基于ASO-BP神经网络的生物量反演模型研究

Research on Biomass Inversion Model Based on ASO-BP Neural Network

本文选择六盘山森林区为论文研究区域,光学数据选择Landsat 8 OLI,微波雷达数据选择具有L波段的ALOS-2 PALSAR-2,并结合森林资源清查数据作为六盘山森林AGB的反演模型研究数据源,对选择的两种数据源的相关特征变量进行提取、分析、筛选,基于经典及改进算法构建六盘山森林AGB反演模型。利用原子优化算法(ASO)优化BP神经网络模型构建新的森林地上生物量反演模型——基于原子优化算法改进的BP神经网络(ASO-BP)森林AGB反演模型,通过对两种生物量反演模型精度的对比与评价,最终选择精度最高的ASO-BP反演模型比较适用于六盘山森林地上生物量反演,完成六盘山森林地上生物量的估算和分析。

This paper selects Liupanshan forest area as the research area,selects Landsat 8 OLI as the optical data,and selects ALOS-2 PALSAR-2 with L band as the microwave radar data,and uses the forest resource inventory data as the data source of Liupanshan forest AGB inversion model research,extracts,analyzes,and filters the relevant characteristic variables of the selected two data sources,and constructs the Liupanshan forest AGB inversion model based on the classic and improved algorithms.ASO is used to optimize the BP neural network model and construct a new forest aboveground biomass inversion model-the improved BP neural network(ASO-BP)forest AGB inversion model based on the atomic optimization algorithm.By comparing and evaluating the accuracy of the two biomass inversion models,the ASO-BP inversion model with the highest accuracy was ultimately selected to be more suitable for the aboveground biomass inversion of the Liupanshan forest,completing the estimation and analysis of aboveground biomass of Liupanshan forest.