Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification
Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification
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DOI：
10.1016/j.rse.2007.02.014
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发表时间：
2007-09-14
影响因子：
13.5
通讯作者：
Stange, Yulia
中科院分区：
文献类型：
--
作者：
Johansen, Kasper;Coops, Nicholas C.;Stange, Yulia
陆地生态系统测绘通过纳入气候、自然地理、地表物质、土壤和植被结构方面的信息，为土地和资源管理人员提供了重要信息。这项研究的主要目的是确定高空间分辨率卫星图像数据的能力，以区分植被结构阶段的河岸和邻近的森林生态系统的定义使用不列颠哥伦比亚省陆地生态系统制图（TEM）计划。高空间分辨率的QuickBird图像，在2005年6月捕获的，重合的字段数据覆盖的河岸区的丢失的鞋溪和邻近的森林温哥华岛，不列颠哥伦比亚省，在此分析中使用。计算半变异函数来评估植被结构阶段的可分离性，并评估哪些空间尺度最适合用于计算灰度共生纹理措施，以最大限度地提高结构类分离。空间自相关程度表明，TEM方案中的大多数植被结构类型都可以区分，3 × 3像素和11 × 11像素的窗口大小最适合图像纹理计算。使用这些窗口大小，纹理分析表明，共生对比度，相异性，同质性纹理措施，在光谱的可见光部分的频带的基础上，提供了最显着的统计差异植被结构类。随后，一个面向对象的分类算法被应用到光谱和纹理变换的QuickBird图像数据映射植被结构类。使用光谱和纹理图像波段产生了最高的分类精度（整体精度= 78.95%）。图像纹理的加入使植被结构的分类精度提高了2- 19%。结果表明，植被结构的信息可以有效地映射从高空间分辨率的卫星图像数据，提供了一个额外的工具，正在进行的航空照片的解释。(c)2007年爱思唯尔公司All rights reserved.
Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 X 3 pixels and 11 X 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy= 78.95%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2-19%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellite image data, providing an additional tool to ongoing aerial photograph interpretation. (c) 2007 Elsevier Inc. All rights reserved.