In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
416 
Source image (LANDSAT TM) of this study 
contained 7 spectral bands. For display purpose 3 bands 
were selected which helped selection of training areas. For 
this study R: 7 G: 4 B: 3 were found suitable. 
In training stage location, size, shape and 
orientation of each pixel type for each class was analysed to 
categories the satellite image accordingly. Some regions 
have been selected from Mirdita (district of Albania) area 
for five land cover class through training process. 
In classification stage each pixel was categorised into 
land cover class to which it closely resembles. If the pixel 
was not similar to the training data, then it was labelled as 
unknown. Supervised classification of ER Mapper was used 
for image classification. ‘Minimum Distance Classification’ 
was used as it gave best result among all supervised 
classification available in ER Mapper. The land cover was 
classified according to the FAO Land Cover Classification 
System (LCCS) (Di Gregorio, A., & Jansen, L.J.M. 1996, 
1997), a comprehensive, standardized a priori classification 
system, created for mapping exercises and independent of 
the scale or mapping method. The classification uses a set 
of independent diagnostic criteria that allow correlation 
with existing classifications and legends. The system could 
therefore serve as an internationally agreed reference base 
for land cover. The methodology is applicable at any scale 
and is comprehensive in the sense that any land cover 
identified anywhere in the world can be readily 
accommodated. 
Soil types and erosion features, obtained from 
traditional sources, will linked to each land cover mapped 
unit as attributes into a GIS system. This results in a 
comprehensive database, which provides useful information 
for agriculture, forestry and urban development planning, 
for environment protection, and for many other 
applications. The data collected in the database allow for 
different kinds of spatial analyses, which are necessary in 
land management. As the database developed using 
ArcView, a common GIS software package, it will be easy 
to combine the database with other data sets, existing or in 
preparation, for a variety of different applications. 
5. CONCLUSSIONS 
In context of Albania, Remote Sensing technology is 
an non well unexplored field. By using this rarely used tool 
a land cover classification of Albania was done. LANDSAT 
TM image was used for this study. The land cover was 
classified according to the FAO Land Cover Classification 
System (LCCS). 
REFERENCES 
1. Di Gregorio, A., & Jansen, L.J.M. 1996. FAO Land 
Cover Classification System: A Dichotomous, 
Modular-Hierarchical Approach. Paper presented at 
the Federal Geographic Data Committee Meeting - 
Vegetation Subcommittee and Earth Cover Working 
Group, Washington, 15-17 October 1996. 
2. Di Gregorio, A., & Jansen, L.J.M. 1997. A new 
concept for a land cover classification system. 
Proceedings of the Earth Observation and 
Environmental Information 1997 Conference. 
Alexandria, Egypt, 13-16 October 1997. 
3. FAO. 1995. Planning for sustainable use of land 
resources. Towards a new approach. FAO Land and 
Water Bulletin No. 2. 
4. http://www.fao.org/docrep/003/x0596e/x0596e00.htm 
5. Meyer, W.B., & Turner II, B.L. 1992. Human 
Population Growth and Global Land Use/Land Cover 
Change, pp. 39-61, in: Ann. Rev. Ecol. Syst., No. 23. 
6. Mueller-Dombois, D., & Ellenberg, J.H. 1974. Aims 
and Methods of Vegetation Ecology. New York and 
London: John Wiley. 
7. Nasreen, I.K., 1999: Present status of geoinformatics 
technology in Bangladesh: special Nègre, T. 1995. 
Report of the Preparatory Mission: Outlines for the 
Africover Classification System. Draft document. 
FAO, Rome. 
8. Nghia C.X., 1999, Results of afforesting protective 
forest in the sand areas, Journal of agriculture and 
development of rural area, Vol 9, Ha Noi, 13-14. oi. 
9. Olaf B. and Uta S., 2002. Development and 
perspectives of landscape ecology, Kluwer Academic 
Punlishers, Netherlands. Operationalization of Remote 
Sensing, ITC. 
10. Ostir, K., 2006: Daljinsko zaznavanje.- Zalozba ZRC, 
p. 250, Ljubljana. 
11. Reichert, P., & Di Gregorio, A. 1995. Preparation of a 
forest cover map and reconnaissance forest inventory 
of Albania. Technical Report of FAO Project 
GCP/ALB/002/IDA. Remote Sensing Journal 2(2), 33- 
41. 
12. Sokal R. 1974. Classification: purposes, principles, 
progress, prospects. Science 185 (4157): 1115-1123. 
13. Clawson, Marion, and Stewart, Charles L., 1965, Land 
use information. A critical survey of U.S. statistics 
including possibilities for greater uniformity: 
Baltimore, Md., The Johns Hopkins Press for 
Resources for the Future, Inc., 402 p. 
14. Gordon, S., 1980. Utilizing Landsat imagery to 
monitor land use change: A case study in Ohio. 
Remote Sensing of Environment, 9, pp. 189-196