Yerel İklim Zonlarının CBS Ortamında Vektör-Tabanlı Haritalanması

Year 2021, Volume: 36 Issue: 4, 929 - 940, 29.12.2021

Müge Ünal Çilek

https://doi.org/10.21605/cukurovaumfd.1040496

Cited By: 1

Abstract

Kentsel ısı adası çalışmalarında yaygın olarak kullanılan Yerel İklim Zonları (YİZ) kentsel alanda iklimsel farklılıkların belirlenebilmesi için kentin iklim temelli sınıflanmasını içermektedir. Bu yüzden YİZ sınıflarını görselleştirilmesi için haritalama yöntemleri geliştirilmiştir. Manuel, raster ve vektör tabanlı olmak üzere üç tür haritalama yöntemi bulunmasına rağmen hassas ve doğru değerlendirme yapan vektör tabanlı yöntem yaygın olarak kullanılmamaktadır. Bu yüzden çalışmada vektör tabanlı YİZ haritası üretilmesinde CBS-tabanlı bir yaklaşımın Adana kenti örneğinde gerçekleştirilmesi amaçlanmıştır. Bu doğrultuda çalışmanın yöntemi 5 basamaktan oluşmaktadır. a) sınıflama kriterlerinin belirlenmesi, b) her bir sınıflama kriterinin Arc-GIS aracılığı ile haritalanması, c) YİZ haritasının oluşturulması için karar ağacının oluşturulması, d) karar ağacı doğrultusunda sınıflama kriterlerinin çakıştırılması, e) bulgular doğrultusunda önerilerin geliştirilmesi. YİZ sınıflarının vektör tabanlı sınıflanması, çözünürlükten kaynaklı olabilecek sınıflama hatalarını en aza indirgemiş ve yüksek doğruluğa sahip YİZ haritasının oluşturulmasını sağlamıştır. Çalışma sonucunda elde edilen YİZ haritasının kent iklimi ile ilgili çalışmalarda altlık olabilmesi ve karar vericilere yol gösterici olması beklenmektedir.

Keywords

Vektör-tabanlı, Yerel İklim Zonları, Coğrafi Bilgi Sistemleri (CBS), Çok Kriterli Karar Analizleri (ÇKKA)

A Vector-Based Mapping in GIS Environment to Classify Local Climate Zone

Abstract

Local climate zones (LCZ), which are widely used in urban heat island studies, include climate-based classification of the city to determine the climatic differences in the metropolitan area. Therefore, mapping methods have been developed to visualise LCZ classes. Compared to the raster-based mapping method, the vector-based mapping method, which makes a more precise and accurate evaluation, is not widely used due to the difficulty in creating and obtaining a dataset. This study aims to implement a GIS-based approach in creating a vector-based LCZ map in the example of Adana City, Turkey. The method of the study consists of five steps: a) determination of classification criteria; b) mapping of each classification criteria via Arc-GIS; c) creation of the decision tree for the creation of the LCZ map; d) overlapping of the classification criteria in line with the decision tree; e) development of suggestions in line with the findings. Vector-based LCZ classification has minimised the classification mistakes that may arise from resolution and has enabled the creation of a highly accurate LCZ map. The LCZ map obtained from the study is expected to be a base map in studies on urban climate studies and to guide decision-makers.

Keywords

Vector-based, Local Climate Zone, Geographic Information System (GIS), Multi-Criteria Decision Analysis (MCDA)

