哨兵 1a-2a 采用基于对象的图像分析方法进行洪水绘图和洪水范围评估

Donya Azhand, S. Pirasteh, Masood Varshosaz, H. Shahabi, Salimeh Abdollahabadi, Hossein Teimouri, Mojtaba Pirnazar, Xiuqing Wang, Weilian Li
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摘要

摘要本研究介绍了从哨兵图像中提取洪水范围并绘制地图的方法。在此,我们提出了一种利用 Sentinel-1A 和 Sentinel-2A 图像从基于对象的图像分析(OBIA)中提取洪水泛滥区域的算法,以绘制和评估从事件开始到一周后的洪水范围。本研究使用 OBIA 中的多尺度参数进行图像分割。首先,我们在 Sentinel-1A 图像上应用我们提出的算法确定了洪水泛滥区域。然后,为了评估洪水对各土地利用/土地覆被类别(LULC)的影响,我们在事件发生后使用 OBIA 对 Sentinel-2A 图像进行了分类。此外,我们还使用阈值法比较了应用 OBIA 的拟议算法,以确定计算参数用于变化检测和洪水范围绘图的效率。研究结果表明,当比例参数为 60 时,分割过程的最佳性能为 0.92,对象适宜度指数 (Object Fitness Index, OFI)。结果还显示,在洪水开始时,有 2099.4 平方公里的研究区域被洪水淹没。此外,我们还发现受淹面积最大的 LULC 类别是农田和果园,分别为 695.28 平方公里(32.4%)和 708.63 平方公里(33.7%)。相比之下,其余约 33.9% 的水淹面积发生在其他类别(即养鱼场、建筑群、裸地和水体)。每个尺度参数的结果对象都通过对象纯度指数(OPI)、对象匹配指数(OMI)和 OFI 进行了评估。最后,我们使用全球定位系统(GPS)结合实地数据的总体准确度(OA)方法显示,LULC、洪水地图(即使用我们提出的算法)和阈值方法的准确度分别为 93%、90% 和 89%。
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Sentinel 1a-2a Incorporating an Object-Based Image Analysis Method for Flood Mapping and Extent Assessment
Abstract. This study presents flood extent extraction and mapping from Sentinel images. Here we suggest an algorithm for extracting flooded areas from object-based image analysis (OBIA) using Sentinel-1A and Sentinel-2A images to map and assess the flood extent from the beginning to one week after the event. This study used multi-scale parameters in OBIA for image segmentation. First, we identified the flooded regions by applying our proposed algorithm on the Sentinel-1A. Then, to evaluate the effects of the flood on each land-use/land cover (LULC) class, Sentinel-2A images is classified using the OBIA after the event. Besides, we also used the threshold method to compare the proposed algorithm applying OBIA to determine the efficiency in computing parameters for change detection and flood extent mapping. The findings revealed the best performance for the segmentation process with an Object Fitness Index (OFI) is 0.92 when the scale parameter of 60 is applied. The results also show that 2099.4 km2 of the study area is flooded at the beginning of the flood. Furthermore, we found that the most flooded LULC classes are agricultural land and orchards with 695.28km2 (32.4%) and 708.63 km2 (33.7%), respectively. In comparison, about 33.9% of the remaining flooded area has occurred in other classes (i.e., fish farm, built-up, bare land and water bodies). The resulting object of each scale parameter was evaluated by Object Pureness Index (OPI), Object Matching Index (OMI), and OFI. Finally, our Overall Accuracy (OA) method incorporated field data using the Global Positioning System (GPS) shows 93%, 90%, and 89% for LULC, flood map (i.e., using our proposed algorithm), and threshold method, respectively.
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