I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia
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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. 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)
