Ruiliang Pu University of South Florida
Shawn Landry University of South Florida

22E

The forest leaf area index (LAI) is an important structural parameter to quantify the structure and function of a forest ecosystem. Modern remote sensing techniques may offer an alternative for measuring and mapping forest LAI at a landscape scale. Given the fact of penology of forest plant and spectral and spatial/textural characteristics of high resolution satellite imagery, it is significant that assessing the potential of multiseasonal high resolution Plleiades satellite imagery for estimating and mapping forest plant LAI. For this case, four multiseasonal Pleiades images (acquired in FEB, MAY, AUG and NOV, 2015) were collected to cover a mixed natural forest area in Florida, USA, and corresponding seasonal in situ LAI were measured. Then, LAI seasonal changes and mapping accuracies would be analyzed and compared among the four season images.  In this study, spectral/spatial features (SFs) were first extracted, selected, and tested, including band reflectance, various vegetation indices and 1st and 2nd-order statistical texture measures from the four season images;  then, a canonical correlation analysis was performed with each of four data sets of SFs and LAI measurements; and finally linear regression models were used to predict and map the forest plant LAIs with canonical variables calculated from image data.  The experimental results indicate that for estimating and mapping the seasonal forest LAIs, (i) MAY image has resulted in the highest LAI mapping accuracy, then AUG, and FEB images and NOV image in the lowest accuracy; and (ii) AUG image produced maximum variation range of forest LAI in the study area, then MAY and NOV images, and FEB image had a minimum variation of LAI.  The results demonstrate that late spring seasonal (MAY) image significantly improved forest LAI mapping accuracies compared to other seasonal images (p<0.01), and the maximum variation of forest LAI could be obtained with a typical summer season image (AUG), suggesting a significant seasonal effect on the forest LAI mapping. Therefore, it is important to choose appropriate seasonal remote sensing data for mapping forest plant LAI.

10:30 Evaluating Seasonal Effect on Mapping Forest LAI Using Multiseasonal Pleiades Satellite Imagery, Ruiliang Pu