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Monitoring Desertification based on Geographic Information System and Multi- Spctral and Multi-Temporal Satellite Data Case Study; Damghan Playa
Alavi Panah, S.K.
Assistant professor
College of Geography
Department of Geography
University of Tehran, Iran
salavipa@chamran.ut.ac.ir
Ehsani, A.H.
Post graduated of Natural Resources Engineering
University of Tehran, Iran.
Abstract
Land cover changes, due to human activities, are the main subjects of regional planning. Changes detection is a major application of remotely sensed data. In this study, which carried out based on visual –and digital procedures, various changes are identified, and were detected during 23 years for three times. The digital images of MSS (20 July, 1977), TM (7 Sep., 1988) and ETM+ (20 July, 2000) were used. The three multi-source images were geometrically and radio metrically calibrated to each other and then the different methodologies, such as overlaying, images differencing and post classification comparisons were applied. The obtained results have shown that during 23 years, drastic changes occurred in relation to desertification and 68% of the occurred changes are in between 1985-2000. Based on the obtained results we concluded that Landsat MSS, TM, and ETM+ data are powerful to map the changes. From the obtained results we concluded that extensive fieldwork are necessary to map the occurred changes for the study period.
1. Introduction
Desertification has been variously defined in the Literature (Gao, 1977), with no single definition being unanimously accepted. According to UNCOD (1978), it refers to the diminution or destruction of the biological potential of the land that can lead ultimately to desert-like conditions. This definition is adopted here with the modification that the process is limited to an arid environment. The expansion of desertified areas or their rehabilitation to productive use is inevitably accompanied by changes from vegetated to denuded cover or vise versa. Monitoring of these changes is ideally accomplished from multi-temporal remotely sensed data. Remote sensing has successfully been applied to the monitoring of desert expansion (Luk, 1983). And to the assessment of the factors that may cause desertification ( Hanan, et al., 1991). Visual interpretation of Landsat enable us to identify areas endangered by desertification in Desert areas.
Remote sensing and GIS are land-related technologies and are therefore very useful in the implementation of the land component of a suitable development strategy (Anthony Gar-on Yeh and Li, 1996). Townshend et al. (1989) used Landsat TM data to formulate a dynamic process-based model to delimit processes in the Chott el. Djerid, in which the contributions of dissolved salts, surface run-off and aeolian processes, and their changes over time are evaluated. Change detection involves the use of multitemporal data sets to discriminate areas of land cover change between dates of imagery (Lillesand and Kiefer, 1994).
Goossens and Van Ranst ( 1996) showed the possibility of multitemporal analysis using TM and MSS classification images in the Nile delta in Egypt. Ideally, a change detection method should be based on a system that 1) has a systematic period between overflights (e.g. 18 days), 2) reduces displacement effect, 3) records imagery of the same area at the same time of the day each time to minimise the sun angle effects, 4) keeps the same scale and, 5) records reflected radiant flux in useful spectral regions. Ideally, change detection methods should involve data acquired by the same sensor with the same spectral and spatial resolution. Agricultural crops typically have unique crop calendars in each geographic region. Analysis of two-date imagery of the same area and the same time can provide information on how some land cover types are changing in a period. It should be noted that the nature of change detection problem in general is so that digital change detection is complex (Jensen, 1983), especially change detection methods that use two different remote sensor data. When two different remote sensor data are used some important considerations, such as difference in resolution must be taken into account. The selected area is located in the Central Iranian Deserts.
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