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Comparing parametric and nonparametric approaches for estimating forage availability using remote sensing and

Sajad Alimahmoodi Sarab1, Farajollah Tarnian2, Ebrahim Karimi Sangchini3

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Summary

Accurate rangeland forage availability estimation is vital for management. This study found that combining the MSAVI2 index with potential evapotranspiration and Transou indices using C&RT regression offers a robust method for arid environments.

Keywords:
Linear regressionMSAVI2PETRandom forestRemote sensingTransou index

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Area of Science:

  • Rangeland Ecology
  • Remote Sensing
  • Environmental Modeling

Background:

  • Effective rangeland management relies on precise forage availability assessments.
  • Hot semi-arid regions present unique challenges for accurate forage estimation.
  • Integrating remote sensing and climatic data can improve vegetation monitoring.

Purpose of the Study:

  • To develop and validate a robust method for estimating forage availability in hot semi-arid rangelands.
  • To identify optimal vegetation and climatic indices for forage mass prediction.
  • To compare the performance of different machine learning algorithms for this estimation.

Main Methods:

  • Field measurements of forage mass using the cutting and weighing method on 58 sample plots.
  • Analysis of Sentinel-2 remote sensing data and climatic indicators (e.g., potential evapotranspiration).
  • Application of Multiple Linear Regression (MLR), Random Forest (RF), and Classification and Regression Trees (C&RT) models, with k-fold cross-validation.

Main Results:

  • Correlation analysis identified MSAVI2, potential evapotranspiration index, and Transou index as significant variables.
  • Initial models showed moderate performance (R² up to 0.68 for C&RT).
  • Integrating vegetation and climatic indices significantly improved model accuracy, with C&RT achieving an R² of 0.91.

Conclusions:

  • The combination of MSAVI2, potential evapotranspiration, and Transou indices, processed via C&RT regression, provides a highly accurate method for estimating forage availability.
  • This approach is particularly effective in hot, semi-arid, and arid rangeland environments.
  • The study highlights the potential of integrated remote sensing and climatic data for sustainable rangeland management.