Mangrove forest zonation by using high resolution satellite data

Mangrove forest zonation by using high resolution satellite data

Sr. Suvit Vibulsresth,Dr. Surachai Ratanasermpong,
Dr. Darasri Downreang,Chaowalit Silapathong
Remote Sensing Division,
National Research Council of Thailand
Bangkok 10900

Abstract
Mangrove forest is one of the valuable resources, as a multiple economic, ecological, scientific and cultural resource now and for future generations. It was in 1987 that remote sensing technique was applied in this area and the results from classification of LANDSAT-MSS data were used as base-line information for the mangrove land use zoning into 3 principal zones: Preservation, Economic A and Economic B zones which cover the area of 427 Sq. m 1,997 sq. km and 1,300 sq. km respectively for the whole country.

In this study high resolution data of LANDSAT-TM and SPOT-PLA which can provide more effective information than LANDSAT-MSS, were used for mangrove forest land use zone monitoring. These satellite data were recorded in late 1988. It was found that for the selected test site of Eastern Seaboard of Thailand: 1) The FCC of TM bands 4-5-3/R-G-B with linear seaboard enhancement technique differentiates mangrove forest from other vegetations, 2) SPOT-PLA shows better shrimp farming pattern and built-up area and 3) the combination of TM4-TM5-PLA/R-G-B via digital classification could satisfactory depict the mangrove forest zonation which corresponds to the dominant species.

Introduction
Mangrove forest is one of the most important coastal ecosystems. Since the last 10 years, a number of mangrove forest area has been destroyed mainly by human activities. In Thailand, mangrove forest area was seriously reduced from a total of 2,873 sq, m, in 1974 to 1,964 sq. km in 1986. Therefore in 1988, to alleviate the deterioration situation, the zonation of mangrove forest land use was performed with the aid of LANDSAT-MSS image interpretation. Three zones of mangrove land use were defined as preservation zone, Economic A and Economic B zones, covering areas of 427 sq.km 1,997sq.km and 1,300 sq, km respectively.

However, the zonation of mangrove forest corresponding to species distribution,that is one of the dominant characteristics of mangrove forest has never been successively carried out of LAND-SAT data. In this study, high resolution data of LANDSAT-TM and SPOT-Panchromatic are used to classify. The species zonation of mangrove forest are at Khlung test site in Chanthaburi.

Description of the study area
Khlung, a district of Chanthaburi province, is in the south-eastern Thailand. This coastal zone is predominated by 2 seasons, wet (May-October) and dry (November-April), with the means annual temperature of 26.5 C.

The mangrove forest of Khlung can be classified according to its species distribution into 3 major zones (Vibulsresth, et.al. 1985) as follows (Fig.1) :

Class I. Dominated by Rhizophoraceace (Rhizophora mucronate, R. apiculata and Bruguiera sp) which are distributed along the coast line and river banks. Some Avicennia sp. Can also be found in the area that the soil is more sandy.
Class II. This class is dominated by the dwarf pure stand of Ceriops tagal. It is found in the inner zone next to class I where the physical condition is poor with bad soil and long period of flooding. In the transition zone between class I and class II Xylocarpus sp. and Acrostic hum aurum are often found.
Class III. This class is further inland and is composed of many species such as Lumnitzera spp. Excoeccaria sp. Xylocarpus sp. and Melaleuca leucodendron (on sandy soil) etc. The soil in this zone is more stiff and flooded during exceptionally high tide.

Figure 1 Dominant species zonation of mangrove forest
of khlung, Chanthaburi. (After Vibulsresth et. al., 1985)

Methodology
The remotely sensed data used in this study are the CCTS of LANDSAT-5 TM (Scene 128-51) and SPOT-Panchromatic (scene 266-325) acquired on the same date of 20 December 1988 with I hour difference. Aerial infrared photograph at a scale of 1:20,000 which were taken is October 1985 were also used as ground truth for training area selection. The processing of digital performed on Dipt. Aries II at RSD/NRCT.

The SPOT-Panchromatic image was first geometrically registered to UTM topographic maps (scale 1:50,000) using a 1st degree polynomial interpolation and a cubic convolution transformation method. Then, the LANDSAT-TM image was registered to the corrected panchromatic image and resampled from the pixel size of 30 metres to 10 metres using the same transformation method.

The combination of SPOT-Panchromatic and TM 4 and TM 5 images of LANDSAT was selected for this study base on their range of electromagnetic wave-bands which include visible, near infrared and short wave infrared. This should yield better results to both false color composite and automatic Classification with the fact that the visible region (panchromatic 0.51-0.73 micron) is the chlorophyll absorption band while the near infrared region (TM 4 0.6-0.90 micron) (TM 1.55 - 1.75 micron) responds tot eh humidity condition of soil and vegetation.

