Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)

<strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change <strong>Contact:</strong> Viola Heinrich ([e-mail]) <strong>This repository contains</strong>: Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong>&lt;driver&gt;_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_&lt;driver_threshold&gt;.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver. Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed&lt;NUM&gt; - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong>&lt;REGION&gt;-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_&lt;region_disturbance_type&gt;.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below). <strong>Code: </strong>The corresponding code mentioned here can be access here: heinrichTrees/secondary-forest-regrowth-amazon-public (github.com) <strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository. If you need anything else, please contact the corresponding author: Viola Heinrich ([e-mail])**标题**：巴西亚马逊次生林的巨大碳汇潜力及其在减缓气候变化中的作用 **联系方式**：Viola Heinrich（[e-mail]） **本仓库包含**： 压缩包1：`Fig1_data_input.zip`——包含制作主论文图1a-e所需的全部文件。请将工作目录设置至该压缩包解压后的文件夹，通过脚本`Fig1a_f_plot.R`运行（详见下文）。该文件夹包含用于构建图1中恢复模型的6个驱动变量的输入文件，文件格式为`<driver>_assessment_v2.csv`。各列含义如下： A: 次生林林龄； B: 对应林龄的众数地上生物量（Aboveground Biomass, AGB）的50百分位数（中位数），注：单位为生物量而非碳量：Mg/ha/yr； C: 经过偏差校正的AGB值，通过减去列B中的最低AGB值计算得到，以确保林龄1年时的AGB数据接近0Mg/ha/yr； D: 观测到对应林龄的次生林像元数量； E: "Threshold"（阈值）：对应驱动因子的阈值范围，例如`fire_assessmentv2.csv`中的“0火灾”代表对应次生林像元在分析时段内未遭受火灾。 该文件夹还包含图1的恢复模型输出文件，格式为`regrowth_model_<driver_threshold>.RData`，其中`driver_threshold`指代驱动变量名称及其关联的阈值限值。 压缩包2：`Fig2_regions_outline.zip`——包含主论文图2a中识别出的4个区域的边界形状文件（shapefile，.shp格式），以及制作和加载形状文件所需的关联文件。 压缩包3：`Fig1g_2b_e_variable_importance.zip`——包含随机森林分析（random forest analysis）的输出文件，用于评估整个亚马逊区域（`whole_Amazon`子文件夹）以及图2a中识别的不同区域的变量重要性。文件格式为可在R语言中加载的.RDS文件，可通过脚本`Fig1g_2b_e_plot.R`生成对应图表。文件名格式说明如下：以研究区域名称开头，例如`whole_Amazon`或`NE_sector`；中间部分为`importance_conditionalTrue/False`，用于标识是否使用条件置换（conditional permutation）法计算重要性（True为使用，False为未使用）；末尾`seed<NUM>`代表设置的随机种子（random seed）编号，用于提取样本数据。例如`whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS`代表在设置随机种子为200以提取代表整个亚马逊区域的随机样本时，使用样本量2500完成的条件置换重要性评估。其余文件为随机森林输出的.RDS文件。请注意，用于生成随机森林模型及重要性评估的代码未包含在此仓库中——该代码运行需耗时数日，因此仅上传了输入与输出文件。如需了解更多细节，请联系通讯作者（见文末）。 压缩包4：`Fig3_data_input.zip`——包含制作主论文图3a-d所需的全部文件。请将工作目录设置至该压缩包解压后的文件夹，通过脚本`Fig3_plot.R`运行（详见下文）。该文件夹包含用于构建图3中恢复模型的6个驱动变量的输入文件，格式为`<REGION>-Group.csv`。各列含义详见前文说明。其中列E的“threshold”用于标识图3中4类次生林恢复子类的编码规则：11 = 无干扰；12 = 仅遭受火烧；21 = 仅发生多次皆伐；22 = 同时遭受火烧与多次皆伐干扰。该代码可将AGB数据转换为地上碳量（Aboveground Carbon, AGC）。文件夹还包含图3的恢复模型输出文件，格式为`regrowth_model_<region_disturbance_type>.RData`，其中`region_disturbance_type`代表研究区域及受干扰类型。 压缩包5：`Fig4_5_carbon_sink_2017.zip`——包含两个子文件夹： a) **Map_aggre_0.1deg**：该文件夹包含2016-2017年间亚马逊次生林AGC的损失、增益与净变化的.tiff格式文件（及关联文件），数据已聚合至0.1度网格单元，每个单元包含该单元内次生林的总损失/增益量。 b) **secondary_forest_by_region_and_disturbance**：该文件夹包含2016和2017年原始分辨率（30m）的次生林数据.tiff文件（及关联文件），数据已按图2识别的区域及干扰类型（如有）进行拆分。关联文件包含.dbf文件，其中包含额外数据[详见文件夹内的`README.txt`文件]——在地理信息系统（GIS）软件中加载数据后，可查看次生林像元的林龄，打开属性表可获取该像元的更多关联数据，例如建模得到的对应像元AGB值。该文件夹中的文件可用于制作图4d与图5，详见代码仓库中的脚本`Fig4_Fig5_plot.R`（见下文）。 **代码**：本文提及的对应代码可通过以下地址获取：heinrichTrees/secondary-forest-regrowth-amazon-public (github.com) **数据使用规范**：若使用本仓库或本研究相关的任何代码或数据，请引用Heinrich等人2021年发表的论文、本研究原始论文以及本数据集仓库的DOI。如需其他协助，请联系通讯作者：Viola Heinrich（[e-mail]）