Land cover mapping using remotely sensed observation



DATA ANALYSIS AND RESULTS
Two high spatial resolution satellite Landsat TM scenes were chosen in this study for seasonal agricultural vegetation analysis on 28 February 1994 and 27 November 1994. The aim of the classification analysis is to categorize all of the pixels in the imageries into two classes; vegetation and non-vegetation. Basically, the process can be divided into three simple steps, the pre-processing, data classification and output. In the pre-processing, the classes were established by using polygons for training sites. They are delineated by spectrally homogeneous sub areas, which have, class name given. In the classification stage, three supervised classification methods were selected to classify the images. Maximum Likelihood, Minimum Distance-to-Mean, and Parallelepiped were applied in the present study. Two methods of accuracy assessment used in this study were the Kappa statistic and overall accuracy. The Kappa statistic is a statistical method of assessing the accuracy that takes into account the chance of random agreement. This statistic has been used by many researchers in their studies [Selamat, et al., (2002), Dymond and Johnson, (2002)]. The produced results in this study are shown in Table 1 and the accuracy assessment results are shown in Table 2 and 3. Finally, the coverage of seasonal agricultural vegetation was determined. The classified seasonal agricultural vegetation maps are shown in Figure 2 for 28-2-1994 and Figure 3 for 27-11-1994.


Table 1. Statistic analysis for the increasing vegetation and urban areas
Classes 28-2-1994(km2) 27-11-1994 (km2)
Vegetation 13.0023 11.7712
Non- Vegetation 100.9557 102.1868
Total 113.9580 113.9580


Table 2. The Kappa coefficient for the two images
Classification method Kappa coefficient
28-2-1994 27-11-1994
Maximum Likelihood 0.8651 0.9102
Minimum Distance-to-Mean 0.7152 0.7925
Parallelepiped 0.5961 0.6584


Table 3. The overall classification accuracy for the two images
Classification method Overall classification accuracy (%)
28-2-1994 27-11-1994
Maximum Likelihood 86.2154 90.2561
Minimum Distance-to-Mean 75.2152 78.2151
Parallelepiped 54.0210 61.3653



Figure 2. The seasonal agricultural vegetation map: 28-2-1994 [Colour Code: Green = vegetation and Blue = Non-vegetation]



Figure 3. The seasonal agricultural vegetation map 27-11-1994 [Colour Code: Green = vegetation and Blue = non-vegetation]


CONCLUSION

This analysis has demonstrated the necessity of a spatial approach in studying seasonal agricultural vegetation over AlQassim, Saudi Arabia. The Maximum Likelihood classifier produced high degree of accuracy. This study classified two seasonal agricultural vegetation coverage maps with a reasonable accuracy.

ACKNOWLEDGEMENTS:

We would like to thank the technical staff who participated in this project. Thanks are extended to USM for support and encouragement.

REFERENCES:
  • Bruzzone, L., Cossu, R. and Vernazza, G. (2002). Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images. Information Fusion 3, 289 –297.
  • Dymond, C. C. and Johnson, E. A., 2002, Mapping vegetation spatial patterns from modeled water, temperature and solar radiation gradients. Journal of Photogrammetry and Remote Sensing, 57, 69–85.
  • Rees, W.G., Williams, M. and Vitebsky, P., 2003, Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge. Remote Sensing of Environment, 85, 441–452.
  • Selamat, I., Nordin, L., Hamdan, N., Mohti, A. and Halid, M., 2002, Evaluation of TiungSAt data for land cover/use mapping application. Proceeding of the Seminar Kumpulan Pengguna TiungSAT-1, Jabatan Remote Sensing dan Sains Geoinformasi, Fakulti Kejuruteraan Dan Sains Geoinformasi Universiti Teknologi Malaysia and Astronautic Technology (M) Sdn Bhd.
Page 2 of 2
| Previous |