Total Reflected Radiance Index -An Index To Support Land Cover Classification
Application Of TRR Index For Land Cover Classification
An algorithm for land cover classification by using TRR index in combination with other image invariant could be proposed as on Fig. 5
Fig. 4
| NDVI |
Maximum likelihood class. |
TRR index and other invariant |
| To 127 : water |
Water |
To 1: water |
| To 150: bare surf. |
Cl.Fo.: closed forest |
To 50: closed forest |
| To 175:lo w NDVI |
Op. Fo.: Open forest |
To 51: Open forest |
| To 200:medium NDVI |
Bush: Bush land |
To 52: Bush land |
| To 255:high NDVI |
Grass: Gras land |
To 53: Grass land |
| |
Lang:Settlement in rural area |
To 54: Settlement in rural area |
| |
Lo. Ve.: Surface with low portion of vegetation To |
55: Surface with low portion of vegetation |
| |
Ba. So.:Bare soil |
To 62: Bare soil |
| |
We. La.: Wet land |
To 70: Wet land |
| |
|
To 71: Wetland vegetation (grass) |
| |
Ba.su.: Bare surface |
To 80: Bare surface |
| |
Cloud: Cloud |
To 250: Cloud |
The proposed algorithm has been realized by Microsoft FORTRAN PowerStation 4.0 for Windows 95. Classification by the above algorithm was compared with NDVI and maximum likelihood methods. On Fig. 4a is classified image of study area by level slicing of NDVI image, Fig. 4B shows result of maximum likelihood and the Fig.4C is result of classification and the Fig.4c is result of classification by the above algorithm.
Fig. 5 Computation flowchart of
land cover classification by using
TRR index
When compare these three classification results we can see that all three methods classify correctly areas with and without vegetation. However, due to impact of topographical and tree canopy shadow the NDVI method shows less accuracy in forest cover classification. Because closed forest has lower NDVI than open forest and bush land so it was classified to class of medium NDVI while open forest was classified to class of high NDVI. By supervised classification and method with TRR index the above inaccurate classification can be avoided. While all land cover categories with low NDVI value are classified into single object in the first classification method, Maximum likelihood method and TRR index method provides finer classification capabilities. The ML method classifies and cover according to how training data was selected. The TRR index method provides the finest abilities to discriminate land cover by combination of TRR index and other image invariant.
Conclusion
The above research result has pointed out a possible way to improve accuracy of land cover classification. The TRR index can be used in combination with NDVI index for fast land cover classification in global and regional scale. When spectral channels of the input data cover both visible and infrared region, it is possible to establish TRR index differently for each spectrum and use them with NDVI index to achieve higher classification accuracy.
Acknowledgment
The author thanks NASDA-ESCAP joint ADEOS research project for the sponsorship to undertake this research. The author expresses also acknowledgement to Fundamental Research Program of Vietnam and Institute for Geography, NCST of Vietnam for supporting this research.
Reference
-
David A. Hastings
-
Land cover Classification: Some new Technique, New Source Data.
- Proceeding of the 18th Asian conference on Remote Sensing, 20-24 October, 1997 kuala Lumpur, Malaysia
- B.L. Turner II, David Skole, Steven Sanderson, Gunther Fisher, Louise Fresco and Rik Leemans
- Land-Use and Land-Cover Change, Science/Research Plan
- IGBP Report No. 35, HDP Report No. 7
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Nguyen Dinh Duong
- Semi -Automatic Land cover Classification Using ADEOS/AVNIR Multispectral Data
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Proceedings of the 18th Asian conference on Remote Sensing, 20-24 October, 1997 Kuala Lumpur, Malaysia.
