Estimating leaf area index (LAI) of black wattle (Acacia mearnsii) using Landsat ETM+ satellite imagery.

Abstract

Leaf area index (LAI) is an important variable in models that attempt to simulate carbon, nutrient, water and energy fluxes for forest ecosystems. LAI can be measured either directly (destructive sampling) or by using indirect techniques that involve estimation of LAI from light penetration through canopies. Destructive sampling techniques are laborious, expensive and can only be carried out for small plots. Although indirect techniques are non-destructive and less time consuming, they assume a random foliage distribution that rarely occurs in nature. Thus a technique is required that would allow for rapid estimation of LAI at the stand level. A means of getting this information is via remotely sensed measurements of reflected energy with an airborne or satellite-based sensor. Such information on an important plant species such as Acacia mearnsii (Black Wattle) is vital as it provides an insight into its water use. Landsat ETM+ images covering four study sites In KwaZulu-Natal midlands encompassing pure stands of Acacia mearnsii were processed to obtain four types of vegetation indices (VIs). The indices included: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) and vegetation index 3 (VB). Ground based measurements of LAI were made using destructive sampling (actual LAI) and LAI-2000 optical instrument, (plant area index, PAl). Specific leafarea (SLA) and leaf area (LA) were measured in the field for the entire sample stands to estimate their LAI values. The relationships between the various VIs and SLA, actual LAI and PAl values measured by LAI-2000 were evaluated using correlation and regression statistical analyses. Results showed that the overall mean SLA value of Acacia mearnsii was 8.28 m2kg-1 SLA showed strong correlations with NDVI (r=0.71, p<O.Ol) and RVI (r=0.76, p<O.Ol) and a moderate correlation with TVI (r=0.66, p<0.05). Regression analysis revealed that SLA had significant relationship with RVI (R2=0.59) and NDVI (R2=0.51). Actual LAI values showed strong correlation with PAl values (r=0.86) and the analysis revealed that 74 % of the variation in the relationship between actual LAI and PAl values could be explained by regression. PAl values were strongly correlated with NDVI (r=0.75,p<O.Ol) and moderately correlated with RVI (r=O.63, p<O.05) and TVI (r=O.58, p<O.05). Actual LAI was strongly correlated with NDVI (r=O.79, p<O.Ol) and moderately correlated with RVI (r=O.61, p<O.05). Out of the various VIs examined in this study, NDVI was found to have a better relationship with actual LAI values (R2=O.62) and with PAI values (R2=O.56); while VB didn't show any significant relationship with SLA, PAl or actual LAl. In conclusion, preliminary estimate of SLA of Acacia mearnsii could be obtained from RVI or NDVl. The relationship obtained between PAl and actual LAI values was satisfactory, thus the regression equation can be used to calibrate the LAI-2000 plant canopy analyzer. Because NDVI was observed to have a good relationship with actual LAI and PAl, LAI of Acacia mearnsii can be estimated from Landsat ETM+ satellite imagery with a reasonable degree of accuracy. These results can satisfactorily be used as inputs into models that attempt to estimate water use by Acacia mearnsii.