Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series
"> Figure 1
<p>LULC maps of the study areas (<b>a</b>,<b>b</b>). In Fuente de Piedra (<b>a</b>), the classification has been performed using photointerpretation and field inventories [<a href="#B20-remotesensing-08-00795" class="html-bibr">20</a>]. The classification in Camargue (<b>b</b>) is a product of the project SWOS, elaborated by Tour du Valat using Landsat images for 2015 and field inventories [<a href="#B21-remotesensing-08-00795" class="html-bibr">21</a>]. In order to make both classification compatible and for the sake of simplification, some classes have been merged, e.g., the class “wetlands” includes marshlands, temporal water bodies and salt marshes; “open spaces” includes areas with little vegetation, dunes and some pastures; “urban” includes all sorts of pavement or concrete; “forests” includes coniferous, as well as broad-leaved forests.</p> "> Figure 2
<p>Month by month changes detected by S1-omnibus in the Lagoon of Fuente de Piedra marked with letters (<b>a</b>–<b>k</b>) in the upper left corner of each image. Their corresponding water table and precipitation levels can be found in the chart. Water table and precipitations were recorded by a limnograph and pluviometer at the center of the lagoon, marked with a yellow circle in “a”. The same Landsat 8 band 4 image has been used as the background in (<b>a</b>–<b>k</b>).</p> "> Figure 3
<p>Frequency of change in Fuente de Piedra and Camargue. Colors indicate how many times a pixel has changed over the 12-month period. The charts aggregate the frequencies of change by area (Landsat 8 band 4 used as the background image).</p> "> Figure 4
<p>Frequencies of change aggregated in LULC classes in Fuente de Piedra (<b>a</b>) and Camargue (<b>b</b>). Each chart accounts for the proportion of pixels of each LULC class where a change was detected once, twice, three or four or more times. The gray portion of the bar corresponds to areas where no change was detected.</p> "> Figure 5
<p>Changes detected in Fuente de Piedra by the pairwise change detection approach (yellow) overlaid on top of the S1-omnibus change detection results (blue). Changes in water level and in most crops are well detected by both approaches. Subset <b>A</b> shows how the S1-omnibus is capable of detecting changes in patches of crops matching the LULC map better (in the LULC map, orange is olive groves and beige herbaceous crops); Subset <b>B</b> shows how S1-omnibus can even detect whole patches of change that are missed with the pairwise approach (Landsat 8 band 4 used as the background image).</p> "> Figure 6
<p>Changes detected by S1-omnibus (blue), by Landsat-CVA (yellow) and by both methods (red). Subsets <b>A</b> and <b>B</b> show a closer look at the two areas of change indicated in the main image. In chart, light blue color represents the area detected as change by either method.</p> ">
Abstract
:1. Introduction
2. Study Areas
3. Materials and Methods
3.1. Imagery and Preprocessing
3.2. SAR-Based Change Detection
3.3. S1-Based and Landsat-Based Change Detection Comparison
4. Results
4.1. S1-Omnibus Approach
4.2. Comparison of S1-Omnibus Time Series and Pairwise Change Detection Approaches
4.3. Landsat-Based and Sentinel-1-Based Change Detection Comparison
5. Discussion
Potentials and Limitations of the Application
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
CVA | Change Vector Analysis |
DCT | Dynamic Cover Types |
ESA | European Space Agency |
GRD | Ground Range Detected |
IR-MAD | Iteratively Re-Weighted Multivariate Alteration Detection |
IW | Interferometric Wide |
LULC | Land Use Land Cover |
LULCC | Land Use Land Cover Change |
SAR | Synthetic Aperture Radar |
SLC | Single Look Complex |
SNAP | Sentinel Application Platform |
SWOS | Satellite-based Wetlands Observation Service |
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Fuente de Piedra (Spain) | Camargue (France) | ||
---|---|---|---|
Sentinel 1 | Landsat | Sentinel 1 | Landsat |
11 November 2014 | |||
02 December 2014 | |||
31 January 2015 | |||
24 February 2015 | |||
15 March 2015 | 09 March 2015 * | 08 March 2015 | |
20 April 2015 | 02 April 2015 | 01 April 2015 | 15 April 2015 |
26 May 2015 | 12 May 2015 * | 07 May 2015 | 17 May 2015 |
19 June 2015 | 05 June 2015 | 12 June 2015 | 02 June 2015 |
25 July 2015 | 07 July 2015 | 18 July 2015 | 20 July 2015 |
18 August 2015 | 16 August 2015 * | 18 August 2015 | 21 August 2015 |
23 September 2015 | 25 September 2015 | 28 September 2015 | 06 September 2015 |
17 October 2015 | |||
22 November 2015 | 12 November 2015 | ||
28 December 2015 | |||
21 January 2016 | |||
26 February 2016 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Muro, J.; Canty, M.; Conradsen, K.; Hüttich, C.; Nielsen, A.A.; Skriver, H.; Remy, F.; Strauch, A.; Thonfeld, F.; Menz, G. Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series. Remote Sens. 2016, 8, 795. https://doi.org/10.3390/rs8100795
Muro J, Canty M, Conradsen K, Hüttich C, Nielsen AA, Skriver H, Remy F, Strauch A, Thonfeld F, Menz G. Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series. Remote Sensing. 2016; 8(10):795. https://doi.org/10.3390/rs8100795
Chicago/Turabian StyleMuro, Javier, Morton Canty, Knut Conradsen, Christian Hüttich, Allan Aasbjerg Nielsen, Henning Skriver, Florian Remy, Adrian Strauch, Frank Thonfeld, and Gunter Menz. 2016. "Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series" Remote Sensing 8, no. 10: 795. https://doi.org/10.3390/rs8100795