# Introduction

Current change detection systems use a variety of image processing tools to make changes visible, but typically rely on manual interpretation by expert analysts to delineate the change areas. Most systems look for changes between only two images: one "before" and one "after". Change -detection techniques in common use include: subtracting spectral bands between the images, subtracting a feature space, principal components analysis, and change vector analysis. Satellite remote sensing is widely accepted as a technique to study land use and land cover change Dynamics .The use of satellite data for compiling land use change area is becoming substitute for data derived from time consuming satellite images interpretation. Better assessment of land use land cover change using digital analysis of remotely sensed satellite data can help decision maker to develop effective plans for the management of land.

Numbers of researchers have used remotely sensed satellite data for change detection, and a number of approaches and techniques have been developed. Locating and characterizing areas of significant change using remotely sensed data is important for many applications. These include: resource management, urban planning and impacts of human activities on the environment Landsat satellite imagery reveals that in the last 10 years, wetlands that once covered as much as 20,000 square km (7,700 square miles) in parts of Iraq and Iran have been reduced to about 15 percent of their original size. The ecosystem has been damaged and, as a result, a number of plant and animal species face possible extinction. (UNEP, 2004) Two main approaches to digital change detection have been reported. Both involve multitemporal images and can be categorized as separate data set or single data set analysis. Separate data set analysis involves classification of each-date imagery separately into land cover classes. The results were subsequently compared. Single data set analysis involves co-registering and re-sampling multi-temporal images into the same dataset and matamatical transformation, mainly image differencing and rationing, is then applied to the raw co-registered images to produce a residual image indicating the relative change in reflectance between the two dates. This technique is gives slightly more accurate result. (See, for example, Nelson, 1982;jenson andtoll, 1982: woodwell et al., 1983;Singh, 1986;Quarmby et al., 1987).


# II.


# Digital Change Detection Techniques

Change detection is the process of identifying differences in the state of an object or phenomenon by Author : Nahrain University, IRAQ. E-mail : salahsrc123@hotmail.com n recent years there has been a significant amount of research put forth in the development of change detection methods using remotely sensed data. Scientists studying global change may find the ability to monitor land-surface changes over time the most important use of satellite image data. The repetitive coverage, consistent data characteristics, and digital format of the image data provided by several satellite systems makes their respective data readily conducive to the production of a digital "change" database in which the spatial and temporal dimensions of land-use and land-cover change can be detected and evaluated.


# Global

I observing it at different times. Change detection generally operates by detecting numerical differences in corresponding pixel values between dates. Many digital algorithms have been applied for change detection purposes ( Kwarteng and Chavez Jr !998).

The basic reason in using remote sensing data for change detection is that changes in the object of interest will result in changes in radiance values or local texture that are separable from changes caused by other factors, such as differences in atmospheric conditions, illumination and viewing angle, soil moisture etc. It may further be necessary to require that changes of interest be separable from expected or uninteresting events, such as seasonal, weather, tidal or diurnal effects.

Digital change detection techniques may be categorized into two basic approaches: the comparative analysis of independently produced thematic labeling or classifications of imagery from different dates; or simultaneous analysis of multi-temporal data sets. Within these two approaches, there are a number of methods and techniques such as post classification comparison, image rationing and principle components analysis (this list is not exhaustive).

III.


# Methodology

The   bands 4,3,2. Landsat Multispectral data provide the longest duration archive of moderately high spatial resolution satellite image data for monitoring the types and rates of land-surface change imposed by human activity. The derivation of change information from Landsat data generally consists of co-registering the data of two or more images of the same area acquired at different time; adjusting the radiometric properties of the data to normalize for varying observation and atmospheric conditions; implementing a change detection method on the combined data sets; and producing an output product that can effectively convey land-surface change on an image or in statistical basis. Although the consistent data characteristics of Landsat data enable ready production of change images, the procedures of change image production can also be implemented on multiple data sets of non-similar data characteristics, allowing combination of Landsat MSS data with data from other sensors, such as Landast Thematic Mapper (TM) and SPOT Satellite.  


# Temporal Comparison Results

The change detection procedure used has involved a classified images derived for each date. This approach as described above was performed on unsupervised and supervised classifications of Landsat data from 2 dates and subsequently compares the classified images. Hence the output image was greatly dependent upon the accuracy of the classified images. Figures (3-a, b, c, d) and Table (1        MSS-1975 andTM-2002 images. Table (3) shows the result of qualitative evaluation as well as the rate of land use -land cover changes carried out through the analysis of unsupervised and supervised classification statistics summary reports.   These changes caused by IRAQ-IRAN war, and as a result of damming upstream as well as drainage schemes since the 1970s and due to massive drainage works implemented in southern Iraq in the early 1990s, following the second Gulf war.

V.


# Results and Discussion

Change detection has become a useful tool for detecting landcover changes from remotely sensed imagery. It has enabled resource managers to observe changes over large areas and provided long-term monitoring capabilities. Generally we can conclude that digital change detection techniques using temporal remote sensing data are useful to assist human analysts of remote sensing data, and provide detailed information for detecting and assessing land cover and land use dynamics. 


# Conclusion

The obtained results from temporal classification change detection method showed that there are noticeable and clear changes in the land use and land cover in the area for the period 1972-2002. There is no much differences in classification results for supervised and unsupervised techniques.

