Kentsel Yeşil Alan Kalitesinin LiDAR Nokta Bulutu Verileri Kullanılarak Haritalanması

Yıl 2020, Cilt: 2 Sayı: 2, 23 - 33, 24.12.2020

Derya Gülçin

Öz

Yapı yoğunluğu ve vejetasyon karakteristiklerini dikkate alan kentsel peyzaj çalışmalarında, üç boyutlu veri kullanılarak yeşil alanların mekânsal bağlantılığın kentsel yeşil alan kalitesi ile nasıl bütünleştirileceği konusunda yapılmış çalışma oldukça sınırlıdır. Bu araştırma, son on yılda yapılaşma hızının yüksek olduğu bir kampüs alanı ve yakın çevresinde yürütülmüştür. Araştırmanın temel amacı, kentsel yeşil alanların mekânsal dağılımını, bağlantılılık konsepti çerçevesinde analiz etmek ve yaşam kalitesi ile ilişkilendirmektir. Araştırma kapsamında, LiDAR (Light Detection and Ranging) nokta bulutu verileri kullanılarak hem yapı hem de vejetasyon karakteristikleri dikkate alınmış, nokta başına düşen hacim hesaplamaları yapılmış ve kentsel vejetasyon indeksi (KVI) haritası oluşturulmuştur. Mekânsal bağlantılılığı sağlayan habitat ünitelerinden merkez ve koridorlar morfolojik mekânsal patern analizi ile belirlenerek KVI ile ilişkilendirilmiştir. Sonuç haritası, kampüs alanındaki yeşil alanların yakın çevresindeki doğal alanlarla bir bütün olarak ele alınıp tasarlandığını göstermektedir. Araştırma alanındaki yeşil alanların %60,1’inin çok yüksek; %10,39’unun yüksek, %12,22’sinin orta, %7,16’sının düşük, %9,29’unu çok düşük kalitede olduğu belirlenmiştir. Herhangi bir kalite değerine sahip olmayan yeşil alanlar ise, araştırma alanının %0,83’ünü oluşturmaktadır. Buna ek olarak, hızlı yapılaşmaya karşın kentsel yeşil alanların araştırma alanında göreceli olarak dengeli dağıldığı gözlemlenmiştir. Bu çalışmanın özgün yönü, LiDAR nokta bulutu verileri kullanılarak kentsel yeşil alanların kalitesinin nasıl haritaya aktarılabileceğini gösteren bir yöntem akışı sunmasıdır.

Anahtar Kelimeler

Kentsel Peyzaj, LiDAR, Uzaktan Algılama, Kentsel Vejetasyon İndeksi, Kentsel Ormancılık

Destekleyen Kurum

Tübitak

Proje Numarası

2219

Teşekkür

Bu araştırma, Tübitak 2219-Yurt Dışı Doktora Sonrası Araştırma Burs Programı tarafından desteklenmiştir. Tübitak’a vermiş olduğu destek için çok teşekkür ederim. GuidosToolbox yazılımının geliştiricisi Dr. Peter Vogt’a yeşil alanların mekânsal dağılımı ve bağlantılılığı hakkında verdiği teknik destekten dolayı teşekkür ederim. UBC Kampüs Planlama Komitesi’ne, araştırma alanındaki yapı bilgisini sağladığı için teşekkürler.

