数据集选择了能够反映植物资源获取策略和生态系统功能的六个关键植物功能性状，包括比叶面积（SLA）、叶干物质含量（LDMC）、叶氮浓度（LNC）、叶磷浓度（LPC）、叶面积（LA）和木质密度（WD）。在中国的 1430 个采样点共采集了 3447 种种子植物的 34589 个原位性状测量值，并利用两种机器学习模型（随机森林和提升回归树）生成了空间植物功能性状图（∼1 km）以及环境变量和植被指数。为获得最佳估计值，进一步应用加权平均算法合并两个模型的预测结果，得出最终的空间植物功能性状图。", "ds_source": "
本研究使用了21个气候变量，包括19个生物气候变量、太阳辐射（RAD）和干旱指数（AI）。19 个生物气候变量和太阳辐射数据来自 1970 年至 2000 年的 WorldClim 2.1 版（https://www.worldclim.org/data/worldclim21.html）。\n
人工影响数据来自国际农业研究磋商组织空间信息联合会（CGIAR-CSI）1970 年至 2000 年的数据http://www.csi.cgiar.org 。气候数据的空间分辨率为1公里。\n
本研究纳入了12个土壤变量，代表了土壤性质的不同方面，即土壤质地、容重（BD）、pH值和土壤养分。所有土壤变量均提取自中国土壤数据库进行地表建模http://globalchange.bnu.edu.cn/research/soil2\n
地形变量是高程。高程数据基于SRTM V4.1数据库（https://www.resdc.cn/data.aspx?DATAID=123。 从中国STRM 90m数据集中提取。空间分辨率为 1 km。\n
本研究包括三类植被指数。首先，EVI是从MOD13A3 V006产品中提取的（https://lpdaac.usgs.gov/products/mod13a3v006/）。 该产品以1公里的空间分辨率提供月平均值，范围为2000年1月至2018年12月。其次，还从MOD13A3 V006产品中提取MODIS反射率数据，包括MIR反射率、NIR反射率、红色反射率和蓝色反射率。第三，MERIS陆地叶绿素指数（MTCI）是从自然环境研究委员会地球观测数据中心（NEODC，2005年）（https://data.ceda.ac.uk/）中提取的。 从2002年6月到2011年12月，MTCI数据以4.63公里的空间分辨率和范围在全球范围内提供。需要注意的是，有效的MTCI值应大于1，因此我们的研究删除了任何小于1的值。", "ds_process_way": "
1.从 TRY 和中国数据库以及公开发表的文献中收集了六种植物功能性状的实地测量数据，并根据植物生长形态、叶片类型和叶片物候对植物物种的功能性状进行了分类。在避免了气候、土壤、地形和植被指数之间的共线性之后，使用了多个网格预测因子。\n
2.使用随机森林和提升回归树分别训练每个 PFT 的植物功能性状与预测因子之间的关系。\n
3.利用土地覆被图（100 米）计算每个 PFT 在 1 千米网格单元内的空间丰度。根据步骤 2 中每个 PFT 的丰度及其预测性状值，计算出 1 千米网格单元内的群落加权性状值。为了减少不同单一模型的变异性，我们采用了集合模型算法，根据随机森林树和提升回归树的交叉验证 R2 值，合并它们的预测结果，得出植物功能性状的最终空间分布图。", "ds_quality": "
将本研究与全球范围内的研究进行了比较，因此，从全球性状图中提取了中国的数据。在与之前的研究进行定量比较之前，我们进行了两个步骤，以使数据产品尽可能具有可比性，并提高不同研究之间的一致性。首先，由于全球性状图的空间分辨率（主要是 0.5°）与我们的研究不同，我们将以往研究的数据产品和我们的地图重新采样到 0.5°的空间分辨率。除了 Vallicrosa 等人（2022 年）以 1 千米的空间分辨率生成 LNC 和 LPC 的全球地图外，我们还比较了 Vallicrosa 等人（2022 年）与我们的研究在 1 千米空间分辨率下的频率分布。其次，我们的研究侧重于自然植被，因此使用全球性状图过滤掉了非自然植被（如耕地）。我们从两个方面对我们的地图与之前的研究进行了定量比较。性状图之间的比较采用频数图和空间相关性。", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "中国", "ds_acq_lon_east": 60.0, "ds_acq_lat_south": 0.0, "ds_acq_lon_west": 140.0, "ds_acq_lat_north": 50.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 772839315, "ds_files_count": 8, "ds_format": "tif，excel", "ds_space_res": "1km", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "ea602038-d362-4681-9d63-e5634ced4d92.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": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45", "170.4510" ], "quality_level": 3, "publish_time": "2024-05-22 16:19:10", "last_updated": "2026-01-13 16:43:38", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6491.2024", "i18n": { "en": { "title": "Maps of plant functional traits with 1-km spatial resolution in terrestrial ecosystems across China", "ds_format": "tif，excel", "ds_source": "
This study used 21 climate variables, including 19 bioclimatic variables, solar radiation (RAD), and drought index (AI). 19 bioclimatic variables and solar radiation data from WorldClim version 2.1 from 1970 to 2000（ https://www.worldclim.org/data/worldclim21.html ）.\n
The artificial impact data is sourced from the Consultative Group on International Agricultural Research (CGIAR-CSI) Spatial Information Consortium from 1970 to 2000 http://www.csi.cgiar.org The spatial resolution of climate data is 1 kilometer.\n
This study included 12 soil variables representing different aspects of soil properties, namely soil texture, bulk density (BD), pH value, and soil nutrients. All soil variables were extracted from the Chinese soil database for surface modeling http://globalchange.bnu.edu.cn/research/soil2\n
The terrain variable is elevation. Elevation data based on SRTM V4.1 database（ https://www.resdc.cn/data.aspx?DATAID=123 Extract from the Chinese STRM 90m dataset. The spatial resolution is 1 km.\n
