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A comparative study of LULC classifiers for analysing the cover management factor and support practice factor in RUSLE model

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Abstract

Soil erosion is a natural occurrence in landforms but is a major threat that causes essential soil nutrients loss and it is hazardous to agriculture practices as depletion of soil makes the land less productive. The major factors influencing soil erosion are rainfall erosivity factor (R), soil erodibility factor (K), length and steepness factor (LS), cover management factor (C) and support practice factor (P). In most of the earlier research studies, the estimation of soil erosion has been done with Revised Universal Soil Loss Equation (RUSLE) but accuracy in analysis of C and P factors has not been considered. The main objective of the study is to explore the influence of different LULC classifier in determining the rate of erosion using RUSLE model. This study utilizes five classifiers such as Maximum Likelihood Classifier (MLC), Random Trees Classifier (RTC), Support Vector Machine Classifier (SVM) and Artificial Neural Network (ANN) and Mahalanobis Distance (MD) to generate the C and P factor. The Accuracy assessment, a statistical method is used to validate the classified Land Use / Land Cover (LULC). The obtained results show that the respective annual mean soil losses based on MLC, MD, ANN, SVM, RTC are 1.95, 2.08, 1.71, 2.19 and 1.85 tons−1 ha−1 yr−1. The estimated annual mean soil loss by using different classifiers is validated using a statistical method Receiver operating curve / Area under Curve. The accuracy level of RTC is 0.800 of Area Under the Curve (AUC) which is higher compared to conventional classifiers like MLC, MDC, ANN, SVM with the values 0.793, 0.707, 0.693, 0.793 of AUC, respectively. RTC, a Machine learning-based classifier, is observed to be more accurate than the conventional classifiers. The insights obtained from the present study will be very useful for land use planning and management and to undertake soil and water conservation measures.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

    Vinoth Kumar Sampath & Nisha Radhakrishnan

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  1. Vinoth Kumar Sampath
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  2. Nisha Radhakrishnan
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Contributions

Vinoth Kumar S: Conceptualization, Framing Methodology, Analysis, Data Curation, Preparation of original draft;

Nisha Radhakrishnan: Supervision, Guidance, Reviewing, Editing, Endorsed the final manuscript.

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Correspondence to Vinoth Kumar Sampath.

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Sampath, V.K., Radhakrishnan, N. A comparative study of LULC classifiers for analysing the cover management factor and support practice factor in RUSLE model. Earth Sci Inform 16, 733–751 (2023). https://doi.org/10.1007/s12145-022-00911-7

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