Peyzaj Mimarlığında Konumsal Bilgi Teknolojilerinin Kullanımı

Year 2019, Volume: 1 Issue: 1, 55 - 67, 10.07.2019

Süha Berberoğlu , Ahmet Çilek

Abstract

Günümüzde artan veri kaynakları bilgi
teknolojilerinin kullanım alanını yaygınlaştırmıştır. Verilerin sistematik bir
şekilde toplanarak depolanması, uygun ortamlarda işlenmesi ve kullanıcılar için
bilgiye dönüştürülmesi ancak bilgi teknolojilerinin kullanımı ile mümkün
olmaktadır. Hızlı bir şekilde gelişen bilgisayar teknolojisi peyzaj tasarım ve
planlama çalışmalarında uluslararası platformlarda etkin bir araç haline
gelmiştir. Özellikle konumsal bilgi teknolojilerinin (uzaktan algılama, CBS,
konumsal modeller ve GPS) her alanda kullanılabilirliği ve bu teknolojilerin
iki boyuttan üçüncü boyuta taşınması ile büyük etkinlik sağlamaktadır. Sonuç
olarak bu çalışmada, konumsal modellerin ve üç boyutlu modelleme çalışmalarının
peyzaj planlama ve peyzaj tasarımında kullanımı değerlendirilmiştir.

