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

doi:10.13466/j.cnki.lyzygl.2020.05.018

摘要/Abstract

摘要：

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

Abstract:

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.

Key words: leaf area index, genetic algorithm, BP neural network, Sentinel-2 image, Pinus densata

中图分类号:

S758.4

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]. 林业资源管理, 2020,(5): 123-130.

TAN Dehong, SHU Qingtai, ZHAO Hongying, WANG Keren, YUAN Zijian. Optimized BP Neural Network Model Based on Genetic Algorithm to Estimate The Leaf Area Index of Pinus densata[J]. FOREST RESOURCES WANAGEMENT, 2020,(5): 123-130.

图/表 11

表1

表2

表3

植被指数公式

植被指数	公式	文献号
归一化差值植被指数(NDVI)	(NIR-RED)/(NIR+RED)	21
归一化光谱指数(NDSI)	(R₇₈₃-R₇₀₅/(R₇₈₃+R₇₀₅)	22
比值型光谱指数(RSI)	R₇₈₃/R₇₀₅	22
土壤调节植被指数(SAVI)	(1+L)(R₈₆₅-RED)/(R₈₆₅+RED+L)(L=0.25)	23
垂直植被指数(PVI)	(0.355NIR-0.149RED)²+(0.3355RED-0.852NIR)²	24
红边位置植被指数(S2REP)	705+35×[0.5×(R₇₈₃+R₆₆₅)-R₇₀₅]/(R₇₄₀-R₇₀₅)	25
简单比率(MSR)	(NIR/RED-1)/ $(NIR / RED + 1)$	26

表3

图1

表4

表5

图2

图3

图4

图5

表6

参考文献 28

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波段	波段名称	波长范围/nm	分辨率/m
B1	气溶胶及海岸带波段	422~456	60
B2	蓝波段	439~533	10
B3	绿波段	538~583	10
B4	红波段	646~684	10
B5	植被红边	695~714	20
B6	植被红边	731~749	20
B7	植被红边	769~797	20
B8	近红外波段	773~907	10
B8a	近红外波段	847~881	20
B9	水蒸气波段	932~958	60
B10	卷积云波段	1337~1412	60
B11	短波红外波段	1539~1682	20
B12	短波红外波段	2078~2320	20

植被指数	相关系数	植被指数	相关系数
NDSI(783,705)^*	0.454	MSR(783,705)	0.335
NDSI(783,740)	0.280	NDVI^*	0.504
NDSI(740,705)	0.263	S2REP^*	0.508
MSR	0.398	RSI	0.306
MSR(740,705)	0.263	SAVI^*	0.478
MSR(783,740)	0.280	PVI^*	0.501

LAI取值范围	像元个数	百分比/%
0~2	396719	18.38
2~3	1746277	80.93
3~4	14792	0.69