Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band
Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band
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DOI：
10.3390/rs14246374
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发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
A. Salem;L. Beni
中科院分区：
文献类型：
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作者：
A. Salem;L. Beni
由于全球变暖导致的强降雨和飓风，洪水事件变得更加激烈和频繁。准确的洪水范围地图是应急管理机构和洪水救济计划的重要信息来源，以便将其资源定向到受影响最严重的地区。在洪水制图方面，合成孔径雷达（SAR）数据优于光学数据，特别是在与城市地区和关键基础设施相邻的植被地区和森林中。利用各种可用的SAR传感器调查洪水地图，并比较它们的性能，可以确定合适的SAR传感器，用于绘制不同土地覆盖的淹没地区，如森林和植被地区。在这项研究中，我们研究了基于Sentinel1b、c波段和无人机合成孔径雷达（UAVSAR） l波段数据的极化配置在植被和开阔地区洪水边界划定中的性能。利用机器学习方法——随机森林分类算法，对当天同一研究区域的洪水事件传感器数据集进行处理，并将其分为五类土地覆盖。我们比较了SAR数据集的线性极化、对偶极化和全极化的分类结果。l波段全极化数据分类在洪水制图中获得了最高的精度，因为全极化SAR数据的分解允许基于其散射机制识别土地覆盖特征。
Flood events have become intense and more frequent due to heavy rainfall and hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies and flood relief programs to direct their resources to the most affected areas. Synthetic Aperture Radar (SAR) data are superior to optical data for floodwater mapping, especially in vegetated areas and in forests that are adjacent to urban areas and critical infrastructures. Investigating floodwater mapping with various available SAR sensors and comparing their performance allows the identification of suitable SAR sensors that can be used to map inundated areas in different land covers, such as forests and vegetated areas. In this study, we investigated the performance of polarization configurations for flood boundary delineation in vegetated and open areas derived from Sentinel1b, C-band, and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band data collected during flood events resulting from Hurricane Florence in the eastern area of North Carolina. The datasets from the sensors for the flooding event collected on the same day and same study area were processed and classified for five landcover classes using a machine learning method—the Random Forest classification algorithm. We compared the classification results of linear, dual, and full polarizations of the SAR datasets. The L-band fully polarized data classification achieved the highest accuracy for flood mapping as the decomposition of fully polarized SAR data allows land cover features to be identified based on their scattering mechanisms.