I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia
收藏资源简介：
Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89 000 ha), Poland (Milicz forest district - 21 000 ha) and Slovenia (Snežnik forest - 4700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100 000 ha including about 64 000 ha of forest and consist of more than 42 million trees of 51 different species. For each virtual landscape we provide a table (in .csv format) with the following columns:<br> - cellID25: the unique ID of each 25x25 m² cell<br> - sp: species latin names<br> - n: number of trees<br> - dbh: tree diameter at breast height (cm)<br> - h: tree height (m) We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. In v1.0.1, useless system files were removed. Finally, we provide a proof of how multiplying the trees dbh by the α correction coefficient makes it possible to reach the cells BA value derived from the ALS mapping (see algorithm presented in the associated Open Research Europe article). Below is an example of R code that opens the datasets and creates a tree density map. ------------------------------------------------------------<br> # load package<br> library(raster)<br> library(dplyr) # set work directory<br> setwd() # define path to the I-MAESTRO_data folder # load tree data<br> tree <- read.csv2('./milicz/trees.csv', sep = ',') # load spatial data<br> cellID <- raster('./milicz/cellID25.asc') # convert raster into dataframe<br> cellIDdf <- as.data.frame(cellID) # calculate tree density from tree dataframe<br> dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n)) # merge the two dataframes<br> dens <- left_join(cellIDdf, dens) # add density to raster<br> cellID$dens <- dens$n # plot density map<br> plot(cellID$dens)
本研究公开三套数据集，分别对应欧洲三处大型林地景观：法国鲍日地质公园（Bauges Geopark，面积89000公顷）、波兰米利茨林区（Milicz forest district，面积21000公顷）以及斯洛文尼亚斯内日尼克森林（Snežnik forest，面积4700公顷），数据精度可达单木尺度。单木数据集通过融合样地调查数据、植被图与机载激光扫描（Airborne Laser Scanning, ALS）数据生成。上述三处景观（以下简称虚拟景观）总面积逾10万公顷，其中林地面积约6.4万公顷，涵盖51个不同树种的总计超4200万棵单木。针对每套虚拟景观，我们提供了格式为.csv的数据表，其字段如下： - cellID25：每个25m×25m栅格单元的唯一标识符 - sp：树种拉丁学名 - n：单木数量 - dbh：树木胸径（单位：厘米） - h：树木树高（单位：米）此外，每套虚拟景观还附带格式为.asc的栅格文件，其中存储了各栅格单元的cellID25标识符，可实现数据的空间可视化。在v1.0.1版本中，冗余系统文件已被清理。最后，我们提供了验证方法：将树木胸径与α校正系数相乘，即可得到由ALS测绘得到的栅格单元胸高断面积（BA）值，具体算法详见关联的《Open Research Europe》论文。以下为用于加载数据集并生成树木密度分布图的R代码示例： ------------------------------------------------------------ # 加载依赖包 library(raster) library(dplyr) # 设置工作目录 setwd() # 定义I-MAESTRO_data文件夹路径 # 加载单木数据 tree <- read.csv2('./milicz/trees.csv', sep = ',') # 加载空间栅格数据 cellID <- raster('./milicz/cellID25.asc') # 将栅格转换为数据框 cellIDdf <- as.data.frame(cellID) # 基于单木数据框计算树木密度 dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n)) # 合并两个数据框 dens <- left_join(cellIDdf, dens) # 将密度值赋值至栅格 cellID$dens <- dens$n # 绘制树木密度分布图 plot(cellID$dens)
