Remote Sensing Estimation of Forest Lai in Close Canopy Situation

This study attempts to find a new and better approach to estimate forest LAI in fully closed canopy condition. Although there have been many previous studies to estimate LAI using optical remote sensor data, there are not enough evidences whether the red and near-IR reflectance are still effective to estimate forest LAI in closed canopy situation. In this study, we have conducted a simple correlation analysis between LAI and spectral reflectance at two different settings: 1) spectral measurements on the multiple-layers of leaf samples and 2) Landsat ETM+ reflectance with field-measured LAI on the close canopy forest stands. In both cases, the correlation coefficients between LAI and spectral reflectance were higher in short-wave infrared (SWIR) and visible wavelength regions. Although the near-IR reflectance showed positive correlations with LAI, the correlations strength is weaker than in SWIR and visible region. The higher correlations were found with the spectral reflectance data measured on the simulated vegetation samples than with the ETM+ reflectance on the actual forests. In addition, there was no significant correlation between the forest LAI and NDVI, in particular when the LAI values were over three and full canopy situation. The SWIR reflectance may be important factor to improve the potential of optical remote sensor data to estimate forest LAI in close canopy situation. * Corresponding author INTRODUCTION Forest leaf area index (LAI) has been one of important structural variables to understand the process of forest ecosystems and can be used to measure the activities and the production of plant ecosystem (Pierce and Running, 1988; Bonan, 1993). The measurement of LAI on the ground is very difficult and requires a great amount of time and efforts (Gower et al., 1999). This is particularly true in forest where the canopy structure is much more complex than the grasslands and agriculture systems. Since plant canopy is composed of leaves, which is a direct source of the energy-matter interactions that are observed by earth-observing remote sensing systems, LAI has been an attractive variable of interest in vegetative remote sensing. There have been many attempts to estimate LAI using various types of remote sensor data since the early stage of space remote sensing (Badwhar et al., 1986; Peterson et al., 1987; Turner et al., 1999). Remote sensing estimation of LAI has been primarily based on the empirical relationship between the field-measured LAI and sensor observed spectral responses (Curran et al., 1992; Peddle et al., 1999). As a single value to represent the remotely sensed spectral responses of green leaves, spectral vegetation indices, such as normalized difference vegetation index (NDVI) or simple ratio, are frequently used to indirectly estimate LAI. Normalized difference vegetation index (NDVI) has been a popular index with which to estimate LAI across diverse ecosystems. However, large portion of such studies to estimate LAI using NDVI were dealing with semi-arid vegetation and agricultural systems where the canopy closure is less than 100%. Recent studies have shown that the NDVI many not be very sensitive to values of LAI in particular at the forest ecosystem having the close canopy condition that the LAI value is relatively high (Chen and Cihlar 1996, Turner et al. 1999) The objectives of this study are to analyse the relationship between spectral reflectance and LAI in fully canopy condition and to find a methodology to estimate LAI in forest where the canopy closure is closed to 100% and LAI values are high. Although there were several studies dealing with the remote sensing estimation of LAI in forest, the study sites were generally not close canopy situation (Turner et al., 1999; Lefsky et al., 1999). The forest vegetation has very dense canopy closure in Korea as well as many other temperate and tropical forests around the world. Considering the environmental value of these forest ecosystems, more effective and accurate method to estimate forest LAI would be very beneficial.