A thesis submitted in partial fulfillment of the requirement for the degree of Master of Science, School of Advanced Technologies

Thesis (M.Sc.) - Asian Institute of Technology, 2000

Tea is very important cash crop for Sri Lanka as it is the number one foreign exchange earner for the country. Monitoring tea yield is vital from both management of tea plantation and commercial purpose. Tea yield depends on various land use and environment parameters. Remote sensing provides useful information on existing crop condition. In this analysis an attempt has been made to predict tea yield using remote sensing and other key parameters in GIS environment. Leaf area index (LAI) is one main key factor useful in crop growth models that may be derived from optical remote sensing data. The LAI during the plucking stage is an important state variable in tea yield modeling. The LAI is a major factor determining crop reflectance and is often used in crop reflectance modeling. Therefore, the relationship between tea LAI and optical remote sensing parameter (NDVI) was investigated and this is very important in developing yield prediction models for tea. A model with a logarithmic function was adopted to find the relationship between NDVI (satellite image) and measured LAI. Satellite derived LAI values and existing spatial, meteorological and agronomic variables with statistical regression analysis and analytical capabilities of GIS were used to develop a model for tea yield estimation. Although different methods were tried to find the best model by using multiple regression, a model develop by using weights of considered variables selected as the suitable model for predicting tea yield as it gives a 95% level of significance.