Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches

Abstract

Accurate agricultural land use (LU) map is essential for many agro-environmental applications. With advances in technology, object-based image classification and non-parametric machine learning algorithms evolved. Still, no particular method has universal applicability. This paper compares robust non-parametric machine learning algorithms, random forest (RF) and support vector machine (SVM), and a common parametric algorithm maximum likelihood (MLC) based on multiple Landsat 8 images. We have also assessed the classifier performance relative to the choice either pixel-based (PB) or field-based (FB) approach. The study area, a semi-desert irrigated region, lies in Khorezm province and Republic of Karakalpakstan in Uzbekistan. Accuracy assessment showed higher overall accuracy (OA) and kappa index (KI) of the nonparametric machine learning FB-RF and FB-SVM algorithms over the PB-RF, PB-SVM and PB-MLC algorithms. The lowest OA and KI occurred with the parametric FB-MLC. Based on the results, the FB machine learning non-parametric algorithms are recommended for mapping irrigated croplands.

Keywords

• Land use (LU) mapping
• random forest
• support vector machine
• maximum likelihood
• field-based
• Uzbekistan