An Exploratory Application of Remote Sensing Technologies and Statistical Analysis to Provide Rapid and Cost Effective Inundation Predictions for the Tonle Sap Lake Floodplain System
Elydia Binte Azman
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
Grace Eu Yi Ying
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
Lim Yean Yue Gladys
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
Seah Yiting
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
B. S. Wu *
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
K. N. Irvine
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
*Author to whom correspondence should be addressed.
Abstract
Aims: The objective of this study is twofold: i) explore a simple, empirical relationship based on freely available, remotely sensed data and the water levels recorded at Prek Kdam (one week prior) to predict total inundated area of the Tonle Sap flood plain; and ii) use the relationship to provide a preliminary demarcation of flood risk zones around the Tonle Sap Lake.
Study Design: This study is designed to predict inundation in the Tonle Sap Lake, Cambodia.
Place and Duration of Study: Tonle Sap Lake, with data and satellite images for the period between June 2008 and November 2013.
Methodology: Three approaches were adopted in this study: 1) Classification: to examine flooded regions during wet seasons by Landsat images. 2) Regression model: to explore the relationship between the flooded areas and water level at Prek Kdam, near the mouth of the Tonle Sap Lake. 3) Visualization and estimation: To observe dynamics of inundation and predict the potential flooded areas based on the regression.
Results: The adoption of GIS and remote sensing helps the delineation of flood zones. The results of the statistical analysis demonstrated a strong linear relationship between water levels at Prek Kdam and flooded areas at the Tonle Sap Lake. Together with the adoption of GIS and remote sensing technologies, the regression model can be further used to support flood prediction, management and regional planning.
Conclusion: This research develops a flood warning tool for the government and the public to intuitively evaluate the potential flooding areas in the Tonle Sap Lake during monsoon seasons. It can further help the government prepare for flood risk management and develop a sustainable environment.
Keywords: Tonle Sap Lake, floodplain, remote sensing, GIS, regression model