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Improved characterization of dryland degradation using trends in vegetation/ rainfall sequential linear regression (SERGS-TREND)

Abel, Christin ; Brandt, Martin ; Tagesson, Torbern LU and Fensholt, Rasmus (2018) 2018-July. p.2988-2991
Abstract

Land degradation in drylands has been investigated extensively over recent decades and several remote sensing based techniques attempt to decouple the human influence from the natural climate variability, but are contested in literature. We introduce a novel approach termed SeRGS-TREND that is designed to monitor land degradation by suppressing the impact from climate variability and highlight vegetation disturbances may it be human or climate-induced. SeRGS-TREND is based on the interpretation of the slope of a linear regression analysis within a sequentially moving window along the temporal axis of the time series of remote sensing data. The use of a moving window increases the probability of a statistically significant linear... (More)

Land degradation in drylands has been investigated extensively over recent decades and several remote sensing based techniques attempt to decouple the human influence from the natural climate variability, but are contested in literature. We introduce a novel approach termed SeRGS-TREND that is designed to monitor land degradation by suppressing the impact from climate variability and highlight vegetation disturbances may it be human or climate-induced. SeRGS-TREND is based on the interpretation of the slope of a linear regression analysis within a sequentially moving window along the temporal axis of the time series of remote sensing data. The use of a moving window increases the probability of a statistically significant linear vegetation-rainfall relationship (VRR), which in turn provides an improved statistical basis for the results produced and thereby confidence in the assessment of degradation. We test and compare SeRGS-TREND and the commonly used RESTREND by simulating different degradation scenarios and find that SeRGS reveals both, more significant and more exact information about degradation events (e.g. starting and end point) while keeping the VRR correlation coefficients high, thus rendering results more reliable.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Drylands, Land degradation, SeRGS-TREND, Time series analysis
host publication
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
volume
2018-July
article number
8518816
pages
4 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85064169378
ISBN
978-1-5386-7149-8
9781538671504
DOI
10.1109/IGARSS.2018.8518816
language
English
LU publication?
yes
id
14188260-be6c-4d49-99b6-8269e77f6b32
date added to LUP
2019-05-09 14:58:15
date last changed
2019-05-14 04:59:42
@inproceedings{14188260-be6c-4d49-99b6-8269e77f6b32,
  abstract     = {<p>Land degradation in drylands has been investigated extensively over recent decades and several remote sensing based techniques attempt to decouple the human influence from the natural climate variability, but are contested in literature. We introduce a novel approach termed SeRGS-TREND that is designed to monitor land degradation by suppressing the impact from climate variability and highlight vegetation disturbances may it be human or climate-induced. SeRGS-TREND is based on the interpretation of the slope of a linear regression analysis within a sequentially moving window along the temporal axis of the time series of remote sensing data. The use of a moving window increases the probability of a statistically significant linear vegetation-rainfall relationship (VRR), which in turn provides an improved statistical basis for the results produced and thereby confidence in the assessment of degradation. We test and compare SeRGS-TREND and the commonly used RESTREND by simulating different degradation scenarios and find that SeRGS reveals both, more significant and more exact information about degradation events (e.g. starting and end point) while keeping the VRR correlation coefficients high, thus rendering results more reliable.</p>},
  author       = {Abel, Christin and Brandt, Martin and Tagesson, Torbern and Fensholt, Rasmus},
  booktitle    = {2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings},
  isbn         = {978-1-5386-7149-8 },
  language     = {eng},
  pages        = {2988--2991},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Improved characterization of dryland degradation using trends in vegetation/ rainfall sequential linear regression (SERGS-TREND)},
  url          = {http://dx.doi.org/10.1109/IGARSS.2018.8518816},
  doi          = {10.1109/IGARSS.2018.8518816},
  volume       = {2018-July},
  year         = {2018},
}