高时空分辨率绿洲土地覆被地图，对厘清干旱区生态演化与社会发展过程具有重要价值。然而绿洲相关研究起步较晚，加之绿洲景观高度破碎、土地覆被类型转换频繁，构建高时空分辨率绿洲土地覆被数据集面临诸多挑战，因此，专门针对绿洲的此类数据产品仍然匮乏。\n
基于此，本文构建一套逐年 30 米土地覆被制图框架，融合 Landsat 卫星影像、机器学习算法、时间序列分割模型（LandTrendr）与主成分分析方法。基于该框架，依托谷歌地球引擎（GEE）平台生成 1987—2024 年中国绿洲 30 米逐年土地覆被数据集（OasisMap30）。基于 6300 余个目视解译样本的精度验证表明， OasisMap30 整体精度高于 90%。结合目视解译样本与第三方验证数据开展多产品交叉对比，OasisMap30 在分类精度与误差控制方面优势显著。此外，将该数据集与多款 30 米不透水面、耕地、地表水体专题产品对比，发现 OasisMap30 与现有数据一致性良好。\n
基于 OasisMap30 数据集，本文解析了中国绿洲土地覆被格局演变特征。结果显示：1987—2024 年我国绿洲总面积扩张 45.87%，增量达 775 万公顷，扩张核心驱动力为耕地开垦与草地修复。其中，404 万公顷荒漠转化为草地，319 万公顷荒漠开垦为耕地；不透水面扩张 58 万公顷，地表水体增加 35 万公顷；同时存在大量地类内部转换，例如 312 万公顷草地转为耕地。\n
综上，这套时序连续、高分辨率的 OasisMap30 数据集可为绿洲景观格局演变、社会 - 生态响应、空间格局优化等研究提供有力支撑，助力绿洲区域可持续发展。\n
数据集共划分 7 类土地覆被：不透水面、地表水体、耕地、灌木、林地、草地与裸地。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "1987-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "中国", "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": "open-access", "ds_total_size": 6752658049, "ds_files_count": 0, "ds_format": "GeoTIFF", "ds_space_res": "30m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "Albers等面积投影", "ds_thumbnail": "3808cedc-34aa-4244-8651-8faa911c6e71.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-07-01 09:49:43", "last_updated": "2026-07-01 09:49:43", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.landcover.db7473.2026", "i18n": { "en": { "title": "OasisMap30: a 30 m annual land cover dataset of China's oases from 1987 to 2024", "ds_format": "GeoTIFF", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
High spatio-temporal resolution maps of oasis land cover are valuable for understanding ecological and societal development processes in dryland regions. However, the relatively late development of oasis research, combined with the highly fragmented structure of oases and their frequent land cover transitions, has made it challenging to construct high spatio-temporal resolution land cover datasets for oases. As a result, dedicated data products of this kind are still lacking. \r\n
Here, we developed a framework for annual 30 m land cover mapping that integrates Landsat satellite imagery, machine learning, a temporal segmentation approach (LandTrendr), and principal component analysis.Using this framework, we produced a 30 m resolution annual land-cover dataset for Chinese oases (OasisMap30) for the period 1987–2024 on the Google Earth Engine (GEE) platform. Accuracy assessment based on more than 6300 visually interpreted samples demonstrates high accuracy of OasisMap30 (overall accuracy > 90 %). In cross-product comparisons based on visually interpreted and third-party test samples, OasisMap30 exhibits considerable advantages in terms of classification accuracy and error reduction. Moreover, in comparison with several 30 m resolution thematic products for impervious surface, cropland, and surface water, we found an impressive consistency between OasisMap30 and these datasets. \r\n
Using OasisMap30, we investigated the changes in land cover patterns of Chinese oases. The results show that oasis area expanded by 45.87 % (+7.75 Mha) between 1987 and 2024, primarily driven by cropland expansion and grassland restoration. Specifically, 4.04 Mha of desert were restored to grassland, and 3.19 Mha were converted from desert to cropland. In addition, OasisMap30 reveals the expansion of impervious surfaces (0.58 Mha) and surface water (0.35 Mha), as well as conversions among land cover types, such as the conversion of 3.12 Mha of grassland to cropland. \r\n
Overall, the consistent, high-resolution OasisMap30 data can substantially support studies on the evolution of oasis landscape patterns, socio-ecological responses, and spatial pattern optimization, thereby contributing to the sustainable development of oasis regions. \r\n
The dataset includes seven land-cover types: impervious surface, surface water, cropland, shrubland, forest, grassland and barren land.", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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, "belong_to_nieer": false, "ds_topic_tags": [ "绿洲", "土地覆被", "LandTrendr", "干旱区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 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, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "唐强", "email": "qiangtang@swu.edu.cn", "work_for": "西南大学地理科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "唐强", "email": "qiangtang@swu.edu.cn", "work_for": "西南大学地理科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "唐强", "email": "qiangtang@swu.edu.cn", "work_for": "西南大学地理科学学院", "country": "中国" } ], "category": "基础地理" }