Spatio-temporal modelling of wetland ecosystems using Landsat time series: case of the Bajo Sinú Wetlands Complex (BSWC)– Córdoba– Colombia

Figures & data

Figure 1. Location of BSWC in Córdoba – Colombia.

Figure 2. Methodological phases.

Table 1. Selected scenes for spatio-temporal modelling of BSWC.

	Scene
Period	10–54	9–54	Season	Sensor
1991	March 14	March 14	Dry	Landsat TM 4
	June 30	June 22	Wet
2003	February 28	February 3	Dry	Landsat 7 ETM +
	June 25	June 2	Wet
2015	February 3	February 12	Dry	Landsat 8 Oli-Tirs
	June 2	June 11	Wet

Figure 3. Selection of Landsat images based on seasonal rainfall conditions.

Table 2. Water indexes used in different scientific research projects.

Index	Acronym	Equation	Source
Normalized Difference Water Index	NDWI	(Green−NIR)/(Green+NIR)	(McFeeters 1996)
Normalized Difference Water Index de Xu’s or ModifiedNormalizade Difference Water Index	Xu’s NDWI	(Green−MIR)/(Green+MIR)	(Li et al. 2013; Xu 2006)
Normalized Difference Moisture Index	NDMI	(NIR−MIR)/(NIR+MIR)	(Wilson and Sader 2002)
Water Ratio Index	WRI	(Green+Red)/(Green+MIR)	(Shen and Li 2010)
Automated Water Extraction Index	AWEI	4*(Green−MIR)−[(0.25*NIR)+(2.75*SWIR)]	(Feyisa et al. 2014)
Index of Hydrographic Studies Center	I_CEDEX	(NIR/Red)−(NIR/MIR)	(Díaz-Delgado et al. 2006)

Figure 4. Decision tree for index selection.

Table 3. Raster operations used for modelling the dynamics of the BSWC. (DS: Dry season, WS: Wet season).

Change Dynamics	Raster operation	Criteria
Permanent water Storage capacity	DS91-DS2003	Influence of the hydroelectric plant
	DS2003-DS2015	Changes overtime (2003–2015)
Maximum capacity of water storage	WS91-WS2003	Influence of the hydroelectric plant
	WS2003-WS2015	Changes overtime (2003–2015)

Figure 5. (a) Infrared colour composition. (b) NDWI Image (Xu 2006).

Figure 6. (a) WRI index, (b) NDMI index, and (c) NDWI (Xu 2006).

Table 4. Results of the accuracy assessments of digital classifications.

XUS INDEX	Source of verification	Total number of points	No water	Water	Producers’ Accuracy (%)	Users’ Accuracy (%)	Overall Classification Accuracy (%)	Overall Kappa
14 March 1991	Landsat – spectral signatures	150	67	71	89.87	94.67	92.00	0.84
10 June 1991	Aero-photography	150	71	69	94.52	92.00	93.33	0.87
28 February 2003	Landsat – spectral signatures	150	67	65	89.04	86.67	88.00	0.76
25 June 2003	Landsat – spectral signatures	150	71	65	94.20	86.67	90.67	0.81--
3 February 2015	Field data	120	54	51	89.47	85.00	87.50	0.75
2 June 2015	Field data	120	58	48	96.00	80.00	88.33	0.77

Table 5. Metrics associated with BSWC for inter-seasonal and inter-annual periods.

	Dry Season			Wet Season
Year	(TA) Total surfacewaterbodies (hectares)	(Nwb) Numberwaterbodies	(Aawb)Averagesize (hectares)	(TA) Total surfacewaterbodies (hectares)	(Nwb) Numberwaterbodies	(Aawb)Averagesize (hectares)
1991	7269	749	9.71	20,135	429	46.90
2003	7930	884	9.00	13,500	719	18.77
2015	6428	699	9.20	8,826	323	27.32

Figure 7. (a) Location of permanent waterbodies (dry season) in 1991 and (b) location of permanent waterbodies in 2003.

Figure 8. Spatiotemporal dynamics of the BSWC for wet season in 1991 and 2003.

Figure 9. (a–c) Disappearance of permanent waterbodies in BSWC between 1991, 2003 and 2015.

Figure 10. Spatio-temporal dynamics of BSWC for wet season in 2003 and 2015 periods.

McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. doi:10.1080/01431169608948714.

 Web of Science ®Google Scholar

Li, W., D. Zhiqiang, F. Ling, D. Zhou, H. Wang, Y. Gui, B. Sun, and X. Zhang. 2013. “A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI.” Remote Sensing 5 (11): 5530–5549. doi:10.3390/rs5115530.

 Web of Science ®Google Scholar

Xu, H. 2006. “Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery.” International Journal of Remote Sensing 27 (14): 3025–3033. doi:10.1080/01431160600589179.

 Web of Science ®Google Scholar

Wilson, E. H., and S. A. Sader. 2002. “Detection of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery.” Remote Sensing of Environment 80 (3): 385–396. doi:10.1016/S0034-4257(01)00318-2.

 Web of Science ®Google Scholar

Shen, L., and L. Changchun. 2010. Water Body Extraction from Landsat ETM+ Imagery Using Adaboost Algorithm. In 2010 18th International Conference on Geoinformatics 1–4. Beijing: IEEE. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5567762&isnumber=5567473.

 Google Scholar

Feyisa, G. L., H. Meilby, R. Fensholt, and S. R. Proud. 2014. “Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery.” Remote Sensing of Environment 140: 23–35. doi:10.1016/j.rse.2013.08.029.

 Web of Science ®Google Scholar

Díaz-Delgado, R., J. Bustamante, D. Aragonés, and F. Pacios. 2006. Determining Water Body Characteristics of Doñana Shallow Marshes through Remote Sensing, 3662–3664. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.703.3899&rep=rep1&type=pdf

 Google Scholar