Short- Term change detection with precise geometric correction and sub-pixel land cover characterization of modis
Results of change detection
Root mean squared errors (RMSE) are calculated between the six pair of V, S and W images both
filtered and unfiltered images. The result is shown in Table1. RMSE of filtered images decreased in three
categories compared with unfiltered ones. This result shows filtering algorithm was performed well to
reduce the noise originated from the mis-registration. The derived three RMSEs of filterd images were
selected as threshold values for the land cover change detection.
Fig.9 shows the subtracted images between 10 Mar. 2002 and unfiltered 12 Mar. 2002 images for V, S,
and W respectively. Fig.10 shows the subtracted image for filtered images for V, S, and W respectively.
Bright values indicate areas of high errors of that category. Flat region (white circle in Fig.9(a)) has
comparatively smaller noise and mountainous region shows higher noise. The error images in Fig.9 and the
corresponding digital elevation model (Fig.11) are clearly similar. This implies that differences of the slope
effect strongly affected the result. To increase the accuracy of short-term land cover change detections, it is
necessary to reduce differences of slope effect, cloud screening, atmospheric conditions and
mis-registrations. However, the proposed method was able to detect short- term land cover changes with
high accuracy.
Fig.9. Subtracted images between 10 Mar. 2002 and unfiltered 12 Mar. 2002 images
Fig.10. Subtracted images between 10 Mar. 2002 and filtered 12 Mar. 2002 images
Fig.11 Digital elevation map (DEM) of study area (300 x 300 km 2 )
Conclusions
This research examined the possibilityof detecting short-term land cover change using MODIS images
with spatial resolution of 250m in visible and near infrared channels. Firstly, pattern matching was used to
evaluate the accuracy of geometric correction by WebMODIS. As a result, it is verified that the accuracy
was within one pixel for the study area. Secondly, short- term land cover change detection was performed by.using the simple subtractive operation in sub- pixel based land cover characterization and the spatial filtering
technique. As a result, errors were found in mountainous area for unfiltered images, and the error images
were quite similar to digital elevation model. The differences of slope effect, atmospheric conditions and
mis-registration may cause a lot of mis-detection. However, the filtering operation performed well to reduce
the errors originated from the mis-registration. In this scene, the proposed method was able to detect
short- term land cover change in pixel level with high accuracy. However, some mis-detection still remained.
It is necessary to remove difference of slope effect, atmospheric conditions and mis-registration to improve
accuracy of short-term land cover change detections. Since it is assumed that there is no change between
two image because of their short-term- difference, this paper only discuss how to reduce the noise related
with mis-registration. As a future work, the other errors should be examined regarding the
Sun-Target- Sensor Geometry problems.
Acknowledgments
A part of this work was supported by ACT-JST “Research and Development for Applying Advanced
Computational Science and Technology of Japan Science and Technology Corporation”.
References
- WebMODIS home page, http://webmodis.iis.u- tokyo.ac.jp/, (20 Oct. 2002 Access)
Y.Yamagata, M.Sugita and Y.Yasuoka, 1997, Development of VSW index algorithm and application (in
Japanese), Journal of the Remote Sensing Society of Japan, Vol.17, No.1, pp.54 -63.
