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

Abstract

This research is focused on the application of water indexes derived from historical Landsat image series to quantify the impact caused by anthropic activities on wetlands. This work is focused specifically on the Bajo Sinú Wetlands Complex (BSWC) located on the northern Colombian Caribbean Coast. We modelled the spatio-temporal dynamics of the BSWC in three specific periods (1991, 2003 and 2015) and during two seasons: dry and wet. We used data from Landsat 4 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (Oli-Tirs) images, based on which we evaluated seven different water indexes in order to select the one which best describes changes in the BSWC.The modelling consisted of the identification of spatio-temporal changes over the BSWC caused by two main pressure factors: (1) anthropic activity in the Sinú River Watershed, which is the main water source of the BSWC and (2) the launch of the Urrá Hydroelectric Dam Project located in the upper basin of the Sinú River in 2000.The most suitable water index was found to be the Normalized Difference Water Index (NDWI), based on which we acquired water index images and digital classifications. Reliability of these products was rated in terms of the Overall Classification Accuracy values above 87% and a Kappa index between 0.75 and 0.86.We found that over the 25 year study period, the maximum water storage capacity decreased by 56.2%, the number of the waterbodies reduced by 24.7%, and the average size of the waterbodies decreased by 41%. All the results indicate a deterioration of the water storage capacity in the BSWC.

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

“…Landsat satellites (MSS, TM, ETM +, and OLI) that continuously provide medium resolution images are among the most widely used optical sensors in environmental research in the last five decades. The use of Landsat satellite images has an important place in numerous studies where the water surface areas of wetlands are extracted or the temporal changes are determined [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. The ability of cloud computing programs has been significantly improved in recent years, and they have shown great application potential in large-scale land cover mapping [44].…”