Havza çalışmalarında kullanılan CORINE 2006 arazi sınıflandırma verilerinin doğruluğunun araştırılması

Ateşoğlu, Ayhan

doi:10.17099/jffiu.21070

Cited by 11 publications

(10 citation statements)

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“…CORINE are land-cover/-use data generated by the computer-assisted visual-interpretation method over satellite imagery and RS data according to land-cover/-use classifications set by the European Environment Agency [50]. CORINE is employed to utilize the terrain-classification degree of RS data in a GIS environment [51]. In satellite imagery, the coordinates of each pixel were defined according to the coordinates of the ground control points (rectification).…”

Section: Dd = L/a (1)mentioning

confidence: 99%

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Evaluation of Groundwater Potential by GIS-Based Multicriteria Decision Making as a Spatial Prediction Tool: Case Study in the Tigris River Batman-Hasankeyf Sub-Basin, Turkey

Çelik

2019

Water

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The Tigris River Batman-Hasankeyf region sub-basin drainage area is in the Upper Tigris basin and lies between the area where the Batman stream joins the river and the Yanarsu stream flows into the river. Intensive agricultural activities are carried out in this region, and irrigation is generally obtained from groundwater just as it moves away from the riverfront. The study area is a valuable basin for both Turkey and the Middle East. In this study, the effectiveness of the Geographic Information System (GIS)-based multicriteria decision-making (MCDM) analytic hierarchy process (AHP) as a spatial prediction tool was utilized in exploring the groundwater potential of the drainage area. In the analysis, eight hydrological and hydrogeological criteria were considered as influencing factors, namely, geomorphology, geology, rainfall, drainage density, slope, lineament density, land use, and soil properties. The weights of these criteria were determined through the AHP method; the Arc GIS 10.2.2 program and its submodules were used. The major findings of the study were that groundwater-potential index values of the basin were derived. Groundwater-potential-zone evaluation of the basin was obtained as follows: very poor (19%), poor (17%), moderate (34%), good (17%), very good (13%); and groundwater potential zone (GWPZ) maps of the sub-basin were created. 2 of 16 model [8]. Most of these methods are based on multivariate statistical techniques [9]. In contrast, the analytical hierarchy process (AHP) is considered to be a simple, effective, transparent, and reliable technique [10]. The AHP method is easy to identify in a useful way by integrating geographic information system (GIS) and remote sensing (RS) data. Groundwater parameters such as precipitation, aquifers, land use, and soil type can easily be defined as spatial data in a GIS environment. Thus, the method is very useful for identifying potential groundwater areas and is well-suited for the useful identification of groundwater-potential regions [11][12][13]. AHP is an approach that was developed by Thomas L. Saaty in the early 1980s [14]. It was developed with planned criteria according to a certain stage, evaluating the weights of these criteria, and comparing the criteria with other similar studies [15,16]. It is possible and preferable to determine hydrological and hydrogeological parameters that influence groundwater formation and their effects on groundwater formation and generate potential groundwater maps with GIS-based Multicriteria Decision-Making (MCDM) techniques. GIS-based multicriteria decision analysis (MCDA) has been extensively used in the field of hydrological and water resources [16,17]. It has gained popularity in the last decade as it provides GIS-based mapping [18].Most current studies [19][20][21][22] widely applied RS-and GIS-based AHP-MCDM techniques for the assessment of Groundwater Potential Zones (GWPZs) with effective results. When these studies were examined, it was found that land use (LU), geomorphology (GM), geology (G), recharge...

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Section: Dd = L/a (1)mentioning

confidence: 99%

“…Environment Agency [50]. CORINE is employed to utilize the terrain-classification degree of RS data in a GIS environment [51]. In satellite imagery, the coordinates of each pixel were defined according to the coordinates of the ground control points (rectification).…”

Section: Dd = L/a (1)mentioning

confidence: 99%

Evaluation of Groundwater Potential by GIS-Based Multicriteria Decision Making as a Spatial Prediction Tool: Case Study in the Tigris River Batman-Hasankeyf Sub-Basin, Turkey

Çelik

2019

Water

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“…Sistem birinci düzeyde 5 genel sınıf, ikinci düzeyde 15 ana sınıf ve üçüncü düzeyde 44 temel sınıftan oluşmaktadır. Arazi varlığının korunması, kullanılması ve geliştirilmesi açısından birçok bilgiyi birlikte gösteren bu sistem arazi örtüsü ve kullanım sınıfları haritalarının üretilmesi gibi birçok alanda önemli avantajlar sağlamaktadır (Ateşoğlu, 2016). Ülkemizde "Çevre Düzenleme Planları" oluşturulurken de arazi örtüsü veri teması olarak CORINE sınıflandırma sitemi kullanılmaktadır.…”

