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The k-nearest neighbors (kNN) method is widely employed in national forest inventory applications using remote sensing data. The objective of this study was to evaluate the kNN method for stand volume estimation by combining LANDSAT/ETM+ data with 622 field sample plots from the Japanese National Forest Inventory (NFI) in Kyushu, Japan. The root mean square error (RMSE) and relative RMSE of the volume estimates rapidly decreased as the number of nearest neighbors (k) increased up to five, and then it slightly declined. They were consistently smaller for the Euclidean distance than for the Mahalanobis distance. The estimation errors (RMSE and relative RMSE) were 169.2 m 3 /ha and 66.2%, respectively (k = 10). The relative RMSE was similar to the previous studies. The estimated values were more accurate towards the mean value of the total volume, with an overestimation of the low volumes and an underestimation of the high volumes. We found a significant linear relationship between the observed stand volumes and estimated errors, which suggests that systematic errors may be reduced using this linearity. This research concluded that the kNN method is suitable for estimating stand volumes in Kyushu.

The mountain zone of Yakushima Island is covered with a mixed conifer-broadleaved forest dominated by old-growth Cryptomeria japonica (L.f.) D. Don trees. Even though Yakushima Island has been frequently struck by typhoons with wind velocities exceeding 55 m s À1 , the Cr. japonica trees in the mountain zone have survived for thousands of years without fatal damage. To evaluate the effect of storms on tree growth, the relationships between the stem diameter at breast height (DBH) and the heights of Cr. japonica and coexistent tree species were investigated. Two models based on an expanded allometric equation and a discontinuous piecewise allometric equation, respectively, to represent DBH-height relationships were evaluated. In all plots, the DBH-height relationship of Cr. japonica was discontinuous between small DBH and large DBH individuals. The tops of the large DBH individuals of Cr. japonica were lost to strong winds. However in each instance, they occupied the highest position in the canopy, even if they had lost their tops. In contrast, the DBH-height relationships of subcanopy broadleaved species were continuous in many plots and the equilibrium heights of the dominant broadleaved species were similar and almost in the same order regardless of the canopy heights of Cr. japonica. These results revealed a constant vertical structure in the Cr. japonica forest on Yakushima Island. Our results demonstrate a vertical niche segregation in the forest under high wind pressures and such vertical structure enables effective use of forest space and increases the basal area density.

Information about forest biomass distribution is important for sustainable forest management and monitoring fuelwood supply. The objective of this study is to develop an accurate forest biomass map for Kampong Thom Province, Cambodia. We used a new technique (object-based approach) and a conventional technique (pixel-based approach) for the estimation of forest biomass using Landsat Enhanced Thematic Mapper Plus (ETM?). The object-based approach created segments of images, and calculated statistical and textural attributes. Our results showed that estimation accuracy of the object-based approach, with the use of band 1 and an exponential fit, was the best (R 2 = 0.76), and this accuracy was comparable to that of the pixel-based approach (R 2 = 0.67). Although several textural variables were related to forest biomass, they did not contribute significantly to improvement of estimation accuracy. However, the object-based method can be used for image segmentation so that the image objects are spectrally more homogeneous within individual regions than with their neighbors. Hence, they can be regarded as management units for policy-related spatial decisions. Therefore, it is possible to select either of the two methods depending upon what the situation demands.

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