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Poster Session 3
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Comparison of Different Sensors and Analysis Techniques for Tropical
Mangrove Forest Mapping
Data Analysis
Systematic investigations were carried out to identify the sterngths and limitations of various sensors and sensor combinations as well as different analysis techniques. In particular, the analysis steps shown in Table 2 were performed [4].
Table 2: Different analysis steps perfo0rmed within the current study (from [5])
| ID |
Analysis Methodology |
Data Source |
| 1 |
Visual interoperation |
individually: Landsat, spot, MOS, ERS-1,JERS-1 |
| 2 |
Image rectification |
all images rectified to UTMproi. |
| 3 |
speckle filtering (Gamma MAP) |
ERS-1,JERS-1 |
| 4 |
Texture analysis (variance, homogeneous contrast, dissimilarity) |
all ERS-1, images |
| 5 |
Image stratification (GIS based) |
Spot, Landsat, ERS-1, JERS-1 |
| 6 |
Spectral/temporal signature analysis |
Spot, Landsat, Spot & ERS-1, Landsat & ERS-1, ERS-1 & JERS-1, mERS-1 |
| 7a |
Digital image classification |
SPOT |
| 7b |
|
Landsat |
| 7c |
|
ERS-1 & Spot |
| 7d |
|
ERS-1 & JERS-1 |
| 7e |
|
multi-temporal ERS-1 |
| 8 |
Integration of GIS for classification enhancement |
as7 |
| 9 |
Classification accuracy assesment |
as 7 |
The center piece of the study is the systematic compariso9n of the five different digital classification methods shown as ID 7a-c in Table 2. Digital classifications were carried out based on both, single data sources such as Spot, Landsat or ERS-1, as well as combinations of different sensors such as Spot &ERS-1. Training sample selection for the maximum likelihood classification was based on detailed ground surveys carried out at different times of the year.
Results
The overall classification accuracies based on data from the various sensors showed that the highest accuracy is achieved combining ERS-1 and Spot imagery. The overall accuracy was 87.3% considering the following six classes: homogeneous rhizophora, ho9mogeneous nypa, mixed open mangrove forest, mixed dense mangrove forest, water, and rubber plantations. The classification accuracy based on r5adar data alone was significantly lower and reached 52.1% for combined ERS-1 & JERS-1 image data, which was also similar as for ERS-1 data alone. For comparison, the classification based on the Landsat image showed a relatively low accuracy of68.6%, which can partly be att5aibted to some misclassifications due to cloud cover. The Spot image, for comparison, was practically cloud-free and resulted in a classification accuracy of84.8% [1,4].
Taking the example of the combined ERS-1 & Spot classification, the error matrix for the classification of mangrove types is shown in Table 3. If the four mangrove types are considered as one class (mangroves) and a discrimination with the other non-mangrove classes such as rubber, water, dense forest, built-up areas and other land-cover is performed, the user's classification accuracy is 92.1 and 89.7%, respectively, for mangroves versus non-mangrove classes. The classified image is shown in Figure 2.
Table 3: Error matrix for the classification of mangrove types based on ERS-1 & Spot images (from [5] ).
| Class |
Omission(%) |
Commission (%) |
Map accuracy(%) |
| Rhizophora |
11.1 |
15.6 |
76.9 |
| Nypa palms |
9.7 |
12.9 |
80.0 |
| Mixed dense |
22.4 |
10.0 |
71.4 |
| Mixed open |
26.7 |
33.3 |
55.0 |
| Rubber |
8.9 |
13.3 |
80.4 |
| Water |
0.0 |
0.0 |
100.0 |
Figure 2: Classified land-cover map based on images of spot and ERS-1 (from[1]). Legend: Rhizophora (green), nypa (Yellow), mixed open mangroves (orange), mix dense mangroves (brown), rubber (red), water (blue).
An independent test of the different classification procedures shown in Table 2 (ID7a-e) was carried out on a different study area, located 50 km south of the area shown in Fig. 1. The results were very similar to the ones reported above, with overall accuracies of 83.8%, 48.0% and 72.9% for the spot & ERS-1, ERS-1 & JERS-1 and Landsat based classifications, respectively [1,5].
Conclusions and Recommendations
Based on the study of different sensors and analysis techniques of mangrove forests at a study area in Thailand, the following conclusions can be drawn:
-
The use of cloud-free optical data is most suitable for the discrimination of mangrove form non mangrove areas.
- The best discrimination, also between different mangrove types, was achieved when both data sources, optical (Spot) and radar (ERS) data, were combined.
- At least four mangrove classes can be identified using optical data , namely homogeneous rhizophora, homogeneous nypa , mixed dense and mixe3d open mangrove open.
- The inclusion of radar data gives additional information on the approximate age of rhizophora stands, the presence of clear-cut areas or abandoned paddy fields. Multi-storied , uneven-aged mixed forest has a higher radar backscattering compared to more homogeneous stands. Clear-cut areas where tree trunks, twigs, stumps and other debris are still left on the ground give a strong radar backscattering signal.
- For the use of radar data the use of multi-temporal imagery is crucial, as is the selection of acquisition dates . Changes are occurring mainly in the non-mangrove areas.
- The inclusion of GIS-based data such as topographic information , or soil information increases the mapping accuracy.
For the similar to the one carried out within this study, and if available resources allow , it is recommended to use an integrated approach which includes the use of optical and radar together with GIS based information.
References
-
J. Aschbacher, C. P. Giri, R. S. Ofren, P. N. Tiangco, J-P. Delsol, T. B. Suselo, S. Vibulsresth, and T. Charuppat, "Tropical Mangrove Vegetation Mapping U sing Advanced Remote Sensing And GIS Technology"; Final Report, Asian Institute of Technology, 1994,90 pp.
- S. Aksornkoae, "Ecology and Management of Mangroves", IUCN Publ., Bangkok, Thailand,
1993.
- D. R. Paudyal and J. Aschbacher "ERS-l SAR data calibration at the Indian National Remote
Sensing Agency"; Asia-Pacific Remote Sensing Journal, Vol. 6, No.2, Jan. 1994, pp. 117-120.
- J. Aschbacher, R. Ofren, J-P. Delsol, T. B. Suselo, S. Vibulsresth and T. Charuppat,"An Integrated Comparative Approach to Mangrove Vegetation Mapping using Advanced Remote .Sensing and GIS Technologies: Preliminary Results"; J. Hydrobiologica, Vol. 295, nos. 1-3, Kluwer Academic Press, Jan. 1995, pp. 285-294.
- J. Aschbacher, C. P. Giri, R. S. Ofren, P. Tiangco, D. R. Paudyal: "Comparison of different sensors and analysis techniques for tropical mangrove forest mapping"; Proc. IGARSS '95, 10-14 July 1995, Firenze/Italy; pp. 2109-2111.
Acknowledgement
The authors wish to warmly thank the staff of the Remote Sensing Laboratory of the Asian Institute of Technology for their continued and tireless support of the current work. In particular , the support of Mrs..Zhang, Ms. Srisa-ang and Mr. Shankar is acknowledged. Thanks are also given to Mr. Apan and Mrs. Bevis who supported the initial phase of the project.
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