Abstract:

Change patterns of each ecological factor,such as spatial and periodic distribution of wind, sun light and temperature, redistribution of precipitation, are closely relate to canopy closure in stand forest. To properly estimate the distribution of canopy closure is a foundation of recognizing and utilizing ecological service function of forest. Due to the complexity of objective world and uncertainty of remote sensing data,we don't always find out the variables that significantly impact the estimation of canopy closure but in term of common sense select sufficient variables to analyze. In this paper,Bootstrap approach based on partial least squares regression and RMS_q principle based on least squares estimate were used to find out optimal variables to construct the estimation model of canopy closure. The results showed using the Bootstrap approach attributed to improve the estimation precision of regression models. Additionally,despite of more variables, the Bootstrap approach worked on well while the RMS_q carried out slowly.

Key words: canopy closure estimation models, remote sensing, RMS_q principle, Bootstrap approach, partial least square regression method