Keywords

konumsal modeller, 3B, gelişim modelleri, peyzaj, peyzaj mimarlığı

References

• Bamler R, Hart P (1998) Synthetic aperture radar interferometry, Inverse Problems 14 R-1–R54. Printed in the UK
• Bell EJ (1974) Markov analysis of land use change: An application of stochastic processes to remotely sensed data. Socio-economic Planning Sciences, 8, 311–316.
• Bell EJ, Hinojosa RC (1977) Markov analysis of land use change: continuous time and stationary processes. Socio-Econ Planning Science 11, 13–17.
• Bergen KM, Goetz SJ, Dubayah RO, Henebry GM, Hunsaker CT ve ark (2009) Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research. 114:1–13. doi: 10.1029/2008JG000883.
• Bourne LS (1971) Physical adjustment processes and land use succession: a review and central city example. Economic Geography 47, 1–15.
• Buermann W, Saatchi S, Smith TB, Zutta BR, Chaves JA, Milá B, Graham CH (2008) Predicting species distributions across 8 | MOTHES ET AL. the Amazonian and Andean regions using remote sensing data. Journal of Biogeography, 35, 1160–1176. https://doi.org/10.1111/j.1365- 2699.2007.01858.x
• Clarke KC, Hoppen S (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environment and Planning A 24, 247-261.
• Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and re-mote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2014.02.013
• De Grandi GF, Mayaux P, Malingreau JP, Rosenqvist A, Saatchi S, Simard M (2000) New perspectives on global ecosystems from wide-area radar mosaics: Flooded forest mapping in the tropics, Int. J. Remote Sens., 21, 1235–1249, doi:10.1080/014311600210155.
• Drewett JR (1969) A stochastic model of the land conversion process. Regional Studies, 3, 269–280.
• Godin G, Beraldin JA, Taylor J, Cournoyer L, Rioux M, El-Hakim S, Baribeau R, Blais F, Boulanger P, Domey J, ve ark (2002) Active Optical 3D Imaging for Heritage Applications., IEEE Comput. Graph. Appl. 22, 24–36.
• González-Jorge H, Martínez-Sánchez J, Bueno M, Arias P (2017) Unmanned Aerial Systems for Civil Applications: A Review. Drones, 1 (1), 2. https://doi.org/10.3390/drones1010002.
• Hermann A, Kuttner M, Hainz-renetzeder C, Konkoly-gyuró É, Tirászi Á, Brandenburg C, Allex B, Ziener K, Wrbka T (2014) Assessment framework for landscape services in European cultural landscapes : An Austrian Hungarian case study 37, 229–240.
• Hermann A, Schleifer S, Wrbka T (2011) The concept of ecosystem services regarding landscape research: a review. Living Rev. Landscape Res. 5, 1 (Online Article): cited (10.11.2012), http://www.livingreviews.org/lrlr-2011-1
• Holland J (1995) Hidden Order. How adaptation builds complexity Reading, Massachusetts, USA: Helix Books.
• Imhoff M, Lawrence WT, Stutzer DC, Elvidge CD (1997) Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the US. Remote Sensing of Environment, 59, 105−117
• Jahan S (1986) The determination of stability and similarity of Markovian land use change processes: A theoretical and empirical analysis. Socio-economic Planning Sciences, 20, 243–251.
• Kauffman S (1993) Origins of Order. Self-Organization and Selection in Evolution Oxford, UK: Oxford University Press.
• Kramer J (1996) Integration of a GIS with a local scale self-modifying cellular automaton urban growth model in Southeastern Orange County, NY. Department of Geography. New York, Hunter College: 73.
• Logofet DO, Lesnaya EV (2000) The mathematics of Markov models: What Markov chains can really predict in forest successions. Ecological Modeling, 126, 285–298.
• Luenberger DG (1979) Introduction to dynamic systems theory, models, and applications. New York: Wiley.
• Muller RM, Middleton J (1994) A Markov model of land-use change dynamics in the Niagara region, Ontario, Canada. Landscape Ecology, 9, 151–157.
• Norgaard RB (2010) Ecosystem services: from eye-opening metaphor to complexity blinder. Ecol. Econ. 69, 1219–1227
• Oğuz H (2004) Modeling Urban Growth and Land Use/Land Cover Change In The Houston Metropolitan Area From 2002 – 2030. Doctor of Philosophy. Texas A&M University
• Prigogine I, Stengers I (1984) Order out of chaos. Man’s new dialogue with nature Toronto, CA: Bantam Books.
• Remondino F, Barazzetti L, Nex F, Scaioni M, Sarazzi D (2012) UAV photo-grammetry for mapping and 3d modeling – current status and future perspectives. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-1/, 25-31. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-25-2011
• Robinson VB (1978) Information theory and sequences of land use: An application. The Professional Geographer, 30, 174–179.
• Saatchi SS, Houghton RA, Dos Santos Alvalá RC, Soares JV, Yu Y (2007) Distribution of aboveground live biomass in the Amazon basin. Global Change Biology, 13: 816-837. doi:10.1111/j.1365-2486.2007.01323.x
• Sefercik UG, Schunert A, Soergel U, Watanabe K (2012) Yüksek Çözünürlüklü Terrasar-X Verilerinin 3b Kalite Değerlendirmesi- Barselona Örneği, UZAL-CBS 2012.
• Silva EA, Clarke KC (2005) Complexity, emergence and cellular urban models: lessons learned from applying SLEUTH to two Portuguese metropolitan areas, European Planning Studies, 13, 93-115
• Sümer E, Türker M (2009) Üç Boyutlu Bina Modelleri İçin Otomatik Bina Yüz Dokusu Çıkarımı, TMMOB Harita ve Kadastro Mühendisleri Odası 12. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara.
• Tanrıöver AA (2011) Adana Kentsel Gelişiminin Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Kullanılarak Modellenmesi, Doktora tezi, Ç.Ü. Peyzaj Mimarlığı A.D., 203s.
• Toffoli T (1998) Cellular Automata as an Alternative to rather than an approximation of differential equations, Physica D, 10,117–127.
• Wegener M (2001) New spatial planning models. Int. J. Appl. Earth Obs. Geoinf. 3, 224–237. https://doi.org/https://doi.org/10.1016/S0303-2434(01)85030-3
• White R, Engelen G (1994) Cellular Dynamics and GIS: Modeling spatial complexity, Geographical Systems, 1, 237–253.
• Wilson A (2000) Complex Spatial Systems: The Modeling Foundations of Urban and Regional Analysis Harlow, England: Prentice Hall.