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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Performance of different spectral and textural aerial photograph features in multi-source forest inventory
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Performance of different spectral and textural aerial photograph features in multi-source forest inventory

机译：多源森林资源清单中不同光谱和纹理航空照片特征的表现

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Most multi-source forest inventory (MSFI) applications have thus far been based on the use of medium resolution satellite imagery, such as Landsat TM. The high plot and stand level estimation errors of these applications have, however, restricted their use in forest management planning. One reason suggested for the high estimation errors has been the coarse spatial resolution of the imagery employed. Therefore, very high spatial resolution (VHR) imagery sources provide interesting data for stand-level inventory applications. However, digital interpretation of VHR imagery, such as aerial photographs, is more complicated than the use of traditional satellite imagery. Pixel-by-pixel analysis is not applicable to VHR imagery because a single pixel is small in relation to the object of interest, i.e. a forest stand, and therefore it does not adequately represent the spectral properties of a stand. Additionally in aerial photographs, the spectral properties of the objects are dependent on their location in the image. Therefore, MSFI applications based on aerial imagery must employ features that are less sensitive to their location in the image and that have been derived using the spatial neighborhood of each pixel, e.g. a square-shaped window of pixels. In this experiment several spectral and textural features were extracted from color-infrared aerial photographs and employed in estimation of forest attributes. The features were extracted from original, normalized difference vegetation index and channel ratio images. The correlations between the extracted image features and forest attributes measured from sample plots were examined. Additionally, the spectral and textural features were used for estimating the forest attributes of sample plots, applying the k nearest neighbor estimation method. The results show that several spectral and textural image features that are moderately or well correlated with the forest attributes. Furthermore, the accuracy of forest attribute estimation can be significantly improved by a careful selection of image features.
机译：迄今为止，大多数多源森林资源清查（MSFI）应用程序都是基于使用中分辨率卫星图像，例如Landsat TM。但是，这些应用程序的高地块和林分等级估计误差限制了它们在森林管理规划中的使用。提出高估计误差的原因之一是所使用图像的粗略空间分辨率。因此，非常高的空间分辨率（VHR）图像源为展位级库存应用提供了有趣的数据。但是，VHR图像（如航空照片）的数字解释比传统卫星图像的使用更为复杂。逐像素分析不适用于VHR图像，因为单个像素相对于感兴趣的对象（即森林林分）很小，因此不能充分代表林分的光谱特性。另外，在航空摄影中，物体的光谱特性取决于它们在图像中的位置。因此，基于航拍图像的MSFI应用程序必须采用对图像中的位置不太敏感的特征，并且这些特征是使用每个像素的空间邻域得出的，例如一个正方形的像素窗口。在该实验中，从彩色红外航拍照片中提取了一些光谱和纹理特征，并将其用于森林属性的估计。从原始的，标准化的差异植被指数和通道比图像中提取特征。检查了提取的图像特征与从样地测得的森林属性之间的相关性。此外，使用k最近邻估计方法，光谱和纹理特征用于估计样地的森林属性。结果表明，一些光谱和纹理图像特征与森林属性具有中等或良好的相关性。此外，通过仔细选择图像特征可以显着提高森林属性估计的准确性。

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