基于遗传算法优化BP神经网络模型估测高山松叶面积指数
收稿日期: 2020-08-19
修回日期: 2020-10-15
网络出版日期: 2020-11-30
基金资助
国家自然科学基金项目(31860205);国家自然科学基金项目(31460194);2020年云南省教育厅科学研究基金项目(2020Y0403)
Optimized BP Neural Network Model Based on Genetic Algorithm to Estimate The Leaf Area Index of Pinus densata
Received date: 2020-08-19
Revised date: 2020-10-15
Online published: 2020-11-30
叶面积指数(LAI)是衡量森林生产力的重要指标,遥感技术为实现大尺度估测叶面积指数提供支持。以香格里拉市高山松为研究对象,以Sentinel-2多光谱影像为信息源,结合地面样地实测LAI,通过相关性分析筛选出与LAI显著相关的植被指数,采用BP神经网络和遗传算法(GA)优化BP神经网络建立高山松LAI估测模型,基于像元尺度对研究区高山松LAI进行遥感估测。研究表明:1)Sentinel-2影像红边波段构建的植被指数与LAI有较高的相关性; 2)遗传算法优化前后,BP神经网络模型的决定系数(R2)为0.289和 0.508,均方根误差(RMSE)为0.340和0.314,通过遗传算法优化后,BP神经网络建模精度更高和对实测LAI的变化趋势预测更加准确。研究结果可为低纬度高海拔地区森林LAI的研究提供参考。
关键词： 叶面积指数; 遗传算法; BP神经网络; Sentinel-2影像; 高山松
谭德宏 , 舒清态 , 赵洪莹 , 王柯人 , 袁梓健 . 基于遗传算法优化BP神经网络模型估测高山松叶面积指数[J]. 林草资源研究, 2020 , 0(5) : 123 -130 . DOI: 10.13466/j.cnki.lyzygl.2020.05.018
The leaf area index (LAI) is an important index to measure forest productivity.Remote sensing technology provides support for large-scale of LAI estimation.This study takes Pinus densata as the research object in Shangri-La city,uses Sentinel-2 multi-spectral images as the information source,and combines the measured LAI on the ground sample plots,selects vegetation index which is significantly correlated with LAI through correlation analysis,uses BP neural network model and genetic algorithm (GA) to optimize BP neural network to establish LAI of Pinus densata estimation model.Research shows that:1) Sentinel-2 image red-edge band vegetation index has a high correlation with LAI;2) The coefficient of determination (R 2) of the BP neural network model before and after genetic algorithm optimization were 0.289 and 0.508,and the root mean square errors (RMSE) were 0.340 and 0.314.The BP neural network modeling accuracy is higher after the genetic algorithm optimization and the actual measurement the forecast of the change trend of LAI is more accurate.This research result can provide a reference for the study of forest LAI in low latitude and high altitude areas.
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