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Identifying potential critical transitions in a forest ecosystem using satellite data

Lamsal, Krishna LU (2017) In Lund University GEM thesis series NGEM01 20171
Dept of Physical Geography and Ecosystem Science
Abstract
Ecosystems can undergo changes that are slow and gradual, or an abrupt change - a sudden nonlinear dynamics that can bring catastrophic changes and ultimately leads to the change in the structure and functioning. Non-linear and discontinuous changes sometimes may cause the system to shift into another undesirable state, called ‘regime shifts’ or ‘critical transition’. Recent model based and simulation studies have identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition or tipping points. However, these studies lack an empirical base and studies in real world ecosystems are largely missing. Therefore, this study attempted to identify potential critical transitions and... (More)
Ecosystems can undergo changes that are slow and gradual, or an abrupt change - a sudden nonlinear dynamics that can bring catastrophic changes and ultimately leads to the change in the structure and functioning. Non-linear and discontinuous changes sometimes may cause the system to shift into another undesirable state, called ‘regime shifts’ or ‘critical transition’. Recent model based and simulation studies have identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition or tipping points. However, these studies lack an empirical base and studies in real world ecosystems are largely missing. Therefore, this study attempted to identify potential critical transitions and tipping points in a Mediterranean type forest ecosystem. In this study, we argue that time series of enhanced vegetation index (EVI) derived from MODIS satellite images can help to identify potential tipping points and critical transitions. The long term increasing trend and changes in the statistical properties of the observed time series of metric-based early warning indicators of critical transition and tipping points - autocorrelation-at-lag-1, standard deviation and skewness are used to identify the potential transitions and tipping points. The study quantified early warning indicators for the Northern Jarrah Forest (NJF) ecosystem, but the strongest signals did not flag any forest that showed any signs of an impending shift. In contrast, it largely identified the areas that were mined in the past and are susceptible to human interference and land use change. Some forest pixels are identified but it did not show any collapse while monitored using imagery from Google Earth at different time. There might be several possible reasons why the results indicated a non-tipping forest. The possible false indication of tipping points could be possibly due to the environmental and climatic variability that might have triggered the rise in indicators to act as a source of false alarm of impending critical transition or tipping point. On the other hand, it could also be that the NJF forest ecosystem is not yet close to tipping points. This study shows that detecting critical transitions and tipping points in real world ecosystems remains challenging and may not be as promising and straightforward as suggested by simulation studies. The gaps in evidences of tipping points in real world examples could be filled by analyzing high resolution and high frequency data, integrating remote sensing with process based approach dynamic vegetation models and validate the results with ground observations. (Less)
Popular Abstract
Ecosystems can undergo changes that are slow and gradual, or an abrupt change - a sudden nonlinear dynamics that can bring catastrophic changes and ultimately leads to the change in the structure and functioning. Non-linear and discontinuous changes sometimes may cause the system to shift into another undesirable state, called ‘regime shifts’ or ‘critical transition’. The critical transitions occur at a point called ‘tipping point’ – defined as a point at which a change becomes significant (i.e. crosses the threshold) that a dynamic system suddenly switches to another state. Tipping points can cause drastic changes in ecosystems in a relatively short time-span. The scientific community has accepted the concepts of critical transition... (More)
Ecosystems can undergo changes that are slow and gradual, or an abrupt change - a sudden nonlinear dynamics that can bring catastrophic changes and ultimately leads to the change in the structure and functioning. Non-linear and discontinuous changes sometimes may cause the system to shift into another undesirable state, called ‘regime shifts’ or ‘critical transition’. The critical transitions occur at a point called ‘tipping point’ – defined as a point at which a change becomes significant (i.e. crosses the threshold) that a dynamic system suddenly switches to another state. Tipping points can cause drastic changes in ecosystems in a relatively short time-span. The scientific community has accepted the concepts of critical transition theory and methods to detect early warning signals of impending regime shifts or critical transition have been developed. These were focused on spatial and temporal indicators, such as autocorrelation-at-lag-1, skewedness and variance. However, most of these studies were based on theoretical and mathematical models, and lack an empirical base. Therefore, this study attempted to identify potential critical transitions and tipping points in a real-world example by using time series of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images.

The research attempted to identify whether the areas with high levels of early warning indicators are undergoing critical transitions, and analyze if generic early warning indicators of tipping points - autocorrelation-at-lag-1, variance, and skewness increase before a critical transition in a forest ecosystem. In this study, we argue that time series of enhanced vegetation index derived from MODIS satellite images can help to identify potential tipping points and critical transitions. The long term increasing trend and changes in the statistical properties of the observed time series of metric-based early warning indicators of critical transition and tipping points - autocorrelation-at-lag-1, standard deviation and skewedness - are used to identify the potential transitions and tipping points.