Section: Metodunclassified

Determination of coastal areas of recreational activities in the city of Trabzon

Işık¹,

Demirel²

2015

J FAC FOR ISTANBUL U

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Abstract:The size of the areal use in planning allows for increase in recreational diversity. When considered that recreational needs are the times that people spend to make use of their spare time, it is obvious that the decisions taken based on the user will lead the planner to more accurate planning targets. While determining the recreational planning decisions, decisions should be evaluated in an integrated approach and in accordance with the requests and demands of users. This study aimed at taking planning decisions required for the recreational connections which are on the coast and for the improvement of people's social life between the coastal city and coastal users. Within the study, the diversity of Trabzon coastal area in terms of recreational activity based on measurement and observation was revealed. In the study, subclasses determined for Trabzon coast were formed by sticking to CORINE classification system, and the coast was divided into 9 sections considering the size and clarity of the area. In CBS environment, each section was digitized according to separate layers and areas with ArcGIS program in terms of recreational area usages. As a result of the study, it was revealed that the diversity of activity was more intense in sections where areal use was more intense depending on the size of the area.Keywords: Recreation, coastal recreation, recreation planning, recreational activity, coastal city Trabzon kenti'nde kıyı rekreasyonel etkinlik alanlarının belirlenmesi Özet: Planlamalarda alansal kullanımların büyüklüğü, rekreasyonel çeşitliliğin artmasına olanak sağlamaktadır. Rekreasyonel ihtiyaçların kişilerin boş zamanlarını geçirdikleri vakitler olduğu düşünüldüğünde, kullanıcıya bağlı alınan kararların planlamacıyı daha doğru planlama hedeflerine götüreceği ortadadır. Rekreasyonel planlama kararları belirlenirken kararlar bütüncül bir yaklaşım içerisinde, kullanıcıların istek ve talepleri doğrultusunda değerlendirilmelidir. Bu araştırma, kıyıda ihtiyaç duyulan ve insanların sosyal yaşamlarını arttırmaya yönelik olarak kıyı kenti ve kıyı kullanıcıları arasındaki rekreasyonel bağlantılar için gerekli olan planlama kararlarının alınmasını amaçlamıştır. Araştırma kapsamında, ölçüm ve gözleme dayalı Trabzon kıyı alanının rekreasyonel etkinlik açısından çeşitliliği ortaya koyulmuştur. Çalışmada CORINE sınıflandırma sistemine bağlı kalınarak, Trabzon kıyısı için belirlenen alt sınıflar oluşturulmuş, alanın büyüklüğü ve anlaşılırlığı düşünülerek kıyı 9 bölüme ayrılmıştır. CBS ortamında her bir bölüm rekreasyonel alan kullanımları açısından ArcGIS programı ile ayrı katmanlara ve alanlara göre sayısallaştırılmıştır. Çalışmanın sonucunda, alansal kullanımların daha fazla olduğu bölümlerde alan büyüklüğüne bağlı olarak etkinlik çeşitliliğininde fazla olduğu sonucu ortaya çıkmıştır.Anahtar kelimeler: Rekreasyon, kıyı rekreasyonu, rekreasyonel planlama, rekreasyonel etkinlik, kıyı kenti GİRİŞDünya nüfusunun 2003 verilerine göre %40'ından çoğu (2.38 milyon) kıyı alanları ve 100 km mesafesinde yerleşmiştir. 1...

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“…CLC maps were prepared by dividing 44 land cover classes under 5 main classes in order to inspect vegetation groups broadly (Bossard, 2000). These maps have been used frequently in Turkey that were supported by a project which was called 'Land Cover Classification Project' initiated in 1998 in the name of the European Union adaptation process (Karagülle & Kendüzler, 2007;Ateşoğlu, 2016).…”

Section: Introductionmentioning

confidence: 99%

Vegetation Cover Change Analysis of Phytogeographic Regions of Turkey Based on CORINE Land Cover Datasets from 1990 to 2018

Akturk

Güney

2021

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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Aim of study:The current state of the land cover and its temporal changes are one of the most popular topics nowadays, and it is a very useful subject in the protection of ecosystems and combating global climate change. The main objective of this study was to reveal vegetation cover change on Turkey and its phytogeographic region within the last 28 years.Area of study: The study area borders of the Republic of Turkey, covers an area of 779.646 square kilometers. In addition, Turkey is home to three phytogeographic regions (Mediterranean, Euro-Siberia, and Irano-Turanian) with different floristic features. Both phytogeographic regions and Turkey were examined individually in this study.

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Havza çalışmalarında kullanılan CORINE 2006 arazi sınıflandırma verilerinin doğruluğunun araştırılması

Cited by 11 publications

References 0 publications

Evaluation of Groundwater Potential by GIS-Based Multicriteria Decision Making as a Spatial Prediction Tool: Case Study in the Tigris River Batman-Hasankeyf Sub-Basin, Turkey

Evaluation of Groundwater Potential by GIS-Based Multicriteria Decision Making as a Spatial Prediction Tool: Case Study in the Tigris River Batman-Hasankeyf Sub-Basin, Turkey

Determination of coastal areas of recreational activities in the city of Trabzon

Vegetation Cover Change Analysis of Phytogeographic Regions of Turkey Based on CORINE Land Cover Datasets from 1990 to 2018

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