  This paper presents a distributed approach that scales up to segment tree
crowns within a LiDAR point cloud representing an arbitrarily large forested
area. The approach uses a single-processor tree segmentation algorithm as a
building block in order to process the data delivered in the shape of tiles in
parallel. The distributed processing is performed in a master-slave manner, in
which the master maintains the global map of the tiles and coordinates the
slaves that segment tree crowns within and across the boundaries of the tiles.
A minimal bias was introduced to the number of detected trees because of trees
lying across the tile boundaries, which was quantified and adjusted for.
Theoretical and experimental analyses of the runtime of the approach revealed a
near linear speedup. The estimated number of trees categorized by crown class
and the associated error margins as well as the height distribution of the
detected trees aligned well with field estimations, verifying that the
distributed approach works correctly. The approach enables providing
information of individual tree locations and point cloud segments for a
forest-level area in a timely manner, which can be used to create detailed
remotely sensed forest inventories. Although the approach was presented for
tree segmentation within LiDAR point clouds, the idea can also be generalized
to scale up processing other big spatial datasets.
  Highlights: - A scalable distributed approach for tree segmentation was
developed and theoretically analyzed. - ~2 million trees in a 7440 ha forest
was segmented in 2.5 hours using 192 cores. - 2% false positive trees were
identified as a result of the distributed run. - The approach can be used to
scale up processing other big spatial data
