. This result suggests that the inventory polygons are homogenous with regards to height (and related variability) and may be characterized with LiDAR, independent of actual flight path.

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A 5,159km profiling airborne LiDAR data set consisting of 56 parallel flight lines (fls) systematically spaced one kilometer apart acquired over the State of Delaware (USA) in y2000 are used to test the accuracy and precision of LiDAR-based forest inventory estimates. Nonparametric techniques is employed to develop simple linear regressions (SLRs) relating ground-measured biomass to laser height and crown closure. The ground-laser models are used to estimate total aboveground dry biomass at the county (3 counties in Delaware - 1,124km^2, 1,542km^2 and 2,539km^2) and State (5,205km^2) levels. The laser estimates are compared to U.S. Forest Service-Forest Inventory and Analysis (FIA) estimates from a 1999 ground-based survey of 215 FIA plots. In addition, the 56-fl data set is treated as a population and subsampled to test three variance estimators. The three variance estimators include weighted versions of the simple random sampling (SRS) estimator, a successive differences (SD) estimator, and a Newton's Method (NM) estimator. Results, constrained to this particular 56 fl data set and post-stratification, indicate the following: (1) Using all 56 fls in conjunction with the nonparametrically derived SLRs, LiDAR-based estimates of biomass are within 4%-24% at the county level and 14%-18% at the state level. (2) Across the 3 counties and State, considering the full range of flight line sampling intensities (from 2 to 28km between fls), the SRS estimator most closely tracks systematic sampling variability. The SD estimator is most conservative, consistently overestimating biomass variability by 〜15%. (3) When a limited, more realistic range of inter-flight line distances from 2-6km between parallel fls is considered, the behavior of the SRS estimator changes markedly. The SD and NM estimators overestimate systematic standard errors (SEs) by 〜18%, whereas the SRS estimator becomes the most conservative, overestimating systematic SEs by 〜30%. This SRS role reversal from least to most conservative as the distance between fls decreases suggests that the fls spaced 2-6km apart are, in Delaware, spatially autocorrelated. We suggest that analysts employ the SD or NM estimators when fls are closely spaced, e.g., 2-6km apart. (4) Inclusion of prediction error, i.e., the residual noise around regression lines used to predict, for instance, biomass as a function of profiling LiDAR height measurements, adds approximately 0.7-1.2 t/ha (an 8-15% increase) to the biomass standard error, averaged across strata and sampling intensities. (5) The positive relationship between the distance between flight lines and the systematic standard error appears to be generally linear (albeit noisy) for a given cover type and study area. Figures are provided illustrating the empirical relationship between flight line distance and systematic SE, by stratum within study area. These may be used to guide the design of airborne LiDAR-based forest surveys on areas from 1,000-5,000km^2.

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The objective of the present study is to develop a transparent and verifiable model of volume estimation for conifer plantations using remote sensing. The model is to be independent from observation, that is, ground truths are not necessary for parameter fitting and model construction. To achieve it, the estimation process is divided into two steps: direct measurements of physical parameters of stand, and a stand volume estimation by an external model. As an external model, we adopt the stand density chart, which is a robust and general model of stand volume growth and estimation based on a semi-empirical growth model of even-aged stands. It can estimate a stand volume from a dominant height and a density, which can be directly measured by remote sensing. In the present study, firstly, the iso-height curves, a sub-model of the stand density chart, are created from the external inventory data. Then, the dominant heights and densities are directly observed by means of the airborne remote sensing. Finally stand volumes are estimated by the observed dominant heights and densities using the iso-height curves. We found that the new iso-height curves predicted the stand volume very well. The dominant height estimation was reasonably accurate, too. However, there was a room for improvement for the stand density estimation. This method will contribute the implication of the remote sensing technology to forest management by sharing the concept and values with foresters.

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Stand attributes such as stand density, stand height, stand volume, are important factors for sustainable forest management. This study aimed to estimate stand attributes in Cryptomeria japonica and Chamaecyparis obtusa stands in Japan from single tree detection using small-footprint airborne LiDAR data. Twenty circular sample plots of 0.04ha were established for this study. Their stand densities were estimated from the number of treetops derived from airborne LiDAR data using the local maximum filtering method. Stand densities derived from the field survey in the sample plots were compared with those obtained from airborne LiDAR data. The coefficient of determination between them was 0.92. Stand densities which were estimated from the airborne LiDAR data, were underestimated in both young and mature stands. Stand heights, which were estimated from the airborne LiDAR data, were slightly overestimated, but they were almost the same as the mean heights of dominant standing trees. Allometric equations between diameter at breast height (DBH) and crown area obtained from airborne LiDAR data were determined for each of two species, i.e., Cryptomeria japonica and Chamaecyparis obtusa, and DBH of individual trees was estimated from the airborne LiDAR data. Stand volumes were estimated from the cumulative individual volumes, which were derived from volume formulas with two variables, i.e., DBH and height, both obtained from airborne LiDAR data. Stand volumes derived from the field survey were compared with those obtained from the airborne LiDAR data. The coefficient of determination was 0.86. Stand volumes which were estimated from the airborne LiDAR data, were underestimated because of the lack of suppressed tree volume; however, the degree of underestimation was relatively low.

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