Forest volume estimation based on spectral and textural information from the Landsat 8 satellite
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摘要: 以福建省将乐县国有林场为研究对象，通过外业实地调查得到样地蓄积量：以Landsat 8卫星遥感图像为数据源，对遥感图像进行处理，获取多光谱影像的波段光谱值、植被指数和波段组合值，并筛选出全色波段的最优纹理生成窗口与纹理特征；通过多元回归分析方法，分别建立仅以光谱因子为自变量和结合光谱信息和纹理特征的蓄积量估测模型，并比较两者之间的精度。实验结果表明：光谱因子的多元线性回归方程的相关系数为0.853，联合光谱和纹理特征因子反演的多元回归方程的相关系数为0.926。同时利用检验数据，得出模型的预测精度：光谱因子蓄积量的估算方程精度为79.81%，联合反演蓄积量的估算方程精度为85.98%。研究表明：引入纹理特征后蓄积量的预测精度得到一定程度的提高，利用Landsat 8全色波段的纹理特征进行蓄积量估测具有良好的应用前景。图3表6参22Abstract: On the Jiangle State Forest Farm of Fujian Province forest volume was obtained by field investigation and by Landsat 8 observations that utilized band spectral values, vegetation indexes, derivatives of bands, and optimal textural measurements derived from the panchromatic band using varied window sizes. Through multiple regression analysis, volume estimation models were produced with independent variables of 1） only spectral factors and 2） combined spectral factors and textural volume. Then, validation was conducted using field survey data to test and compare model prediction accuracy. Experimental results showed R2 = 0.727 6 for the spectrally based volume estimation model and R2 = 0.857 5 for the combined model. Model prediction accuracy was 79.8% for the single spectral based volume estimation model and 86.0% for the combined model. Therefore, the improved prediction accuracy using textural information from the panchromatic band with images of Landsat 8 for forest volume estimation and application of this procedure should be considered when determining forest volume.［Ch, 3 fig. 6 tab. 22 ref.］
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Key words:
- forest mensuration /
- forest volume /
- Landsat 8 images /
- band spectra /
- texture information /
- estimation models
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链接本文:
https://zlxb.zafu.edu.cn/article/doi/10.11833/j.issn.2095-0756.2015.03.008
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