Scale dependence in remotely sensed biodiversity: Leveraging continental‐scale imaging spectroscopy from the National Ecological Observatory Network
6 affiliations
- 1Department of Ecology & Evolutionary Biology University of Colorado‐Boulder Boulder Colorado USA
- 2Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado‐Boulder Boulder Colorado USA
- 3Department of Forest & Wildlife Ecology University of Wisconsin‐Madison Madison Wisconsin USA
- 4Department of Civil and Environmental Engineering University of California Merced Merced California USA
- 5Department of Ecology & Evolutionary Biology University of California Santa Cruz Santa Cruz California USA
- 6WKID Solutions, LLC Boulder Colorado USA
Abstract
Biodiversity is under threat globally, with significant implications for the ecosystem processes that underpin human well‐being. Effective conservation efforts require scalable, replicable metrics to detect and monitor changes in biodiversity. However, a persistent challenge is deciding on the spatial scale over which to quantify biodiversity—including when using metrics derived from remote sensing—which is inherently scale‐dependent. Understanding the scaling properties of remote sensing metrics is thus important for biodiversity change detection and assessment. We address this challenge by investigating the scale dependence of two remotely sensed vegetation diversity metrics, spectral richness and divergence, across 15 diverse ecosystems that are part of the United States National Ecological Observatory Network (NEON). Our continental‐scale analysis builds on the success of similar studies that have shown scale dependence of spectral richness in select forest ecosystems. Our results corroborate prior findings that show that spectral richness follows well‐established ecological scaling laws by adhering to the sub‐linear scaling expected for species–area and functional diversity area relationships. We compare these scaling relationships to the null expectation of randomly distributed pixel values, demonstrating that empirical scaling relationships are non‐random. Comparing diverse ecosystems using the same data and methods, we show how scaling parameters encode important information on the relative roles of climate, geomorphology, and ecosystem structure on vegetation‐based biodiversity metrics. By advancing our understanding of the scale dependence of remotely sensed biodiversity metrics, this study lays a foundation for leveraging remote sensing data in global biodiversity monitoring and conservation.
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BibTeX
@article{hayden2026scale,
title = {{Scale dependence in remotely sensed biodiversity: Leveraging continental‐scale imaging spectroscopy from the National Ecological Observatory Network}},
author = {Meghan T. Hayden and Matthew W. Rossi and Laura E. Dee and Kyle Kovach and Cibele H. Amaral and Jacob Nesslage and Madeline Slimp and Rachel S. Meyer and E. Natasha Stavros},
journal = {Remote Sensing in Ecology and Conservation},
year = {2026},
month = oct,
doi = {10.1002/rse2.70068},
url = {https://doi.org/10.1002/rse2.70068},
note = {CC BY},
}
RIS
TY - JOUR
TI - Scale dependence in remotely sensed biodiversity: Leveraging continental‐scale imaging spectroscopy from the National Ecological Observatory Network
AU - Meghan T. Hayden
AU - Matthew W. Rossi
AU - Laura E. Dee
AU - Kyle Kovach
AU - Cibele H. Amaral
AU - Jacob Nesslage
AU - Madeline Slimp
AU - Rachel S. Meyer
AU - E. Natasha Stavros
PY - 2026
DA - 2026/10/31
JO - Remote Sensing in Ecology and Conservation
DO - 10.1002/rse2.70068
UR - https://doi.org/10.1002/rse2.70068
AB - Biodiversity is under threat globally, with significant implications for the ecosystem processes that underpin human well‐being. Effective conservation efforts require scalable, replicable metrics to detect and monitor changes in biodiversity. However, a persistent challenge is deciding on the spatial scale over which to quantify biodiversity—including when using metrics derived from remote sensing—which is inherently scale‐dependent. Understanding the scaling properties of remote sensing metrics is thus important for biodiversity change detection and assessment. We address this challenge by investigating the scale dependence of two remotely sensed vegetation diversity metrics, spectral richness and divergence, across 15 diverse ecosystems that are part of the United States National Ecological Observatory Network (NEON). Our continental‐scale analysis builds on the success of similar studies that have shown scale dependence of spectral richness in select forest ecosystems. Our results corroborate prior findings that show that spectral richness follows well‐established ecological scaling laws by adhering to the sub‐linear scaling expected for species–area and functional diversity area relationships. We compare these scaling relationships to the null expectation of randomly distributed pixel values, demonstrating that empirical scaling relationships are non‐random. Comparing diverse ecosystems using the same data and methods, we show how scaling parameters encode important information on the relative roles of climate, geomorphology, and ecosystem structure on vegetation‐based biodiversity metrics. By advancing our understanding of the scale dependence of remotely sensed biodiversity metrics, this study lays a foundation for leveraging remote sensing data in global biodiversity monitoring and conservation.
SN - 2056-3485
ER -