Home Articles Change detection and assesment using multi temporal satellite image for North-East Mediterranean...

Change detection and assesment using multi temporal satellite image for North-East Mediterranean Coast

Tuncay Kuleli
University of Cukurova Faculty of Fisheries 01330 Adana, Turkey
Corresponding author. Tel.: +90-322-338 6249; fax:+90-322-338 6439
Email: [email protected]

Abstract
In this study, land use change detection of the north-eastern Mediterranean coast within approximately eight years period was conducted. Two Landsat images of different dates (1992, 2000) were processed and analyzed, geometrically corrected (registered) and digitized to obtain more detailed information and identify the change. Based on the combined use of multi temporal satellite imagery and ancillary data, such as topographic maps and field check (ground truth) data, land use maps with different classes were prepared, showing the substantial rate of change, and usefulness of Landsat data in detailed mapping and landuse change detection studies.

1. Introduction
Many coastal areas are undergoing dramatic changes and experiencing the impact of human activities dealing with economic, landuse/abuse, and resource development. There occurs a highly dramatic loss of habitat for wildlife including fish species surrounding many coastal areas worldwide. Remote Sensing data can be used as important tool to evaluate and monitor landuse and land-cover changes. Global coverage, High spatial resolution, and revisit capabilities of modern remote sensing satellites provide us with a large amount of valuable data for accurate land use estimation. Land-use/land-cover change is a widespread, accelerating, and significant process. Land-use/land-cover change is driven by human actions, and, in many cases, it also drives changes that impact humans. Modelling these changes is critical for formulating effective environmental policies and management strategies.

2. Study area
The Çukurova Delta is located in the south of Adana on the south-eastern Turkish Mediterranean coast. It represents an internationally important coastal strip ecosystem, 110 km long.

The Çukurova Delta is the largest coastal river basin in Turkey and created by the sediments from the Berdan, Seyhan and Ceyhan Rivers and Taurus Mountains over two thousand years. The southern part of Tarsus City (now inland) was a Roman harbour demonstrating that extensive eroded material has been carried from Taurus Mountains [1, 2] and deposited in the delta. With a 110 km long coastal strip, Çukurova Delta encloses an area of about 5000 km².

Whilst the upper Delta basin has important fertile soils for agriculture, the lower part is still an internationally important coastal ecosystem containing biologically diverse habitats and biotopes.

The wetlands and coastal ecological areas in the Delta are, from west to east: Tuzla, Akyatan, Agyatan and Yumurtalik Lagoons. The Delta contains valuable habitats for nearly 200.000 water birds over-winter every year. Two hundred and sixty eight bird species have so far been recorded [3].

As well as having importance for wildlife, the coastal lagoons are also of economic importance for “Dalyan Fishing” [4]. Dalyan fishing is a system based on the catchments of the mature fish flocks by using the tights during the period when the fish flocks move between sea and lagoon. However, it is done only during fishing season and is a utility coming from the nature.

The Delta is also a breeding and nesting area for three globally threatened sea turtles; Caretta caretta, Chelonia mydas, and Trionix triunguis. [5, 6, 7, 8]. This delta is important not only for sea organisms, but also for some endemic vegetation. A type of Aleppo pine (Pinus halepensis), not common in Turkey, can be seen in Çamlik Lagoon, and it is surrounded by sand dunes and lagoons with wetlands. This site has a high floristic diversity [9]. There are also some endemic halophytic plants in the Delta [10, 11, and 12].

The area of dunes is decreasing mainly because of agricultural activities, but Turkey’s largest natural coastal sand dunes still survive in the Delta [11, 12, 13].

Figure 1. Study area
3. Methods of Study
Most change detection techniques fall into five general categories: manual, write function memory insertion, image enhancement, multi-date data classification and comparison of two independent landcover classifications [14, 15]. In this study, post-classification change detection method was utilised.

Post-classification change detection is used to compare two independently prepared classified images. Two supervised classifications are produced using the same information classes to facilitate a comparison of two images. This procedure not only allows areas of no change to be identified, but in areas where change has occurred, the nature of the change can be determined [16]. However, in traditional supervised classification change detection, changes must be known in order to be sampled [17].

Landsat imageries of dates 1992 and 2000 were considered for digital image processing. The features of Landsat satellite images were given in Table 1.

Table 1. The features of the Landsat satellite images used in the study

Landsat Thematic Mapper 4 – 5
Spectral Resolution (m) Spatial Resolution (m) Temporal Resolution (day) Radiometric Resolution
Band 1 (0.45 – 0.52) 30 x 30 16 days 8 bits
Band 2 (0.52 – 0.60) 30 x 30 16 days 8 bits
Band 3 (0.63 – 0.69) 30 x 30 16 days 8 bits
Band 4 (0.76 – 0.90) 30 x 30 16 days 8 bits
Band 5 (1.55 – 1.75) 30 x 30 16 days 8 bits
Band 6 (10.4 – 12.5) 120 x 120 16 days 8 bits
Band 7 (2.08 – 2.35) 30 x 30 16 days 8 bits

The satellite images used in the study were purchased from Landsat as their geometric and radiometric corrections being already processed.

Unsupervised classification process ISODATA (Iterative Self Organizing Data Analysis Technique) was applied to image data sets. A preliminary thematic raster layer was created, which gave similar results to using a minimum distance classifier. From these data, signatures were created. This thematic layer can be used for analyzing and manipulating the signatures before actual classification takes place [18]. ISODATA algorithm produced 12 spectral clusters after the generalization on the fieldwork, and six landuse/land cover classes were determined: agriculture, wetland, sand dune, vegetation, shallow water, deep water.
4. Results
In this study, the images belonging 1992 and 2000 was evaluated. From the Figure 2. it was clearly seen that much of the sand dune of 1992 was changed into agricultural areas in 2000.

Figure 2. The classified image shows land use and land cover of the area in 1992 and 2000

The numerical and proportional values concerning landuse/land cover change were given in Table 2.

Table 2. The results of analysis based on the comparison of classified images of the dates 1992 and 2000

Year / km2 Year / km2 Difference / km2 Change Land use
1992 2000 1992-2000 %  
1.35 1.00 0.35 25.87 Decrease Deep Water
0.31 0.66 -0.35 53.27 Increase Shallow Water
0.30 0.71 -0.42 58.58 Increase Swamp
0.31 0.63 -0.33 51.52 Increase Pasture
2.15 3.79 -1.63 43.10 Increase Agriculture
4.84 2.47 2.38 49.05 Decrease Sand Dune
9.26 9.26 0.00    

According to the obtained data, it was determined that there was a decrease of 25.87% from 1991 to 2000 in the wetland surface. Similarly, a increase of 53.27% and decrease 49.05% was determined in the shallow of wetland and sand dune respectively. On the other hand, swamp area, pasture area created by human and agricultural area are seemed to have experienced some dramatic increases of 58.58%, 51.52% and 43.10% respectively.

In the light of these data, it is understood that the increase of 43.10% in agricultural area was mostly obtained from sand dune and, on a smaller scale, from shallow water.

That there occurs a decrease of 25.87% in deep water against an increase in shallow water and swamp shows that the extent of the lagoon is liable to get shallow quickly. agriculture, wetland, sand dune, vegetation, shallow water, deep water.

5. Conclusion
The transformation of the sand dune and wetland into agriculture in a short term like eight years poses a serious threat to the wetland. It cannot be possible to talk about the wetland of Yumurtalik provided this change lasts without curtailing its speed. In order to prevent this rapid change, sustainable agricultural policies and eco-system restorations are indispensable for the region.

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