Multi-Model Estimation of Forest Canopy Closure by Using Red Edge Bands Based on Sentinel-2 Images

Yiying Hua,Xuesheng Zhao

doi:10.3390/f12121768

Abstract

In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images.

Highlights

Forest canopy closure (CC), an essential parameter of forest structure and the forest environment, is defined as the ratio between the total shadow area projected on the ground by direct sunlight and the total area of the forest [1]
The use of remote sensing represents an effective approach for determining the distribution of forest canopy closure at a regional scale and thereby provides a convenient and feasible method for monitoring and managing forest resources
We introduced vegetation indices calculated from red edge bands into estimation models and analyzed their applicability

Summary

Introduction

Forest canopy closure (CC), an essential parameter of forest structure and the forest environment, is defined as the ratio between the total shadow area projected on the ground by direct sunlight and the total area of the forest [1]. CC is widely used in forest resource inventory, forest quality evaluation, landscape construction and other fields [2,3,4]. In forest management, CC is a significant basis for determining forest and sub-compartment divisions, as well as serving as an important indicator for determining tending and cutting intensity. CC is a basic ecological factor that has a significant influence on multiple forest parameters, including canopy interception, throughfall and forest illuminance. CC is an important parameter with respect to the estimation of aboveground biomass [7,8], and more accurate estimates obtained based on CC can contribute to the management of forests with regard to wildlife habitats. The effective inversion and monitoring of forest CC can facilitate the accurate evaluation of forest benefits and better serve forest resource monitoring and national forest ecological security and timber strategies

Methods

Results

Discussion

Conclusion