Comparative Analysis of Supervised Machine Learning Algorithms for Forest Habitat Mapping in Cyprus

Maria Prodromou (), Ioannis Gitas, Christodoulos Mettas, Marios Tzouvaras, Chris Danezis and Diofantos Hadjimitsis
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Maria Prodromou: ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus
Ioannis Gitas: Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Christodoulos Mettas: ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus
Marios Tzouvaras: ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus
Chris Danezis: ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus
Diofantos Hadjimitsis: ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus

Sustainability, 2025, vol. 17, issue 13, 1-32

Abstract: Mapping dominant forest habitats is essential for guiding reforestation practices, especially in areas affected by fires. This study focuses on identifying dominant forest habitats in selected forested areas in Cyprus using supervised, pixel-based classification algorithms to support the planning of post-fire reforestation actions. For this study, three classifiers were provided by the Google Earth Engine (GEE) platform. Specifically, the Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART) were implemented utilizing Sentinel-1 and Sentinel-2 data as well as topographic features and the tree density. Eight dominant forest habitats were mapped, including the Mediterranean pine forests with endemic Mesogean pines, Sarcopoterium spinosum phrygana, Thermo-Mediterranean and pre-desert scrub, Olea and Ceratonia forests, scrub and low forest vegetation with Quercus alnifolia , endemic forests with Juniperus , Cedrus brevifolia forests and Mediterranean pine forests with endemic Mesogean pines. The results revealed that RF and SVM outperformed CART. While SVM achieved the highest overall accuracy (OA) of 84.67%, it exhibited sensitivity to hyperparameter adjustments. In contrast, RF demonstrated greater stability and generalization across habitat types, attaining a reliable OA of 82.24%, making it the preferred classifier for this study.

Keywords: forest habitat mapping; Google Earth Engine; Sentinel; remote sensing; supervised classification; Mediterranean forests (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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