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Forest cover type classification at Mt. Asama using ALOS fully Polarimetric PALSAR data, in Nagano prefecture, central Japan

机译:山的森林覆盖类型分类在日本中部长野县,浅间使用了ALOS的完全极化PALSAR数据

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摘要

PALSAR (Phased Array L-band Synthetic Aperture Radar) was installed on the Japanese ALOS (Advanced Land Observing Satellite) that was launched on 24 January 2006. This is the first satellite regularly operated with fully polarimetric SAR in the world, and is considered useful for monitoring forest and biomass, as well as topography and land use. Forest cover types and stand volume classifications were produced for Mt. Asama using unsupervised and supervised classifications from the backscatter matrix using polarimetric PALSAR data. The Mt. Asama region was selected for study because of its gentle geographic features and large number of representative Japanese larch (Larix kaempferi), red pine (Pinus densifoliu), and sub-alpine conifer (Abies mariesii and Tsuga diversifolia) forests. The unsupervised classification data allowed the forest region to be distinguished from fields, rice fields, pastures, residential areas, roads, and bare ground. We also produced forest cover type and volume classification images using a supervised classifier with GIS and field data, with a classification accuracy of 11.0-56.5%. The accuracy of highest volume classes was 301-450 m~3/ha (56.5%) and 451-800 m~3/ha (49.6%). This may have been because the influence of geographic features such as slope azimuth and ridges were larger than that of forest cover types. Therefore, polarimetric PALSAR data are suitable for monitoring deforestation and detecting changes in forest cover types in a homogenous, large forested region without complex mountainous geographic features.
机译:PALSAR(相控阵L波段合成孔径雷达)安装在2006年1月24日发射的日本ALOS(高级陆地观测卫星)上。这是世界上第一颗定期以全极化SAR方式运行的卫星,被认为是有用的用于监视森林和生物量以及地形和土地使用。制作了山的森林覆盖类型和林分数量分类。 Asama使用来自极化散射PALSAR数据的反向散射矩阵的无监督和有监督分类。山。选择浅间地区进行研究是因为其温和的地理特征以及大量的代表性日本落叶松(Larix kaempferi),赤松(Pinus densifoliu)和亚高山针叶树(Abies mariesii和Tsuga diversifolia)森林。无监督分类数据使森林区域与田地,稻田,牧场,居民区,道路和裸露地面区分开。我们还使用带有GIS和现场数据的监督分类器,制作了森林覆盖类型和体积分类图像,分类精度为11.0-56.5%。最高体积等级的精度为301-450 m〜3 / ha(56.5%)和451-800 m〜3 / ha(49.6%)。这可能是因为诸如坡度方位角和山脊等地理特征的影响大于森林覆盖类型的影响。因此,极化PALSAR数据适用于在没有复杂山区地理特征的同质,大森林地区中监测森林砍伐和检测森林覆盖类型的变化。

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