Estimation of stand volumes using the k-nearest neighbors method in Kyushu, Japan

Abstract

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.

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

“…The size of trees to be measured was different for each circle; trees >1 cm, 5 cm and >18 cm in diameter at breast height (DBH) were measured in the S, M and L circles, respectively. In each plot, the tree height was measured for at least 20 trees, which were expected to be selected from a wide range of tree sizes to construct a DBH-height curve for the plot [23,25,31,38,39]. We used the information of species, DBH and tree height from the 40 measurement variables to calculate the plot-level stand volume.…”

“…In addition, although fixed values of t = 0, 1 and 2 are usually used in the k-NN technique, the optimum value of t increases as k increases [49]. However, given its interpretive advantages [21] and the ease of comparing its results with those of previous studies [7,10,25,40,42,[50][51][52], we used a simple k-NN technique with a fixed value of t = 2 in order to investigate the effectiveness of additional feature variables in this study. First, we created prediction models without FTYPE and then created prediction models using FTYPE as a dummy variable.…”

“…The FRMS plots were geo-referenced, i.e., the plot data could also be used for estimating forest attributes via the k-NN technique, combined with satellite image data. For example, Kajisa et al [25] estimated the stand volume in the Kyushu region of Japan using FRMS plots and satellite image data, and concluded that the k-NN technique was suitable for this purpose in their study area. Kajisa et al [25] proposed a simple method using only satellite image data as an explanatory variable.…”

“…For example, Kajisa et al [25] estimated the stand volume in the Kyushu region of Japan using FRMS plots and satellite image data, and concluded that the k-NN technique was suitable for this purpose in their study area. Kajisa et al [25] proposed a simple method using only satellite image data as an explanatory variable. They estimated the stand volume of coniferous and broad-leaved forests simultaneously without stratifying forests according to a priori information.…”

“…FRMS data, which is a systematically sampled set of forest inventory data in Japan, was used as in situ data in the analysis, as with Kajisa et al [25]. We compared the stand volume estimates obtained using three Landsat 7 Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons.…”