FOREST ECOSYSTEM PROCESSES AT THE WATERSHED SCALE - SENSITIVITY TO REMOTELY-SENSED LEAF-AREA INDEX ESTIMATES

Recent research has shown that general trends in forest leaf area inde x along regional climatic gradients can be adequately characterized by using ratios of near-infrared and red reflectances. However it has pr oven difficult to represent properly the spatial distribution of Leaf Area Index (LAI) at subregional scales such as small catchments. The k ey problem at Thematic Mapper scale is the variation in canopy closure and understorey contribution, which dramatically influences near-infr ared reflectance from conifer forests. In this paper, a new spectral i ndex is presented to estimate LAI of conifer forests using a combinati on of Red, NIR and mid-IR reflectances from the Landsat Thematic Mappe r (TM). A simulation system (RHESSys) was used first, to generate pote ntial vegetation patterns around a watershed in order to test them aga inst remotely-sensed vegetation patterns, and secondly, to test the se nsitivity of forest ecosystem processes to LAI estimated from combinat ions of the Thematic Mapper data. The relation between Normalised Diff erence Vegetation Index (NDVI) and LAI is poorly defined at TM scale b ecause of the outsized contribution of understorey vegetation and back ground materials to the NIR reflectance in open canopies. The mid-IR c orrection factor acting as a scalar for canopy closure scaled down the inflated NDVI in the open canopies, resulting in an improved relation between NDVI and LAI. LAI estimates from the MIR corrected NDVI bette r represented the vegetation patterns in Soup Creek watershed than tho se from uncorrected NDVI both in terms of magnitude and spatial patter ns. Simulations using LAIs derived from corrected NDVI showed lower ra tes evapotranspiration and net photosynthesis. Differences in mean res ponses of evapotranspiration and photosynthesis were as large as 8 cm and 2 ton C ha-1 yr-1 respectively between simulation runs using LAIs from corrected and uncorrected NDVI.