There are increasing in dry land and deep water areas and decreasing in the wet land and shallow water areas. Also there is decline in spatial extent of date palm and marsh shrubs. These changes caused by IRAQ-IRAN war and as a result of damming upstream as well as drainage schemes since the 1970s. This page is intentionally left blank


# References Références Referencias


# Global
![study area covers part of Al Hammar Marshes and Shatt Al-Arab. Two Landsat images of different dates, Landsat MSS 1975 in three bands 4,2,1(near IR -0.8-1.1, red -0.6-0.7 , green -0.50 -0.60 micron ) with 80 meters resolution and Landsat TM 2002, in three bands 4,3,2 (near IR -0.76-0.90, red -0.63-0.69 , green -0.52-0.60 micron ) with 30 meters resolution were used in this work.](image-2.png "")
1![Figure (1-a) : False-color composite of Landsat 1975 MSS bands 4,2,1.](image-3.png "Figure ( 1")
![Used images were preliminarily geometrically corrected. Then , image to image registration was done in order to register the 2002 image with image 1975 (master image).Keeping the root mean square error (RMSE ) less than 0.5 pixels, an image to image transformation model (third order polynomial) and nearest neighbor resampling was calculated.](image-4.png "")
2![Figure 2 : Resample of Landsat 2002 image. Post-Classification Comparison as change detection technique used in this study to compare temporal land cover/land use change in study area. Post classification Involves the classification of each of the images independently, followed by a comparison of the corresponding pixel (thematic) labels to identify areas where change has occurred. There are two general approaches to multispectral classification: supervised classification in which an analyst selects sites of known cover type; and unsupervised (clustering) classification, in which clusters of pixels are generated based on spectral similarities. Both supervised and unsupervised classification requires human expertise to generate the classified imagery. The comparison of separately classified images can be carried out visually, or by computer.Computers are better at quantitative analysis, but humans are able to discern patterns and shape much better, and the effect of mis-registrations may be much reduced. The advantages of post-classification comparison are that it minimizes the search space and the effects of seasonal and atmospheric differences](image-5.png "Figure 2 :")
![) summarizes the area occupied (number of pixels) by each class of the images (MSS-1975 and TM -2002) by unsupervised classification methods (K-mean and Iso-data methods). A -Unsupervised classification of 1975 -MSS image by isodata method. may introduce spurious change detections. Postclassification comparison has been used, e.g., to detect: non-urban to urban or forest to cropland conversion, and changes in general land use, wetlands and forests. is that classification errors, which are usually common, between the scenes. The main problem of this approach B -Unsupervised classification of 1975-MSS image by k-mean method. C -Unsupervised classification of 2002-TM image by isodata method. D -Unsupervised classification of 2002-TM image by k-mean method.](image-6.png "")
3![Unsupervised classification by k -mean and isodata methods for 1975 and 2002 images.Global Journal of Human Social ScienceVolume XII Issue XII Version ITable 1 : Unsupervised classification statistics summary report for MSS-1975 and TM-2002 images.Supervised Classification for the images of each date was carried out using minim distance method. The training samples selected were based on land cover and terrain. Six different land cover classes were selected including deep water, shallow water, date palms and weeds, wet land1, wet land2, and dry land .](image-7.png "Figure 3 :")
![Figure (4-a,b) and Table (2) summarizes the area occupied (number of pixels) by each class of the images ( MSS-1975 and TM -2002) by supervised classification methods (Minim distance method ) .](image-8.png "")
![-Supervised classification by minim distance method for MSS-1975 image. B -Supervised classification by minim distance method for TM-2002 image.](image-9.png "A")
4![Supervised classification by minim distance method for mss-1975 and TM-2002 image.](image-10.png "Figure 4 :")
![By visual interpretation of landastMSS (1975)   andTM (2002)  false-color composite images, dense marsh vegetation (marsh grass) appears as dark red patches in 1975 image (fig1-a). But in 2002 TM image (Fig 1-b) most of the Marsh lands had dried out. It appear as olive to grayish-brown patches indicating low vegetation cover on moist to dry ground. The very light to grey patches are an area of expose ground with no vegetation, which may be salt flats where before there were lakes. The elongated red patches along the banks of the Shatt Al-Arab River are Date Palm groves. The date palm belt long Shatt Al-Arab appears as a dark red hue in 1975. Healthy vegetation is characterized by a distinctively strong reflectance in the near infrared represented by red tones in these satellite images. In 2002 image, the intensity of infrared reflectance in the date belt had considerably diminished denoted by red brown indicating stressed and dead vegetation, and the replacement of palms by reeds and desert scrub.By applying change detection and considering the output figures and tables, we can find there are noticeable changes in land use/ land cover classes' area between 1975 and 2002 images. There are increasing trends to dry land and deep water and decline trends to wet land and shallow water.](image-11.png "")
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1. Jenson, J.R. and Toll, D.R., 1982. Detecting residential Land use development at the Urban fringe, Photogrammetric Engineering and Remote Sensing, 48, 629-643 2. Kwarteng, A. Y. and Chavez Jr, P. S. 1998. Change detection study of Kuwait city and environs using multi -temporal Landsat thematic mapper data. International journal of remote sensing, Vol19, Issue 9. 3. Nelson, R.E 1982 Detecting forest canopy change using Landsat. AgRISTARS Report TH 83918 , NASA / Goddard Space Flight centre Greenbelt , MD 80 pp 4. Quarmby, A.A. Townshend, J.R.G. and Cushine j.L. 1987 Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in South-east England , VI.
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			Temporal Change Detection of AL-Hammar Marsh-IRAQ using Remote Sensing Techniques