Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region
"> Figure 1
<p>Overview of the study area. The Sundarbans region is covered by six Landsat scenes (blue squares). Red squares indicate the areas from which non-mangrove training data was selected, where each square has an area of approximately 3000 km<sup>2</sup>. Mangrove training data was taken from the Global Mangrove Watch (GMW) 2010 classification.</p> "> Figure 2
<p>Overview of processing steps.</p> "> Figure 3
<p>Area covered by mangroves over time, based on the final yearly classifications. Extent for 1988 and 1989 is represented by a dotted red line because the classification of mangroves in these years was considered less reliable due to a combination of the extreme weather experienced by Bangladesh in 1988 and spin-up effects of the modelling approach.</p> "> Figure 4
<p>Left: A false colour Landsat 5 image from February 2010 where R = NIR, G = SWIR1, and B = Red. Right: Comparison of CCDC classification vs. GMW classification for 2010 showing additional mangroves captured by CCDC.</p> "> Figure 5
<p>Examples of loss and gain of mangroves between the 1988 and 2017 classification maps. The top row shows a false colour RGB image where R = NIR, G = SWIR1, and B = Red. The bottom row shows pixels classified as mangrove by CCDC in green. Location A shows a loss of mangroves while location B shows formation of new land with mangroves establishing.</p> "> Figure 6
<p>Example NDVI trajectories for four pixels where CCDC recorded a break in the 2 months after Sidr made landfall. (<b>A</b>) Pixel that did not recover from Sidr and was classified as water once a new model had been initialized; (<b>B</b>) Pixel that was significantly damaged by Sidr but recovered and was still classified as mangrove once a new model had been initialized; (<b>C</b>) Pixel showing minor damage from Sidr; (<b>D</b>) Pixel showing a possible area of new land formation where an establishing trend in NDVI can be seen. Once established the pixel remains classified as mangrove even after a high magnitude disturbance. M = Classified as mangroves, W = classified as water.</p> "> Figure 7
<p>Overall NDVI trend from January 1988 to June 2018 for all pixels classified as mangrove at some point (union of all classifications). Stretched to <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>2</mn> <mi>σ</mi> </mrow> </semantics></math> to better show trend distribution.</p> "> Figure 8
<p>Maps showing (<b>A</b>) distribution of NDVI break magnitude recorded by CCDC and (<b>B</b>) distribution of year recovered from Cyclone Sidr in terms of NDVI trend. NYR = Not Yet Recovered.</p> "> Figure 9
<p>Box plot of break size vs. year of recovery (left axis) and cumulative recovery over time (right axis). NYR = Not Yet Recovered.</p> "> Figure A1
<p>Plot showing gaps in the estimation of number of pixels damaged by Cyclone Sidr caused by the Landsat 7 Scan Line Corrector failure. While the CCDC algorithm uses temporal modelling to interpolate between observations, the change detection process itself relies on real observations being available which can be compared to the fitted model.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data and Pre-Processing
2.3. Classification of Mangroves Using CCDC
2.3.1. The CCDC Algorithm
2.3.2. Generation of Yearly Classification Maps
Model Classification
