Classification Of Forest In Malaysia Using Jers-1 Sar And Landsat Tm Data
Hideki Saito1, Khali Aziz Hamzah2 and Haruo Sawada
1Forestry and Forest Products Research Institute
P.O. Box 16 Tsukuba Norin Danchi, Tsukuba Ibaraki 305, Japan
Tel: 0298-73-3211 ext. 636 FAX: 0298-73-3799
E-mail :rslsaito@ffpri.affrc.go.jp
2Forest Research Institute of Malaysia
Kepong, Kuala Lumpur 52109, Malaysia
Tel: 03-6342633 FAX: 603-6367753
E-mail :khali@frim.gov.my
Abstract
We can get SAR images in both wet and dry seasons, as they are available in all-weather. In addition, SAR observes objects with the same energy, thus we can compare any SAR images that are acquired at different time directly. These characteristics are effective for monitoring a forest in Malaysia that is often covered by cloud and has different condition due to seasons. The purpose of this study is to improve the classification accuracy of forest area in east coast Malaysia using JERS-1 SAR data and Landsat TM data. However SAR data alone is not enough for obtaining good result on forest classification. Therefore we attempted to combine SAR and TM data for the classification. Their results are as follows; (1) The classification accuracy of peat swamp forest, hill forest and grass land are improved, (2) It is effective to use average filtered image of SAR, (3) It is also effective to use two SAR data that are acquired in different seasons. Combining TM and SAR data is quite useful for the classification of forest in Malaysia. These results indicate that SAR is closely related to the forest volume and structure of forest in Malaysia. This point must be the subject of future research.
1. Introduction
Lately, decreasing and degrading of tropical forest have become important problem at not only local or regional level but also global level. Remote sensing technology has been available for monitoring tropical forest. Spatial and spectral resolutions of Landsat TM are so high that it is suitable sensor for monitoring of tropical forest. But it is restricted to acquire the observation data, because optical sensor, such as Landsat TM and SPOT HRV, were obstructed by cloud. Compared with these sensor, SAR has some advantages in observing the forest. (1) SAR data is possible to be acquired in both dry and wet seasons, as it is available in all-weather. (2) SAR observes objects with the same energy, thus we can directly compare SAR images that are acquired at different time. Thus these characteristics allow us to monitor tropical forest periodically. (3) L-band SAR digital number and forest volume have deep relationship, since it can penetrate the canopy layer and is reflect by forest floor and tree stem or branch (L. L. Hess, 1990). There is a study about classification using Landsat TM together with ERS-1 SAR. It says that the combination of Landsat TM and ERS-1 SAR data can ~ potentially lead to improved land cover mapping (R.P.H.M. Schoemakers et al., 1993). It is considered to be admitted that the combination of Landsat TM and JERS-1 SAR can improve the classification accuracy.
2. Study Area and Data
A test site was selected in forest area in the south part of Pekan village (102 30'E, 3 30'N), Pahang in the east coast of Peninsular Malaysia. Peat swamp forests are major occupation in this area and ii oil palms are planted around peat swamp forest. The topology is flat, especially in the peat swamp forest. The weather is seasonal tropical climate, and temperature keeps high and it has small seasonal changes. Both wet and dry seasons are found in this area. Monthly temperatures and monthly mean precipitation at Kuantan, Pahang, MALA YSIA are represented in Figure 1 and 2 (NOAA, 1987-1994). Kuantan is northern part of Pekan and distance between both towns is about 50 km. The data that were used in this study are represented in Table 1.
Table 1 Attribute of Data that are used in this study
| Satellite |
Sensor |
Path -Row |
Acquisition |
Season |
| Landsat |
TM |
126-58 |
24 June, 1992 |
Dry Season |
| JERS-1 |
SAR |
118-29 |
17 July, 1993 |
Dry Season |
| ERS-1 |
SAR |
118-29 |
13 October, 1993 |
Beginning of Wet Season |
Figure 1 Monthly Mean Temperature of Kuantan, Malaysia
Figure 2 Monthly Precipitation of Kuantan, Malaysia
3. Methodology
3.1 Reducing Speckle Noise
To minimize effects of speckle noise, a median filter is applied to SAR image. The median filter can remove very high or low values in its windows, so it is expected to reduce the effect of speckle noise. The window size of that filter was 5 by 5.
3.2 Overlay
Both of SAR data were overlaid to TM data and the pixel spacing of both was set to 30 m, the pixel size of TM data, using nearest neighbor interpolation as a resampling method.
3.3 Texture Analysis of SAR
The statistical values such as average and standard deviation of the gray level histogram of n by n windows can be used as a textual information (J. A. of RS, 1993). Average and Standard deviation filter of 5 by 5 windows were applied to median filtered SAR image to get textural information of SAR in this study.
3.4 Normalized Difference Vegetation Index (NDVI) of TM
NDVI are calculated by following expression;
NDVI - (TM4 - TM3 +TM4 + TM3)
NDVI shows as a high value for denser vegetation, while NDVI very low in desert, or non-vegetation region (J.A. of RS, 1993).
