巴西亚马逊地区树叶和大气二氧化碳中的 LBA-ECO CD-02 碳和氮同位素

简介: 本数据集展示了巴西亚马逊州马瑙斯附近原始森林中叶片组织和大气CO2的碳氮同位素比值及浓度变化。研究于2004年和2006年旱季进行,包含不同冠层高度的叶片样本与大气空气瓶样本,以及高原KM34塔的气象和CO2通量数据。数据集由3个CSV文件组成,分别记录气体样本、叶片样本及气象通量信息,为理解亚马逊生态系统的碳氮循环提供了重要参考。

​LBA-ECO CD-02 C and N Isotopes in Leaves and Atmospheric CO2, Amazonas, Brazil

简介

本数据集报告了巴西亚马逊州马瑙斯附近亚马逊国家科学研究所 (INPA) ZF2 保护区 (34 公里) 原始森林叶片组织和大气二氧化碳 (CO2) 的 13C/12C 变化结果、叶片组织的 15N/14N 比值以及沿地形梯度的叶片碳氮浓度。2004 年和 2006 年旱季,研究人员在冠层不同高度采集了叶片样本,并在该梯度沿线三个位置的不同高度采集了大气空气瓶样本。此外,数据集还包含来自高原 KM34 塔的同步气象、大气 CO2 和 CO2 通量测量数据。本数据集包含 3 个逗号分隔的数据文件。

摘要

Data are presented in three comma-delimited ASCII files:

File #1: CD02_Gas_Samples_13C_2004_2006.csv

File #2: CD02_Foliar_13C_15N_2004_2006.csv

File #3: CD02_Met_and_flux_data_2004_2006.csv

File #1: CD02_Gas_Samples_13C_2004_2006.csv

Column Heading Units/format Description
1 Year YYYY Year in which samples were collected: 2004 or 2006
2 Month Month in which samples were collected: August = 8 or October = 10
3 Day Day of the month in which samples were collected
4 Time HH:MM Start of sample collection in local time. Local time is GMT -4
5 Flask Flask identification number for laboratory purposes
6 Position Location within the landscape: Valley, Slope, Plateau, and Campinarana
7 Sample_type Samples were collected in flasks and are either Atmospheric gas samples or Soil respiration samples collected from a 40 L chamber placed on the soil surface
8 Height m Height in meters above the ground at which the sample was collected for the atmospheric samples
9 conc_CO2 ppm Concentration of carbon dioxide in the flask samples in parts per million (ppm)
10 inv_conc_CO2 ppm-1 Inverse concentration of carbon dioxide in the sample calculated as 1/column 9
11 delta_13C_R1 parts per mil Isotopic ratio of 13C/12C in carbon dioxide referenced to PDB, measured on a continuous flow isotope-ratio mass spectrometer (Finigan Delta Plus) at CENA
12 delta_13C_R2 parts per mil 13C/12C ratio measured in second aliquot from the same sample where available
13 delta_13C_R3 parts per mil 13C/12C ratio measured in third aliquot from the same sample where available
14 delta_13C_Avg parts per mil Mean isotopic ratio of 13C/12C in carbon dioxide based on 1, 2, or 3 measurements

Missing data are represented as -9999

File #2: CD02_Foliar_13C_15N_2004_2006.csv

Column Heading Units/format Description
1 Year YYYY Year in which samples were collected: 2004 or 2006
2 Month Month in which samples were collected: August = 8 or October = 10
3 Day Day of the month in which samples were collected
4 Location Topographic location of sampling point: Valley, Slope, Plateau, and Campinarana or not reported. In 2006 Campinarana was added to the list as a forest type that only occurs at lower slope and valley locations.
5 Height m Height of sampling location in meters above ground level or not recorded
6 Sample_id Internal sample id
7 Sample_type Type of material sampled: leaves with mature and young leaves distinguished in 2006 or litter
8 Canopy_position Location within the canopy based on sample height: Canopy, Understory, Soil, or not identified
9 Species Species identification where noted: In 2004 species were identified only with local name where possible in 2006 species were identified with scientific names
10 Description Notations from original field notebooks
11 delta_15N per mil Isotopic ratio of 15N/14N in the leaf sample relative to atmospheric N, measured on a continuous flow isotope-ratio mass spectrometer (Finigan Delta Plus) at CENA
12 delta_13C per mil Isotopic ratio of 13C/12C in the leaf or litter sample referenced to PDB, measured on a continuous flow isotope-ratio mass spectrometer (Finigan Delta Plus) at CENA
13 conc_C percent Concentration of carbon in the leaf or litter sample measured by dry combustion and reported in percent by dry weight
14 conc_N percent Concentration of nitrogen in the leaf or litter sample measured by dry combustion and reported in percent by dry weight
15 C_to_N Mass based ratio of carbon to nitrogen in the leaf or litter sample calculated by dividing column 13 by column 14
16 Notes Comments from the field notebooks; no comment or missing comment = none