Yearly Maps
Validation
2.4. Long Term Vegetation Trends in the Sundarbans
2.5. Investigation of Dynamics around Cyclone Sidr
3. Results
3.1. Classification of Mangroves Using CCDC
3.2. Long Term Vegetation Trends in the Sundarbans
3.3. Investigation of Dynamics around Cyclone Sidr
4. Discussion
4.1. Classification of Mangroves Using CCDC
4.2. Long Term Vegetation Trends in the Sundarbans
4.3. Investigation of Dynamics around Cyclone Sidr
4.3.1. Comparison to Previous Damage Estimates
4.3.2. Impact of Sidr on Mangrove Extent
4.3.3. Estimation of Recovery from Sidr
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ARCSI | Atmospheric and Radiometric Correction of Satellite Imagery |
ASTER | Advanced Spaceborne Thermal Emission and Reflection Radiometer |
CCDC | Continuous Change Detection and Classification |
Fmask | Function of mask |
GDAL | Geospatial Data Abstraction Library |
NetCDF | Network Common Data Form |
NIR | Near-infrared |
ODC | Open Data Cube |
SAR | Synthetic Aperture Radar |
SCW | Super Computing Wales |
SLC | Scan Line Corrector |
SWIR | Short Wave Infrared |
UNESCO | United Nations Educational, Scientific and Cultural Organization |
USGS | United States Geological Survey |
Appendix A
Year | Change | Net Loss | Net Gain | Total |
---|---|---|---|---|
1988 | - | - | - | 6368.3 |
1989 | 39.9 | 86.7 | 126.6 | 6408.2 |
1990 | 171.8 | 19.8 | 191.6 | 6580.0 |
1991 | 32.6 | 11.9 | 44.5 | 6612.6 |
1992 | 15.2 | 5.6 | 20.8 | 6627.8 |
1993 | 7.7 | 3.7 | 11.4 | 6635.5 |
1994 | 2.9 | 4.5 | 7.4 | 6638.4 |
1995 | 10.7 | 4.7 | 15.4 | 6649.1 |
1996 | 2.0 | 5.7 | 7.7 | 6651.1 |
1997 | 5.0 | 7.0 | 12.0 | 6656.1 |
1998 | 4.4 | 6.7 | 11.1 | 6660.5 |
1999 | 2.9 | 7.0 | 9.9 | 6663.4 |
2000 | 2.5 | 6.5 | 9.0 | 6665.9 |
2001 | 0.4 | 10.4 | 10.8 | 6666.3 |
2002 | −0.7 | 12.3 | 11.6 | 6665.6 |
2003 | 1.5 | 12.9 | 14.4 | 6667.1 |
2004 | 5.5 | 11.9 | 17.4 | 6672.6 |
2005 | −3.5 | 12.4 | 8.9 | 6669.1 |
2006 | −3.0 | 10.6 | 7.6 | 6666.1 |
2007 | −90.1 | 105.0 | 14.9 | 6576.0 |
2008 | 82.5 | 21.7 | 104.2 | 6658.5 |
2009 | −2.5 | 17.4 | 14.9 | 6656.0 |
2010 | 9.2 | 9.6 | 18.8 | 6665.2 |
2011 | −2.0 | 6.4 | 4.4 | 6663.2 |
2012 | −3.2 | 7.0 | 3.8 | 6660.0 |
2013 | −3.8 | 9.6 | 5.8 | 6656.2 |
2014 | −7.1 | 14.1 | 7.0 | 6649.1 |
2015 | −3.4 | 10.0 | 6.6 | 6645.7 |
2016 | −40.6 | 43.8 | 3.2 | 6605.1 |
2017 | 12.5 | 10.7 | 23.2 | 6617.6 |
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Reference | ||||||
---|---|---|---|---|---|---|
Mangrove | Water | Other | Total | User’s (%) | ||
Classifier | Mangrove | 11,835 | 531 | 219 | 12,585 | 94.0 |
Water | 174 | 22,422 | 367 | 22,963 | 97.6 | |
Other | 684 | 609 | 8599 | 9892 | 86.9 | |
Total | 12,693 | 23,562 | 9185 | 45,440 | ||
Producer’s (%) | 93.2 | 95.2 | 93.6 | 94.5 |
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Awty-Carroll, K.; Bunting, P.; Hardy, A.; Bell, G. Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region. Remote Sens. 2019, 11, 2833. https://doi.org/10.3390/rs11232833
Awty-Carroll K, Bunting P, Hardy A, Bell G. Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region. Remote Sensing. 2019; 11(23):2833. https://doi.org/10.3390/rs11232833
Chicago/Turabian StyleAwty-Carroll, Katie, Pete Bunting, Andy Hardy, and Gemma Bell. 2019. "Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region" Remote Sensing 11, no. 23: 2833. https://doi.org/10.3390/rs11232833