Change Detection Based on Remote Sensing Information Model and its Application on Coastal Line of Yellow River Delat

Keywords: Change detection, Yellow River delta, Remote sensing information model.

Abstract
Information about change is necessary for updating land cover maps and the management of natural resources. Many researches have been undertaken to develop methods of obtaining change information. Based on the summarization of the methods on change information extracted from remotely sensed data, the paper promotes the method of change detection based remote sensing information model. This method is applied to detect the coastal line change of Yellow River Delta (YRD). It lays the foundation for research on the change relation of natural and human activity impact each other, and finally aids to study the regional geographic feature through more than 10 years remote sensing images in YRD.

1. Introduction
Information about change is necessary for updating land cover maps and the management of natural resources. The information may be obtained by visiting sites on the ground and/ or extracting it from remotely sensed data. For many of the physical and cultural features on the landscape there are optimal time periods during which these features may best be observed. Remotely sensed data acquired at the fixed time interval becomes an important factor. Many researches have been undertaken to develop methods of obtaining change information. Change detected from different temporal images usually reflect natural and human activity impact each other and then can be used to study how to form the regional geographic feature.

As we know, the Yellow River Delta is a delta grows fastest in the world. Because of its new land forming and unstable environment, its development is far more backward than other famous large river delta. However, the Yellow River Delta has a good geographic location, rich natural resources and tremendous developing potentiality. Its development is of great importance to the development of North China.

This paper first summarizes the methods on change information extracted from remotely sensed data. Then based on the different object models the method of change detection is mainly discussed. Finally through more than 10 years change result of Yellow River Delta, we analyze the coastal line change.

2 Change Detection Methods Based Remote Sensing Data

2.1 General Methods
Temporal feature is important and special in all the system characteristics. Because spatial and spectral information can be seen from images, temporal feature is relatively abstract, it is difficult to reflect directly. Its feature only can be seen by the change of spatial and spectral feature. Some commonly used change detection algorithms are summarized and analyzed as the following table.

Table 1 Main algorithms of change detection

Algorithm and example	Method procedure	Problem
Image Transformation (e.g. PCA).Fung et al., 1987, 1988.(Eastman et al., 1993, Bauer et al.,1994)	Composition of different temporal data, then classified or using PCA to transform all together	No classified change information
Image Arithmetic Change Detection.Price et al., 1992.	Arithmetic operation among different bands, e.g. Dijk=Bvijk(1)-Bvijk(2)+C	No classified change information
Post-Classification Comparison (Rutchey et al., 1994)	Classification of remote sensing images, then comparison pixel by pixel	Relay on classification accuracy and multiple classification
Data sources aided change detection(Lunetta et al., 1991.	Map data or GIS aid to analyze	Relay on quality of aided information
Spectral vector change (Michalek et al., 1993, Johnson et al., 1998)	Comparison with spectral vector of different temporal CMpixel= [Bvijk(2)-Bvijk(1)]2	Suitable for region of spectral change greatly

2.2 Based on Remote Sensing Information Model Change Detection
Remote sensing information is a complicated information. It is the comprehensive behavior from a certain environment. The behaviors of images varied largely with different ground features due to their different radiation and scattering characters to visual light, infrared and microwave. As a result, we can not build their remote sensing information models for ground feature under a unified mode. Three levels of model are conducted to describe the ground feature as follows:

1. Spectrum vector based remote sensing information model for ground feature:
   Some features, such as water body, vegetation, scene of forest fire, possess distinctive spectrum characteristic of multiple bands. Remote sensing information is often corresponding to ground object. This model is the starting point for a study. It is called primitive class model.
2. Multi-source information based remote sensing information model for ground feature:
   Most of ground features can not rely on just a single information model. Especially when coming across the appearance of different objects but equally spectra or equally spectra but different objects, it is necessary to use multi-temporal remote sensing information or other supplementary data (supported with GIS data base) to build its corresponding remote sensing information model for ground features. Based on primitive class model, this model is created by fresh supplying new element terms according to derivate mechanism.
3. Geo-knowledge based remote sensing information model for ground feature:
   Some ground features or appearances, especially the recessive information, can be recognized only through complicated processing and deeply analyzing. This kind of remote sensing information model for ground features not only includes spectrum vector characteristics of ground features but also needs to add geo-knowledge and expert knowledge and experience as well as the process of operating the knowledge and experience. For the reason, the study to this model deals with wide range of fields and is very complicated.

