Abstract
Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.
Highlights
Tropical forests comprise nearly 50% of the global forest cover [1]
Leaf Area Index at the Plot Level Calculated from Inventory Data 3.1
We used published Leaf area index (LAI) data that were measured near our research area or came from areas with a similar forest type to evaluate the plot-level LAI calculated from the forest inventory data
Summary
Tropical forests comprise nearly 50% of the global forest cover [1]. Understanding the growth stage of forests is an important basis for understanding their ecological responses to climate change [2]. Selective logging is an important harvesting method in forested areas throughout the humid tropics [4]. Logging results in many changes in the forest structure and contributes substantially to gross carbon fluxes in the Brazilian Amazon and in other tropical regions [5]. Selective logging can be monitored by determining the changes in the forest gap fraction [4] or LAI [6]. Measurements of LAI in selective logging areas are important for monitoring the impact of forest management and for understanding the carbon cycle dynamics in tropical forests
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