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首页> 外文期刊>International journal of applied earth observation and geoinformation >Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel
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Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel

机译：使用源自WorldView-2多光谱图像的图像纹理预测以色列干旱地区森林的森林结构参数

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Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 multispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differentiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30 m × 30 m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r 0.75, p < 0.01), the BA and the entropy of blue band (r 0.73, p < 0.01), and the GC and the contrast of blue band (r 0.71, p < 0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determination coefficient (R~2) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56 ha~(?1) for the NT; 0.54 and 1.79 m~2 ha~(?1) for the BA; 0.42 and 27.18 m~3 ha~(?1) for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R~2 > 0.50). In conclusion, cross-validated statistics confirmed that the structural parameters including the BA, SDDBH, and GC can be predicted and mapped with a reasonable accuracy using the texture features extracted from the spectral bands of WorldView-2 image.
机译：通过基于现场的数据收集方法估算森林结构参数既昂贵又费时。卫星遥感技术是在大型森林地区建模和绘制结构参数的低成本替代方案。当前的研究调查了使用WordView-2多光谱卫星图像预测以色列旱地人工林森林结构参数的潜力。图像纹理特征与几个结构参数之间的关系，例如树数（NT），基础面积（BA），茎体积（SV），克拉克·埃文斯指数（CEI），直径差异指数（DDI），传染指数（ CI），基尼系数（GC）和乳房高度直径的标准偏差（SDDBH）使用了相关分析。这些变量是从30 m×30 m正方形图获得的。对于与场图相对应的像素，计算了作为一阶纹理特征的灰度标准偏差（SDGL）和基于灰度共生矩阵（GLCM）的二阶纹理变量。相关分析的结果表明，森林结构参数与图像纹理特征显着相关。计算出SDDBH与红色条带的对比度（r 0.75，p <0.01），BA和蓝色条带的熵（r 0.73，p <0.01）与GC和对比度之间的关系的最高相关系数的蓝色带（r 0.71，p <0.01）。使用逐步多元线性回归分析，将每个森林结构参数建模为根据卫星图像得出的纹理测度的函数。最佳拟合模型的确定系数（R〜2）和均方根误差（RMSE）值分别为NT的0.38和109.56 ha〜（？1）。 BA的0.54和1.79 m〜2 ha〜（？1）; SV为0.42和27.18 m〜3 ha〜（？1）; CEI为0.23和0.16; DDI为0.32和0.05; CI为0.25和0.06; GC为0.50和0.05; SDDBH分别为0.67和0.70。采用留一法交叉验证技术来验证最佳拟合模型（R〜2> 0.50）。总之，交叉验证的统计数据证实，可以使用从WorldView-2图像光谱带中提取的纹理特征，以合理的精度预测和映射包括BA，SDDBH和GC在内的结构参数。

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