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An Empirical Bayesian Approach to Quantify Multi-Scale Spatial Structural Diversity in Remote Sensing Data

Schuh, Leila A; Santos, Maria J; Schaepman, Michael E; Furrer, Reinhard (2022). An Empirical Bayesian Approach to Quantify Multi-Scale Spatial Structural Diversity in Remote Sensing Data. Remote Sensing, 15(1):14.

Abstract

Landscape structure is as much a driver as a product of environmental and biological interactions and it manifests as scale-specific, but also as multi-scale patterns. Multi-scale structure affects processes on smaller and larger scales and its detection requires information from different scales to be combined. Herein, we propose a novel method to quantify multi-scale spatial structural diversity in continuous remote sensing data. We combined information from different extents with an empirical Bayesian model and we applied a new entropy metric and a value co-occurrence approach to capture heterogeneity. We tested this method on Normalized Difference Vegetation Index data in northern Eurasia and on simulated data and we also tested the effect of coarser pixel resolution. We find that multi-scale structural diversity can reveal itself as patches and linear landscape features, which persist or become apparent across spatial scales. Multi-scale line features reveal the transition zones between spatial regimes and multi-scale patches reveal those areas within transition zones where values are most different from each other. Additionally, spatial regimes themselves can be distinguished. We also find the choice of scale need not be informed by typical length-scales, which makes the method easy to implement. The proposed multi-scale approach can be applied to other contexts, following the roadmap we pave out in this study and using the tools available in the accompanying R package StrucDiv.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
07 Faculty of Science > Institute of Mathematics
07 Faculty of Science > Institute of Theoretical Astrophysics and Cosmology
08 Research Priority Programs > Global Change and Biodiversity
Dewey Decimal Classification:910 Geography & travel
510 Mathematics
Scopus Subject Areas:Physical Sciences > General Earth and Planetary Sciences
Uncontrolled Keywords:General Earth and Planetary Sciences multi-scale landscape features; spatial structural diversity; Empirical Bayesian model; structural diversity entropy; landscape patterns
Language:English
Date:21 December 2022
Deposited On:10 Feb 2023 11:49
Last Modified:28 Jan 2025 02:42
Publisher:MDPI Publishing
ISSN:2072-4292
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3390/rs15010014
Project Information:
  • Funder: University Research Priority Program on Global Change and Biodiversity of the University of Zurich
  • Grant ID:
  • Project Title:
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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