Browsing by Author "Gowsigan, PT"

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    item: Conference-Abstract
    Comparative analysis of water hyacinth dynamics in North Bolgoda Lake, Sri Lanka: a classification based on high-resolution aerial imagery and satellite-imagery
    (Department of Earth Resources Engineering, 2023-08-28) Dawalagala, HS; Radampola, A; Gowsigan, PT; Chaminda, SP; Dassanayake, SM; Jayawardena, CL
    Water hyacinth (WH) is an invasive aquatic plant that has established its presence in tropical and subtropical regions around the globe. Its widespread existence has resulted in societal, economic, and ecological impacts that are mostly intolerable. Understanding and monitoring the spatial and seasonal dynamics of WH in the respective environments could provide insights to mitigate its environmental impact. This study attempts to identify seasonal patterns of WH within north Bolgoda Lake over four years (2019-2022). The methodology includes a pixel-based random forest (RF) classification utilising five distinct spectral indices in conjunction with raw Sentinel-2 spectral bands, operationalised through the Google Earth Engine (GEE) platform. The aerial imageries were classified using Esri ArcGIS Pro software. The outcomes of this study indicate an increase of WH proliferation during the wet season (May-November) over the dry season (December- April) with an overall accuracy of 82% for aerial imagery and 98% for satellite imagery. Data fusion techniques are proposed to overcome the limitations of employing two different forms of remote sensing data individually. Despite the challenges, this study reveals important insights into the scalability of input data to specific requirements and under restricted conditions.
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    Google earth engine based assessment of seasonal spatial distribution of water hyacinth in north Bolgoda lake, Sri Lanka
    (IEEE, 2023-12-09) Radampola, A; Dawalagala, HS; Gowsigan, PT; Dassanayake, SM; Jayawardena, CL; Chaminda, SP; Abeysooriya, R; Adikariwattage, V; Hemachandra, K
    Water hyacinth (WH), a highly invasive aquatic plant is found on every continent except Antarctica. Due to the significant damages caused by these plants to the society, economy and to aquatic ecosystems, it is important to monitor the spatial distribution with respect to seasonal variability to help employ prompt and effective mitigation strategies. In this study, we characterized the seasonality of WH over four years (2019-2022) for the North Bolgoda lake, Sri Lanka. To identify high incidence areas the lake was divided into four sections (A, B, C and D). A pixel based Random Forest (RF) classification utilizing five spectral indices along with the raw spectral bands of Sentinel-2 scenes were used to map the hyacinth coverage with an accuracy greater than 98% by using the GEE (Google Earth Engine) platform. We found that the highest abundance of hyacinth for all sections occurs in the wet season (May – November) and that section A has the maximum WH coverage in all seasons followed by sections C, B and D. This study provides a freely available automated framework for continuous monitoring of WH to help improve environmental decision making.