Chlorophyll measurement from Landsat TM imagery

Water quality is an important factor for human health and quality of life. This has been recognized many years ago. Remote sensing can be used for various purposes. Environmental monitoring through the method of traditional ship sampling is time consuming and requires a high survey cost. This study uses an empirical model, based on actual water quality of chlorophyll measurements from the Penang Strait, Malaysia to predict chlorophyll based on optical properties of satellite digital imagery. The feasibility of using remote sensing technique for estimating the concentration of chlorophyll using Landsat satellite imagery in Penang Island, Malaysia was investigated in this study. The objective of this study is to evaluate the feasibility of using Landsat TM image to provide useful data for the chlorophyll mapping studies. The chlorophyll measurements were collected simultaneously with the satellite image acquisition through a field work. The in-situ locations were determined using a handheld Global Positioning Systems (GPS). The surface reflectance values were retrieved using ATCOR2 in the PCI Geomatica 10.1.3 image processing software. And then the digital numbers for each band corresponding to the sea-truth locations were extracted and then converted into radiance values and reflectance values. The reflectance values were used for calibration of the water quality algorithm. The efficiency of the proposed algorithm was investigated based on the observations of correlation coefficient (R) and root-mean-square deviations (RMS) with the sea-truth data. Finally the chlorophyll map was color-coded and geometrically corrected for visual in terpretation. This study shows that the Landsat satellite imagery has the potential to supply useful data for chlorophyll studies by using the developed algorithm. This study indicates that the chlorophyll mapping can be carried out using remote sensing technique by using Landsat imagery and the previously developed algorithm over Penang,- Malaysia.