Date

2022-11-01

Author

Listiani, Indira Aprilia
Leloğlu, Uğur Murat
Zeydanli, Ugur
Caliskan, Bilgehan Kaan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

141
views

0
downloads

Cite This

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.

Subject Keywords

Feature extraction, Google earth engine (GEE), Machine learning, Maquis, Random Forest, Sentinel-2, DISTRIBUTION MODELS, FOREST ALLIANCES, LAND-COVER, VEGETATION, CLIMATE, DISCRIMINATION, CLASSIFICATION, DIVERSITY, DETECT, MAP

URI

https://hdl.handle.net/11511/100670

Collections

Graduate School of Natural and Applied Sciences, Article