首页> 外文会议>2015 International Conference on Optical Instruments and Technology: Optical Sensors and Applications >Combining geostatistical models and remotely sensed data to improve vegetation classification in horqin sandy land
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Combining geostatistical models and remotely sensed data to improve vegetation classification in horqin sandy land

机译:结合地统计学模型和遥感数据改善科尔沁沙地植被分类

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摘要

On different degrees of desertification land, there exists different vegetation communities, and spatial structure differences are obvious among different vegetation communities. This study implemented variogram calculation using typical sample selected from the image, adopting a common global optimization method to fit them into the spherical model. The results showed that the difference is obvious among different vegetation communities for the sill and range, such as, the sill and range are smaller for sample variogram of Artemisia halodendron and Salix flavida community than that of Artemisia halodendron and Caragana microphylla community, and the range for sample variogram of Agriophyllum arenarium community is bigger than that of Artemisia halodendron and Salix flavida community, but smaller than that of Artemisia halodendron and Caragana microphylla community. Incorporating the difference of the spatial structure characterization into the vegetation classification can improve sample separation, thereby increasing the overall classification accuracy.
机译:在不同程度的荒漠化土地上,存在不同的植被群落,不同植被群落之间空间结构差异明显。本研究使用从图像中选择的典型样本实施了方差图计算,并采用一种通用的全局优化方法将其拟合到球形模型中。结果表明,不同植被群落的门槛和幅度差异明显,如黄花蒿和柳柳群落的变异函数的底纹和幅度小于黄花蒿和小叶锦鸡儿群落的幅度和幅度,且范围沙丁鱼群落的样本变异图大于青蒿和柳柳群落,但小于蒿和小叶锦鸡儿群落。将空间结构特征的差异纳入植被分类可以改善样品分离,从而提高总体分类精度。

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