Tipamat Upanoi1,2 and Nitin K. Tripathi1
1 Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand
2Phuket Marine Biological Center, P.O. Box 60,Maung, Phuket 83000, Thailand
[email protected]
Introduction
In research on mangrove remote sensing could provide useful information for mangrove management. Remote sensing data derived from different satellite such as LANDSAT MSS data can provide us information about the area extent, conditions and boundary of coastal wetlands. IRS LISS II LANDSAT TM data have also proved extremely useful for wetland mapping as well as for delineation high and low water lines Likewise; it is possible to distinguish mangroves from other plant communities (Nayak, 1993). Further, multi-date satellite data could be used effectively to find out the changes in the area extent of mangroves. For example, the 1986 TM and 1993 IRS LISS II data have helped to quantify the changes in the area cover of mangroves since both the sensors have similar resolution (Krishnamoorthy, 1997).
NDVI is one of techniques that could be applied for vegetation change monitoring in which many researchers study (Galvão et al., 2000; Sader and Winne 1992).
This study aims to demonstrate a simple and logical technique to display and quantify mangrove forest change on different date interval. The results from this study could be providing draft information on mangrove changes.
Methodology
Study area
A study area of approximately 200 square kilometers was selected in Phang nga Bay, Southwest Thailand. The area is covered by mangrove forests of Krabi bay, Krabi province (figure 1) which ranges from 08º00´00´´–08º07´45´´N in latitude and from 98º51´45´´–99º00´00´´E in longitude. The topography is generally flat, with a few small mountains on the northern part of the area. The flat topography causes a very large tidal range with extended mudflats. The dominant land cover is agriculture such as para rubber plantation and oil palm plantation. Three main rivers are found in the area and reach the inner part of Phang-nga Bay at Krabi Bay. The mangroves are located around the three main rivers. Krabi town is located near the shoreline close to the mangrove forest. Krabi estuary is an important estuary which is located in front of Krabi town. It divides into Klong Ji Lad and Klong Krabi. Mangrove forests area generally found along the estuarine canal on the lower part of the area. Due to mangrove concession over the past 60 year, the Rhizophora was clear cut. The secondary mangrove forest is dominated by Rhizophora sp., Bruguiera sp., and Xylocarpus sp. could be found. Aquaculture ponds for shrimp farming and villages spreading surround the mangrove forests.
Figure 1 Study area
Materials and methods
Materials
- Digital datasets of LANDSAT5 Thematic Mapper (TM) Satellite image on Krabi Bay, which are acquired on 1995, 2000 from Geo-Informatics and Space technology Development Agency (GISTDA).
- Digital dataset of LANDSAT7 Enhanced Thematic Mapper Plus (ETM+) Satellite image on Krabi Bay, which is acquired on 2002 from GISTDA.
Methods
Three dates of satellite imagery were acquired. Landsat TM data were obtained 15 December 1995 and 12 February 2000. Landsat ETM+ data was acquired on 9 February 2002. The wavebands representing of near-infrared and visible-red region were extracted from each Landsat dataset. The Normalized Difference Vegetation Index (NDVI) is used to transform multi-spectral data into a single image band which representing vegetation distribution.
The NDVI values indicate the amount of green vegetation present in the pixel. Higher NDVI values indicate more green vegetation. In the ENVI system, NDVI were computed according to the standard algorithm:
NVDI = (NIR-Red)/ (NIR+Red)
Valid results fall between -1 and +1.
The NDVI which computed from each of the three years (1995-2000-2002), were applied to ISODATA unsupervised classification. Change detection was taken an account on the changes which happen on 1995 to 2002. A statistic test (t-test single factors) was applied in order to compare 2 means on NDVI value among years. Change areas were significantly different at 95 % (p