Mapping the Spatial Quality of Urban Green Spaces Using LiDAR Data

Öz

Studies on how to map the spatial connectivity of urban green spaces using three-dimensional data that take into account life quality and vegetation characteristics are limited. This research was carried out on a campus and its environs where the construction rate of new buildings was high in the last decade. The main purpose of the research was to map the spatial quality of urban green spaces associated with the ecological connectivity. Within the scope of the research, both building density and vegetation characteristics were taken into consideration by using LiDAR (Light Detection and Ranging) data, the volumes of buildings and vegetation types were computed, and an Urban Vegetation Index (UVI) map was created. Core and bridge areas that provided spatial connectivity were determined by MSPA (Morphological Spatial Pattern Analysis), and they were associated with UVI. The results suggest that the green spaces in the campus area were designed to enhance connectivity with the natural areas surrounding the campus. It was determined that 60.1% of the green spaces in the research area provided very high, 10.39% high, 12.22% medium, 7.16% low, and 9.29% very low quality respectively. Areas that did not provide quality constitute 0.83% of the research area. In addition, despite the rapid construction, it has been observed that the urban green spaces were relatively evenly distributed in the research area. The novelty of this study is to present a methodological approach to mapping the spatial connectivity of urban green spaces using LiDAR data.

Anahtar Kelimeler

Urban landscape, LiDAR, Remote Sensing, Urban Vegetation Index, Urban Forestry

Proje Numarası

2219

Kaynakça

• Alonzo M, Bookhagen B & Roberts, D A (2014). Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment, 148, 70-83.
• Alves A, Gersonius B, Kapelan Z, Vojinovic Z &Sanchez A (2019). Assessing the Co-Benefits of green-blue-grey infrastructure for sustainable urban flood risk management. Journal of Environmental Management, 239, 244-254.
• Amati M & Taylor L (2010). From green belts to green infrastructure. Planning Practice & Research, 25 (2), 143-155.
• Anguelovski I, Irazábal‐Zurita C & Connolly J J (2019). Grabbed urban landscapes: Socio‐spatial tensions in green infrastructure planning in Medellín. International Journal of Urban and Regional Research, 43 (1), 133-156.
• Berland A, Shiflett S A, Shuster W D, Garmestani A S, Goddard H C, Herrmann D L & Hopton M E (2017). The role of trees in urban stormwater management. Landscape and Urban Planning, 162, 167-177.
• Bierwagen B G (2007). Connectivity in urbanizing landscapes: The importance of habitat configuration, urban area size, and dispersal. Urban Ecosystems, 10 (1), 29-42.
• Bilgili B C, Gökyer E, Özyavuz M & Çorbacı Ö L (2018). Peyzaj Tasarımında Coğrafi Bilgi Sistemleri Kullanımının Değerlendirilmesi: Çankırı Karatekin Üniversite Yerleşkesi Örneği. Düzce Üniversitesi Ormancılık Dergisi, 14, 1-17.
• Casalegno S, Anderson K, Cox D T, Hancock S & Gaston K J (2017). Ecological connectivity in the three-dimensional urban green volume using waveform airborne lidar. Scientific Reports, 7, 45571.
• Coutts C & Hahn M (2015). Green infrastructure, ecosystem services, and human health. International Journal of Environmental Research and Public Health, 12 (8), 9768-9798.
• Dalponte M & Coomes D A (2016). Tree‐centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods in ecology and evolution, 7 (10), 1236-1245.
• De la Sota, C, Ruffato-Ferreira V J, Ruiz-García L & Alvarez S (2019). Urban green infrastructure as a strategy of climate change mitigation. A case study in northern Spain. Urban Forestry & Urban Greening, 40, 145-151.
• Di Giulio M, Holderegger R & Tobias S (2009). Effects of habitat and landscape fragmentation on humans and biodiversity in densely populated landscapes. Journal of environmental management, 90 (10), 2959-2968.