References

• 1. Stewart, I., Oke, T., 2009. Classifying Urban Climate Field Sites by “Local Climate Zones”: the Case of Nagano, Japan. In: The Seventh International Conference on Urban Climate. The Seventh International Conference on Urban Climate, 29 June-3 July 2009, Yokohama, Japan, 1–5.
• 2. Stewart, I.D., Oke, T.R., 2012. Local Climate Zones for Urban Temperature Studies. Bulletin of the American Meteorological Society, 93(12), 1879–1900.
• 3. Kántor, N., Unger, J., 2011. The Most Problematic Variable in the Course of Human-biometeorological Comfort Assessment-The Mean Radiant Temperature. Central European Journal of Geosciences, 3(1), 90-100.
• 4. Chen, Y., Zheng, B., Hu, Y., 2020. Mapping Local Climate Zones Using ArcGIS-Based Method and Exploring Land Surface Temperature Characteristics in Chenzhou, China. Sustainability, 12(7), 2974.
• 5. Zheng, Y., Ren, C., Xu, Y., Wang, R., Ho, J., Lau, K., Ng, E., 2018. GIS-based Mapping of Local Climate Zone in the High-density City of Hong Kong. Urban Climate, 24, 419–448.
• 6. Bechtel, B., Daneke, C., 2012. Classification of Local Climate Zones Based on Multiple Earth Observation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5.4: 1191-1202.
• 7. Ng, E., Yuan, C., Chen, L., Ren, C., Fung, J.C.H., 2011. Improving the Wind Environment in High-density Cities by Understanding Urban Morphology and Surface Roughness: A Study in Hong Kong. Landscape and Urban Planning, 101(1), 59–74.
• 8. Ren, C., Lau, K.L., Yiu, K.P., Ng, E., 2013. The Application of Urban Climatic Mapping to the Urban Planning of High-density Cities: The Case of Kaohsiung, Taiwan. Cities, 31, 1–16.
• 9. Perera, N., 2015. Climate-sensitive Urban Public Space: a Sustainable Approach to Urban Heat Island Mitigation in Colombo, Sri Lanka. University of Moratuwa, Department of Architecture, PhD Thesis, Sri Lanka, 273.
• 10. Gál, T., Bechtel, B., Unger, J., 2015. Comparison of Two Different Local Climate Zone Mapping Methods. ICUC9-9th International Conference on Urban Climates, Toulouse, France (20-24 July).
• 11. TSMS, 2019. Turkish State Meteorological Service [online]. Available from: https://mgm.gov.tr/eng/forecast-cities.aspx.
• 12. Unal Cilek, M., Cilek, A., 2021. Analyses of Land Surface Temperature (LST) Variability Among Local Climate Zones (LCZs) Comparing Landsat-8 and ENVI-met Model Data. Sustainable Cities and Society, 69, 102877.
• 13. Estacio, I., Babaan, J., Pecson, N.J., Blanco, A.C., Escoto, J.E., Alcantara, C.K., 2019. GIS-based Mapping of Local Climate Zones Using Fuzzy Logic and Cellular Automata. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, 42 (4/W19), 199–206.
• 14. Zhou, X., Okaze, T., Ren, C., Cai, M., Ishida, Y., Watanabe, H., Mochida, A., 2020. Evaluation of Urban Heat Islands Using Local Climate Zones and the Influence of Sea-land Breeze. Sustainable Cities and Society, 55 (April 2019), 102060.
• 15. Ng, E., Cheng, V., 2012. Urban Human Thermal Comfort in Hot and Humid Hong Kong. Energy and Buildings, 55, 51–65.
• 16. Bartesaghi Koc, C., Osmond, P., Peters, A., Irger, M., 2018. Understanding Land Surface Temperature Differences of Local Climate Zones Based on Airborne Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2724–2730.
• 17. Quan, J.L., 2019. Enhanced Geographic Information System-based Mapping of Local Climate Zones in Beijing, China. Science China Technological Sciences, 62(12), 2243–2260. 18. Stewart, I.D., 2013. Local Climates of the City. Architectural Design, 83(4), 100–105.
• 19. Gholami, R., Beck, C., 2019. Towards the Determination of Driving Factors of Varying LST-LCZ Relationships: A Case Study Over 25 Cities. Geographica Pannonica, 23(4), 289–307.
• 20. Bande, L., Manandhar, P., Marpu, P., Battah, M., Al, 2020. Local Climate Zones Definition in Relation to ENVI-met in the City of Dubai, UAE. IOP Conference Series: Materials Science and Engineering, 829(1), 012013.
• 21. Ochola, E.M., Fakharizadehshirazi, E., Adimo, A.O., Mukundi, J.B., Wesonga, J.M., Sodoudi, S., 2020. Inter-local Climate Zone Differentiation of Land Surface Temperatures for Management of Urban Heat in Nairobi City, Kenya. Urban Climate, 31 (November), 100540.