Extraction of vegetation is an important step for agricultural, forest and greenery mapping. The proposed method examines the complex process of land cover vegetation pattern classification using an IRS-1C LISS III image. Pre-processing was done by employing partial differential equation (PDE). Normalized differential vegetation index (NDVI) was applied to separate vegetation features from the ...

Land cover of Finnish Lapland was classified to 16 land cover classes using optical IRS LISS, Spot XS and MODIS satellite images, ancillary GIS data and decision tree classifier. The aim of this study was to test decision tree classifier for land cover classification and study the effects of its parameters to classification result. In the best case, the overall accuracy was about 68% for all 16...

This paper reports results of an experiment LRVE (Leaf Area Index Retrieval and Validation Experiment) that was conducted over agricultural areas of Central India during winter season of 2001-02, aimed at relating field measurements of LAI to space borne IRS LISS-III data, preparation of site-level LAI maps and validation of MODIS-based 1 km LAI global fields,. Measurements of fieldlevel LAI, a...

estimation of vegetation cover attributes, such as the leaf area index (lai), is an important step in identifying the amount of water use for some plants. the goal of this study is to investigate the feasibility of using irs liss-iii data to retrieve lai. to get a lai retrieval model based on reflectance and vegetation index, detailed field data were collected in the study area of eastern iran....

the preparation of vegetation cover maps by used the land inventory and a traditional method has a lot of cost and time. but today, remote sensing is one of the main sources of data collection and information production for study and monitoring land resources, and was efficient tools for providing quickly and timely data and information needs for program planning in the natural resource filed. ...