毛乌素沙地位于国家“三区四带”生态安全战略格局中“黄河重点生态区”的核心区域，属于北方农牧交错带西段的防风固沙关键生态功能区。受长期的农牧开发与生态恢复工程双重影响，该区域地表景观高度破碎，导致现有大尺度开源土地分类产品常出现误判或适用性较差的问题。为此，本数据集专门针对沙地复杂地表特征进行定制化解译，旨在弥补全国尺度产品在局部脆弱区的可用性缺陷，为生态评估提供更为准确的基础底图。\n
本数据产品基于1980-2025年共10期Landsat系列遥感影像（5年时间步长）生产，空间分辨率为30 m。数据集采用一级土地利用/覆被分类体系，涵盖乔木林、灌木林、农田、水体、草地、裸沙地及建设用地7个大类。在生产流程上，首先通过野外实地考察、低空无人机正射图及多源开源数据融合，构建了高可信度地面样点集；然后依托ENVI平台对比筛选多种监督分类算法，引入混淆矩阵定量评价与专家目视解译的主客观协同机制；最后经人工交互式检查与地物边界修正，生成全序列空间分布产品。经验证，全序列数据集的平均Kappa系数为0.71，平均总体精度达80.36%，能够充分满足区域生态演替长期监测与宏观土地变化科学的研究需求。\n
本数据集为栅格格式（GeoTIFF），各像元数值（Value）以整型编码存储，唯一对应一种特定的土地利用/覆被类型。分类体系共包含7个大类，具体的栅格代码映射关系如下：\n1：草地\n2：灌木林\n3：农田\n4：乔木林\n5：裸沙地\n6：水体\n7：建设用地", "ds_source": "
本数据集的主体遥感数据源自Landsat系列卫星影像（涵盖MSS、TM、ETM+和OLI传感器）。时间序列严格覆盖1980至2025年的半年代际节点，空间分辨率统一为30 m（早期MSS影像均重采样至30 m）。为消除物候干扰，影像均选取植被生长季（6–10月）且全景云量低于10%的优质景幅；对极少数由于多云导致质量欠佳的年份，采用相邻年份同季影像进行替代。辅助交叉验证的开源数据包括：中国年度土地覆盖数据集（CLCD, https://doi.org/10.5194/essd-13-3907-2021）、Esri 全球 10 米土地覆盖数据（Esri 10m, https://doi.org/10.1109/IGARSS47720.2021.9553499）、中国 250 米灌溉农田空间分布图（CIrrMap250, https://doi.org/10.5194/essd-16-5207-2024）以及全球地表水数据集（GSWE, https://doi.org/10.1038/nature20584）。", "ds_process_way": "
1. 分类样本集构建\n
解译样点来源于野外实地考察、低空无人机正射影像以及多套高精度开源产品（CLCD、Esri 10m、CIrrMap250、GSWE 等）的交集置信区。共筛选并提取高质量训练与验证样本1808个。各感兴趣区（ROI）的杰弗里斯-马图西塔距离（J-M Distance）均大于1.7，确保了样本在光谱特征上具备高度可分性。\n
2. 多算法分类与结果优选\n
依托ENVI 5.6 软件，综合遥感影像与样本数据，分别采用支持向量机、最小距离、最大似然、马氏距离、平行六面体与神经网络六种算法开展监督分类。在初步评估中，剔除了对沙地复杂地表分类效果欠佳的平行六面体与神经网络算法。随后，利用混淆矩阵与目视解译相结合的主客观协同评价方法，对剩余四种算法的结果进行交叉对比，从而针对不同年份的影像特征动态优选最佳分类算法。最后，辅以精细的人工交互式目视修正以消除斑块噪声，最终生成本数据集。", "ds_quality": "
本数据集基于混淆矩阵进行Kappa系数与总体精度检验，结果如下：\n
1980年（Kappa 0.74，总体精度84.28%）；\n
1985年（Kappa 0.62，总体精度76.26%）；\n
1990年（Kappa 0.74，总体精度85.12%）；\n
1995年（Kappa 0.74，总体精度85.50%）；\n
2000年（Kappa 0.73，总体精度81.39%）；\n
2005年（Kappa 0.77，总体精度83.39%）；\n
2005年（Kappa 0.77，总体精度83.39%）；\n
2010年（Kappa 0.69，总体精度76.43%）；\n
2015年（Kappa 0.71，总体精度77.63%）；\n
2020年（Kappa 0.73，总体精度81.32%）；\n
2025年（Kappa 0.64，总体精度72.30%）；\n
平均（Kappa 0.71，总体精度80.36%）.", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2025-12-31 00:00:00", "ds_acq_place": "毛乌素沙地", "ds_acq_lon_east": 110.49833333333333, "ds_acq_lat_south": 37.47694444444445, "ds_acq_lon_west": 107.39444444444445, "ds_acq_lat_north": 39.353611111111114, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 115540052, "ds_files_count": 0, "ds_format": "GeoTIFF", "ds_space_res": "30米", "ds_time_res": "5年", "ds_coordinate": "无", "ds_projection": "WGS 1984 UTM Zone 49N", "ds_thumbnail": "82d5b261-3468-49b1-b09f-3958957aaeab.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "None", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-05-20 16:53:53", "last_updated": "2026-05-20 17:11:51", "protected": false, "protected_to": "2027-05-10 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.nieer.db7325.2026", "i18n": { "en": { "title": "Long-term land use/cover dataset of the Mu Us Sandy Land (1980-2025)", "ds_format": "GeoTIFF", "ds_source": "
