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Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

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  • Mareike Ließ
  • Johannes Schmidt
  • Bruno Glaser

Abstract

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

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  • Mareike Ließ & Johannes Schmidt & Bruno Glaser, 2016. "Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-22, April.
  • Handle: RePEc:plo:pone00:0153673
    DOI: 10.1371/journal.pone.0153673
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    Cited by:

    1. Johannes Schmidt & Nik Usmar & Leon Westphal & Max Werner & Stephan Roller & Reinhard Rademacher & Peter Kühn & Lukas Werther & Aline Kottmann, 2023. "Erosion Modelling Indicates a Decrease in Erosion Susceptibility of Historic Ridge and Furrow Fields near Albershausen, Southern Germany," Land, MDPI, vol. 12(3), pages 1-11, February.
    2. Gerald Forkuor & Ozias K L Hounkpatin & Gerhard Welp & Michael Thiel, 2017. "High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
    3. Masoud Zolfaghari Nia & Mostafa Moradi & Gholamhosein Moradi & Ruhollah Taghizadeh-Mehrjardi, 2022. "Machine Learning Models for Prediction of Soil Properties in the Riparian Forests," Land, MDPI, vol. 12(1), pages 1-15, December.
    4. Kennedy Were & Syphyline Kebeney & Harrison Churu & James Mumo Mutio & Ruth Njoroge & Denis Mugaa & Boniface Alkamoi & Wilson Ng’etich & Bal Ram Singh, 2023. "Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya," Land, MDPI, vol. 12(4), pages 1-19, April.
    5. Carmen Cianfrani & Aline Buri & Eric Verrecchia & Antoine Guisan, 2018. "Generalizing soil properties in geographic space: Approaches used and ways forward," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-17, December.

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