For main objects such as water, vegetation and non-vegetable, they can be extracted only by establishment of spectral feature model. For example,


Figure 1 Hierarchy of ground objects




Figure 2 Spectral curve of different objects
Water and land
With the increase of bands, the spectral reflectance value of water body decrease, i.e. b_ij1>b_ij2>b_ij3>b_ij4>b_ij 5>b_ij7.Meanwhile, band 5,7 can be used to select threshold to segment water. But due to the special effection of bedload in YRD, CH3, CH4 >CH2. So, we first using water spectral feature model to extract water, then within the region of water body, classification is conducted to obtain different depth classification map (shown as figure 3). It simply and distinctly reflects the water region distribution of YRD.


Figure 3 Water classification map
Vegetation and non-vegetation
Vegetation has high reflectance value in band 4.and in band 3 there is low reflectance. For this character, b_ijv=b_ij4/b_ij3 is often taken as the index to identify vegetation region and non-vegetation region. Certainly, there is much other vegetation indexes. When it is used in image segmentation, we define the following rules:
b_ijv>a1; vegetation region
Pixel(i,j) bijv <=a2; non-vegetation region
Between them.confused region

Figure 4 Based on spectral model segmentation 
of vegetation and non-vegetation
Sandy land and general land
For non-vegetation region, we can farther separate it into bare soil, build-up soil, residence etc. But for coastal region, sandy soil is leading object. Because spectral value of sand is lower than other kinds of non-vegetation, especially band 7 (including water). According to the rule, we can extract sandy soil in the non-vegetation region (shown as figure 5).


Figure 5 Based on spectral model 
segmentation of sandy soil
3. Study Area
The history of the Modern Yellow River Delta is less than 100 years. The construction of the modern delta mainly occurred after the northward moving of the Yellow River in 1855. Restrained by the embankment from Lijin country to upstream in Henan and Shandong provinces, the destination of water and sediments turned from the Yellow Sea to Bohai Sea. From Lijin to downstream, with the segmental extend of the man-made dykes, and also affected by the Coriolis force, the orientation of the river mouth changed from north and northeast to east and southeast. From Lijin to downstream, the lower research of the main stream went southward near Laizhou Bay. The river thus produced several sub-delta in order, just like a Chinese fan. From 1976 to now, there are apparent change for the Modern Yellow River Delta. We can use different temporal remote sensing images to analyze the change of coastline line.

4. Application on Coastal Line Chenge of YRO

4.1 Multi-Temporal Images
For 1996 TM, 1992 TM, 1988 TM, 1982 MSS and 1976 MSS different temporal images, a fundamental requirement is that they be spatially referenced. First precise geometric correction is conducted, then spatial resolution is sampled from 80m to 30m. Finally, using the object model information.different information can be extracted from different temporal images.

4.2 Coastal Line Change Detection
Through different temporal image, the boundary of water body can be extracted clearly. We can distinctly see the change process of Yellow River delta.


Figure 6 Change process of coastal line in YRD
5. Conclusion
Through more than 10 years change result of Yellow River Delta from remote sensing images, we can clearly see the change of Yellow River delta on the route of Yellow River and coastal line. The method can be used to other thematic information. The change relation of natural and human activity impact each other are analyzed and then can be used to study how to form the regional geographic feature.

References

• Jensen, J.R., 1996, Introductory Digital Image Processing: A Remote Sensing Perspective, 2 nd Ed. Prentice-Hall, Englewood Cliffs, NJ, p330.
• Xu D., 1990, Study of Remote Sensing for Huanghe River Mouth, Beijing: Meterological Press.
• Yang X., 1996, Satellite Monitoring of Dynamic Environmental Change of the Actival Yellow River Delta. ISPRS XX-VII, Vienna, Austria, 1996. (omited)