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Forest Ecosystems 2020, 7(4): 64 https://doi.org/10.1186/s40663-020-00276-7
Research | Open Access | Issue | Published: 26 November 2020

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Show Author's Information Huiyi Su1,  Wenjuan Shen1,  Jingrui Wang3,  Arshad Ali1,  Mingshi Li1,2( )
College of Forestry, Nanjing Forestry University, Nanjing, 210037, China
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
Shenzhen Academy of Environmental Sciences, Shenzhen, 518000, China
Keywords:
National forest inventory, Forest aboveground biomass, Random forest co-kriging, ALOS PALSAR, Landsat TM, Digital elevation model
Cite this article:
Su H, Shen W, Wang J, et al. Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests. Forest Ecosystems, 2020, 7(4): 64. https://doi.org/10.1186/s40663-020-00276-7
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Abstract Full text About this article
Background

Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.

Methods

Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China.

Results

The proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha-1, the mean absolute error ranged from 6.54 to 32.32 t∙ha-1, the bias ranged from − 2.14 to 1.07 t∙ha-1, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha-1) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area.

Conclusions

Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.


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Abstract
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About this article

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Show Author's information Huiyi Su1, Wenjuan Shen1, Jingrui Wang3, Arshad Ali1, Mingshi Li1,2( )
College of Forestry, Nanjing Forestry University, Nanjing, 210037, China
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
Shenzhen Academy of Environmental Sciences, Shenzhen, 518000, China

Abstract

Background

Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.

Methods

Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China.

Results

The proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha-1, the mean absolute error ranged from 6.54 to 32.32 t∙ha-1, the bias ranged from − 2.14 to 1.07 t∙ha-1, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha-1) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area.

Conclusions

Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

Keywords: National forest inventory, Forest aboveground biomass, Random forest co-kriging, ALOS PALSAR, Landsat TM, Digital elevation model

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Publication history

Received: 27 April 2020
Accepted: 09 November 2020
Published: 26 November 2020
Issue date: December 2020

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© The Author(s) 2020.

Acknowledgements

Acknowledgements

The authors would like to acknowledge the United States Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and Japan Aerospace Exploration Agency (JAXA) for providing the image data. Special thanks go to the Guangdong Provincial Center for Forest Resources Monitoring for sharing their forest inventory data.

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