借助多源遥感数据快速准确地获取淹没水体及地物信息并进行科学有效的洪涝评估，对降低洪涝带来的危害具有十分重要的意义。\n
本研究利用SAR数据不受云雨天气影响等特点，通过两星协作将GF-3灾前、灾中雷达影像与Sentinel-1A灾中雷达影像结合，分别发挥其分辨率优势与时间优势，协同监测提取洪涝灾害水体，并运用阈值分割法、面向对象法和随机森林等多种方法对比研究实现洪涝水体信息的提取，得到洪涝水体相关数据集。研究借助高分辨光学影像GF-3、GF-6等，实现对淹没地物的准确提取与统计，得到淹没地物数据集；最后，通过提取的水体和地物信息，构建危险性评估模型和应急性评估模型，获取洪涝灾害综合评价数据集。数据成果、洪涝灾害提取与评估算法可为洪涝灾害防治、应急救援工作等提供科学依据。", "ds_source": "
1. GF-3、GF-6卫星影像数据：高分辨率对地观测系统山东聊城数据与应用中心\n
2. Sentinel-1A、Sentinel-2B卫星影像数据：欧洲航天局 （https://scihub.copernicus.eu/）\n
3. DEM数据：日本ALOS对地观测卫星12.5m数字高程产品（https://earthdata.nasa.gov/）\n
4.沂南县各类统计数据：临沂统计信息网（http://tjj.linyi.gov.cn/)", "ds_process_way": "
1. 遥感数据预处理：\n
(1)GF-3数据预处理\n
(2)Sentinel-1A数据预处理\n
(3)哨兵二号数据预处理\n
(4)高分六号数据预处理\n
2. 洪涝水体提取：基于ENVI软件，对SAR影像分别采用阈值分割、面向对象和随机森林共3种分类方法进行洪涝淹没范围提取。以总体精度、Kappa系数等作为指标，验证各分类方法的灾前水体信息提取结果。经对比，选择使用面向对象分类方法的提取结果确定洪涝淹没范围。\n
3. 淹没地物提取：基于ENVI软件，对影像分别采用面向对象、随机森林和最小距离法监督分类共3种分类方法进行土地利用类型分类。经对比，选择使用面向对象分类方法的分类结果提取淹没地物。\n
4. 建立评估模型：首先进行因子的选取并进行归一化处理，结合层次分析法和熵值法确定因子权重，并通过加权综合法分别得到危险性评估模型和应急性评估模型，最后进行精度验证。", "ds_quality": "
数据精度：\n
(1)基于面向对象分类方法的灾前水体信息提取结果的总体精度为95.97%，Kappa系数为0.9104，查全率、查准率和虚警率分别为0.9532、0.9862和0.0138。\n
(2)基于面向对象分类方法的淹没地物提取结果Kappa系数为96.67%。\n
(3)评估结果的精度验证基于Python爬虫技术，位于较高危险等级及以上区域的受灾点占比为90.32%，应急性评估的精度为92.9%。", "ds_acq_start_time": "2019-07-10 00:00:00", "ds_acq_end_time": "2020-08-16 00:00:00", "ds_acq_place": "山东省沂南县", "ds_acq_lon_east": 118.72527777777778, "ds_acq_lat_south": 35.30722222222222, "ds_acq_lon_west": 118.11, "ds_acq_lat_north": 35.770833333333336, "ds_acq_alt_low": 69.0, "ds_acq_alt_high": 747.0, "ds_share_type": "login-access", "ds_total_size": 55023620, "ds_files_count": 4, "ds_format": "tif", "ds_space_res": "10m,15m,30m", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "768b0c96-cad8-4b28-9457-76db4366b821.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-10-20 15:25:44", "last_updated": "2025-04-24 16:12:48", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.Hydro.db2453.2022", "i18n": { "en": { "title": "Data set of flood water extraction and comprehensive disaster assessment in Yinan County, Shandong Province (2020)", "ds_format": "TIF", "ds_source": "
1. GF-3 and GF-6 satellite image data: Shandong Liaocheng Data and Application Center of High Resolution Earth Observation System\n
2. Sentinel-1A and Sentinel-2B satellite image data: European Space Agency（ https://scihub.copernicus.eu/ ）\n
3. DEM data: Japan ALOS Earth observation satellite 12.5m digital elevation product（ https://earthdata.nasa.gov/ ）\n
4. Various statistical data of Yinan County: Linyi Statistical Information Network（ http://tjj.linyi.gov.cn/ )", "ds_quality": "
Data precision:\n
(1) The overall accuracy of the pre disaster water body information extraction results based on the object-oriented classification method is 95.97%, the Kappa coefficient is 0.9104, and the recall, precision and false alarm rates are 0.9532, 0.9862 and 0.0138 respectively.\n
(2) The Kappa coefficient of the submerged object extraction result based on the object-oriented classification method is 96.67%.\n
(3) The accuracy verification of the assessment results is based on Python crawler technology. The proportion of disaster affected points located in areas with higher hazard level and above is 90.32%, and the accuracy of emergency assessment is 92.9%. \"", "ds_ref_way": "", "ds_abstract": "
