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)

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...
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Autor*in:	Sun Y [verfasserIn] Ao Z [verfasserIn] Jia W [verfasserIn] Chen Y [verfasserIn] Xu K [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	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

Übergeordnetes Werk:	In: iForest - Biogeosciences and Forestry - Italian Society of Silviculture and Forest Ecology (SISEF), 2019, 14(2021), 1, Seite 353-361
Übergeordnetes Werk:	volume:14 ; year:2021 ; number:1 ; pages:353-361

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3832/ifor3705-014

Katalog-ID:	DOAJ058291504

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520			\|a 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.
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topic_facet	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 Forestry
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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. 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callnumber-first	S - Agriculture
author	Sun Y
spellingShingle	Sun Y misc SD1-669.5 misc Down Dead Wood Volume (DDWV) misc Ordinary Least Squares (OLS) Model misc Linear Mixed Model (LMM) misc Geographically Weighted Regression (GWR) Model misc Deep Neural Network (DNN) Model misc Geographically Weighted Deep Neural Network (GWDNN) Model misc Forestry 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)
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topic_title	SD1-669.5 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) 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
topic	misc SD1-669.5 misc Down Dead Wood Volume (DDWV) misc Ordinary Least Squares (OLS) Model misc Linear Mixed Model (LMM) misc Geographically Weighted Regression (GWR) Model misc Deep Neural Network (DNN) Model misc Geographically Weighted Deep Neural Network (GWDNN) Model misc Forestry
topic_unstemmed	misc SD1-669.5 misc Down Dead Wood Volume (DDWV) misc Ordinary Least Squares (OLS) Model misc Linear Mixed Model (LMM) misc Geographically Weighted Regression (GWR) Model misc Deep Neural Network (DNN) Model misc Geographically Weighted Deep Neural Network (GWDNN) Model misc Forestry
topic_browse	misc SD1-669.5 misc Down Dead Wood Volume (DDWV) misc Ordinary Least Squares (OLS) Model misc Linear Mixed Model (LMM) misc Geographically Weighted Regression (GWR) Model misc Deep Neural Network (DNN) Model misc Geographically Weighted Deep Neural Network (GWDNN) Model misc Forestry
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title	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)
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title_full	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)
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title_sort	geographically weighted deep neural network model for research on the spatial distribution of the down dead wood volume in liangshui national nature reserve (china)
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title_auth	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)
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	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)
url	https://doi.org/10.3832/ifor3705-014 https://doaj.org/article/21955fe42ea94a3a96b513765f9051fe https://iforest.sisef.org/contents/?id=ifor3705-014 https://doaj.org/toc/1971-7458
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up_date	2024-07-03T17:07:42.320Z
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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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Down Dead Wood Volume (DDWV)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ordinary Least Squares (OLS) Model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Linear Mixed Model (LMM)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Geographically Weighted Regression (GWR) Model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep Neural Network (DNN) Model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Geographically Weighted Deep Neural Network (GWDNN) Model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield 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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)

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