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ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022, v6.0

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DataCite Commons2025-04-17 更新2025-05-18 收录
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https://catalogue.ceda.ac.uk/uuid/95913ffb6467447ca72c4e9d8cf30501
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This dataset comprises estimates of forest above-ground biomass (AGB) for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 6. Compared to version 5, version 6 consists of an update of the maps of AGB for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and new AGB maps for 2007 and 2022. AGB change maps have been created for consecutive years (e.g., 2020-2019), for a decadal interval (2020-2010) as well as for the interval 2010-2007. The pool of remote sensing data includes multi-temporal observations at L-band for all biomes and for all years and extended ICESat-2 observations to calibrate retrieval models. A cost function that preserves the temporal features as expressed in the remote sensing data has been refined to limit biases between the 2007-2010 and the 2015+ maps. The data products consist of two (2) global layers that include estimates of: 1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots per unit area 2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset) Additionally provided in this version release are aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km). In addition, files describing the AGB change between two consecutive years (i.e., 2016-2015, 2017-2016, 2018-2017, 2019-2018, 2020-2019, 2021-2020, 2022-2021), over a decade (2020-2010) and over 2010-2007 are provided. Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly. Data are provided in both netcdf and geotiff format.

本数据集涵盖2007、2010、2015、2016、2017、2018、2019、2020、2021及2022年的森林地上生物量(above-ground biomass, AGB)估算结果。 这些估算结果基于各年份的地球观测数据融合生成,涉及的数据源包括哥白尼哨兵-1(Copernicus Sentinel-1)任务数据、Envisat卫星的先进合成孔径雷达(Advanced Synthetic Aperture Radar, ASAR)仪器数据,以及日本宇宙航空研究开发机构(Japan Aerospace Exploration Agency, JAXA)的先进陆地观测卫星(Advanced Land Observing Satellite, ALOS-1和ALOS-2)数据,同时辅以其他地球观测来源的补充信息。本数据集由欧洲空间局(European Space Agency, ESA)气候变化倡议(Climate Change Initiative, CCI)计划下的生物量CCI团队完成制作。 本次发布的数据为第6版。相较于第5版,第6版更新了2010、2015、2016、2017、2018、2019、2020、2021年的AGB地图,并新增了2007年与2022年的AGB地图。团队已生成连续年份(如2020-2019年)、十年间隔(2020-2010年)以及2010-2007年间隔的AGB变化地图。本次遥感数据源池覆盖所有生物群区、所有年份的L波段多时序观测数据,并扩充了ICESat-2观测数据用于校准反演模型。同时优化了保留遥感数据所表征的时序特征的代价函数,以缩小2007-2010年与2015年及之后年份AGB地图间的偏差。 本数据集包含两类全球图层产品: 1) 地上生物量(AGB,单位:吨/公顷,即Mg/ha)估算结果(栅格数据集)。其定义为单位面积内所有活立木的木质部分(树干、树皮、枝条及细枝)的烘干质量,不包括伐桩与根系。 2) 逐像素AGB不确定性估算结果,以Mg/ha为单位的标准差形式表征(栅格数据集)。 本次版本发布还提供了聚合数据产品。AGB及AGB变化数据图层的聚合产品可在更低空间分辨率(1、10、25及50km)下获取。 此外,还提供了描述连续年份间AGB变化(即2016-2015、2017-2016、2018-2017、2019-2018、2020-2019、2021-2020、2022-2021)、十年尺度(2020-2010年)以及2010-2007年尺度AGB变化的文件。每一类AGB变化产品均包含两套地图:AGB变化的标准差,以及AGB变化的质量标记。需注意,AGB变化量可直接通过两幅AGB地图相减得到,因此未直接提供该变化量本身。 数据以netCDF与GeoTIFF两种格式提供。
提供机构:
NERC EDS Centre for Environmental Data Analysis
创建时间:
2025-04-17
搜集汇总
数据集介绍
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背景与挑战
背景概述
该数据集提供了2007-2022年全球森林地上生物量的估计,结合了多种卫星数据,并包含生物量变化和质量评估。数据以多种分辨率和格式提供,适用于气候变化研究。
以上内容由遇见数据集搜集并总结生成
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