Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring

Evan R Muise; Margaret E Andrew; Nicholas C Coops; Txomin Hermosilla; A. Cole Burton; Stephen S Ban

doi:10.1038/s41598-024-64615-2

Back

Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring

Journal article

Open access

Peer reviewed

Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring

Evan R Muise, Margaret E Andrew, Nicholas C Coops, Txomin Hermosilla, A. Cole Burton and Stephen S Ban

Scientific reports, Vol.14(1), 13717

2024

DOI: https://doi.org/10.1038/s41598-024-64615-2

PMCID: PMC11178816

PMID: 38877188

Files and links (1)

pdf

Published3.69 MBDownload View

Published (Version of Record)CC BY V4.0, Open Access

Abstract

Biodiversity

Community composition

Complementarity

Coniferous trees

Ecological function

Ecosystem structure

Ecosystems

Environmental gradient

Forest ecosystems

Forests

Lidar

Mixed forests

Monitoring systems

Population genetics

Productivity

Remote sensing

Remote sensing systems

Structure-function relationships

Terrestrial ecosystems

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

Details

Title: Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring
Authors/Creators: Evan R Muise - University of British Columbia
Margaret E Andrew - Murdoch University
Nicholas C Coops - University of British Columbia
Txomin Hermosilla - Natural Resources Canada
A. Cole Burton - University of British Columbia
Stephen S Ban - Ministry of Environment
Publication Details: Scientific reports, Vol.14(1), 13717
Publisher: Nature Publishing Group
Identifiers: 991005675725407891
Murdoch Affiliation: Centre for Terrestrial Ecosystem Science and Sustainability; School of Environmental and Conservation Sciences
Language: English
Resource Type: Journal article

Metrics

22 File views/ downloads

37 Record Views

See more details