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Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity

Authors :
Lingling Liu
Jeffrey R. Smith
Amanda H. Armstrong
Domingo Alcaraz-Segura
Howard E. Epstein
Alejandra Echeverri
Kelley E. Langhans
Rafael J. P. Schmitt
Rebecca Chaplin-Kramer
Source :
Remote Sensing, Vol 15, Iss 23, p 5593 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Satellite-derived Ecosystem Functional Types (EFTs) are increasingly used in ecology and conservation to characterize ecosystem heterogeneity. The diversity of EFTs, also known as Ecosystem Functional Diversity (EFD), has been suggested both as a potential metric of ecosystem-level biodiversity and as a predictor for ecosystem functioning, ecosystem services, and resilience. However, the impact of key methodological choices on patterns of EFTs and EFD have not been formally assessed. Using Costa Rica as a study system, we compared EFTs and EFD, derived from MODIS and Landsat data using different methodological assumptions, at both national and local extents. Our results showed that the regional spatial patterns of EFTs and EFD derived from 250 m MODIS and 30 m Landsat are notably different. The selection of sensors for deriving EFTs and EFD is dependent on the study area, data quality, and the research objective. Given its finer spatial resolution, Landsat has greater capacity to differentiate more EFTs than MODIS, though MODIS could be a better choice in frequently cloudy areas due to its shorter revisiting time. We also found that the selection of spatial extent used to derive EFD is critical, as smaller extents (e.g., at a local rather than a national scale) can show much higher diversity. However, diversity levels derived at smaller extents appear to be nested within the diversity levels derived at larger extents. As EFTs and EFD continue to develop as a tool for ecosystem ecology, we highlight the important methodological choices to ensure that these metrics best fit research objectives.

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
23
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.55cfaa2096a44ee59b36588af8928555
Document Type :
article
Full Text :
https://doi.org/10.3390/rs15235593