林分结构多样性参数与纹理信息相关分析中最优窗口的确定

Determining optimal window size based on correlation between texture information and stand structural diversity indices

【目的】遥感影像中的纹理指数在林业领域的应用日益广泛,窗口大小对纹理指数与林分结构多样性指数的相关性影响较大,因此研究试图利用纹理指数与林分结构多样性指数的相关性大小确定最优窗口,以期实现林分结构多样性指数的准确预估。【方法】基于广西壮族自治区一类调查的68块杉木纯林或混交林样地,计算得出林分结构多样性指数;根据SPOT5全色波段,分别计算7种窗口(3×3、5×5、7×7、9×9、11×11、13×13和15×15)下的纹理指数,对窗口大小与纹理指数两者进行Pearson相关性分析,并确定最优窗口。【结果】熵、同质性、角二阶矩阵、对比度、非相似度、变化量以及平均值与林分结构多样性的相关性随窗口的变化不显著,但纹理相关性指数与DBH标准差的相关系数的变化率达到了86%。【结论】基于DBH标准差与纹理指数相关关系的变化规律,确定研究区最优窗口大小为11×11。

[Objective] Image textural features derived from optical remote sensing images have been widely used in forestry. Because window sizes can significantly influence the correlation between forest structural diversity and image textural features, in this study we attempted to determine the optimal window size based on the correlation coefficient for accurate estimation of forest structural diversity. [ Method] We first calculated forest structural diversity indices for 68 national forest inventory plots in the Gnangxi Zhuang Autonomous region. Secondly, based on the SPOT-5 panchromatic band, we extracted textural features using different window sizes （3×3, 5×5, 7×7, 9×9, 11×11, 13×13 and 15×15）. Finally, we performed Pearson correlation analysis between image textural features and forest structural diversity indices to determine the optimal window size. [ Result] The results showed that the correlation between forest structural diversity indices and textural features, i.e. entropy, homogeneity, angular second matrixes, contrast, dissimilarity, variation and mean value, exhibited fewer significant differences in response to changes in window size. In contrast, the change rate of the correlation coefficient between DBH standard deviation and image correlation index was as high as 86%. [ Conclusion ] Consequently, we determined the optimal window size to be 11 × 11 pixels.