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Computing maps of forest structural diversity: Aggregate late

Year: 2025
Type: article
Abstract: Local forest biodiversity hotspots are small areas within a landscape or a single stand. Such hotspots are characterized by a high variability in e.g. tree species composition, size distribution or spatial pattern, in comparison to the surrounding areas. Their identification is of interest, e.g. for conservation. Remote sensing allows quantifying forest structure in terms of different diversity indices at large scales, typically restricted to a fixed spatial resolution. This contradicts the ecological meaning of local diversity, which varies as a function of the measurement scale. When data are instead measured at the single-tree level, the two concepts of scale can be separated. For example, remote sensing allows for single tree detection and, combined with field data, production of a tree pattern of interest as an output. We emphasize and illustrate the importance of distinguishing scales, particularly when producing forest diversity maps. Further, as an alternative to the indices being computed at systematically or randomly selected locations in the landscape, we present diversity indicators defined through individual trees' neighbourhoods. We give examples of species, size and structural diversity maps. We define species, size and structural indices unconditional and conditional on there being a tree at a spatial location and illustrate benefits of conditional indices on simulated data. We also analyse two point patterns from Panama and Finland. We conclude that a tree-level data should not be aggregated to any technical scale before the indicators are computed. Separation of scales also helps produce indicator maps that are comparable across different studies.
DOI: 10.1016/j.ecolind.2025.114046
Language: English
Open Access status: gold
Estimated APC paid: $3,650
OpenAlex ID: W4413690688
FWCI: 0.571
Cites: 41
Cited by: 2
Related to: 10