Evaluating micro-satellite data for monitoring environmental dynamics

Quantitative monitoring of vegetation change over time is essential in understanding the environmental processes of which are importance in climate change and global warming models. However, obtaining such information has been a challenge especially for vegetation phenology due to the lack of appropriate methods for quantitative assessment.The aim of this research was to test the relationships between ground-based measurement of leaf area index (LAI) and vegetation indices (VI) derived from satellite remote sensing instruments to quantitatively monitor vegetation dynamics in a broadleaf and coniferous forest in the UK. A regression model was used to predict LAI at both sites and the resulting estimates of LAI had a root mean square error of 0.51 at the broadleaf site and 1.1 at the coniferous forest site. Improvements in the accuracy of ground-based LAI measurements and radiometric and atmospheric correction of the data are expected to increase the accuracy of such LAI estimates in future.