The study of land use survey in the tropics using Multi Season and Multi Sensor Remote Sensing data
Conclusion
A couple of conclusions of this study are summarized blow.
- The development o image registration method.
The image registration method developed in this study significantly reduced the registration errors among input images also simplified the data processing of multi season and multi sensor images
- Improvement of the accuracy of the training data classification
The combination of three images acquired in different seasons improved the accuracy of training data classification .
We are now improving our method to employ geological data soil data and DTM of the test site these data are expected to improve the land use classification of this study.
Acknowledgements
We thank TDD Ministry of agriculture and cooperatives Thailand especially mr. Manu OMAKUPT Ms Promchit TRAKULDIST and Mr Anusorn Chantanaoj for assisting our field survey.
Table 5 Acuracy of training data classificaion(%)
| One sensor |
Two sensor |
Three sensors |
Four sensors |
| H |
87.9 |
H+T |
96.0 |
H+T+ME |
98.9 |
H+T+Me+Ms
99.7 |
| T |
90.1 |
H+Me |
96.3 |
H+T+WS |
99.2 |
| ME |
89.9 |
H+MS |
97.4 |
H+ME+Ms |
98.1 |
| MS |
87.9 |
ME+MS |
96.8 |
H+Me+Ms |
99.4 |
| |
T+MS |
98.1 |
H........HRV T........TM |
| Me+Ms |
98.2 |
ME.....MESSR T.......M |
Table 6 The error matrix of classified results by MESSR
| |
Classified land use |
| Urban |
Paddy |
Rubber |
Coconut |
Forest |
Mangrove |
Mine |
Water |
Original
Land
Use |
Urban area |
560 |
11 |
0 |
11 |
0 |
0 |
21 |
0 |
| Paddy |
5 |
556 |
34 |
24 |
0 |
0 |
9 |
0 |
| Rubber |
0 |
642 |
14 |
40 |
40 |
0 |
0 |
0 |
| Coconut |
4 |
11 |
39 |
91 |
0 |
0 |
0 |
0 |
| Forest |
0 |
4 |
6 |
42 |
361 |
22 |
0 |
15 |
| Mangrove |
5 |
13 |
0 |
0 |
16 |
485 |
0 |
2 |
| Mine |
5 |
0 |
0 |
0 |
0 |
0 |
282 |
0 |
| Water area |
20 |
0 |
0 |
0 |
20 |
0 |
2 |
534 |
Table 7 The error matrix of classified results by MSS
| |
Classified land area |
| Urban |
Paddy |
Rubber |
Coconut |
Forest |
Mangrove |
Mine |
Water |
Original
Land Use |
Urban area |
425 |
134 |
0 |
28 |
0 |
0 |
12 |
0 |
| Paddy |
65 |
499 |
0 |
2 |
0 |
16 |
0 |
46 |
| Rubber |
0 |
5 |
666 |
0 |
25 |
0 |
0 |
0 |
| Coconut |
0 |
8 |
0 |
137 |
0 |
0 |
0 |
0 |
| Forest |
0 |
0 |
9 |
20 |
418 |
3 |
0 |
0 |
| Mangrove |
4 |
8 |
0 |
0 |
0 |
506 |
0 |
0 |
| Mine |
8 |
0 |
0 |
0 |
0 |
0 |
279 |
0 |
| Water area |
6 |
33 |
0 |
6 |
0 |
0 |
28 |
503 |
Table 8 The error matrix of classifed results by MESSR and MSS
| |
Classified land uses |
| Urban |
Paddy |
Rubber |
Coconut |
Forest |
Mangrove |
Mine |
Water |
Original
Land Use |
Urban area |
580 |
10 |
0 |
1 |
0 |
0 |
12 |
0 |
| Paddy |
9 |
615 |
0 |
4 |
0 |
0 |
0 |
0 |
| Rubber |
0 |
3 |
689 |
02 |
0 |
0 |
2 |
0 |
| Coconut |
2 |
4 |
0 |
139 |
0 |
0 |
0 |
0 |
| Forest |
0 |
0 |
0 |
1 |
449 |
0 |
0 |
0 |
| Mangrove |
4 |
5 |
0 |
0 |
0 |
509 |
0 |
3 |
| Mine |
1 |
0 |
0 |
0 |
0 |
0 |
286 |
0 |
| Water area |
1 |
0 |
0 |
0 |
0 |
0 |
5 |
570 |
accuracy = 98.2%
