Akademik Veri Yönetim Sistemi
ECOLOGICAL INFORMATICS, cilt.71, 2022 (SCI-Expanded)
Maquis, which provides numerous ecosystem services and constitutes an integral part of the Mediterranean ecosystem, is highly heterogeneous. However, despite its importance and heterogeneity, maquis is generally mapped as a single class, while forests are mapped for management purposes. Detailed mapping of the maquis formations is necessary to understand their ecology and manage them sustainably. This study presents a method that generates alliance-level maps of the maquis ecosystems through satellite images using various machine learning techniques with different feature combinations and evaluates the proposed approach in the Mediter-ranean region of Southern Turkey, which has an area of 95,000 km2. Multi-temporal images extract information from vegetation phenology, while topographic and meteorological data are used to improve the classification. Cross-validation is performed using a ground-truth data set of approximately 7500 polygons. Results show that cost-effective and accurate maquis classification at the alliance level is possible using a combination of envi-ronmental features, multi-spectral, and multi-temporal satellite images. Adding environmental features to remotely sensed classification has improved the accuracy by 18%. The Random Forest (RF) algorithm improves classification accuracy by 7.3% and 14.6% relative to Support Vector Machine and Quadratic Discriminant Analysis algorithms, respectively. With the help of newly introduced features, we have succeeded in mapping 11 alliances with 64.2-82.7% overall accuracy. We believe the proposed classification approach will help improve the mapping of the shrubland ecosystems, which will significantly affect natural resource management, con-servation, and adaptation to climate change.