Missing data are represented as -9999

File #3: CD02_Met_and_flux_data_2004_2006.csv

Column Heading Units/format Description
1 Year YYYY Year in which data were collected: 2004 or 2006
2 DOY Day of the year (DOY) on which data were collected. For 2004, data were collected from day 195 = July 13 thru day 218= August 5. For 2006, data were collected from day 260=September 17 thru day 290=October 17
3 Time Sampling time in decimal hours. Time given is the end of the 30 minute sampling period
4 Temp_Air degrees C Air temperature in degrees Celsius
5 RH percent Relative humidity in percent
6 E_act kPa Actual water vapor pressure reported in kilopascals
7 E_sat kPa Saturated water vapor pressure in kilopascals
8 VPD kPa Vapor pressure deficit reported in kilopascals
9 F_CO2 umol/m2/s Carbon dioxide flux measured at 53 m height
10 CO2_conc ppm Atmospheric carbon dioxide concentration measured at 53 meters above ground level
11 u_star m/s Friction velocity measured at 53 meters above ground level in meters per second

Missing data are represented by -9999
All data were measured at the top of the KM34 tower at 53 meters above ground level. The tower is located on the plateau.
All data are averages from a 30 minute sampling period: For the meteorological data each sampling period included 60 measurements (scan time interval 30 seconds); for the flux data each sampling period includes 18,000 samples (scan time interval 0.1 seconds)

Site boundaries: (All latitude and longitude given in decimal degrees)

Site (Region) Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude Geodetic Datum
Amazonas (Manaus) - ZF2 km 34 (Amazonas (Manaus)) -60.20910 -60.0000 -2.50000 -2.60900 South-American Datum, 1969 (SAD-69)

代码
!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassify

import pandas as pd
import leafmap

url = "https://github.com/opengeos/NASA-Earth-Data"
df = pd.read_csv(url, sep="\t")
df

leafmap.nasa_data_login()

results, gdf = leafmap.nasa_data_search(
short_name="CD02_C_N_Isotopes_1097",
cloud_hosted=True,
bounding_box=(-60.21, -2.61, -60.0, -2.5),
temporal=("2004-08-02", "2006-10-21"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)

gdf.explore()

leafmap.nasa_data_download(results[:5], out_dir="data")

引用

de Araujo, A.C., J.P.H.B. Ometto, A.J. Dolman, B. Kruijt, M.J. Waterloo and J.R. Ehleringer. 2012. LBA-ECO CD-02 C and N Isotopes in Leaves and Atmospheric CO2, Amazonas, Brazil. Data set. Available on-line [http://daac.ornl.gov ] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

