Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data

Forests are a major component of the global carbon cycle, and accurate
estimation of forest carbon stocks and fluxes is important in the context
of anthropogenic global change. Airborne laser scanning (ALS) data sets
are increasingly recognized as outstanding data sources for high-fidelity
mapping of carbon stocks at regional scales. We develop a tree-centric
approach to carbon mapping, based on identifying individual tree crowns
(ITCs) and species from airborne remote sensing data, from which
individual tree carbon stocks are calculated. We identify ITCs from the
laser scanning point cloud using a region-growing algorithm and
identifying species from airborne hyperspectral data by machine learning.
For each detected tree, we predict stem diameter from its height and
crown-width estimate. From that point on, we use well-established
approaches developed for field-based inventories: above-ground biomasses
of trees are estimated using published allometries and summed within plots
to estimate carbon density. We show this approach is highly reliable:
tests in the Italian Alps demonstrated a close relationship between field-
and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are
invisible from the air, and a correction factor is required to accommodate
this effect. An advantage of the tree-centric approach over existing
area-based methods is that it can produce maps at any scale and is
fundamentally based on field-based inventory methods, making it intuitive
and transparent. Airborne laser scanning, hyperspectral sensing and
computational power are all advancing rapidly, making it increasingly
feasible to use ITC approaches for effective mapping of forest carbon
density also inside wider carbon mapping programs like REDD++.