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

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 - 4,700 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:
- cellID25: the unique ID of each 25x25 m² cell
- sp: species latin names
- n: number of trees
- dbh: tree diameter at breast height (cm)
- 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.

The v2.0.0 presents the algorithm in its final state.

Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (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.

------------------------------------------------------------
# load package
library(raster)
library(dplyr)

# set work directory
setwd()              # define path to the I-MAESTRO_data folder

# load tree data
tree <- read.csv2('./milicz/trees.csv', sep = ',')

# load spatial data
cellID <- raster('./milicz/cellID25.asc')

# convert raster into dataframe
cellIDdf <- as.data.frame(cellID)

# calculate tree density from tree dataframe
dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))

# merge the two dataframes
dens <- left_join(cellIDdf, dens)

# add density to raster
cellID$dens <- dens$n

# plot density map
plot(cellID$dens)