​At regional and national levels, additional and comprehensive information is needed to model policy scenarios, and to forecast impacts on environmental policies and decisions related to land evaluation and agricultural planning. One of the first steps in these efforts is to systematically describe land cover and land use. Data describing land cover are required in many research analyses and modelling studies. Many of these data can be estimated from space-based observations at various scales and can be obtained on a multi-temporal basis globally (IGBP,1992).

Many existing data sets, especially for land applications, have been derived from hard copy maps at various scales and from tabular sources. Since variables are required to be noticed in a uniform geographic and temporal dimensions suggests the use of satellite remote sensing as the major new source (Rasool, 1987). Many of these data have the potential to be acquired globally and can be used to derive a number of important monitoring and modelling parameters. Data describing land cover are required in many research analyses and modelling studies. Many of these data can be estimated from space-based observations at various scales and can be obtained on a multi-temporal basis globally (IGBP,1992).

National statistics, aggregated for regional and global applications on agriculture, fisheries, forestry, etc., are often not consistent. Harmonized definitions and classifications will increase the value of land cover and land use statistics. Also existing land cover and land use data tend to be sectoral in nature. Primary data sources, sampling structures, classifications, nomenclature, and data formats differ; many data are based on estimates and interpolations; and datasets are often static (no indications of changes or variation). As a result, area estimates of land cover and land use often vary widely. Most scientists recommend the use of remote sensing data, validated by ground truthing for land use mapping.

This project primarily aims to determine the present status of land cover types and the nature of the land cover transformation for selected target countries in the Asian region at the scale 1:1 million on a regular basis, a vital information for the regional monitoring of the land cover dynamics. The use of remotely sensed data particularly the NOAA AVHRR data was considered a major tool for such purpose. It also provides an opportunity to recognize areas of major land cover transformation ("hot spots") which will serve as an early warning system that will allow for a more detailed analysis and to derive criteria for any future action plan.

List of reports:

1. Main report
2. Bangladesh
3. Cambodia
4. Lao P.D.R
5. Malaysia
6. Myanmar
7. Nepal
8. Pakistan
9. Thailand
10. Vietnam