A geographically weighted deep neural network model for research on the spatial distribution of the down dead wood volume in Liangshui National Nature Reserve (China)

doi:10.3832/ifor3705-014

A geographically weighted deep neural network model for research on the spatial distribution of the down dead wood volume in Liangshui National Nature Reserve (China)

Yuman Sun⁽¹⁾, Ziqi Ao⁽¹⁾, Weiwei Jia⁽¹⁾ , Ying Chen⁽¹⁾, Kai Xu⁽²⁾

iForest - Biogeosciences and Forestry, Volume 14, Issue 4, Pages 353-361 (2021)
doi: https://doi.org/10.3832/ifor3705-014
Published: Jul 27, 2021 - Copyright © 2021 SISEF

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Geo-mapping

In natural forest ecosystems, there is often abundant down dead wood (DDW) due to wind disasters, which greatly changes the size and structure of forests. Accurately determining the DDW volume (DDWV) is crucial for sustaining forest management, predicting the dynamic changes in forest resources and assessing the risks of natural disasters or disturbances. However, existing models cannot accurately express the significant spatial nonstationarity or complexity in their spatial relationships. To this end, we established a geographically weighted deep neural network (GWDNN) model that constructs a spatially weighted neural network (SWNN) through geographic location data and builds a neural network through stand factors and remote sensing factors to improve the interpretability of the spatial model of DDWV. To verify the effectiveness of this method, using 2019 data from Liangshui National Nature Reserve, we compared model fit, predictive ability and residual spatial autocorrelation among the GWDNN model and four other spatial models: an ordinary least squares (OLS) model, a linear mixed model (LMM), a geographically weighted regression (GWR) model and a deep neural network (DNN) model. The experimental results show that the GWDNN model is far superior to the other four models according to various indicators; the coefficient of determination R², root mean square error (RMSE), mean absolute error (MAE), Moran’s I and Z-statistic values of the GWDNN model were 0.95, 1.05, 0.77, -0.01 and -0.06, respectively. In addition, compared with the other models, the GWDNN model can more accurately depict local spatial variations and details of the DDWV in Liangshui National Nature Reserve.

Keywords

Down Dead Wood Volume (DDWV), Ordinary Least Squares (OLS) Model, Linear Mixed Model (LMM), Geographically Weighted Regression (GWR) Model, Deep Neural Network (DNN) Model, Geographically Weighted Deep Neural Network (GWDNN) Model

Authors’ address

(1)

0000-0001-7318-8997

School of Forestry, Northeast Forestry University, Harbin 150040 (China)

(2)

CosmosVision Robot of Anhui Zhong’an Chuanggu Technology Park, Hefei 230000 (China)

Corresponding author

Weiwei Jia
jiaww2002@163.com

Citation

Sun Y, Ao Z, Jia W, Chen Y, Xu K (2021). A geographically weighted deep neural network model for research on the spatial distribution of the down dead wood volume in Liangshui National Nature Reserve (China). iForest 14: 353-361. - doi: 10.3832/ifor3705-014

Academic Editor

Maurizio Marchi

Paper history

Received: Nov 27, 2020
Accepted: Jun 03, 2021

First online: Jul 27, 2021
Publication Date: Aug 31, 2021
Publication Time: 1.80 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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