It is of great significance to reduce the damage caused by flood to quickly and accurately obtain the information of submerged water body and surface features and conduct scientific and effective flood assessment with the help of multi-source remote sensing data.\n
This study makes use of the characteristics of SAR data that are not affected by cloud and rain weather, and combines the GF-3 pre disaster and disaster radar images with Sentinel-1A disaster radar images through the cooperation of two satellites, giving play to its resolution advantages and time advantages respectively, monitoring and extracting flood water bodies cooperatively, and using threshold segmentation method, object-oriented method and random forest and other methods to achieve the extraction of flood water body information through comparative research, so as to obtain the relevant data sets of flood water bodies. With the help of high resolution optical images GF-3, GF-6, etc., the accurate extraction and statistics of submerged objects are realized, and the submerged object data set is obtained; Finally, through the extracted water body and surface feature information, the risk assessment model and emergency assessment model are constructed to obtain the comprehensive assessment data set of flood disaster. Data results and flood disaster extraction and evaluation algorithms can provide scientific basis for flood disaster prevention and emergency rescue.
", "ds_time_res": "日", "ds_acq_place": "Yinan County, Shandong Province", "ds_space_res": "10m,15m,30m", "ds_projection": "", "ds_process_way": "1. Remote sensing data preprocessing:\n
(1) GF-3 Data Preprocessing\n
(2) Sentinel-1A data preprocessing\n
(3) Sentinel 2 data preprocessing\n
(4) Data preprocessing of Gaofen No.6\n
2. Extraction of flood water body: Based on ENVI software, three classification methods, namely threshold segmentation, object-oriented and random forest, are used to extract the flood inundation range from SAR images. The overall accuracy and Kappa coefficient are used as indicators to verify the pre disaster water body information extraction results of each classification method. After comparison, we choose to use the extraction results of the object-oriented classification method to determine the flooded area.\n
3. Extraction of submerged objects: based on ENVI software, three classification methods including object-oriented, random forest and minimum distance supervised classification are used for image classification of land use types. After comparison, we choose to use the classification results of object-oriented classification method to extract submerged features.\n
4. Establish the evaluation model: firstly, select the factors and normalize them, determine the factor weight by combining the analytic hierarchy process and entropy method, and obtain the risk assessment model and emergency assessment model respectively through the weighted synthesis method. Finally, verify the accuracy.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "洪涝灾害", "危险性评估", "应急性评估" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "山东省沂南县" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "何振芳", "email": "hezhenfang@lcu.edu.cn", "work_for": "聊城大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "何振芳", "email": "hezhenfang@lcu.edu.cn", "work_for": "聊城大学", "country": "中国" } ], "ds_managers": [ { "true_name": "何振芳", "email": "hezhenfang@lcu.edu.cn", "work_for": "聊城大学", "country": "中国" } ], "category": "水文" }