Spatiotemporal growth dynamics of invasive plant distribution in Bolgoda lake, Sri lanka: a gis based approach

Abstract

Inland water bodies in urban areas, such as Bolgoda lake, host vegetation covers that exhibit significant spatio-temporal variations throughout the year. Seasonal weather patterns, anthropogenic activities, such as surface mining, wastewater discharge and invasive plant growth typically govern these dynamics. Measuring, and monitoring, these factors over the spatial extent of these waterbodies require significant efforts. Yet, remote sensing and earth observation data can effectively minimize these efforts. This study employs both Landsat and Sentinel satellite data to estimate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) to develop a relatively accurate classification of the water-vegetation dynamics over the time of interest. The Google Earth Engine and ArcGIS software were used to download and generate the classifications over six years (2016-2021) for four different seasons (i.e., 24 processed images). The classified time series data show that the vegetation cover varies at two temporal frequencies. The annual variation of the water, vegetation, and non-vegetation (other) classes is consistent and cyclic. However, at a finer temporal resolution (i.e., on seasonal cycles), vegetation dynamics fluctuate rapidly. The study offers scope for using the results to support policymakers in optimizing environmental resource management strategies for urban surface water bodies.