Rapid mapping of potential ground effects of the May 2023 Emilia-Romagna rainstorms.

Rapid mapping of the potential ground effects related to the flood events of May 2023 that affected part of the Emilia-Romagna Region, Central Italy. The study aims to a rapid detection of the areas with highest density events on large-scale and low-resolution. The map has been obtained with the methodology described by Notti et al., 2023. This procedure is based on the difference of the Normalized Difference Vegetation Index (NDVI) between the pre-event acquired images and post-event ones. The variation in the index is mostly linked to the processes that occur during the flood event, this allows the functional detection of the phenomena of slope instability. By using the download service of the Copernicus Open Access Hub platform (https://scihub.copernicus.eu/), the following Sentinel-2 images were utilized: Pre-event Sentinel-2 image: 2022-05-13 (A); Post-event Sentinel-2 image: 2023-05-23 (B). For each image, the NDVI has been computed; then the difference NDVIpost-NDVIpre was performed. All the areas characterized by a variation of NDVI (DNVI) <=0.3 and slope >=15° have been identified (the slope was obtained from 10 m DTM TIN Italy, Tarquini et al.,2017). Lastly, the obtained results were converted in polygons. Ancillary information, such as average slope (derived where available also the from 5 m DTM issued by Emilia-Romagna Region) and the intersections with roads, buildings, rivers (available as shapefile from “Geoportale of Emilia-Romagna” region, https://geoportale.regione.emilia-romagna.it /), were added to the obtained polygons. Since the presented work is a rapid, post event, mapping, a very simplified classification was carried out: Slope instability processes (mainly shallow landslides) and secondary hydrographic network; Instability processes mainly related to the bank erosion processes affecting the hydrographic network of the valley floor. Given the limited availability of the high-resolution images, the mapping is solely based on the NDVI analysis performed on Sentinel-2 images. Consequently, the proposed procedure may generate potential false-positive related to: i) land use changes that occurred during the period; ii) residual effects of cloud cover of the May 23 Sentinel-2 image. For this purpose, a preliminary filtering procedure has been performed. However, local residual effects related to cloud coverage remain at this early stage. It is important to note that, given the spatial resolution of the Sentinel 2 data of 10 m, the presented product represent an optimal tool to identify phenomena with dimensions greater than 100-200 m2. Further updates and refinements of the map will be made available as the new satellite images and data are progressively analyzed, as well as with the integration of field surveys still in progress. References Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 and Google Earth Engine, Nat. Hazards Earth Syst. Sci., https://doi.org/10.5194/nhess-23-2625-2023, 2023. Tarquini S., L. Nannipieri (2017). The 10 m-resolution TINITALY DEM as a trans-disciplinary basis for the analysis of the Italian territory: Current trends and new perspectives. Geomorphology, 281, 108-115. Weier, J. and Herring, D. (2000). Measuring Vegetation (NDVI & EVI). NASA Earth Observatory, Washington DC.本数据集针对2023年5月影响意大利中部艾米利亚-罗马涅大区部分区域的洪涝事件，开展潜在地面影响的快速制图。本研究旨在针对大尺度、低分辨率场景下的高事件密度区域进行快速识别。 本次制图采用Notti等人2023年提出的方法完成。该流程基于灾前与灾后影像的归一化植被指数（Normalized Difference Vegetation Index, NDVI）差值开展。指数变化与洪涝事件发生过程中的地表过程高度相关，可实现边坡失稳现象的功能性识别。 借助哥白尼开放访问中心（Copernicus Open Access Hub）平台的下载服务（https://scihub.copernicus.eu/），本研究使用了以下Sentinel-2影像： - 灾前Sentinel-2影像：2022年5月13日（编号A）； - 灾后Sentinel-2影像：2023年5月23日（编号B）。 对每幅影像计算NDVI，随后计算NDVI灾后 - NDVI灾前的差值。本研究识别出所有NDVI差值（ΔNDVI）≤0.3且坡度≥15°的区域（坡度数据源自10米分辨率的意大利TIN数字地形模型，Tarquini等人2017年成果）。最后将识别结果转换为矢量多边形。此外，向生成的多边形中补充了辅助信息，例如平均坡度（部分区域可获取艾米利亚-罗马涅大区发布的5米分辨率数字地形模型数据），以及与道路、建筑、河流的空间交集信息（该类数据可从艾米利亚-罗马涅大区地理门户"Geoportale of Emilia-Romagna"获取，网址：https://geoportale.regione.emilia-romagna.it/）。 鉴于本研究为灾后快速制图工作，仅开展了极简化分类： 1. 边坡失稳过程（以浅层滑坡为主）及次级水文网； 2. 主要与河谷底部水文网岸滩侵蚀相关的失稳过程。 由于高分辨率影像获取受限，本次制图仅基于Sentinel-2影像的NDVI分析开展。因此，本方法可能产生两类潜在假阳性结果：一是研究时段内发生的土地利用变化；二是2023年5月23日Sentinel-2影像中云覆盖残留影响。为此，本研究已开展初步滤波处理，但现阶段仍存在局部云覆盖残留效应。 需要注意的是，鉴于Sentinel-2影像的空间分辨率为10米，本成果仅适用于识别面积大于100-200平方米的地表现象。 随着新卫星影像与数据逐步得到分析，以及仍在进行的野外调查数据整合，本地图将进行进一步更新与优化。 参考文献 Notti, D., Cignetti, M., Godone, D., and Giordan, D.: 利用免费Sentinel-2影像与Google Earth Engine实现浅层滑坡的半自动制图，《自然灾害与地球系统科学》，https://doi.org/10.5194/nhess-23-2625-2023, 2023. Tarquini S., L. Nannipieri (2017). 10米分辨率TINITALY DEM作为意大利地表分析的跨学科基础：当前进展与新视角，《地貌学》，281卷，108-115页。 Weier, J. and Herring, D. (2000). 植被测量（NDVI与EVI），NASA地球观测站，华盛顿特区。