Estimating the leaf area index (LAI) of black wattle from Landsat ETM+ satellite imagery

Synopsis Remote sensing techniques have the potential to provide resource managers with a rapid and economical method of acquiring information related to forest productivity and water use. This study evaluated the utility ofLandsat ETM +satellite imagery to predict canopy attributes ofBlack Wattle (Acacia mearnsii). The study encompassed ground-based measurements ofleaf area index (LAI) and plant area index (PAl) using destructive sampling and LI-COR LAI-2000 plant canopy analyzer, respectively. Vegetation indices (VIs) were estimated from Landsat ETM +images covering four study sites of pure stands of A. mearnsii located in the KwaZulu-Natal Midlands. The indices included: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) and vegetation index 3 (VI3). Relationships between the various vegetation indices, SLA, actual LAI and PAl values were tested using correlation and regression analyses. Results showed strong correlations between LAI and P AI (to calculate LAI), LAI and NDVI, and between P AI and NDVI. No significant correlations were found between VI3 and either P AI or actual LAI. Regression analysis revealed that actual LAI had significant relationships with PAl and NDVI. The results indicate the potential ofthe Landsat ETM +satellite imageries to estimate values ofimportant canopy attributes of A. mearnsii that are related to stand productivity that may be used as inputs into process-based models such as 3-PGS which attempt to estimate stand productivity and water use of commercial plantation tree species.

Hali tarjima qilinmagan