为了量化中国近期全国性恢复工作的生态后果，过去20年森林生物量碳储量变化的空间显性信息至关重要。然而，在全国范围内进行长期生物量追踪仍然具有挑战性，因为它需要连续和高分辨率的监测。本文通过融合多种类型的遥感观测与密集的野外实地测量相结合，通过回归和机器学习方法，表征了2002—2021年中国森林地上和地下生物量碳（AGBC和BGBC）在1km空间分辨率下的变化。森林生物量碳储量增加最为显著的是中国中南部黄土高原、秦岭山脉、西南喀斯特和东南森林。虽然综合使用多源遥感数据为评估森林生物量碳变化提供了有力的工具，但还需要开展进一步的研究，以探索观测到的木质生物量趋势的驱动因素，并评估生物量收益将在多大程度上转化为生物多样性、健康的可持续生态系统。
", "ds_source": "主要通过野外实测、已经发表的文献记载所得到。
", "ds_process_way": "（1）基于2011-2015年大规模的AGBC野外实测数据，标定了基于合成孔径雷达的中国高分辨率森林地上生物量图； \n
（2）参考光学遥感获取的乔木和矮小植被覆盖度，将 AGBC 时间序列扩展到 2002-2021 年；\n
（3）利用基于微波的长期综合 VOD 数据集，校准了某些特定地区的 AGBC 时间序列，以及根据已发表文献中的原位记录，建立随机森林模型，绘制林地 BGBC 图。
", "ds_quality": "在基准 AGBC 测绘过程中，我们将林地的原位 AGBC 数据乘以野外调查期间的林地比例，转换为网格尺度的平均 AGBC。考虑到通过人机交互解译陆地卫星影像开发的中国土地利用/覆盖数据集的整体质量较高，以及本研究使用的 CLCD 数据集中林地分类的生产者精度（PA）和用户精度（UA）分别为 73% 和 85%，基于林地面积分数的尺度转换所引起的基准 AGBC 测绘误差总体上是有限的。短期内气候条件的变化可能会对 BGB 产生微妙的影响，但目前还缺乏有关这种影响的明确知识。相反，木本植被 BGB 更受 AGB（植被密度）的驱动，BGB 与 AGB 之间的关系非常密切（R2≥0.85）。
", "ds_acq_start_time": "2002-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 134.0, "ds_acq_lat_south": 18.0, "ds_acq_lon_west": 74.0, "ds_acq_lat_north": 54.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 940234313, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1km", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "b953825b-c400-47d5-9400-8564a73675c1.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": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-07-19 09:11:07", "last_updated": "2025-05-29 11:33:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6534.2024", "i18n": { "en": { "title": "Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years", "ds_format": "TIFF", "ds_source": "Mainly obtained through field measurements and published literature records.
", "ds_quality": "In the benchmark AGBC surveying process, we multiply the in-situ AGBC data of the forest land by the forest land proportion during the field investigation period, and convert it to the average AGBC at the grid scale. Considering the overall high quality of the Chinese land use/cover dataset developed through human-computer interaction interpretation of land satellite images, and the fact that the producer accuracy (PA) and user accuracy (UA) of forest land classification in the CLCD dataset used in this study are 73% and 85%, respectively, the benchmark AGBC surveying error caused by scale conversion based on forest ground integration is generally limited. Short term changes in climate conditions may have subtle impacts on BGB, but there is currently a lack of clear knowledge about these effects. On the contrary, woody vegetation BGB is more driven by AGB (vegetation density), and the relationship between BGB and AGB is very close (R2≥ 0.85).
", "ds_ref_way": "", "ds_abstract": "In order to quantify the ecological consequences of China's recent nationwide restoration efforts, spatial explicit information on changes in forest biomass and carbon storage over the past 20 years is crucial. However, conducting long-term biomass tracking nationwide remains challenging as it requires continuous and high-resolution monitoring. This article combines multiple types of remote sensing observations with intensive field measurements, and characterizes the changes in aboveground and belowground biomass carbon (AGBC and BGBC) of Chinese forests at a spatial resolution of 1km from 2002 to 2021 through regression and machine learning methods. The most significant increase in forest biomass carbon storage is observed in the Loess Plateau, Qinling Mountains, Southwest Karst, and Southeast forests in central and southern China. Although the comprehensive use of multi-source remote sensing data provides a powerful tool for assessing changes in forest biomass carbon, further research is needed to explore the driving factors behind observed trends in woody biomass and assess the extent to which biomass benefits will translate into biodiversity, healthy and sustainable ecosystems.
", "ds_time_res": "年", "ds_acq_place": "China", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "(1) Based on large-scale field measurements of AGBC from 2011 to 2015, a high-resolution forest aboveground biomass map of China using synthetic aperture radar was calibrated; \n
(2) Extend the AGBC time series to the period of 2002-2021 based on the coverage of trees and short vegetation obtained from optical remote sensing;\n
(3) We calibrated the AGBC time series of certain specific regions using a microwave based long-term comprehensive VOD dataset, and established a random forest model based on in situ records from published literature to draw a forest BGBC map.
", "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": [ "森林生物量碳", "随机森林模型", "森林AGBC测绘" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "冯晓明", "email": "fengxm@rcees.ac.cn", "work_for": " 中国科学院生态环境研究中心", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "冯晓明", "email": "fengxm@rcees.ac.cn", "work_for": " 中国科学院生态环境研究中心", "country": "中国" } ], "ds_managers": [ { "true_name": "冯晓明", "email": "fengxm@rcees.ac.cn", "work_for": " 中国科学院生态环境研究中心", "country": "中国" } ], "category": "生态" }