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Retrieval of forest biophysical variables by inverting a 3-D radiative transfer model and using high and very high resolution imagery

机译:通过反转3-D辐射传输模型并使用高分辨率和超高分辨率图像检索森林生物物理变量

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Obtaining detailed observations of the amount and condition of vegetation is an important issue for describing, understanding and modelling the role of the biosphere in the global carbon cycle. Here, multispectral optical imagery was used for retrieving biophysical variables through the inversion of a 3-D radiative transfer model. Two inversion procedures are presented: a classical procedure for high resolution imagery and an innovative procedure specifically designed for very high resolution imagery (resolution around 1 m). They were tested with SPOT ('Satellite Pour l'Observation de la Terre') and Ikonos images, respectively. One of the objectives was to assess to which extent the inversion of high and very high resolution satellite imagery can help in assessing how Fontainebleau forest (France) was damaged by a very strong storm on December 1999. Retrieved biophysical variables are: Leaf Area Index (LAI), Crown Coverage (CC) and leaf chlorophyll concentration (C_(ab)). Compared with ground measurements, SPOT-derived LAI has a root mean square error (RMSE) of around 1.4 at stand scale. This is not accurate enough to quantify the effects of the storm. However, LAI variation was assessed at a forest scale. On the other hand, the innovative procedure applied to Ikonos data led to more accurate results. For example, the relative error between estimated and ground measured LAI was improved, on average, from 23% (using 20m resolution imagery) to 6% (using very high resolution imagery).
机译:获得详细的植被数量和状况观测资料是描述,理解和建模生物圈在全球碳循环中的作用的重要问题。在这里,多光谱光学影像用于通过反演3D辐射传输模型来检索生物物理变量。提出了两种反演程序:用于高分辨率图像的经典程序和专门为高分辨率图像(分辨率约为1 m)设计的创新程序。他们分别用SPOT(“ Saterite Pour l'Observation de la Terre”)和Ikonos图像进行了测试。目标之一是评估高分辨率和超高分辨率卫星影像的反演有助于评估1999年12月法国枫丹白露森林如何受到强烈风暴的破坏。检索到的生物物理变量为:叶面积指数( LAI),树冠覆盖率(CC)和叶绿素浓度(C_(ab))。与地面测量值相比,SPOT得出的LAI在林分尺度上的均方根误差(RMSE)约为1.4。这还不够准确,无法量化风暴的影响。但是,LAI变化是在森林规模上评估的。另一方面,应用于Ikonos数据的创新程序导致了更准确的结果。例如,估计的LAI和地面测量的LAI之间的相对误差平均从23%(使用20m分辨率的图像)提高到6%(使用非常高分辨率的图像)。

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