The main remote sensing data of this dataset comes from Landsat series satellite images (covering MSS, TM, ETM+, and OLI sensors). The time series strictly covers the six-month generation nodes from 1980 to 2025, with a unified spatial resolution of 30 meters (early MSS images were resampled to 30 meters). To eliminate phenological interference, high-quality scenes with vegetation growth season (June October) and panoramic cloud cover below 10% were selected for the images; For a very small number of years with poor quality due to cloudy weather, adjacent seasonal images are used as substitutes. The open-source data for auxiliary cross validation includes the China Annual Land Cover Dataset (CLCD), https://doi.org/10.5194/essd-13-3907-2021 ）Esri Global 10 meter Land Cover Data (Esri 10m, https://doi.org/10.1109/IGARSS47720.2021.9553499 ）The spatial distribution map of 250 meter irrigated farmland in China (CIrrMap250, https://doi.org/10.5194/essd-16-5207-2024 ）And the Global Surface Water Dataset (GSWE), https://doi.org/10.1038/nature20584 ）.", "ds_quality": "
This dataset is based on confusion matrix for Kappa coefficient and overall accuracy testing, and the results are as follows:\r\n
In 1980 (Kappa 0.74, overall accuracy 84.28%);\r\n
In 1985 (Kappa 0.62, overall accuracy 76.26%);\r\n
In 1990 (Kappa 0.74, overall accuracy 85.12%);\r\n
In 1995 (Kappa 0.74, overall accuracy 85.50%);\r\n
In 2000 (Kappa 0.73, overall accuracy 81.39%);\r\n
In 2005 (Kappa 0.77, overall accuracy 83.39%);\r\n
In 2010 (Kappa 0.69, overall accuracy 76.43%);\r\n
In 2015 (Kappa 0.71, overall accuracy 77.63%);\r\n
In 2020 (Kappa 0.73, overall accuracy 81.32%);\r\n
In 2025 (Kappa 0.64, overall accuracy 72.30%);\r\n
Average (Kappa 0.71, overall accuracy 80.36%)", "ds_ref_way": "", "ds_abstract": "
The Maowusu Desert is located in the core area of the \"Yellow River Key Ecological Zone\" in the national \"Three Zones and Four Belts\" ecological security strategic pattern, and belongs to the key ecological function zone of windbreak and sand fixation in the western section of the northern agricultural pastoral ecotone. Due to the dual impact of long-term agricultural and animal husbandry development and ecological restoration projects, the surface landscape in this area is highly fragmented, leading to frequent misjudgment or poor applicability of existing large-scale open source land classification products. For this purpose, this dataset is specifically tailored to the complex surface features of sandy areas, aiming to compensate for the limited availability of national scale products in local vulnerable areas and provide a more accurate base map for ecological assessment.\r\n
This data product is based on a total of 10 Landsat series remote sensing images (5-year time step) from 1980 to 2025, with a spatial resolution of 30 meters. The dataset adopts a first level land use/cover classification system, covering 7 major categories: tree forest, shrub forest, farmland, water body, grassland, bare sand land, and construction land. In terms of production process, a highly reliable ground sample set was first constructed through field investigations, low altitude drone orthophoto, and multi-source open source data fusion; Then, relying on the ENVI platform, multiple supervised classification algorithms are compared and screened, and a subjective and objective collaborative mechanism of confusion matrix quantitative evaluation and expert visual interpretation is introduced; Finally, through manual interactive inspection and land boundary correction, a full sequence spatial distribution product is generated. After verification, the average Kappa coefficient of the entire sequence dataset is 0.71, with an average overall accuracy of 80.36%, which can fully meet the research needs of long-term monitoring of regional ecological succession and macro land change science.\r\n