Linear enhancement technique applied to the false color composition of panchromatic (blue), TM 5 (green) and TM 4(red) to gain maximum contract of the image. Training area up to 16 classes of land cover in this study area with 7 classes of mangrove forest were selected fro digital classification using maximum forest were selected for digital classification using maximum likelihood classification method.

Results
The false color composite of TM bands 4-53/R/G/B with linear stretch enhancement differentiates mangrove forest from other feature while the FCC of TM 4, TM 5 and SPOT-Panchromatic that was linearly enhanced, show clearly details of both spatial and spectral differences (fig.2). The boundary of each land cover type can be sharply seen. This is particularly true for the linear features such as roads, shrimp dikes, canals etc.

Major classes of land cover can be visually identified. These include 1) mangrove forest which appears in different shades of red depending upon vegetation composition, eg. dense Rhizophora formation in bright red (A) and Melaleuca formation in pale orange (B); 2) aquaculture area which can be clearly identified with its spatial characteristics (C); and 3) open land where light blue color represents paddy fields after harvesting (D) while yellowish green color is moist bare soil (E).

From the digital classification, a total of 16 classes of land cover could be identified within the study area of 454 sq. km. The results are summarized in Table 1 and Fig. 3.

In this study, 7calsses of mangrove forest could be distinguished corresponding to the dominant species as follows :

1) and 2) Rhizophora formation (class I of Vibulsresth et. al., 1985). It is normally distributed along the coast and river banks and could be classified based on its density into 2 classes, namely dense and open.

3) Avicennia formation (class I of Vibulsresth et.al 1985) it forms in narrow strip along the river banks and is dominated by Avicenna ap.

4) and 5) Mixed formation (class 2 and 3 of Vibulsresth et. al 1985). This formation is distributed extensively in the inner zone next Rhizophora formation and could be subdivided into 2 classes as dense and open formations.

Figure 2 False color composite i age of TM 4 (red), TM 5 (Green) and SPOT-Panchromatic (blue)
of mangrove forest at Khlung, Chanthaburi, on 20 December 1988.

Table 1 Results of digital classification of mangrove region at Khulung, chanthaburi,
from the image of 20 December 1988.

No.	Classes	Area Sq. km	%	Color
1	Dense Mangrove Rhizophora	14.03	3.10	Dark green
2	Open Mangrove Rhizophora	30.77	6.80	Light blue
3	Avicennia Mangrove Forest	0.85	0.20	Orange
4	Desnse Mixed Mangrove Forest	39.22	8.60	Yellow
5	Mixed Open Mangrove Forest	60.60	13.30	Yellowish green
6	Mangrove Forest Plantation	0.10	0.01	Bright red
7	Melaleuca Formation	8.36	1.80	Purple
8	Rubber Plantation	10.34	2.30	Pink
9	Orchards and bushes	21.67	4.80	Violet
10	Active Paddy Fields	1.83	0.40	Dark violet
11	Harvested Paddy Fields	54.64	12.00	Tan
12	Moist Bare Soil, Grass Land	23.15	5.10	Brown
13	Sandy Bare Soil	1.53	0.30	White
14	Flooded Aquaculture	66.12	14.60	Navy blue
15	Dry Aquaculture	8.04	1.80	Redish pink
16	Water bodies	117.78	24.60	Dark blue
Unclassified		0.97	0.20	Black
Total		454.00	100

Figure 3 Results of digital classification of mangrove region at Khlung from image of
20 December 1988 (see color codes in Table 1).

6) Mangrove plantation of Rhizophora species.

7) Melaleuca formation. It is found further inland approaching the terrain ecosystem especially in the are of sandy soil.

The results area quite satisfactory when compared to the study of Pumijumnong (1986).

The spectral signature of each formation of mangrove forest obtained from the study (fig, 4) indicates that the zonation of dominant species could be clearly identified by using TM 4 and TM 5 bands.

Figure 4 Spectral signature of mangrove forest species zonation
at Khlung, Chanthaburi.

Conclusion
The study of reveals that the combination of multi-sensor data, SPOT-Panchromatic and Landsat-TM, yields, an excellent representation of ground features particularly on a false color composite image. Panchromatic image (resolution of 10 meters) gives better spatial characteristics whereas LANDSAT-TM offers spectral separabilities that are most useful for vegetation study.

The mangrove land cover zoning could be clearly identified using the combination of Panchromatic, TM 4 and TM 5. The species donation of mangrove forest was satisfactorily performed via digital classification, using these high resolution images particularly TM 4 and TM 5.

It should be noted, however, that only the satellite radiometric values were used to classify land cover type. A higher accuracy could perhaps be achieved with more separation between classes by incorporating some relevant information in the classification.

Reference

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