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Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS)

Abel, Christin; Horion, Stéphanie; Tagesson, Torbern LU ; Brandt, Martin and Fensholt, Rasmus (2019) In Remote Sensing of Environment 224. p.317-332
Abstract

We present a method for remote sensing based monitoring of changes in dryland ecosystem functioning based on the assumption that an altered vegetation rainfall relationship (VRR) indicates changes in vegetation biophysical processes, potentially leading to changes in ecosystem functioning. We describe the VRR through a linear regression between integrated rainfall and vegetation productivity (using NDVI as a proxy) within a combined spatio-temporal window, sequentially moved over the study area and along the temporal axis of a time series. The trend in the slope values derived from such a sequential linear regression, termed SeRGS, thus represents a measure of change in the VRR. Scenarios of land degradation, defined here as a reduction... (More)

We present a method for remote sensing based monitoring of changes in dryland ecosystem functioning based on the assumption that an altered vegetation rainfall relationship (VRR) indicates changes in vegetation biophysical processes, potentially leading to changes in ecosystem functioning. We describe the VRR through a linear regression between integrated rainfall and vegetation productivity (using NDVI as a proxy) within a combined spatio-temporal window, sequentially moved over the study area and along the temporal axis of a time series. The trend in the slope values derived from such a sequential linear regression, termed SeRGS, thus represents a measure of change in the VRR. Scenarios of land degradation, defined here as a reduction in biological productivity, which may be caused by either climatic or anthropogenic factors are simulated for the period 1970–2016 from CRU rainfall and modelled NDVI data to test and evaluate the performance of the SeRGS method in detecting degradation, and compare it against the well-known RESTREND method. We found that SeRGS showed (1) overall more pronounced trends and higher significance levels (p ≤ 0.01) in detecting degradation events and (2) an improved statistical basis for the calculation of trends in the VRR (expressed by high coefficients of determination throughout the period of analysis), which was found to increase the validity of the results produced. Through the implementation of the temporal moving window the effect of inter-annual rainfall variability on vegetation productivity was effectively reduced, thereby enabling a more exact and reliable identification of the timing of degradation events (e.g. start, maximum and end of degradation) by using a time series breakpoint analysis (BFAST). Finally, the SeRGS method was applied using real data for Senegal (seasonally integrated MODIS NDVI and CHIRPS rainfall data 2000–2016) and we discuss patterns and trends. This study provides the theoretical basis for an improved assessment of changes in dryland ecosystem functioning, which is of relevance to land degradation monitoring targeting loss of vegetation productivity.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Changes in ecosystem functioning, Drylands, Land degradation, RESTREND, Sahel, SeRGS, Trend analysis
in
Remote Sensing of Environment
volume
224
pages
16 pages
publisher
Elsevier
external identifiers
  • scopus:85061790582
ISSN
0034-4257
DOI
10.1016/j.rse.2019.02.010
language
English
LU publication?
yes
id
e7418576-badb-4406-9afe-f3b7f19a05e5
date added to LUP
2019-03-01 11:07:12
date last changed
2019-03-27 04:39:33
@article{e7418576-badb-4406-9afe-f3b7f19a05e5,
  abstract     = {<p>We present a method for remote sensing based monitoring of changes in dryland ecosystem functioning based on the assumption that an altered vegetation rainfall relationship (VRR) indicates changes in vegetation biophysical processes, potentially leading to changes in ecosystem functioning. We describe the VRR through a linear regression between integrated rainfall and vegetation productivity (using NDVI as a proxy) within a combined spatio-temporal window, sequentially moved over the study area and along the temporal axis of a time series. The trend in the slope values derived from such a sequential linear regression, termed SeRGS, thus represents a measure of change in the VRR. Scenarios of land degradation, defined here as a reduction in biological productivity, which may be caused by either climatic or anthropogenic factors are simulated for the period 1970–2016 from CRU rainfall and modelled NDVI data to test and evaluate the performance of the SeRGS method in detecting degradation, and compare it against the well-known RESTREND method. We found that SeRGS showed (1) overall more pronounced trends and higher significance levels (p ≤ 0.01) in detecting degradation events and (2) an improved statistical basis for the calculation of trends in the VRR (expressed by high coefficients of determination throughout the period of analysis), which was found to increase the validity of the results produced. Through the implementation of the temporal moving window the effect of inter-annual rainfall variability on vegetation productivity was effectively reduced, thereby enabling a more exact and reliable identification of the timing of degradation events (e.g. start, maximum and end of degradation) by using a time series breakpoint analysis (BFAST). Finally, the SeRGS method was applied using real data for Senegal (seasonally integrated MODIS NDVI and CHIRPS rainfall data 2000–2016) and we discuss patterns and trends. This study provides the theoretical basis for an improved assessment of changes in dryland ecosystem functioning, which is of relevance to land degradation monitoring targeting loss of vegetation productivity.</p>},
  author       = {Abel, Christin and Horion, Stéphanie and Tagesson, Torbern and Brandt, Martin and Fensholt, Rasmus},
  issn         = {0034-4257},
  keyword      = {Changes in ecosystem functioning,Drylands,Land degradation,RESTREND,Sahel,SeRGS,Trend analysis},
  language     = {eng},
  pages        = {317--332},
  publisher    = {Elsevier},
  series       = {Remote Sensing of Environment},
  title        = {Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS)},
  url          = {http://dx.doi.org/10.1016/j.rse.2019.02.010},
  volume       = {224},
  year         = {2019},
}