K. C. Tan
Student
Universiti Sains Malaysia, Malaysia
[email protected]
H S Lim
Lecturer
Universiti Sains Malaysia, Malaysia
[email protected]
M Z Mat Jafri
Lecturer
Universiti Sains Malaysia, Malaysia
[email protected]
K. Abdullah
Lecturer
Universiti Sains Malaysia, Malaysia
[email protected]
ABSTRACT
Land surface temperature (LST) is traditionally used in global change studies because it is important to indicate the issues and themes in earth sciences, due to urban climatology and human-environment interaction. Besides, LST also required for environmental studies since it is a important variable in research purpose, especially in weather and climate change studies. The objectives of our study are to retrieve the LST and find the relationship between LST and NDVI. In this study, the LST over Penang Island, Malaysia was retrieved using ATCOR3_T in the PCI Geomatica image processing software. Then, normalized diffrence vegetation index (NDVI) estimated from the Landsat 7 ETM+. Finally, we find the correlation between LST and NDVI using regression technique. From the result we obtained, we found that LST and NDVI have a strong correlation and feasible to estimate LST with reasonable accuracy.
INTROUCTION
Many researchs have been conducted and monitored by remote sensing through vegetation indices. Among which, the NDVI (Normalized Difference Vegetation Index) is the most widely and traditionally used [Yves and Sobrino (2009)]. NDVI can give an obvious view of vegetation cover with the large coverage area, using satellite image-based analysis. Furthermore, satellite image-based analysis may reduce time consuming and solve the problem due to the sparsely data as compared with in-situ measurement.
Vegetation play an important role in global ecosystems and information about vegetation cover help us to know more about the interation between atmosphere and land (Jiang et al., 2006). All these parameters influents in the effects of climate. Drastic changes in vegetation cover wil give the direct impact on water and energy budgets. This is through the emissivity, albedo and the process of transpiration.
To date this, the land surface temperature (LST) was retrieved. The relationships between LST and NDVI have been studied. LST has been studied for environmental process because it can provide an important knowledge about the surface-atmosphere interations and energy fluxes between the land and atmosphere (Sobrino et al., 2003). Strong negative correlation is observed between LST and NDVI (Javed et al., 2008).
STUDY AREA
The study area is the Penang Island, Malaysia, located within latitudes 5o 12′ N to 5o 30′ N and longitudes 100o 09′ E to 100o 26′ E. The corresponding satellite track for the ETM+ scenes is 128/56. The map of the region is shown in Figure 1. The satellite images were acquired on 6/3/2002.
DATA ANALYSIS AND RESULT
The raw satellite image was used for the retrieval of LST. In this study, we are using Landsat 7 ETM+. The band 6, which is the thermal band for Landsat ETM+, was used to retrieve the LST for our study. LST over Penang Island, Malaysia was retrieved using ATCOR3_T in the PCI Geomatica image processing software. There are lot of methods to retrieve LST, such as using split-window algorithm, mono-window algorithm, etc. All these methods need to consider the factors of water content and surface emissivity. So, we are using PCI Geomatica to retrieve the LST over Penang Island. The built in algorithm had considered all these factors and also the digital elevation model (DEM).
In order to calculate NDVI, firstly, we need to do the radiometric calibration for the band 3 and band 4 of ETM+ image. The DNs of band 3 and band 4 were converted to radiance by the following formula (Chen et al., 2006):
Radiance, L = gain*DN + Offset (1) Where, the gain and offset can be obtained from the header file of the images. Consider that the land surface is Lambertian, we can use the following formula to get surface reflectivity.
From the result we obtained, LST were then regressed against NDVI using linear equation. The graph produced high correlation coefficient and low root-mean-square error, RMSE, 1.968 °C. A good negative correlation agreement between LST retrieval and calculated NDVI (linear correlation coefficient, R, of 0.90) obtained in this study indicates that LST has a good relationship with NDVI. When the value of NDVI is increasing, the retrieval of LST value is decreasing. A NDVI map over Penang, Malaysia was generated using the formula (3) and colour-coded for visual interpretation (Figure 3).
CONCLUSION
This study shows a strong negative correlation between the retrieval LST and calculated NDVI value. Further analysis will be focussed on the relationship of NDVI and retrieval LST using different land classification.
ACKNOWLEDGEMENT
This project was carried out using a short term research grants from Universiti Sains Malaysia. Landsat 7 ETM+ was downloaded from US Geological Survey website. We would like to thank the technical staff who participated in this project. Thanks are extended to USM for support and encouragement.
REFERENCES
- Yves Julien and Jose A. Sobrino, 2009, The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters, Remote Sensing of Environment, Volume 113, 329-334.
- Zhnagyan Jiang, Alfredo R. Huete, Jin Chen, Yunhao Chen, Jing Li, Guangjian Yan, Xiaoyu Zhang, 2006, Analysis of NDVI and scaled difference vegetation fraction, Remote Sensing of Environment, Volume 101, 366-378.
- Sobrino, J. A., El-Kharraz, J. And Li, Z. L., 2003, Surface temperature and water vapour retrieval from MODIS data. International Journal of Remote Sensing, 1-22.
- Javed Mallick, Yogesh Kanti and B.D.Bharathi, 2008, Estimation of land surface temperature over Delhi using Landsat-7 ETM+, J. Ind. Geophys Union, Volume 12, No.3, 131- 140.
- Xiao-Ling Chen, Hong-Mei Zhao, Ping-Xiang Li, Zhi-Yong Yin, 2006, Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes, Volume 104, 133-146.
