Spatial variability of infiltration in tropical watershed using remote sensing technique and infiltration model

• Main Author: Mohammad Khan, Nor Liyana
• Format: Thesis
• Published: 2013
• Subjects: GB Physical geography
• Online Access: http://eprints.utm.my/id/eprint/42211/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:78894
• Institution: Universiti Teknologi Malaysia

Infiltration is an important component of the hydrological cycle and plays significance role in controlling the quality and quantity of surface runoff. Infiltration can also reduce the frequency and extent of downstream flooding in tropical watershed. This research focuses on the study of infiltration using an integration of remote sensing data and Green-Ampt (GA) infiltration model. This study was carried out in the Bekok catchment area, Johor. The objectives of this study are; (i) to evaluate the adaptive filters performance in Advance Land Observation Satellite with Phase Array L-Band Synthetic Aperture Radar (ALOS-PALSAR) for all polarization data (HH, HV, VV, and VH), (ii) to examine linear regression model of ALOS-PALSAR polarization data for retrieving soil moisture in fully vegetated watershed, and (iii) to characterize infiltration for each dominant land use types in the study area using integration of ALOS-PALSAR data and GA infiltration model. The statistical analysis of normalized mean (NM) and Speckle Index (SI) were used to evaluate the performance of the adaptive filter in all polarization of ALOSPALSAR data. The inversion of backscattering regression combined with GA infiltration model was applied to estimate soil moisture from ALOS-PALSAR data and cumulative infiltration respectively. The NM and SI test showed a good performance in Lee and Median filters respectively. A good relationship between estimated and observed soil moisture was found in VV polarization data with the value of R2 equal to 0.708 and significant level greater than 0.05. With regard to infiltration characteristics, it is found that area covered with grass contributing the highest infiltration and followed by area covered by oil palm, shrub, and rubber respectively. Integration of ALOS-PALSAR data and GA infiltration model were useful technique to characterize the temporal and spatial variability of infiltration.