This study includes three types of vegetation indices. Firstly, EVI is extracted from MOD13A3 V006 product（ https://lpdaac.usgs.gov/products/mod13a3v006/ ）. This product provides monthly averages with a spatial resolution of 1 kilometer, ranging from January 2000 to December 2018. Secondly, MODIS reflectance data was extracted from MOD13A3 V006 product, including MIR reflectance, NIR reflectance, red reflectance, and blue reflectance. Thirdly, the MERIS Land Chlorophyll Index (MTCI) was obtained from the Earth Observation Data Center of the Natural Environment Research Council (NEODC, 2005)（ https://data.ceda.ac.uk/ ）Extracted from within. From June 2002 to December 2011, MTCI data was provided globally with a spatial resolution and range of 4.63 kilometers. It should be noted that the effective MTCI value should be greater than 1, so our study removed any values less than 1.", "ds_quality": "
This study was compared with research worldwide, and therefore, data from China was extracted from the global trait map. Before quantitatively comparing with previous studies, we carried out two steps to make the data products as comparable as possible and improve consistency between different studies. Firstly, due to the spatial resolution of the global trait map (mainly 0.5 °) being different from our study, we resampled the data products from previous research and our map to a spatial resolution of 0.5 °. In addition to Vallicrosa et al. (2022) generating global maps of LNC and LPC at a spatial resolution of 1 kilometer, we also compared the frequency distribution of Vallicrosa et al. (2022) with our study at a spatial resolution of 1 kilometer. Secondly, our research focuses on natural vegetation, so we filtered out non natural vegetation (such as cultivated land) using a global trait map. We quantitatively compared our map with previous research from two aspects. The comparison between trait maps uses frequency charts and spatial correlations.", "ds_ref_way": "", "ds_abstract": "
The dataset selected six key plant functional traits that reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen concentration (LNC), leaf phosphorus concentration (LPC), leaf area (LA), and wood density (WD). 34589 in-situ trait measurements of 3447 seed plants were collected from 1430 sampling points in China, and two machine learning models (random forest and lift regression tree) were used to generate spatial plant functional trait maps (∼ 1 km) as well as environmental variables and vegetation indices. To obtain the best estimate, the weighted average algorithm is further applied to merge the prediction results of the two models and obtain the final spatial plant functional trait map.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "1. Field measurement data of six plant functional traits were collected from TRY and Chinese databases, as well as publicly published literature, and the functional traits of plant species were classified based on plant growth morphology, leaf type, and leaf phenology. After avoiding collinearity between climate, soil, terrain, and vegetation indices, multiple grid prediction factors were used.\n
2. Use random forests and boosting regression trees to train the relationship between plant functional traits and predictive factors for each PFT.\n
3. Use a land cover map (100 meters) to calculate the spatial abundance of each PFT within a 1-kilometer grid cell. Calculate the community weighted trait values within a 1-kilometer grid cell based on the abundance and predicted trait values of each PFT in step 2. In order to reduce the variability of different single models, we used the set model algorithm. According to the cross validation R2 value of random forest trees and lifting regression trees, we combined their prediction results to obtain the final spatial distribution map of plant functional traits.", "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_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "比叶面积（SLA）", "叶干物质含量（LDMC）", "叶氮浓度（LNC）", "叶磷浓度（LPC）", "木质密度（WD）", "叶面积LAI" ], "ds_subject_tags": [ "地理学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "吕楠", "email": "nanlv@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": "吕楠", "email": "nanlv@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": "吕楠", "email": "nanlv@rcees.ac.cn", "work_for": "中国科学院生态环境研究中心", "country": "中国" } ], "category": "生态" }