基于开发的随机森林方法，为东南亚（SEA）生成了第一个准确的高分辨率时间序列树木覆盖产品，该产品整合了Planet-Norway的国际气候与森林倡议（NICFI）图像和Sentinel-1合成孔径雷达数据在Google Earth Engine平台上。Planet-NICFI 树木覆盖地图在六年（2016-2021 年）中以良好的准确性和高度一致性进行了测绘。4.77 m 的基线树木覆盖图可以转换为 SEA 的各种分辨率的森林覆盖图，以满足不同用户的需求。此外，该树木覆盖产品可以通过计算孤立的树木数量和监测狭长森林覆盖的移除情况，帮助解决森林覆盖绘图中的四舍五入误差。", "ds_source": "
Planet-NICFI\n
Sentinel-1", "ds_process_way": "
整合Planet-NICFI 和 Sentinel-1 SAR 影像，以生成用于 SEA 的高分辨率 （4.77 m） 年度树木覆盖地图产品，覆盖时间从 2015 年到 2021 年。\n
数据处理过程如下：（1）映射树木覆盖的定义；（2）影像预处理；（3）生成树覆盖图产品的时间序列；（4）统计准确性评估。", "ds_quality": "
该树木覆盖图在用户 accuracy、producer's accuracy 和 overall accuracy。用户的准确性和该树木覆盖图的总体准确率超过 0.083。ESA WorldCover 2020 年 2021 年的表现与该 Planet-NICFI 树木覆盖地图相似。特别是用户的准确性、生产商的准确性和整体的准确性 ESA WorldCover 2020 分别下降了 0.020、0.008 和 0.017 （图 4）。这可能是因为都使用 SAR 影像作为输入，并且应用基于 RF 的机器学习方法对该树木覆盖进行分类。", "ds_acq_start_time": "2016-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "东南亚", "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": 151991462609, "ds_files_count": 9, "ds_format": "tif", "ds_space_res": "4.77m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "6ff8021c-abe0-4af9-b33d-a4df01af9b07.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "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": "2024-11-28 11:01:14", "last_updated": "2025-05-29 11:06:23", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.07173", "i18n": { "en": { "title": "Fine-scale maps of tree cover generated using time-series Planet-NICFI imagery for Southeast Asia (2016-2021)", "ds_format": "tif", "ds_source": "
Planet-NICFI\n
Sentinel-1", "ds_quality": "
The tree coverage map is evaluated in terms of user accuracy, producer's accuracy, and overall accuracy. The accuracy of the user and the overall accuracy of the tree coverage map exceed 0.083. The performance of ESA WorldCover in 2020 and 2021 is similar to that of the Planet NICFI tree cover map. In particular, the accuracy of users, manufacturers, and the overall accuracy of ESA WorldCover 2020 decreased by 0.020, 0.008, and 0.017, respectively (Figure 4). This may be because SAR images are used as input and RF based machine learning methods are applied to classify the tree cover.", "ds_ref_way": "", "ds_abstract": "
Based on the developed random forest method, the first accurate high-resolution time-series tree cover product was generated for Southeast Asia (SEA), which integrates Planet Norway's International Climate and Forest Initiative (NICFI) images and Sentinel-1 synthetic aperture radar data on the Google Earth Engine platform. The Planet NICFI tree cover map was surveyed with good accuracy and high consistency over six years (2016-2021). The baseline tree cover map of 4.77 meters can be converted into forest cover maps of various resolutions in SEA to meet the needs of different users. In addition, this tree cover product can help address rounding errors in forest cover mapping by calculating the number of isolated trees and monitoring the removal of narrow forest cover.
", "ds_time_res": "年", "ds_acq_place": "Southeast Asia", "ds_space_res": "4.77m", "ds_projection": "", "ds_process_way": "Integrate Planet NICFI and Sentinel-1 SAR images to generate high-resolution (4.77 m) annual tree cover map products for SEA, covering the period from 2015 to 2021.\n
The data processing process is as follows: (1) Mapping the definition of tree coverage; (2) Image preprocessing; (3) Generate time series of tree coverage products; (4) Statistical accuracy assessment.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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, "belong_to_nieer": false, "ds_topic_tags": [ "树木覆盖", "Planet-NICFI", "时间序列" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "东南亚" ], "ds_time_tags": [ 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "曾振中", "email": "zengzz@sustech.edu.cn", "work_for": "南方科技大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "曾振中", "email": "zengzz@sustech.edu.cn", "work_for": "南方科技大学", "country": "中国" } ], "ds_managers": [ { "true_name": "曾振中", "email": "zengzz@sustech.edu.cn", "work_for": "南方科技大学", "country": "中国" } ], "category": "生态" }