利用多源、多时相遥感影像，采用分区分层的决策树方法，首先基于e-Cognition遥感解译软件，完成面向对象多尺度影像分割。并通过叠加分析海拔、降水、气温、土壤、植被、地貌等单要素图，得到地学大尺度分区地块的二级类解译结果，在具有地学特征的大尺度分割单元基础上，继续以影像光谱特征作为分类评判依据，依次分层得到荒漠的解译结果。数据精度采用三级质控质检方案，并采用野外采样验证的方法，确保荒漠精度大于85%。
", "ds_source": "MSS、TM、ETM、OLI、Sentinal-2等多源遥感影像。
", "ds_process_way": "利用多源、多时相遥感影像，采用分区分层的决策树方法，首先基于e-Cognition遥感解译软件，完成面向对象多尺度影像分割。并通过叠加分析海拔、降水、气温、土壤、植被、地貌等单要素图，得到地学大尺度分区地块的二级类解译结果，在具有地学特征的大尺度分割单元基础上，继续以影像光谱特征作为分类评判依据，依次分层得到荒漠的解译结果。
", "ds_quality": "本数据是在具有地学特征的大尺度分割单元基础上，继续以影像光谱特征作为分类评判依据，依次分层得到荒漠的解译结果。解译精度优于85%。
", "ds_acq_start_time": "1975-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "新疆全域", "ds_acq_lon_east": null, "ds_acq_lat_south": 34.0, "ds_acq_lon_west": 73.85861, "ds_acq_lat_north": 49.270557, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 0, "ds_files_count": 0, "ds_format": "矢量", "ds_space_res": "1：25万", "ds_time_res": "10-15年一期", "ds_coordinate": "CGCS2000", "ds_projection": "Albers", "ds_thumbnail": "91d68537-e938-4a66-9f78-798909e8c751.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "新疆1：25万荒漠数据（1975-2020年）来源于第三次新疆综合科学考察专项 \"科考数据平台与标准体系建设(2021xjkk1300)\"", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2024-12-09 16:46:34", "first_publish_time": "2024-12-09 16:46:34", "last_updated": "2025-12-15 13:17:36", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.XIEG.XJSEDATA.2022.00005927", "license": null, "extra": null, "files_shape": [], "features": null, "data_level": 0, "i18n": { "en": { "title": "Xinjiang 1: 250,000 desert data (1975-2020)", "ds_abstract": "Using multi-source and multi-temporal remote sensing images, using the zoning and hierarchical decision tree method, first of all, object-oriented multi-scale image segmentation is completed based on e-Cognition remote sensing interpretation software. By superimposing and analyzing single element maps such as altitude, precipitation, temperature, soil, vegetation, and landform, the secondary interpretation results of large-scale geological zoning plots are obtained. On the basis of large-scale segmentation units with geological characteristics, we continue to use The spectral characteristics of the image are used as the basis for classification and evaluation, and the interpretation results of the desert are obtained in turn. The data accuracy adopts a three-level quality control and quality inspection plan, and adopts a field sampling verification method to ensure that the desert accuracy is greater than 85%.
", "ds_source": "Multi-source remote sensing images such as MSS, TM, ETM, OLI, and Sentinal-2.
", "ds_process_way": "Using multi-source and multi-temporal remote sensing images, using the zoning and hierarchical decision tree method, first of all, object-oriented multi-scale image segmentation is completed based on e-Cognition remote sensing interpretation software. By superimposing and analyzing single element maps such as altitude, precipitation, temperature, soil, vegetation, and landform, the secondary interpretation results of large-scale geological zoning plots are obtained. On the basis of large-scale segmentation units with geological characteristics, we continue to use The spectral characteristics of the image are used as the basis for classification and evaluation, and the interpretation results of the desert are obtained in turn.
", "ds_quality": "This data is based on large-scale segmentation units with geological characteristics, and continues to use the image spectral characteristics as the basis for classification and evaluation, and then stratified to obtain the interpretation results of the desert. The interpretation accuracy is better than 85%.
", "ds_acq_place": "Xinjiang whole region", "ds_ref_instruction": "Xinjiang's 1: 250,000 desert data (1975-2020) comes from the third Xinjiang comprehensive scientific expedition special project \"Scientific Research Data Platform and Standard System Construction (2021xjkk1300)\"", "ds_format": "vector", "ds_projection": "Albers", "ds_space_res": "1:250 thousand", "ds_time_res": "10-15 year primary" } }, "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": [ "时空分布", "荒漠" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ "常存" ], "ds_meta_authors": [ "常存", "朱晓蓉" ], "ds_managers": [ "黎秀花" ], "category": "水文" }