基于SAR时间序列的应力植被识别作为密西西比河堤防段边坡失稳指标

K. Hasan, J. Aanstoos, Majid Mahrooghy
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引用次数: 2

摘要

地表植被反映了其生长的土壤的各种特征。在密西西比河下游的土堤上,观察了最近破裂表面和稳定土壤之间的植被类型和生长速度差异。我们尝试使用TerraSAR-X的x波段合成孔径雷达直接表征植被覆盖下的堤防表面。然而,由于其波长较短,大多数反向散射来自植被而不是土壤。因此,在植被生长和生物量最低的时间序列上制作了SAR图像,并使用多时相雷达后向散射模式来识别稳定和不稳定(受滑塌滑坡影响)堤段上健康和受胁迫的植被。野外数据显示,在健康或修复的堤面上植被最旺盛,而在有裂缝的堤面上植被最贫瘠。在7个月内的6个日期,制作了HH和VV和HH-VV图像的三个时间序列。与实地数据和分析的相关性表明,10月至2月的图像在识别目标植被差异方面最有效。在这段时间内,健康的堤防表面和有裂缝的堤防表面(可能导致潜在的堤防失效)之间的植被活力差异最大。利用野外提取的训练多边形对3个时间序列进行独立分类,并从中提取应力植被类别。超过90%的已知滑塌滑块被该分类识别,但导致了大量的“假阳性”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stressed vegetation identification by SAR time series as an indicator of slope instability in Mississippi River levee segments
Surface vegetation reflects various characteristics of the soil on which it grows. Vegetation type and growth rate differences were observed between recently cracked surfaces and stable soil on earthen levees along the lower Mississippi River. We attempted to directly characterize the levee surface beneath the vegetation cover using X-band synthetic aperture radar from TerraSAR-X. Due to its short wavelength, however, most of the backscatter is from the vegetation rather than the soil. Hence a time-series of the SAR imagery was made over the time when vegetation growth and biomass were at their lowest and the multi-temporal radar backscatter pattern was used to identify healthy and stressed vegetation growing over stable and unstable (subject to slump slides) levee segments. Field data showed that vegetation was most vigorous over healthy or repaired levee surfaces and poorest over areas with surface cracks. Three time series of the HH and VV and HH-VV images were made for 6 dates over a 7 month period. Correlation with field data and analysis revealed that imagery from October through February was most effective in identifying the target vegetation differences. In this time span the differences in vigor of the vegetation were greatest between healthy levee surfaces and those with cracks on them (which could lead to potential levee failures). The 3 time series were classified independently using field derived training polygons and the stressed vegetation class was extracted from it. More than 90% of the known slump slides were identified by the classification but a significant number of `false positives' resulted.
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