• Du Toit M J, Cilliers S S, Dallimer M, Goddard M, Guenat S & Cornelius S F (2018). Urban green infrastructure and ecosystem services in sub-Saharan Africa. Landscape and Urban Planning, 180, 249-261.
• Dupras J, Marull J, Parcerisas L, Coll F, Gonzalez A, Girard M & Tello E (2016). The impacts of urban sprawl on ecological connectivity in the Montreal Metropolitan Region. Environmental Science & Policy, 58, 61-73.
• Eroğlu E, Kaya S, Doğan T G, Meral A, Demirci S, Başaran N & Çorbacı Ö L (2018). Determination of the Visual Preferences of Different Habitat Types. Fresenius Environmental Bulletin, 27, 4889-4899.
• Foster J, Lowe A & Winkelman S (2011). The value of green infrastructure for urban climate adaptation. Center for Clean Air Policy, 750 (1), 1-52.
• Gilani H, Ahmad S, Qazi W A, Abubakar S M & Khalid M (2020). Monitoring of Urban Landscape Ecology Dynamics of Islamabad Capital Territory (ICT), Pakistan, Over Four Decades (1976–2016). Land, 9 (4), 123.
• Haq S M A (2011). Urban green spaces and an integrative approach to sustainable environment. Journal of Environmental Protection, 2 (5), 601.
• Hepcan Ş (2013). Analyzing the pattern and connectivity of urban green spaces: A case study of Izmir, Turkey. Urban Ecosystems, 16 (2), 279-293.
• Hermoso V, Morán-Ordóñe, A, Lanzas M & Brotons L (2020). Designing a network of green infrastructure for the EU. Landscape and Urban Planning, 196, 103732.
• Hoang L & Fenner R A (2016). System interactions of stormwater management using sustainable urban drainage systems and green infrastructure. Urban Water Journal, 13 (7), 739-758.
• Iojă C I, Grădinaru S R, Onose D A, Vânău G O & Tudor A C (2014). The potential of school green areas to improve urban green connectivity and multifunctionality. Urban Forestry & Urban Greening, 13 (4), 704-713.
• Kong F, Yin H, Nakagoshi N & Zong Y (2010). Urban green space network development for biodiversity conservation: Identification based on graph theory and gravity modeling. Landscape and Urban Planning, 95 (1-2), 16-27.
• Kronenberg J, Haase A, Łaszkiewicz E, Antal A, Baravikova A, Biernacka M & Khmara Y (2020). Environmental justice in the context of urban green space availability, accessibility, and attractiveness in postsocialist cities. Cities, 106, 102862.
• Langemeyer J, Wedgwood D, McPhearson T, Baró F, Madsen A L & Barton D N (2020). Creating urban green infrastructure where it is needed–A spatial ecosystem service-based decision analysis of green roofs in Barcelona. Science of the Total Environment, 707, 135487.
• LaPoint S, Balkenhol N, Hale J, Sadler J & van der Ree R (2015). Ecological connectivity research in urban areas. Functional Ecology, 29 (7), 868-878.
• Li H, Chen W & He W (2015). Planning of green space ecological network in urban areas: an example of Nanchang, China. International Journal of Environmental Research and Public Health, 12 (10), 12889-12904.
• Liquete C, Kleeschulte S, Dige G, Maes J, Grizzetti B, Olah B & Zulian G (2015). Mapping green infrastructure based on ecosystem services and ecological networks: A Pan-European case study. Environmental Science & Policy, 54, 268-280.
• Mace G M, Norris K & Fitter A H (2012). Biodiversity and ecosystem services: a multilayered relationship. Trends in Ecology & Evolution, 27 (1), 19-26.
• Manning C D, Raghavan P & Schutze H (2008). Introduction to information retrieval. Cambridge University Press: Cambridge.
• Marulli J & Mallarach J M (2005). A GIS methodology for assessing ecological connectivity: application to the Barcelona Metropolitan Area. Landscape and Urban Planning, 71 (2-4), 243-262.
• Mell I C (2017). Green infrastructure: reflections on past, present and future praxis.
• Nor A N M, Corstanje R, Harris J A & Brewer T (2017). Impact of rapid urban expansion on green space structure. Ecological Indicators, 81, 274-284.
