Evidence map›Paper›PMID 30174356›Full record

ArticleJournal of arid environments2018

Albedo estimated from remote sensing correlates with ecosystem multifunctionality in global drylands.

Yanchuang Zhao, Xinyuan Wang, Carlos J Novillo, Patricia Arrogante-Funes, René Vázquez-Jiménez, Fernando T Maestre

Abstract read
In one paragraph

Article in Journal of arid environments, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
  2. Article
  3. Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment.International journal of environmental research and public health · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 3 countries.

Yanchuang ZhaoKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 100094 Beijing, China.
Xinyuan WangKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 100094 Beijing, China.
Carlos J NovilloDepartamento de Tecnología Química y Ambiental, Universidad Rey Juan Carlos, C/ Tulipán s/n, 28933 Móstoles, Spain.
Patricia Arrogante-FunesDepartamento de Tecnología Química y Ambiental, Universidad Rey Juan Carlos, C/ Tulipán s/n, 28933 Móstoles, Spain.
René Vázquez-JiménezCuerpo Académico UAGro CA-93 Riesgos Naturales y Geotecnología, Universidad Autónoma de Guerrero, Av/Lázaro Cárdenas s/n, CU, Chilpancingo, Guerrero 39070, México.
Fernando T MaestreDepartamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, C/ Tulipán s/n, 28933 Móstoles, Spain.
Universidad Rey Juan Carlos · ESChinese Academy of Sciences · CNUniversidad Autónoma de Guerrero · MX

Funding

European Research Council 242658European Research Council 647038
6 · The paper itself

Abstract

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.

Indexed as

desertification monitoringecosystem functionsshortwave domainsoil nutrient cycling

Identifiers

PMID30174356
PMCPMC6112417
OpenAlexW2808884936

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.