本数据集刻画了近 20 年全球森林遭受的主要扰动类型。部分森林斑块可能同时受多种扰动作用，但本数据集仅记录其主导扰动类型。\n
数据集主要采用机器学习分类方法，汇总并计算了18 项森林扰动特征指标，涵盖全球森林扰动总体特征、扰动前特征、扰动后特征、扰动潜在特征、地表覆盖特征及空间区位特征，据此划分出11 类森林扰动类型：其他扰动 (0)、轮垦农业 (11)、林业重植 (12)、人工林更新 (13)、森林砍伐 (14)、森林火灾 (15)、建设用地扩张 (18)、耕地扩张 (19)、洪涝扰动 (20)、油棕种植 (21)、人工林扩张 (22)。\n
全球森林扰动类型占比以林业重植 (26.52%)、轮垦农业 (26.07%)、森林火灾 (12.58%) 为主；新增林地占全球森林扰动面积的 15.47%，天然林大规模砍伐占比 7.94%。人工林扩张、油棕种植扩张、耕地扩张、建设用地扩张对森林造成的扰动占比分别为 4.63%、1.54%、3.88%、0.25%。\n
基于 16000 个样本点的验证结果表明，本数据集总体精度高达95%。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "全球", "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": 7607050926, "ds_files_count": 0, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "3591cec1-4a4c-48d2-a0a6-eaba478dbacd.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "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": "2026-05-11 15:01:35", "last_updated": "2026-05-11 15:12:46", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.ecology.db7320.2026", "i18n": { "en": { "title": "Global high-resolution forest disturbance type dataset", "ds_format": "*.tif", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "This dataset describes the main types of disturbances that forests worldwide have experienced over the past 20 years. There may be some forest patches that are subject to various types of disturbances, but this dataset only records the most significant types of disturbances. Mainly using machine learning classification methods, 18 forest disturbance characteristic indicators including global forest disturbance overall characteristics, pre disturbance characteristics, post disturbance characteristics, disturbance potential characteristics, surface cover characteristics, and spatial location are collected and calculated to determine 11 forest disturbance types: other disturbance (0), shifting cultivation (11), forestry replanting (12), plantation renewal (13), deforestation (14), forest fire (15), built-up expansion (18), cropland expansion (19), flood disturbance (20), oil palm cultivation (21), and artificial forest expansion (22). The Spatial resolution of this dataset is 30m. The main types of global forest disturbance are forestry replanting (26.52%), shifting cultivation (26.07%), and forest fires (12.58%), with newly forests accounting for 15.47% of the global forest disturbance area. The large-scale deforestation of natural forests also accounts for 7.94%. The disturbance caused by plantations, oil palm expansion, cropland expansion, and built-up expansion to forests also accounted for 4.63%, 1.54%, 3.88%, and 0.25%, respectively. The validation results based on 16000 sample points show that the accuracy of this dataset is as high as 95%.", "ds_time_res": "", "ds_acq_place": "global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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": [ "森林", "森林扰动" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "刘时栋", "email": "liusd@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球全国重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘时栋", "email": "liusd@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球全国重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "刘时栋", "email": "liusd@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球全国重点实验室", "country": "中国" } ], "category": "生态" }