Land Cover Change Detection Radio metrically-Corrected
Multi-Sensor Data
Muhamad Radzli Mispan* And Paul M.Mather
Department Of Geography, University Of Nottingham
University Park NG7 2RD Nottingham
United Kingdom
*present address;Strategic, Environemtn and Natural Resources Research Centre,
MARDI. P.O. Box 12301, 50774, Kuala Lumpur.
E-mail :radzali@mardi.my
Abstract
remote sensing offers practical benefit in the field of land cover change detection. However, cloud cover restricts the use of optical remotely sensed data in tropical regions such as Malayisa. Therefore, data from different sensors such as Landsat TM and SPOT HRV are required in order to ensure as complete a templete a temporal coverage as possible. On the other hand, quantitative change detection study requires data to be corrected and converted to physical values such as radiance or reflectance factor. This study desribes a method of using reflectance factor of multi-sensor data for change detection analysis. The radio metrically- corrected multi-sensor data were transformed into uncorrelated component images in N-dimensional space using the Gramm Schmidt orthogonal transformation technique (GSO). The transformation is based on the four stable components and two change components extracted from the image data. The result of the transformation were then used to identify change in land from the image data. The study indicates that the GSO technique provide reliable information on the nature and type of change that is taking place over a period of time.
Introduction
Remote sensing techniques offer benefits inthis field of land cover change. One of the major advantage of satellite remote sensing system is their synoptic and repetitive coverage capability that can used to identify and monitor changes at regional and global scales. The spatio-temporal patterns of change in surface radiance offer reliable information sources on the state and nature of the surface feature and the process of changes that has taken place over a period of time. However, cloud cover restricts the use of optical remotely sensed data in tropical regions such as Malaysia. Therefore, data from different sensors such as Landsat TM and SPOT HRV are required in order to ensure as complete a temporal coverage as possible.
It is also critical that the type of technique and approach used to detect these changes are compatible with the characteristics of the surface features. Thus, for successful change detection analysis, the sections of an appropriate change detection algorithm is also an important factor. The selection should be based on the capability and flexibility of the algorithms the nature and physical of the are and the nature and type of
Change that need to be addressed. This paper discusses methods of change detection utilizing multi-temporal and multi-sensor remote sensing data.
Materials and methods
An attempt is made to employ a Gramm Schmidt Orthogonalisation technique proposed by Collins and Woodcock (1994). In contrast with their approach, this study uses radiometrically corrected data of three homologous bands of green, red and near-infrared channels of landsat TM and SPOT HRV. The radiometric correction efforts are described in Mispan and Mather (1997) and Mispan (1997)
Resources
The study are is located at the central western coast of Peninsular Malaysia (Latitude 3o25'N and longitude 101o 45'E) about 20 km south of Kuala Lumpur. This study used Landsat -5 Thematic mapper ™ and SPOT HRV-2 data acquired on the 6 March 1990 and 26 December 1990 respectively (Table 1). The image processing and analysis were carried out using ERDAS-Imagine software at the Department of Geography, University of Nottinghanm
Table 1: Input reflectance (%) for Gramm-Schmidt orthogonal transformations
| features |
XS1 |
SX2 |
XS3 |
TM2 |
TM3 |
TM4 |
| Wet soil |
7.540 |
9.525 |
18.120 |
6.510 |
7.125 |
15.345 |
| Dry soil |
23.525 |
43.025 |
37.215 |
25.325 |
43.910 |
32.750 |
| Vegetation |
3.21 |
1.713 |
27.748 |
2.633 |
1.630 |
27.959 |
| Water |
4.350 |
2.150 |
1.850 |
4.900 |
2.900 |
2.250 |
| Bare-vegetation |
25.867 |
42.725 |
37.661 |
4.088 |
2.064 |
27.039 |
| Vegetation-bare |
4.796 |
2.389 |
27.759 |
13.519 |
18.833 |
32.741 |
Gramm Schmidt orthotogonalisation
The Gramm-Schmide orthogonalisation (GSO) technique was used by Kauth and Thomas (1976) to derive the coefficients of the Tasselled Cap Transformation (TCA) for singly-data Landsat MSS data. Jackson (1983) describes how the coefficients for n-space indices can be calculated using the Gramm-Schmidt Orthogonalisation technique with minimum data points. The first step in the construction of transformation vectors is the selection of a point in N dimentsional space that acts as the origin of the new co-ordinate system. To obtain the first index, an equation for a line through soil data points must be derived. A minimum of two soil points is required, with points differing considerably in reflectance preferred. For this study, reflectance values of wet and dry soils available in the image were chosen to drive the soil line. This is the first component derived from GSO technique, which can be said to represent the stable brightness index. The second stable component is orthogonal to the first component. The reflectance value of vegetation cover was used to derive this component. The dark dense vegetation 9DDV) wa chosen to represent the vegetative cover. This component can be said to be stable greenness index.
The third stable component, which represents a stable wetness index, is a component orthogonal to the first and second stable components. The representative reflectance for this component is selected from water bodies. The fourth component is a change from bare to vegetation. A similar situation applies to the fifthe component. However in contrast to the fourth component, this component represents a change component from vegetation to bare.
