The emberger bioclimatic classification system for forestry management under climate change: A case study in the büyük Menderes basin, western turkey

README — Emberger Bioclimatic Classification Dataset ## Büyük Menderes Basin (BMB), Western Turkey --- ## Dataset Title Emberger Bioclimatic Classification System for Forestry Management under Climate Change: A Case Study in the Büyük Menderes Basin, Western Turkey ## Authors - Muge Kulahlioglu¹* | [e-mail] - Suha Berberoğlu¹ - Gulnihal Kurt Kayali¹ - Merve Şahinöz² ¹ Remote Sensing and GIS Laboratory, Department of Landscape Architecture, University of Çukurova, 01330 Adana, Turkey ² Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Muencheberg, Germany *Corresponding author ## Data Collection Period - Meteorological station records: 1970–2018 - Remote sensing imagery: 2018 (four seasonal Sentinel-2A scenes) - GIS processing and analysis: 2024–2025 - Climate projections: 2050–2100 --- ## Study Area Büyük Menderes River Basin, western Turkey (~24,976 km²). Located at the Aegean coast of the Mediterranean region. Coordinate Reference System: **WGS 84 / EPSG:4326** --- ## Abstract This dataset contains the spatial and statistical outputs supporting the Emberger bioclimatic classification analysis of the Büyük Menderes Basin (BMB). It includes the basin boundary, current Emberger bioclimatic class polygons derived from 48 years of meteorological data (1970–2018), forest stand type maps produced by object-based image analysis (OBIA) of Sentinel-2A imagery, species-specific polygon and point layers for six dominant stand types (red pine, black pine, oak, maquis, stone pine, juniper), climate change transition polygons under RCP 4.5 and RCP 8.5 scenarios (2050–2100), and the chi-squared / Cramér's V statistical analysis outputs. --- ## File Descriptions ### Study Area | File | Description | |------|-------------| | `bmh.shp` + sidecar files (.dbf, .prj, .shx, .shp.xml, .CPG, .sbn, .sbx) | Büyük Menderes Basin boundary polygon | ### Emberger Bioclimatic Classification | File | Description | |------|-------------| | `emberger.shp` + sidecar files | **Current period (1970–2018)** Emberger bioclimatic class polygons. Attributes include Q index (pluviothermic quotient) and thermal variant (m). Eight bioclimatic classes delineated. | ### Forest Stand Type Classification | File | Description | |------|-------------| | `mescere.tif` + sidecar files (.tfw, .tif.aux.xml, .tif.ovr, .tif.vat.cpg, .tif.vat.dbf, .tif.xml) | Forest stand type raster (10 m resolution) derived from Sentinel-2A OBIA classification | | `mescere.shp` + sidecar files | Forest stand type polygon layer (6 dominant types: red pine, black pine, oak, maquis, stone pine, juniper + non-forest) | | `mescere_son.shp` + sidecar files | **Final analytical layer**: forest stand type polygons with spatially joined Emberger bioclimatic class attributes. Primary dataset for chi-squared analysis. | ### Species-Specific Polygon Layers | File | Description | |------|-------------| | `blackpine.shp` + sidecar files | Black pine (*Pinus nigra*) stand polygons | | `oak.shp` + sidecar files | Oak (*Quercus* spp.) stand polygons | | `makilik.shp` + sidecar files | Maquis (Mediterranean shrubland) stand polygons | | `mkimulti.shp` + sidecar files | Maquis multi-part polygons (used for point extraction) | ### Species Point Layers (Thiessen Polygon Statistical Units) Centroid points derived from stand polygons. Used to generate spatially independent Thiessen polygon statistical units for chi-squared testing. | File | Description | |------|-------------| | `mespoint.shp` + sidecar files | All-species combined centroid points with Emberger Q and m values | | `oakpoint1.shp` + sidecar files | Oak centroid points with Emberger attributes | | `makipoint_updated.shp` + sidecar files | Maquis centroid points with updated Emberger attributes | ### Climate Change Transition Layers Derived from comparison of current (2020) vs. projected (2050, 2100) Emberger classes under RCP 4.5 and RCP 8.5 scenarios (HadGEM2-ES GCM, CMIP5). | File | Description | |------|-------------| | `change50.shp` + sidecar files | Emberger class transition polygons: 2020 to 2050 | | `change100.shp` + sidecar files | Emberger class transition polygons: 2020 to 2100 | ### Statistical Analysis Outputs | File | Description | |------|-------------| | `mespoint1_cramers.csv` | Per-point records: species type x assigned Emberger class. Input table for chi-squared test. | | `mespoint2_cramers.csv` | Cross-tabulation (contingency table): forest stand types x Emberger classes. Used to calculate chi-squared and Cramér's V (V = 0.34, df = 49). | --- ## Methods ### Emberger Q Index Formula Q = 2000P / (M + m + 564.4) - P = mean annual precipitation (mm) - M = mean maximum temperature of the warmest month (degrees C) - m = mean minimum temperature of the coldest month (degrees C) - 564.4 = constant for Kelvin temperature conversion Climate data interpolated from 108 meteorological stations using topography-based ANUSPLIN interpolation method. ### Forest Stand Classification Object-based image analysis (OBIA) applied to Sentinel-2A imagery (10 m resolution) acquired on four dates representing different seasons (January, April, June, September 2018). Validated against OGM (Turkish General Directorate of Forestry) forest inventory maps. ### Statistical Analysis Chi-squared test of independence assessed the association between Emberger bioclimatic classes and forest stand types. Thiessen polygons derived from species occurrence centroids served as spatially independent statistical units. Effect size quantified with Cramer's V. ### Climate Projections RCP 4.5 and RCP 8.5 scenarios modelled using the HadGEM2-ES global circulation model (CMIP5, IPCC 5th Assessment Report). Projection period: 2050–2100. --- ## Software - ESRI ArcGIS Pro 3.x — spatial analysis and map production - Python 3.x (pandas, scipy, seaborn) — statistical analysis and climatograms - QGIS — open-source alternative for viewing all files ## Shapefile Sidecar Files Each shapefile consists of multiple components (.shp, .dbf, .prj, .shx). All components with the same base name belong to the same dataset and must be kept together. --- ## Related Publication Kulahlioglu, M., Berberoğlu, S., Kurt Kayali, G., & Sahinöz, M. (2025). The Emberger Bioclimatic Classification System for Forestry Management under Climate Change: A Case Study in the Büyük Menderes Basin, Western Turkey. Journal of Sustainable Forestry. DOI: [to be added upon acceptance] ## Funding - Çukurova University Scientific Research Projects (BAP), project no. FDK-2019-11829 - Büyük Menderes Basin Landscape Atlas Project, Ministry of Agriculture and Forestry, Turkey ## License Creative Commons Zero (CC0) — Public Domain Dedication https://creativecommons.org/publicdomain/zero/1.0/ --- Dataset version: 1.0 | Prepared: June 2026