该数据是利用landsat8和Sentinel-2遥感影像，同时将大量野外采样数据点作为分类样本，利用随机森林分类算法得到的2010、2020年柴达木盆地土地覆被图。在经过与卫星影像对比明显土地分类后发现分类精度可靠，基本能准确划分不同的土地覆被（Kappa系数大于0.9）。", "ds_source": "
Landsat8和Sentinel-2遥感影像。", "ds_process_way": "
第一步，室内准备，主要包括查阅相关资料，了解柴达木盆地的一些特征，从而为我们后续工作做准备。\n
第二步，野外采样，为了提高分类结果精度，我们在研究区域进行了大量实地采样。样本覆盖面广、数量足。\n
第三步，利用随机森林分类法进行分类，在分类的过程中我们不止利用了样本数据，我们还加入了纹理、坡地等一些关键特征。\n
第四步，分类后处理，主要包括小斑点的去除。\n
第五步，结果质量检验。", "ds_quality": "
本数据是利用大量野外采样得到的样本和随机森林分类法得到的数据集，同时，在分类的时候我们还加入了坡度、纹理等特征。我们是严格控制数据处理过程中的误差。最终，分类结果显示优秀，Kappa系数可以达到0.9以上，并且我们也与卫星观测数据进行了比对，发现大部分土地覆被类型是吻合的。", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "柴达木盆地", "ds_acq_lon_east": 99.25, "ds_acq_lat_south": 35.0, "ds_acq_lon_west": 90.25, "ds_acq_lat_north": 39.31666666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 73644507, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "10m，30m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "ea02a14b-904c-4855-b97c-e8704266baf6.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "4851e874-eafc-4879-812b-ffbdd825e967", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.Hydro.db2441.2022", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2022-09-29 11:01:37", "last_updated": "2025-04-29 16:01:25", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.Hydro.db2441.2022", "i18n": { "en": { "title": "Land cover dataset of Qaidam Basin (2010 and 2020)", "ds_format": "TIF", "ds_source": "
Landsat 8 and sentinel-2 remote sensing images.", "ds_quality": "
This data is a data set obtained by using a large number of field samples and random forest classification. At the same time, we also add slope, texture and other features in classification. We strictly control errors in data processing. Finally, the classification results showed excellent, and the kappa coefficient could reach more than 0.9. Moreover, we also compared with satellite observation data and found that most land cover types were consistent.", "ds_ref_way": "", "ds_abstract": "
This data is the land cover map of Qaidam Basin in 2010 and 2020 obtained by using Landsat 8 and sentinel-2 remote sensing images, and taking a large number of field sampling data points as classification samples and using random forest classification algorithm. After land classification with obvious comparison with satellite images, it is found that the classification accuracy is reliable, and different land cover can be basically accurately divided (kappa coefficient is greater than 0.9).
", "ds_time_res": "年", "ds_acq_place": "Qaidam Basin", "ds_space_res": "10m，30m", "ds_projection": "", "ds_process_way": "The first step, indoor preparation, mainly includes consulting relevant data to understand some characteristics of Qaidam Basin, so as to prepare for our follow-up work.\n
The second step is field sampling. In order to improve the accuracy of classification results, we conducted a large number of field sampling in the study area. The sample coverage is wide and the number is sufficient.\n
The third step is to use the random forest classification method for classification. In the process of classification, we not only use the sample data, but also add some key features such as texture and slope.\n
The fourth step, classification post-processing, mainly includes the removal of small spots.\n
Step 5: result quality inspection.", "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": [ "柴达木盆地", "土地覆被", "随机森林分类算法" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "柴达木盆地" ], "ds_time_tags": [ 2010, 2020 ], "ds_contributors": [ { "true_name": "朱高峰", "email": "zhugf@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "朱永泰", "email": "zhuyt20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "朱高峰", "email": "zhugf@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "其他" }