# Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Sites

**T**he water level in Dorge Sangi has been influenced by the drastic changes in the water level of Lake Urmia, located in the northern border of the study area. Decreases in the extent of Lake Urmia to about 10% of its original size in the last decade have been reported [19,20] as a critical transition toward an alternative state—a salt pan—which is similar to the catastrophic death of the Aral Sea [21]. The variation of the water level in Lake Urmia, derived from Satellite Radar Altimetry data of Jason-3, Jason-1 and TOPEX/P [22] (Figure 2A), shows that this ecosystem dried completely around 2010, and water level fluctuations were lost afterward. The water body in Dorge Sangi displayed serious reduction in both water level and hydroperiods starting in 2003 [23]. The reduction hits a peak in 2006 and lasted until 2013. These changes were the result of regional water resource development plans. These water level reductions were intensified by an increase in the frequency and severity of droughts. To demonstrate the regional decrease in precipitation, Figure 2B displays the standard precipitation index (SPI). The SPI is a meteorological drought index, recommended by the World Meteorological Organization and measures normalized anomalies in precipitation [24].

#### 2.2. Remote Sensing Data

#### 2.2.1. MODIS Data

#### 2.2.2. Spectral Indices

#### 2.3. Leading Indicators for Critical Transition

#### 2.3.1. Metric-Based Indicators

#### 2.3.2. Model-Based Indicators

## 3. Results

#### 3.1. Detecting Critical Transition

#### 3.1.1. Metric-Based Indicators

#### 3.1.2. Model-Based Indicators

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Location of the two case studies: (

**A**) Lake Arpi in Armenia in August 2014; (

**B**) The Dorge Sangi wetland in Iran; the two photos represent the condition of the study area in the spring time of the years 2001 and 2013.

**Figure 2.**Urmia Lake height variation (in meters) for years 2001–2014 based on satellite radar altimetry data (

**A**); Standard Precipitation Index (SPI) for Dorge Sangi during, years 2001–2013, based on measurements from Naghadeh, which is the nearest weather station to Dorge Sangi (

**B**).

**Figure 3.**Mean spectra in Dorge Sangi wetland (

**A**) and Arpi wetland (

**B**) from Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite images (MOD9A1 product) of the five spectral bands (bands 1, 2, 4, 5, and 6) that have been used to calculate the spectral indices in this study. Bars show the standard errors over different pixels in the study area. Different colors show the profiles for two different seasons at the beginning and end of the time series.

**Figure 4.**The time series of four remotely sensed indices, Normalized Difference Vegetation Index (NDVI), Modified Normalized Water Index (MNDWI), Vegetation-Water Ratio (VWR), and Modified Vegetation-Water Ratio (MVWR) used as the variables in (

**A**) Dorge Sangi study site, and (

**B**) Arpi study site. The red line indicates the trend obtained using a moving average with a window size of 3 time steps.

**Figure 5.**Sensitivity analysis of metric-based indicators to rolling window size given the first-differential filter for different spectral indices (columns). First, second, and third rows show autocorrelation at lag-1, standard deviation, and skewness, respectively for the Dorge Sangi (

**A**), and Arpi (

**B**) study sites.

**Figure 6.**Metric-based leading indicators based on NDVI, MNDWI, VWR, and MVWR for Dorge Sangi (

**A**), and Arpi (

**B**) wetlands. The first, second, and third rows show autocorrelation at lag 1 (acf), standard deviation (SD), and skewness of each spectral indices, respectively; the red dotted lines mark the width of the rolling window used to detrend the data; the blue dotted lines mark the moment the trend changes abruptly which could be indicative of a tipping point.

**Figure 7.**Nonparametric drift diffusion jump model of the monitored spectral indices NDVI, MNDWI, VWR, and MVWR versus time in the study sites Dorge Sangi (

**A**), and Arpi (

**B**).

Spectral Indices | Indicators | ||
---|---|---|---|

Autocorrelation | Standard Deviation | Skewness | |

NDVI | 0.296 *|−0.293 *** | −0.514 ***|−0.389 *** | 0.439 *|−0.409 *** |

MNDWI | 0.397 ***|−0.268 *** | −0.782 ***|−0.499 *** | 0.127 ***|0.275 |

VWR | 0.152|0.019 | −0.523 ***|0.698 *** | 0.145|−0.633 |

MVWR | 0.850 ***|−0.225 *** | 0.406 ***|0.285 *** | 0.754 ***|−0.025 *** |

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**MDPI and ACS Style**

Alibakhshi, S.; Groen, T.A.; Rautiainen, M.; Naimi, B.
Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem. *Remote Sens.* **2017**, *9*, 352.
https://doi.org/10.3390/rs9040352

**AMA Style**

Alibakhshi S, Groen TA, Rautiainen M, Naimi B.
Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem. *Remote Sensing*. 2017; 9(4):352.
https://doi.org/10.3390/rs9040352

**Chicago/Turabian Style**

Alibakhshi, Sara, Thomas A. Groen, Miina Rautiainen, and Babak Naimi.
2017. "Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem" *Remote Sensing* 9, no. 4: 352.
https://doi.org/10.3390/rs9040352