数据包含2018年和2019年2期的咸海湖面矢量数据，数据以shp格式存储。基于2018、2019年2期Landsat影像，采用“全域-局部”自适应迭代的阈值分割方法提取得到的湖泊面积信息。
", "ds_source": "原始影像数据来源于美国地质调局（http://glovis.usgs.gov）， 包括2018、2019年Landsat TM/ETM+/OLI数据，分辨率30米。选取的数据满足以下要求：①云覆盖少（云覆盖率<30%）；②阴影较少、色调层次分明；③无噪声。矢量数据产品由中国科学院A类战略性先导科技专项课题（XDA20060301）成果的一部分。
", "ds_process_way": "基于2018、2019年2期Landsat影像，采用“全域-局部”自适应迭代的阈值分割方法提取湖泊面积信息。
", "ds_quality": "自动解译的矢量数据在结合目视修正工作后，取得了较高的精度，能够满足研究需求。
", "ds_acq_start_time": "2020-06-01 00:00:00", "ds_acq_end_time": "2020-06-01 00:00:00", "ds_acq_place": "咸海", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 312530, "ds_files_count": 2, "ds_format": ".shp格式", "ds_space_res": "无", "ds_time_res": "无", "ds_coordinate": "WGS84", "ds_projection": "WGS84", "ds_thumbnail": "75498868-cfc3-4da9-8bdb-4dee9ea5e3d5.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.55" ], "quality_level": 1, "publish_time": "2025-05-06 18:25:51", "first_publish_time": null, "last_updated": "2025-05-28 17:01:29", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.00441", "license": null, "extra": null, "files_shape": [ { "name": "aralsea2018shp.rar", "size": 68850, "is_dir": false }, { "name": "aralsea2019shp.rar", "size": 243680, "is_dir": false } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Change data of Aral Sea surface in 2018 and 2019", "ds_abstract": "The data includes vector data of the Aral Sea Lake surface in 2018 and 2019, and the data is stored in shp format. Based on the Landsat images of 2018 and 2019, the lake area information was extracted using a \"global-local\" adaptive iterative threshold segmentation method.
", "ds_source": "The raw image data comes from the U.S. Geological Survey (http://glovis.usgs.gov), including 2018 and 2019 Landsat TM/ETM+/OLI data, with a resolution of 30 meters. The selected data meets the following requirements: ① Less cloud coverage (cloud coverage rate of 30%);② Less shadows and clear color layers;③ No noise. Vector data products are part of the results of the Class A Strategic Pilot Science and Technology Special Project (XDA20060301) of the China Academy of Sciences.
", "ds_process_way": "Based on the Landsat images of 2018 and 2019, a \"global-local\" adaptive iterative threshold segmentation method is used to extract lake area information.
", "ds_quality": "After combined with visual correction, the automatically interpreted vector data achieves high accuracy and can meet research needs.
", "ds_acq_place": "Aral Sea", "ds_format": ".shp format", "ds_projection": "WGS84", "ds_space_res": "no", "ds_time_res": "no" } }, "license_type": null, "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": [ "湖面" ], "ds_subject_tags": [ "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "咸海" ], "ds_time_tags": [ 2018, 2019 ], "ds_contributors": [ "黄粤", "刘铁" ], "ds_meta_authors": [ "黄粤" ], "ds_managers": [ "刘铁" ], "category": "水文" }