Comparison between biophysical analysis model and dimidiate pixel model for the estimation of forest canopy density

Abstract

Forest canopy density (FCD) is an essential factor in forest resources investigation and plays a vital role in forest resources management. To replace the traditional time-consuming and labor-intensive survey methods, it is of great importance to study how to select a suitable model at specific temporal and spatial scales for rapid and accurate estimation of FCD in large areas. To reduce workload and errors, we present an approach combing Landsat 5 TM data and biophysical analysis algorithms without considering thermal index (TI) information to model FCD in a temperate forest in Northern China. The scaled vegetation index (SVD) and scaled shadow index were used as variables for modeling and mapping FCD. The dimidiate pixel model based on the normalized difference vegetation index was also used to estimate FCD. We compared the forest management inventory data of 2012 with the FCD results from the biophysical analysis model and the dimidiate pixel model. The results showed that the biophysical analysis model (prediction precision P = 93.62 % , coefficient of determination R2 = 0.886, root mean square error RMSE = 0.057) has higher accuracy than the dimidiate pixel model (P = 89.92 % , R2 = 0.725, RMSE = 0.078) in estimating FCD. This study shows that the biophysical analysis model without TI information has better performance than the dimidiate pixel model, and can be used for monitoring large scale FCD.