This dataset is in raster format (GeoTIFF), with each pixel value stored in integer encoding, uniquely corresponding to a specific land use/cover type. The classification system consists of 7 major categories, and the specific grid code mapping relationships are as follows:\r\n1: Grassland\r\n2: Shrubs forest\r\n3: Farmland\r\n4: Tree forest\r\n5: Naked Sand\r\n6: Water body\r\n7: Construction land", "ds_time_res": "", "ds_acq_place": "Maowusu Sandy Land", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
1. Construction of classification sample set\r\n
The interpretation samples were obtained from field investigations, low altitude unmanned aerial vehicle orthophoto images, and the intersection confidence zone of multiple high-precision open source products (CLCD, Esri 10m, CIrrMap250, GSWE, etc.). A total of 1808 high-quality training and validation samples were screened and extracted. The Jeffreys Matuxita distance (J-M Distance) of each region of interest (ROI) is greater than 1.7, ensuring high separability of the spectral features of the samples.\r\n
2. Multi algorithm classification and result optimization\r\n
Based on ENVI 5.6 software, comprehensive remote sensing images and sample data are used to perform supervised classification using six algorithms: support vector machine, minimum distance, maximum likelihood, Mahalanobis distance, parallelepiped, and neural network. In the preliminary evaluation, the parallelepiped and neural network algorithms that have poor classification performance for complex sandy terrain were excluded. Subsequently, a subjective objective collaborative evaluation method combining confusion matrix and visual interpretation was used to cross compare the results of the remaining four algorithms, in order to dynamically select the best classification algorithm for different years' image features. Finally, with the assistance of fine manual interactive visual correction to eliminate plaque noise, this dataset was generated.", "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, "ds_topic_tags": [ "土地利用与覆被", "遥感解译", "景观破碎化", "生态脆弱区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "毛乌素沙地", "北方农牧交错带", "黄河重点生态区" ], "ds_time_tags": [ 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, 2021, 2022, 2023, 2024, 2025 ], "ds_contributors": [ { "true_name": "罗志佳", "email": "luozj_gxnu@163.com", "work_for": "广西师范大学", "country": "中国" }, { "true_name": "连杰", "email": "lianjie@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "张雷", "email": "lkyzhanglei@126.com", "work_for": "内蒙古自治区林业科学研究院", "country": "中国" }, { "true_name": "李衍青", "email": "yanqingli@mailbox.gxnu.edu.cn", "work_for": "广西师范大学", "country": "中国" }, { "true_name": "吴明润", "email": "wumingrun@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "罗志佳", "email": "luozj_gxnu@163.com", "work_for": "广西师范大学", "country": "中国" }, { "true_name": "连杰", "email": "lianjie@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "张雷", "email": "lkyzhanglei@126.com", "work_for": "内蒙古自治区林业科学研究院", "country": "中国" }, { "true_name": "李衍青", "email": "yanqingli@mailbox.gxnu.edu.cn", "work_for": "广西师范大学", "country": "中国" }, { "true_name": "吴明润", "email": "wumingrun@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "连杰", "email": "lianjie@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "沙漠与荒漠化" }