Monitoring Desertification based on Geographic Information System and Multi- Spctral and Multi-Temporal Satellite Data Case Study; Damghan Playa

The study area is located in northeast of Iran between longitude 35o 30' to 36o 5' and latitude of 54o 5' to 54o 58' (fig. 1). The mean average of annual temperature of studied area is 17.1?c.


Fig. 1: Location of study area


The TM is a scanning optical - mechanical sensor that detect reflected or emitted energy from the earth invisible and IR wave lengths. TM band 1-5 and 7 collects reflected energy; band 6 collect emitted energy. The TM sensor has a spatial resolution of 120m for the thermal IR band and 30m for the six reflective bands. Solomons (1984) suggested that it appears the TM can be describe as being twice as effective in providing information as the Landsat MSS. The nearest satellite in the Landsat series, Landsat 7 was launched on April, 15, 1999 and carries the enhanced thematic mapper plus (ETM+) with 30m visible and IR bands 60m spatial resolution thermal band. Since TM and ETM+ bands 1-5 and 7 have the same spatial resolution they can be compared. The purposes of this study area is as following:
  1. Comparing information content of TM and ETM+ thermal and reflective bands.
  2. Comparing the efficiency of information content of TM and ETM+ thermal and reflective bands for change detection.
  3. Studying and detecting various changes, soil salinity and land use/cover maps.
2. Materials and Methods

For this study following documents also were used ;
  1. Digital data of ETM+ dated 20 July 2000 and TM dated 5 Sep. 1988 and MSS were used.
  2. Soil map at 1:50.000 scale
  3. Topographic map at 1:50.000 scale
  4. Geographic map at 1:100.000 scale
  5. Aerial photos at 1:20.000 scale
  6. Published reports
  7. Field checking
The softwares such as ILWIS 3, ARC VIEW 3.1a, PHOTOSHOP 7.0 and EXCEL were used. In this study TM and ETM+ and MSS data using true ground point and GPS were geometrically and radiometricaly calibrated to each others. Figure 2, shows the study area. In this study the raw remotely sensed data from Landsat MSS and TM, and ETM+ obtained on different dates of images of MSS (20 July, 1977), TM (7 Sep., 1988) and ETM+ (20 July, 2000 with some other maps and data have been used for multitemporal analysis. In this method the images from three dates are independently classified and compared. Valuable source of information such as soil/soil salinity observations, explanatory reports correspondant to the time of recorded data and interviewing with farmers were used to improve the field knowledg. As a result, the training classes in the cultivated area are described and defined based on the type, density and their associated soil salinity. In the bare soil, the classes were mainly defined on the of soil salinity level .
2-1. Field work and representative training sites
The field work as one of the most important steps was carried out. Localisation, was one of the main problems for the collection of data and information from the study area. In order to choose representative training sites and to overcome the problems of time and season differences between the last fieldwork and when the remotely sensed data were collected, the following steps were included:
  1. the playa surface conditions were carefully studied with attention being paid to stabilized crusted surface, region of loose, vulnerable, disturbed/non disturbed and coarse clastic particles. because of the dynamic nature of plants and due to the fact that soil salinity in the cultivated area is a complex phenomenon and varying in the time and space, we used the soil salinity data corresponding to the time of reorded remotely sensed data for the training classes of salt affected vegetation, salt affected pistachio. A visual comparision between the standard FCC, s of MSS and TM and ETM+ images and Photomorphic Unit Analysis (PMU) were used to improve the field knowlede in the study for training classes.
Table 1 : Characteristics of training area


2.2 . Spectral signatures evaluation
The validity of the training data was evaluated both from visual examination and from quantitative characterisation. The spectral signatures of the training samples was evaluated by using the reflective and also TM thermal band as the most informative bands based on the obtained result from the calculation of Optimum Index Factor (OIF).

2.3. Sampling techniques
Lillesand, and Kiefer (1994), indicated that all spectral classes constituting each information class must be adequately represented in the training set statistics used to classify an image. In this study the training samples were taken on the conventional FCC where field observations were made. A large enough sample is often needed because the distribution of the sample mean approaches normality as the size of the sample increases. The sampling was performed by displaying the conventional FCC on the colour monitor and then the training samples were carefully assigned. In addition to visual assessment of training samples, during the sampling the class statistics was shown for the given time of sampling. As a result, the land cover types having inherently similar spectral pattern were detected. Finally the classes were determined not only by the occurrence in the field but also by their separability and their spectral signature evaluation. The training samples of TM, ETM+ and MSS imagery are listed in tables. Therefore the classes of the MSS classified image are not completely comparable with the obtained TM and ETM+ classes. Comparing the land cover classification at every pixel in an image with a reference source would appear to ensure adequately accurate assessment. While such “wall - to - wall ” comparison is expensive and defeats the purpose of performing a remote sensing based classification, the training area accuracy indicates very little about the performance of the classifier in other areas (non trained area) in a scene. Therefore other areas of representative land cover type (test areas) different from training samples are used to assess the accuracy of the classified image.

In this study three maximum likelihood classifications with the same threshold were applied based on the training samples (before merging), and b) the classification based on the training samples (after merging. To evaluate the classification accuracy, the same method as TM was used. The test areas were sampled for the assessment of the MSS classification accuracy. The reference sources of data were crossed with the classified images based on the defined classes and the result was tabulated in a contingency table.

2.4. Regrouping
The classes mainly can be regrouped on the purpose of the user. A high classification accuracy was obtained for TM and ETM+ classification images and a high classification accuracy is needed for MSS classification images. Otherwise an accurate result for change detection can not be obtained. Therefore it was necessary to regroup MSS classified image into broad classes in order to increase the classification accuracy. It is also very important to note that the TM and ETM +classified image was also necessary to be regrouped in a logic way to be comparable with the MSS classified image. The same class definitions must be reached with the data sets of different spatial resolution to allow a comparison to be made. For the purpose of this study, a comparison of the two classified images was valid provided that: a) a comparison was made at a land cover classification level no finer than it could be accurately determined by the lower resolution sensor (Landsat MSS), b) appropriate generalisation of the higher resolution data was carried out, c) generalisation of classes was carried out based on the purpose of change detection and increasing accuracy.

2.5. Registration for change detection purpose
The MSS, TM and ETM+ images were geometrically corrected toward the UTM co-ordinates. Then a 3 by 3 majority class filter was applied to the classified TM data set prior to registration. This algorithm was similar to that used by Todd et al. (1980) served to generalised the data to the level of the MSS and reduce the scattered isolated pixels.

2.6. Image classification and accuracy assessment
The training samples which are used to estimate the statistical characteristics of the spectral classes should be typical and represents the norm for each class. The MSS, TM and ETM+ band combinations were examined for classification of MSS, TM and ETM+ imagery with the same method. The accuracy per category were computed by the number of correctly classified pixels by the total number of pixels that were classified in each category (row total). The overall accuracy was also computed by dividing the total number of the correctly classified pixels of each class to the total number of classes. Although the over


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