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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data
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The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data

机译:使用高光谱紧凑型机载光谱成像仪(CASI)数据描绘澳大利亚混合物种森林中的树冠

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In mixed-species forests of complex structure, the delineation of tree crowns is problematic because of their varying dimensions and reflectance characteristics, the existence of several layers of canopy (including understorey), and shadowing within and between crowns. To overcome this problem, an algorithm for delineating tree crowns has been developed using eCognition Expert and hyperspectral Compact Airborne Spectrographic Imager (CASI-2) data acquired over a forested landscape near Injune, central east Queensland, Australia. The algorithm has six components: 1) the differentiation of forest, non-forest and understorey; 2) initial segmentation of the forest area and allocation of segments (objects) to larger objects associated with forest spectral types (FSTs); 3) initial identification of object maxima as seeds within these larger objects and their expansion to the edges of crowns or clusters of crowns; 4) subsequent classification-based separation of the resulting objects into crown or cluster classes; 5) further iterative splitting of the cluster classes to delineate more crowns; and 6) identification and subsequent merging of oversplit objects into crowns or clusters. In forests with a high density of individuals (e.g., regroAqh), objects associated with tree clusters rather than crowns are delineated and local maxima counted to approximate density. With reference to field data, the delineation process provided accuracies > similar to 70% (range 48-88%) for individuals or clusters of trees of the same species with diameter at breast height (DBH) exceeding 10 cm (senescent and dead trees excluded), with lower accuracies associated with dense stands containing several canopy layers, as many trees were obscured from the view of the CASI sensor. Although developed using I-m spatial resolution CASI data acquired over Australian forests, the algorithm has application elsewhere and is currently being considered for integration into the Definiens product portfolio for use by the wider community. (c) 2006 Elsevier Inc. All rights reserved.
机译:在结构复杂的混交林中,树冠的轮廓确定是有问题的,因为它们的尺寸和反射特性各不相同,存在冠层(包括下层)的几层,树冠内部和冠之间存在阴影。为了克服这个问题,已经开发了一种使用eCognition Expert和高光谱紧凑型机载光谱成像仪(CASI-2)数据来描绘树冠的算法,该数据是在澳大利亚昆士兰州中东部Injune附近的森林景观上获取的。该算法有六个组成部分:1)森林,非森林和林下层的区分; 2)对森林区域进行初步分割,并将部分(对象)分配给与森林光谱类型(FST)相关的较大对象; 3)最初将物体最大值识别为这些较大物体中的种子,并将其扩展到冠的边缘或冠簇。 4)随后基于分类将生成的对象分为冠或类。 5)进一步对群集类进行迭代拆分,以描绘出更多的树冠; 6)识别并随后将过度分裂的对象合并为冠或簇。在个人密度较高的森林中(例如regroAqh),与树木簇而不是树冠相关的物体被划定,并且局部最大值被计数为近似密度。参考野外数据,划定过程提供的准确度>类似于同一物种的个体或成簇的树的胸径(DBH)直径超过10 cm的精确度> 70%(范围48-88%)(不包括衰老和死树) ),因为密集度较高的林分(包含数个树冠层)具有较低的精度,因为从CASI传感器的角度来看,许多树木都被遮盖了。尽管使用在澳大利亚森林中获取的I-m空间分辨率CASI数据进行开发,但该算法已在其他地方得到应用,目前正在考虑将其集成到Definiens产品组合中,以供更广泛的社区使用。 (c)2006 Elsevier Inc.保留所有权利。

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