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首页> 外文期刊>Journal of Applied Remote Sensing >Spatial scaling transformation modeling based on fractal theory for the leaf area index retrieved from remote sensing imagery
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Spatial scaling transformation modeling based on fractal theory for the leaf area index retrieved from remote sensing imagery

机译：基于分形理论的遥感影像叶面积指数空间尺度转换建模

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This paper proposes a scaling transfer model based on fractal theory to retrieve the leaf area index (LAI) at different spatial resolutions and to evaluate the scaling bias on the LAI retrieved from coarse resolution images. The LAI scaling transfer model was developed by establishing the double logarithmic linear relationship between the scale n (spatial resolution) and average LAIs of the image at different scales. Thereafter, the influences of four factors, namely, coefficients of LAI retrieval model, image size, spatial resolution, and image standard deviation, which may have impact on the scaling transfer model were analyzed. The results indicated that the scaling transfer model performed well in estimating LAI with a determination coefficient (R-2) value of 96.99% and in evaluating the scaling bias with a root-mean-square error of 0.0188. The scaling transfer model was considerably influenced by the image standard deviation. As the model parameter, the fractal dimension of image was highly correlated with the standard deviation of the normalized difference vegetation index image. Results indicated that the proposed method based on fractal theory is feasible for LAI spatial scaling transformation. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：本文提出了一种基于分形理论的尺度转移模型，以获取不同空间分辨率下的叶面积指数（LAI），并评估从粗分辨率图像获取的LAI上的尺度偏差。通过在尺度n（空间分辨率）和不同尺度的图像平均LAI之间建立双对数线性关系，开发了LAI尺度传递模型。此后，分析了可能对缩放传递模型产生影响的四个因素，即LAI检索模型的系数，图像大小，空间分辨率和图像标准偏差的影响。结果表明，缩放传递模型在估计LAI（确定系数（R-2）值为96.99％）和评估缩放偏差（均方根误差为0.0188）方面表现良好。缩放传递模型受图像标准偏差的影响很大。作为模型参数，图像的分形维数与归一化植被指数图像的标准差高度相关。结果表明，基于分形理论的方法对于LAI空间尺度变换是可行的。 （C）2015年光电仪器工程师协会（SPIE）

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