本数据集通过分析了六种最先进的火灾排放产品的不确定性，并使用三角帽法（TCH）将其合并，产生了一个新的全球火灾排放数据集 FiTCH。FiTCH 数据集提供了 2001 年至 2021 年空间分辨率为 0.1 度的全球年度火灾碳排放量。碳排放量的单位是每像素千兆克碳（Gg C）。堆叠栅格数据共有 21 个波段，每个波段表示自 2001 年以来的一年。本数据集强调了森林火灾监测和管理对于有效减缓气候变化和保护生态系统的重要性。
", "ds_source": "全球火灾同化系统（GFAS）、快速火灾排放数据集（QFED）、全球火灾排放数据库（GFED）、美国国家大气研究中心（NCAR）的火灾清单（FINN）、火灾能量和排放研究（FEER）以及 Xu 等人（2021 年）的数据。
", "ds_process_way": "通过三角帽法将六种数据进行比较分析。", "ds_quality": "
结果表明，全球火灾同化系统（GFAS）、快速火灾排放数据集（QFED）和全球火灾排放数据库（GFED）等卫星产品的火灾排放不确定性较低，而美国国家大气研究中心（NCAR）的火灾清单（FINN）、火灾能量和排放研究（FEER）以及 Xu 等人（2021 年）的数据的不确定性较高。拟议的 FiTCH 数据集的不确定性最低，2001-2021 年的年平均火灾排放量为 1978.47 Tg C。
", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": -90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 66571778, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "10000", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "68bcf5fc-0fa8-4d40-86f7-ab0c6dbca596.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": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.50" ], "quality_level": 3, "publish_time": "2024-12-27 15:58:20", "last_updated": "2026-01-14 10:41:24", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6694.2024", "i18n": { "en": { "title": "Establishing a global fire emission dataset using the triangular hat method (FiTCH) from 2001 to 2021", "ds_format": "TIFF", "ds_source": "The Global Fire Assimilation System (GFAS), Rapid Fire Emissions Dataset (QFED), Global Fire Emissions Database (GFED), National Center for Atmospheric Research (NCAR) Fire Inventory (FINN), Fire Energy and Emissions Study (FEER), and data from Xu et al. (2021)", "ds_quality": "
The results indicate that satellite products such as the Global Fire Assimilation System (GFAS), Rapid Fire Emission Dataset (QFED), and Global Fire Emission Database (GFED) have lower uncertainty in fire emissions, while data from the National Center for Atmospheric Research (NCAR) Fire Inventory (FINN), Fire Energy and Emissions Study (FEER), and Xu et al. (2021) have higher uncertainty. The proposed FiTCH dataset has the lowest uncertainty, with an average annual fire emission of 1978.47 Tg C from 2001 to 2021", "ds_ref_way": "", "ds_abstract": "
This dataset analyzed the uncertainty of six state-of-the-art fire emission products and merged them using the Triangular Cap Method (TCH) to generate a new global fire emission dataset, FiTCH. The FiTCH dataset provides global annual carbon emissions from fires with a spatial resolution of 0.1 degrees from 2001 to 2021. The unit of carbon emissions is gigagrams of carbon per pixel (Gg C). The stacked grid data consists of 21 bands, each representing a year since 2001. This dataset emphasizes the importance of forest fire monitoring and management for effectively mitigating climate change and protecting ecosystems
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "10000", "ds_projection": "", "ds_process_way": "Compare and analyze six types of data using the triangular hat method.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "三角帽法", "FiTCH", "全球", "火灾排放" ], "ds_subject_tags": [ "地质学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "刘猛", "email": "meng.liu@tamu.edu", "work_for": "犹他大学生物科学学院", "country": "中国" }, { "true_name": "杨林清", "email": "linqingyang_bnu@tamu.edu", "work_for": "犹他大学生物科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘猛", "email": "meng.liu@tamu.edu", "work_for": "犹他大学生物科学学院", "country": "中国" }, { "true_name": "杨林清", "email": "linqingyang_bnu@tamu.edu", "work_for": "犹他大学生物科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "刘猛", "email": "meng.liu@tamu.edu", "work_for": "犹他大学生物科学学院", "country": "中国" }, { "true_name": "杨林清", "email": "linqingyang_bnu@tamu.edu", "work_for": "犹他大学生物科学学院", "country": "中国" } ], "category": "其他" }