Abstract
Leaf Area Index (LAI) estimation of crops in cropland and non-crop vegetation in abandoned croplands is important to understand the contributions of crops and vegetation to primary productivity. We used a field spectra-based Normalized Difference Vegetation Index (NDVI) and sampled the LAI of crop and non-crop vegetations (CNCVs) including cultivated and abandoned croplands to develop a simple LAI estimation model. The measured LAI was significantly correlated with field-based NDVI (R2=0.70, p<0.001). QuickBird (QB) imagery of 12 April and 23 May was used for agricultural land (AL) classification, and image obtained on 8 July 2007, was used to validate the field-based LAI estimation model and LAI mapping. Four classes including paddy, corn, abandoned cropland and poly house were classified by the ISODATA clustering technique. Overall accuracy of the classification was 94% with Kappa statistics of 0.92. The final classified image was used to produce a LAI distribution map of CNCVs. The LAI estimation model was evaluated by incorporating it into the July QB image to obtain the observed LAI. The relationship between field-measured and QB-observed LAI appeared to be linear (R2=0.70). Finally, a field-based LAI estimation model was applied to the July QB NDVI image in the area of paddy, corn and abandoned cropland. The LAI map gives us information regarding the status of LAI in CNCVs ranged between <1 to >4. In the LAI map, LAI appeared to be higher in the abandoned croplands than in the croplands. Our LAI maps gave satisfactory estimates of the LAI in each land type. Thus, models using field-spectra based NDVI and sampled LAI could be applied to QB images for remote estimation and mapping of LAI.
