Dataset Information

Quantifying large-scale ecosystem stability with remote sensing data.

ABSTRACT: To fully understand ecosystem functioning under global change, we need to be able to measure the stability of ecosystem functioning at multiple spatial scales. Although a number of stability components have been established at small spatial scales, there has been little progress in scaling these measures up to the landscape. Remote sensing data holds huge potential for studying processes at landscape scales but requires quantitative measures that are comparable from experimental field data to satellite remote sensing. Here we present a methodology to extract four components of ecosystem functioning stability from satellite-derived time series of Enhanced Vegetation Index (EVI) data. The four stability components are as follows: variability, resistance, recovery time and recovery rate in ecosystem functioning. We apply our method to the island of Ireland to demonstrate the use of remotely sensed data to identify large disturbance events in productivity. Our method uses stability measures that have been established at the field-plot scale to quantify the stability of ecosystem functioning. This makes our method consistent with previous small-scale stability research, whilst dealing with the unique challenges of using remotely sensed data including noise. We encourage the use of remotely-sensed data in assessing the stability of ecosystems at a scale that is relevant to conservation and management practices.

SUBMITTER: White HJ

PROVIDER: S-EPMC7582121 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Quantifying large-scale ecosystem stability with remote sensing data.

White Hannah J HJ Gaul Willson W Sadykova Dinara D León-Sánchez Lupe L Caplat Paul P Emmerson Mark C MC Yearsley Jon M JM

Remote sensing in ecology and conservation 20200401 3

To fully understand ecosystem functioning under global change, we need to be able to measure the stability of ecosystem functioning at multiple spatial scales. Although a number of stability components have been established at small spatial scales, there has been little progress in scaling these measures up to the landscape. Remote sensing data holds huge potential for studying processes at landscape scales but requires quantitative measures that are comparable from experimental field data to sa ...[more]

PMID: 33133633

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Project description:Monitoring ecosystem functioning is a significant step towards detecting changes in ecosystem attributes that could be linked to land degradation and desertification in drylands worldwide. Remote sensing-based vegetation indices (VIs) and land surface albedo are two favorite indicators to monitor desertification process due to their close relationship with ecosystem status and to their increasing applicability over multiple spatiotemporal scales. While VIs are routinely used to monitor ecosystem attributes and functions such as vegetation cover and productivity, no previous study has evaluated whether remote sensing-measured albedo is related to the simultaneous provision of multiple ecosystem functions (multifunctionality) in global drylands. In this study, we evaluated the correlation of six albedo metrics (shortwave black-sky albedo, shortwave white-sky albedo, visible black-sky albedo, visible white-sky albedo, near-infrared black-sky albedo and near-infrared white-sky albedo) and two VIs (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) with multifunctionality indices related to carbon, nitrogen and phosphorus cycling measured in 61 dryland ecosystems from all continents except Antarctica. We found a negative relationship between land surface albedo and multifunctionality. Black-sky albedo had a stronger correlation with multifunctionality than white-sky albedo. Visible black-sky albedo showed the strongest correlation with multifunctionality (MUL, -0.314), as well as with functions related to carbon (CCY, -0.216) and nitrogen cycling (NCY, -0.410), while near-infrared (-0.339) and shortwave black-sky albedo (-0.325) showed stronger correlations with functions related to phosphorus cycling (PCY) than visible black-sky albedo (-0.233) did. VIs showed significant positive correlations with MUL, CCY, and NCY, and the magnitudes were higher than those observed between albedo metrics and the multifunctionality indices. However, VIs were not correlated with PCY, which had significant correlations with both shortwave and near-infrared albedo. Though the magnitudes of the correlations observed were not high, which may result from the wide variability in soil and vegetation types in our dataset, our findings indicate that remotely sensed albedo correlates to multifunctionality, which has been linked to alternative states in global drylands. As such, albedo has the potential to monitor changes in dryland ecosystem functioning, which can inform us about the onset of desertification in these areas.

Dataset Information

Quantifying large-scale ecosystem stability with remote sensing data.

Publications

Quantifying large-scale ecosystem stability with remote sensing data.

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