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首页> 外文期刊>GIScience & remote sensing >Linear transformation to minimize the effects of variability in understory to estimate percent tree canopy cover using RapidEye data
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Linear transformation to minimize the effects of variability in understory to estimate percent tree canopy cover using RapidEye data

机译：进行线性变换以最大程度减少林下变化的影响，从而使用RapidEye数据估算树冠覆盖率

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摘要

Variability in understory structure is an important problem in estimating tree canopy cover (TCC) with satellite imagery. Differences between understory structure due to the composition and configuration of herbaceous/shrub species often produce different vegetation index values despite these areas having the same TCC. This study offers a linear transformation approach to minimizing the influence of variability in the understory to accurately estimate percent TCC from RapidEye satellite data. TCC was modeled as a function of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), adjusted (linear transformed) NDVI (NDVI_(adj)), and adjusted NDRE (NDRE_(adj)) using simple linear regression. The coefficient of determination of validation (R_(vld)~2) of the models using NDVI, NDRE, NDVI_(adj), and NDRE_(adj) as explanatory variables were, respectively, 0.50 (RMSE_(vld) = 9.64%), 0.38 (RMSE_(vld) = 10.7%), 0.78 (RMSE_(vld) = 6.61%), and 0.73 (RMSE_(vld) = 7.23%). These results showed that the linear transformation used for standardizing the vegetation index values of understory was an effective approach for estimating TCC.
机译：在利用卫星图像估算树冠覆盖率（TCC）时，林下结构的可变性是一个重要问题。尽管这些地区具有相同的TCC，但由于草本/灌木树种的组成和构造而引起的林下结构之间的差异通常会产生不同的植被指数值。这项研究提供了一种线性变换方法，可最大程度地减少林下层变化的影响，从而根据RapidEye卫星数据准确估算TCC百分比。使用简单归一化植被指数（NDVI），归一化差异红边指数（NDRE），已调整（线性变换）NDVI（NDVI_（adj））和已调整NDRE（NDRE_（adj））的函数对TCC进行建模线性回归。使用NDVI，NDRE，NDVI_（adj）和NDRE_（adj）作为解释变量的模型的确认确定系数（R_（vld）〜2）分别为0.50（RMSE_（vld）= 9.64％）， 0.38（RMSE_（vld）= 10.7％），0.78（RMSE_（vld）= 6.61％）和0.73（RMSE_（vld）= 7.23％）。这些结果表明，用于标准化林下植被指数值的线性变换是估算TCC的有效方法。

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