高分辨率、空间明确的林龄图对于量化森林碳储量和固碳潜力至关重要。。之前在中国进行的全国范围内的林龄估算工作一直受到分辨率稀疏和森林生态系统覆盖不全的限制。由于物种组成复杂、森林面积广阔、实地测量不足以及方法不完善等原因，分辨率稀疏且森林生态系统覆盖不全，从而限制了对中国全国范围内森林年龄的估算。为了应对这些挑战，我们开发了一个将机器学习算法（MLAs）和遥感时间序列分析相结合的框架，用于估算中国的森林年龄。最初，我们根据森林高度、气候、地形、土壤和林龄野外测量，确定并开发用于各种植被划分的森林年龄估计的最佳 MLA，并利用这些 MLA 来确定林龄信息。随后，我们应用LandTrendr时间序列分析来检测1985年至2020年的森林干扰，并利用上次干扰以来的时间作为森林年龄的代理。最终，将LandTrendr的林龄数据与MLA的结果相结合，生成2020年中国林龄图。", "ds_source": "
收集了中国2004-2008年第七次全国森林清查调查（http://www.forestry.gov.cn/，最后访问日期：2023年9月22日）的数据， 用于开发估计森林年龄的模型。该清单涉及基于覆盖全国的667 m2样本地块系统准确地监测国家森林资源。从样地收集的主要信息是树种、林龄、平均树高和地理位置。林分年龄是根据种植时间确定的，或者使用树的胸径来估计。我们总共收集了 58,033 个年龄从 1 年到 480 年不等的田地。样本的平均年龄为34.0岁，标准差为29.6岁。样本样地分布在8个植被分区，每个分区至少包含436个样本样地，用于构建MLAs以估计林龄。", "ds_process_way": "
该框架结合了机器学习算法（MLA）和遥感时间序列分析，用于估计中国森林的年龄。", "ds_quality": "
应用LandTrendr时间序列分析来检测1985年至2020年的森林干扰，并利用上次干扰以来的时间作为森林年龄的代理。最终，将LandTrendr的林龄数据与MLA的结果相结合，生成2020年中国林龄图。对独立田图的验证产生了R2，范围为 0.51 至 0.63。在全国范围内，平均林龄为56.1年（标准差为32.7年）。青藏高原高寒植被区森林最古老，平均植被年限为138.0年，而暖温带落叶阔叶林植被区植被平均年限仅为28.5年。这张30米分辨率的森林年龄图为全面了解中国森林的生态效益和可持续管理中国森林资源提供了重要的见解。", "ds_acq_start_time": "2020-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 140.0, "ds_acq_lat_south": 20.0, "ds_acq_lon_west": 70.0, "ds_acq_lat_north": 50.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 1697750570, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "30m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "793380a4-7a1b-4c98-aa7e-6a526ed27969.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-04-25 17:49:30", "last_updated": "2025-06-30 16:24:55", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6448.2024", "i18n": { "en": { "title": "30 meter resolution forest age map of China (2020)", "ds_format": "tif", "ds_source": "
Collected the seventh national forest inventory survey in China from 2004 to 2008（ http://www.forestry.gov.cn/ The data, last accessed on September 22, 2023, will be used to develop a model for estimating forest age. This list involves accurate monitoring of national forest resources based on a 667 m2 sample plot system covering the whole country. The main information collected from the plot includes tree species, forest age, average tree height, and geographic location. The age of the forest stand is determined based on the planting time or estimated using the diameter at breast height of the tree. We collected a total of 58033 fields ranging in age from 1 year to 480 years old. The average age of the sample is 34.0 years, with a standard deviation of 29.6 years. The sample plots are distributed in 8 vegetation zones, each zone containing at least 436 sample plots, used to construct MLAs for estimating forest age.", "ds_quality": "
Apply LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, and use the time since the last disturbance as a proxy for forest age. Finally, the LandTrendr forest age data was combined with the results of MLA to generate the 2020 Chinese forest age map. The validation of independent field plots resulted in R2, ranging from 0.51 to 0.63. The average forest age nationwide is 56.1 years (with a standard deviation of 32.7 years). The forests in the high-altitude vegetation area of the Qinghai Tibet Plateau are the oldest, with an average vegetation age of 138.0 years, while the vegetation age in the warm temperate deciduous broad-leaved forest vegetation area is only 28.5 years. This 30 meter resolution forest age map provides important insights for a comprehensive understanding of the ecological benefits and sustainable management of China's forest resources.", "ds_ref_way": "", "ds_abstract": "
High resolution and spatially clear forest age maps are crucial for quantifying forest carbon storage and carbon sequestration potential.. The nationwide forest age estimation work previously conducted in China has been limited by sparse resolution and incomplete forest ecosystem coverage. Due to the complex species composition, vast forest area, insufficient field measurements, and incomplete methods, the resolution is sparse and the forest ecosystem coverage is incomplete, which limits the estimation of forest age nationwide in China. To address these challenges, we have developed a framework that combines machine learning algorithms (MLAs) with remote sensing time series analysis to estimate the age of forests in China. Initially, we determined and developed the optimal MLA for estimating forest age for various vegetation classifications based on field measurements of forest height, climate, terrain, soil, and forest age, and used these MLA to determine forest age information. Subsequently, we applied LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, and used the time since the last disturbance as a proxy for forest age. Finally, the LandTrendr forest age data was combined with the results of MLA to generate the 2020 Chinese forest age map.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "30m", "ds_projection": "", "ds_process_way": "This framework combines machine learning algorithms (MLA) and remote sensing time series analysis to estimate the age of Chinese forests.", "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": [ "森林年龄", "高分辨率", "机器学习算法" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "郭庆华", "email": "guo.qinghua@pku.edu.cn", "work_for": " 北京大学遥感与地理信息系统研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "郭庆华", "email": "guo.qinghua@pku.edu.cn", "work_for": " 北京大学遥感与地理信息系统研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "郭庆华", "email": "guo.qinghua@pku.edu.cn", "work_for": " 北京大学遥感与地理信息系统研究所", "country": "中国" } ], "category": "水文" }