Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia
Publication year
Resource type
UNCCD Library
Material Type
article
Highlights
•Random forest outperformed boosted regression tree in prediction of SOC in rangelands.
•Feature selection is necessary to increase model efficiency and accuracy.
•Random forest with genetic algorithm was the most promising model in predicting SOC.
•Fractional cover data (bare ground fraction) is important in determining SOC stocks.
Soil organic carbon (SOC) is pivotal for biological, chemical and physical processes and provides vital information on changes in soil fertility and land degradation. Rangelands, accounting for about 81% of Australian land area, are significant carbon (C) stores and small increases in soil C sequestration over such a vast area represents a considerable climate change mitigation opportunity. Efficient modelling techniques to evaluate the potential to increase rangeland SOC stocks are vitally important to assess their role in the global carbon cycle and quantum abatement. This study aimed to evaluate boosted regression trees (BRT) and random forest (RF) models in predicting SOC stocks from available continuous remotely sensed variables using two feature selection techniques. Dominant variables that affect SOC stocks in the rangelands were also identified. Using field-based measurements of SOC stock collected from 564 data points across the study area and 28 GIS-based environmental variables including climate, topography, radiometry, vegetation and land fractional cover data, we employed stepwise regression (SR, linear approach) and genetic algorithm (GA, nonlinear approach) to select the most informative variables.
The important variables for explaining the observed SOC stocks were rainfall, elevation, Prescott index (PI, a measure of water balance), and land fractional cover (bare ground fraction). The approach proposed here can be extended in areas where field observed data is scarce (e.g. rangelands) to produce more detailed information about SOC stocks. As such, the results of our study are of particular importance in Australian rangelands to provide a statistical and theoretical basis for producing digital SOC stock maps based on readily available remotely-sensed data, with potential for use in similar rangelands conditions internationally.
•Random forest outperformed boosted regression tree in prediction of SOC in rangelands.
•Feature selection is necessary to increase model efficiency and accuracy.
•Random forest with genetic algorithm was the most promising model in predicting SOC.
•Fractional cover data (bare ground fraction) is important in determining SOC stocks.
Soil organic carbon (SOC) is pivotal for biological, chemical and physical processes and provides vital information on changes in soil fertility and land degradation. Rangelands, accounting for about 81% of Australian land area, are significant carbon (C) stores and small increases in soil C sequestration over such a vast area represents a considerable climate change mitigation opportunity. Efficient modelling techniques to evaluate the potential to increase rangeland SOC stocks are vitally important to assess their role in the global carbon cycle and quantum abatement. This study aimed to evaluate boosted regression trees (BRT) and random forest (RF) models in predicting SOC stocks from available continuous remotely sensed variables using two feature selection techniques. Dominant variables that affect SOC stocks in the rangelands were also identified. Using field-based measurements of SOC stock collected from 564 data points across the study area and 28 GIS-based environmental variables including climate, topography, radiometry, vegetation and land fractional cover data, we employed stepwise regression (SR, linear approach) and genetic algorithm (GA, nonlinear approach) to select the most informative variables.
The important variables for explaining the observed SOC stocks were rainfall, elevation, Prescott index (PI, a measure of water balance), and land fractional cover (bare ground fraction). The approach proposed here can be extended in areas where field observed data is scarce (e.g. rangelands) to produce more detailed information about SOC stocks. As such, the results of our study are of particular importance in Australian rangelands to provide a statistical and theoretical basis for producing digital SOC stock maps based on readily available remotely-sensed data, with potential for use in similar rangelands conditions internationally.
Geographical Keywords
Australia
Keywords
soil organic carbon
remote sensing
soil fertility
land degradation
rangelands
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