This study was done in Northern Jarrah Forest (NJF) located in the Southwestern Botanical Province or bioregion of Western Australia, directly east of Perth. In this study, MOD13A1 Version 6 product for vegetation index and the MCD12Q1 land cover data were used. MOD13A1 data was used to prepare and extract the time series of ecosystem variable (EVI) and test early warning indicators of tipping points. MCD12Q1 Land cover data was utilized solely to extract area of interest (AOI). The extracted time series of EVI was used to test the early warning of critical transitions in the time series. All the operations were done in R using the functionalities of raster, rts, MODIStsp and earlywarning packages.

The study quantified early warning indicators for the Northern Jarrah Forest (NJF) ecosystem, but the strongest signals did not flag any forest that showed any signs of an impending shift. In contrast, it largely identified the areas that were mined in the past and are susceptible to human interference and land use change. Some forest pixels are identified but it did not show any collapse while monitored using imagery from Google Earth at different time. There might be several possible reasons why the results indicated a non-tipping forest. The possible false indication of tipping points was found possibly due to the environmental and climatic variability that might have triggered the rise in indicators to act as a source of false alarm of impending critical transition or tipping point, but alternatively, it could also be that NJF forest ecosystem is not yet close to tipping points i.e. forest is still resilient. The results show that detecting critical transitions and tipping points in real-world ecosystems may not be as promising and straightforward as suggested by model simulations. Future studies should focus on filling the gaps in evidences of tipping points in real world examples by analyzing high resolution and high frequency data, integrating remote sensing with process based approach dynamic vegetation models and validate the results with ground observations. (Less)
Please use this url to cite or link to this publication:
author
Lamsal, Krishna LU
supervisor
organization
course
NGEM01 20171
year
type
H2 - Master's Degree (Two Years)
subject
keywords
early warning, critical transition, tipping point, EVI, time series, GEM
publication/series
Lund University GEM thesis series
report number
19
funder
Erasmus Mundus Programme
language
English
additional info
External supervisor: Assistant Professor, Thomas A. Groen, University of Twente
id
8914552
date added to LUP
2017-06-13 12:33:11
date last changed
2017-06-13 12:33:11
@misc{8914552,
  abstract     = {Ecosystems can undergo changes that are slow and gradual, or an abrupt change - a sudden nonlinear dynamics that can bring catastrophic changes and ultimately leads to the change in the structure and functioning. Non-linear and discontinuous changes sometimes may cause the system to shift into another undesirable state, called ‘regime shifts’ or ‘critical transition’. Recent model based and simulation studies have identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition or tipping points. However, these studies lack an empirical base and studies in real world ecosystems are largely missing. Therefore, this study attempted to identify potential critical transitions and tipping points in a Mediterranean type forest ecosystem. In this study, we argue that time series of enhanced vegetation index (EVI) derived from MODIS satellite images can help to identify potential tipping points and critical transitions. The long term increasing trend and changes in the statistical properties of the observed time series of metric-based early warning indicators of critical transition and tipping points - autocorrelation-at-lag-1, standard deviation and skewness are used to identify the potential transitions and tipping points. The study quantified early warning indicators for the Northern Jarrah Forest (NJF) ecosystem, but the strongest signals did not flag any forest that showed any signs of an impending shift. In contrast, it largely identified the areas that were mined in the past and are susceptible to human interference and land use change. Some forest pixels are identified but it did not show any collapse while monitored using imagery from Google Earth at different time. There might be several possible reasons why the results indicated a non-tipping forest. The possible false indication of tipping points could be possibly due to the environmental and climatic variability that might have triggered the rise in indicators to act as a source of false alarm of impending critical transition or tipping point. On the other hand, it could also be that the NJF forest ecosystem is not yet close to tipping points. This study shows that detecting critical transitions and tipping points in real world ecosystems remains challenging and may not be as promising and straightforward as suggested by simulation studies. The gaps in evidences of tipping points in real world examples could be filled by analyzing high resolution and high frequency data, integrating remote sensing with process based approach dynamic vegetation models and validate the results with ground observations.},
  author       = {Lamsal, Krishna},
  keyword      = {early warning,critical transition,tipping point,EVI,time series,GEM},
  language     = {eng},
  note         = {Student Paper},
  series       = {Lund University GEM thesis series},
  title        = {Identifying potential critical transitions in a forest ecosystem using satellite data},
  year         = {2017},
}