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Forest and Grassland Resources Research >

2020 , Vol. 0 >Issue 5: 123 - 130

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2020.05.018

Optimized BP Neural Network Model Based on Genetic Algorithm to Estimate The Leaf Area Index of Pinus densata

  • Dehong TAN ,
  • Qingtai SHU ,
  • Hongying ZHAO ,
  • Keren WANG ,
  • Zijian YUAN
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  • College of Forestry,Southwest Forestry University,Kunming 650224,China

Received date: 2020-08-19

  Revised date: 2020-10-15

  Online published: 2020-11-30

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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

Cite this article

Dehong TAN , Qingtai SHU , Hongying ZHAO , Keren WANG , Zijian YUAN . Optimized BP Neural Network Model Based on Genetic Algorithm to Estimate The Leaf Area Index of Pinus densata[J]. Forest and Grassland Resources Research, 2020 , 0(5) : 123 -130 . DOI: 10.13466/j.cnki.lyzygl.2020.05.018

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