• Ossola A, Locke D, Lin B & Minor E (2019). Yards increase forest connectivity in urban landscapes. Landscape Ecology, 34(12), 2935-2948.
• Pelorosso R,Gobattoni F, Geri F, Monaco R & Leone A (2016). Evaluation of Ecosystem Services related to Bio-Energy Landscape Connectivity (BELC) for land use decision making across different planning scales. Ecological Indicators, 61, 114-129.
• Petras V, Newcomb D J & Mitasova H (2017). Generalized 3D fragmentation index derived from lidar point clouds. Open Geospatial Data, Software and Standards, 2 (1), 1-14.
• Plowright A (2015). Extracting trees in an urban environment using airborne LiDAR. GSS cIRcle Open Scholar Award (UBCV Non-Thesis Graduate Work).
• Pu R & Landry S (2020). Mapping urban tree species by integrating multi-seasonal high resolution pléiades satellite imagery with airborne LiDAR data. Urban Forestry & Urban Greening, 126675.
• R (2020). R: A language and environment for statistical computing, 2020. https://www.R-project.org. (02 Ocak 2020).
• Roussel J R, Auty D De Boissieu F & Meador A S 2020. lidR: Airborne LiDAR data manipulation and visualization for forestry applications. R package version 3.0.3.
• Rusche K, Reimer M & Stichmann R (2019). Mapping and Assessing Green Infrastructure Connectivity in European City Regions. Sustainability, 11(6), 1819.
• Sanesi G, Colangelo G, Lafortezza R, Calvo E & Davies C (2017). Urban green infrastructure and urban forests: A case study of the Metropolitan Area of Milan. Landscape Research, 42 (2), 164-175.
• Serret H, Raymond R, Foltête J C, Clergeau P, Simon L & Machon N (2014). Potential contributions of green spaces at business sites to the ecological network in an urban agglomeration: The case of the Ile-de-France region, France. Landscape and Urban Planning, 131, 27-35.
• Shochat E, Lerman S B, Anderies J M. Warren P S, Faeth S H & Nilon C H (2010). Invasion, competition, and biodiversity loss in urban ecosystems. BioScience, 60 (3), 199-208.
• Sütünç H S (2020). Effects of landscape fragmentation on endemic plant species of Siirt. Journal of Bartın Faculty of Forestry, 22 (2), 422-435.
• Teslenko T (2019). Engaging Students and Campus Community in Sustainability Activities in a Major Canadian University. In Sustainability on University Campuses: Learning, Skills Building and Best Practices. Springer, 3-20
• Tian Y, Liu Y, Jim C Y & Song H (2017). Assessing structural connectivity of urban green spaces in metropolitan Hong Kong. Sustainability, 9 (9), 1653.
• Tompalski P & Wezyk P (2012). LiDAR and VHRS data for assessing living quality in cities-an approach based on 3D spatial indices. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B6.
• Van Vliet J (2019). Direct and indirect loss of natural area from urban expansion. Nature Sustainability, 2 (8), 755-763.
• Velázquez J, Gutiérrez J, Hernando A & García-Abril A (2017). Evaluating landscape connectivity in fragmented habitats: Cantabrian capercaillie (Tetrao urogallus cantabricus) in northern Spain. Forest Ecology and Management, 389, 59-67.
• Venter Z S, Krog N H & Barton D N (2020). Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway. Science of the Total Environment, 709, 10.
• Vogt P (2016). GuidosToolbox (Graphical User Interface for the Description of image Objects and their Shapes). Digital image analysis software collection.
• Wu Z, Chen R, Meadows M E, Sengupta D & Xu D (2019). Changing urban green spaces in Shanghai: trends, drivers and policy implications. Land Use Policy, 87, 104080.
• Wüstemann H, Kalisch D & Kolbe J (2017). Access to urban green space and environmental inequalities in Germany. Landscape and Urban Planning, 164, 124-131.
• Zhang Z, Meerow S, Newell J P & Lindquist M (2019). Enhancing landscape connectivity through multifunctional green infrastructure corridor modeling and design. Urban Forestry & Urban Greening, 38, 305-317.