相关文章
|
8月前
|
存储 关系型数据库 分布式数据库
登顶TPC-C|云原生数据库PolarDB技术揭秘:单机性能优化篇
阿里云PolarDB云原生数据库在TPC-C基准测试中,以20.55亿tpmC的成绩打破性能与性价比世界纪录。此外,国产轻量版PolarDB已上线,提供更具性价比的选择。
|
6月前
|
人工智能 PyTorch 算法框架/工具
ACK AI Profiling:从黑箱到透明的问题剖析
本文从一个通用的客户问题出发,描述了一个问题如何从前置排查到使用AI Profiling进行详细的排查,最后到问题定位与解决、业务执行过程的分析,从而展现一个从黑箱到透明的精细化的剖析过程。
|
6月前
|
存储 SQL 大数据
从 o11y 2.0 说起,大数据 Pipeline 的「多快好省」之道
SLS 是阿里云可观测家族的核心产品之一,提供全托管的可观测数据服务。本文以 o11y 2.0 为引子,整理了可观测数据 Pipeline 的演进和一些思考。
429 35
|
6月前
|
人工智能 安全 应用服务中间件
阿里巴巴 MCP 分布式落地实践:快速转换 HSF 到 MCP server
本文分享了阿里巴巴内部将大规模HSF服务快速转换为MCP Server的实践经验,通过Higress网关实现MCP协议卸载,无需修改代码即可接入MCP生态。文章分析了MCP生态面临的挑战,如协议快速迭代和SDK不稳定性,并详细介绍了操作步骤及组件功能。强调MCP虽非终极解决方案,但作为AI业务工程化的起点具有重要意义。最后总结指出,MCP只是AI原生应用发展的第一步,未来还有更多可能性值得探索。
1172 48
|
6月前
|
开发框架 人工智能 Java
破茧成蝶:阿里云应用服务器让传统 J2EE 应用无缝升级 AI 原生时代
本文详细介绍了阿里云应用服务器如何助力传统J2EE应用实现智能化升级。文章分为三部分:第一部分阐述了传统J2EE应用在智能化转型中的痛点,如协议鸿沟、资源冲突和观测失明;第二部分展示了阿里云应用服务器的解决方案,包括兼容传统EJB容器与微服务架构、支持大模型即插即用及全景可观测性;第三部分则通过具体步骤说明如何基于EDAS开启J2EE应用的智能化进程,确保十年代码无需重写,轻松实现智能化跃迁。
551 42
|
6月前
|
机器学习/深度学习 设计模式 人工智能
深度解析Agent实现,定制自己的Manus
文章结合了理论分析与实践案例,旨在帮助读者系统地认识AI Agent的核心要素、设计模式以及未来发展方向。
1916 103
深度解析Agent实现,定制自己的Manus
|
6月前
|
人工智能 安全 API
Higress MCP Server 安全再升级:API 认证为 AI 连接保驾护航
Higress MCP Server 新增了 API 认证功能,为 AI 连接提供安全保障。主要更新包括:1) 客户端到 MCP Server 的认证,支持 Key Auth、JWT Auth 和 OAuth2;2) MCP Server 到后端 API 的认证,增强第二阶段的安全性。新增功能如可重用认证方案、工具特定后端认证、透明凭证透传及灵活凭证管理,确保安全集成更多后端服务。通过 openapi-to-mcp 工具简化配置,减少手动工作量。企业版提供更高可用性保障,详情参见文档链接。
726 42
|
6月前
|
JSON 安全 Serverless
MCP Server On FC之旅2: 从0到1-MCP Server市场构建与存量OpenAPI转MCP Server
本文介绍了将社区主流STDIO MCP Server一键转为企业内可插拔Remote MCP Server的方法,以及存量API智能化重生的解决方案。通过FunctionAI平台模板实现STDIO MCP Server到SSE MCP Server的快速部署,并可通过“npx”或“uvx”命令调试。同时,文章还探讨了如何将OpenAPI规范数据转化为MCP Server实例,支持API Key、HTTP Basic和OAuth 2.0三种鉴权配置。该方案联合阿里云百练、魔搭社区等平台,提供低成本、高效率的企业级MCP Server服务化路径,助力AI应用生态繁荣。
1032 40
|
6月前
|
机器学习/深度学习 数据采集 安全
MiMo-7B:从预训练到强化学习,解锁语言模型的推理潜能
目前,大多数成功的 强化学习 工作,包括开源研究,都依赖于相对较大的基础模型,例如 32B 模型,特别是在增强代码推理能力方面。业内普遍认为在一个小模型中同时提升数学和代码能力是具有挑战性的。然而,小米MiMo研究团队相信 RL 训练的推理模型的有效性取决于基础模型固有的推理潜力。为了完全解锁语言模型的推理潜力,不仅需要关注后训练,还需要针对推理定制预训练策略。
490 43