Review of studies on tree species classification from remotely sensed data

It is recommended that future research efforts focus stronger on the causal understanding of why tree species classification approaches work under certain conditions or – maybe even more important - why they do not work in other cases as this might require more complex field acquisitions than those typically used in the reviewed studies.

Abstract

"Spatially explicit information on tree species composition of managed and natural forests, plantations and urban
\nvegetation provides valuable information for nature conservationists as well as for forest and urban managers
\nand is frequently required over large spatial extents. Over the last four decades, advances in remote sensing technology
\nhave enabled the classification of tree species from several sensor types.
\nWhile studies using remote sensing data to classify and map tree species reach back several decades, a recent review
\non the status, potentials, challenges and outlooks in this realm is missing. Here, we search for major trends
\nin remote sensing techniques for tree species classification and discuss the effectiveness of different sensors and
\nalgorithms based on a literature review.
\nThis review demonstrates that the number of studies focusing on tree species classification has increased constantly
\nover the last four decades and promising local scale approaches have been presented for several sensor
\ntypes. However, there are few examples for tree species classifications over large geographic extents, and bridging
\nthe gap between current approaches and tree species inventories over large geographic extents is still one of
\nthe biggest challenges of this research field. Furthermore, we found only few studies which systematically described
\nand examined the traits that drive the observed variance in the remote sensing signal and thereby enable
\nor hamper species classifications. Most studies followed data-driven approaches and pursued an optimization of
\nclassification accuracy, while a concrete hypothesis or a targeted application was missing in all but a few exceptional
\nstudies.
\nWe recommend that future research efforts focus stronger on the causal understanding of why tree species classification
\napproacheswork under certain conditions or – maybe even more important -why they do not work in
\nother cases. This might require more complex field acquisitions than those typically used in the reviewed studies.
\nAt the same time, we recommend reducing the number of purely data-driven studies and algorithmbenchmarking
\nstudies as these studies are of limited value, especially if the experimental design is limited, e.g.
\nthe tree population is not representative and only a few sensors or acquisition settings are simultaneously
\ninvestigated.