Dynamic Estimation of Forest Volume Based on Multi-Source Data and Neural Network Model


  •  Dasheng Wu    
  •  Yongquan Ji    

Abstract

It is quite necessary to explore some more efficient and reliable estimation models which could integrate or, in some cases, substitute the traditional and expensive measuring techniques in forest resources management owing to the rising investigation costs. Thanks to their flexibility and adaptability, artificial neural networks (ANN) constitute a valid approach for modelling complex long-lived dynamic forest ecosystems.

The evaluation indexes set was established, including 17 factors: elevation, slope, aspect, surface curvature, solar radiation index, topographic humidity index, tree ages, the soil depth, the A-layer depth of soil, canopy density, Normalized Difference Vegetation Index (NDVI), and the spectral characteristics of the bands from Enhaced Thematic Mapper (ETM+) or Thematic Mapper (TM), Band 1 to Band 5, and Band 7 from Landsat. Then, integrating the remote sensing images of ETM+ or TM, Digital Elevation Model (DEM), and forest resource planning investigation data of fir of the key forestry city of Longquan, Zhejiang Province, China, the membership of each factor was empirically fitted by polynomials, and the forest volumes were estimated via an improved back propagation (BP) neural network (NN) model with Levenberg-Marquardt (LM) optimization algorithm (LM-BP). The results showed that the average individual relative errors (IARE) were from 26.38% to 34.41%; the group relative errors (GRE) were from 2.04% to 6.69%, and all of the group estimation precisions were more than 90% which is the highest standard of overall sampling accuracy about volume of forest resource inventory in China.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1916-9752
  • Issn(Onlne): 1916-9760
  • Started: 2009
  • Frequency: monthly

Journal Metrics

(The data was calculated based on Google Scholar Citations)

  • Google-based Impact Factor (2016): 2.28
  • h-index (December 2017): 31
  • i10-index (December 2017): 304
  • h5-index (December 2017): 22
  • h5-median (December 2017): 27

Contact