Abstract
The forest canopy leaf area index (LAI) is an important structural variable directly affecting functions and structures of terrestrial plant ecosystems. Optical remote sensing techniques may provide an alternative in estimating and mapping plant LAI. However, existing studies on using very high resolution (VHR) multitemporal satellite imagery to map and investigate the seasonal effect on plant LAI at a landscape scale are rare. In this study, we proposed to map and analyze forest LAI using four seasonal Pleiades images and corresponding in situ seasonal LAI measurements collected over a natural forest area in the City of Tampa, Florida, USA. A subset of selected spectral/textural features was used to develop pixel-based seasonal LAI regression models through a two-step feature selection procedure and a canonical correlation analysis. Finally, seasonal changes of the mapped LAIs were analyzed and assessed. Several interesting experimental results were created through this study, including: (i) a set of optimal texture parameters for extracting the 1st- and 2nd-order grey level statistical textures from the Pleiades imagery was determined as a window size 5 x 5, a direction 90 degrees and pixel displacement 4 pixels; (ii) textural features were more important than spectral features in estimating and mapping forest LAI, and red band has a higher power in mapping forest LAI than other three multispectral bands; (iii) the late spring Pleiades image resulted in the highest accuracy for estimating and mapping forest LAI; and (iv) there exists a significant seasonal change of forest LAI in the study area and the seasonal effect on forest LAI mapping can be assessed by using the multi-seasonal VHR satellite imagery at a landscape scale. A novel significance for this study is that it is the first time using both spectral and textural information extracted from the multi-seasonal VHR satellite images to assess the seasonal effect on forest LAI mapping at a landscape scale. Since the experimental results and findings were derived from a relatively small study area, further testing and validation work is needed over different forest ecosystems at a landscape scale.
NSTL
Elsevier