Mapping Urban Green Spaces Based on an Object-Oriented Approach

Yıl 2018, Cilt: 2 - Özel Sayı - International Conference on Science and Technology (ICONST 2018), 71 - 81, 31.12.2018

Derya Gülçin , Abdullah Akpınar

https://doi.org/10.30516/bilgesci.486893

Cited By: 7

Öz

The advent of technology and its implications
on especially remote sensing image processing using High Resolution Satellite
Images (HRSI) to map land cover provide researchers to monitor land changes,
make landscape analyses, and manage land transformation. One of land dynamics
that should be mapped for the sustainability of urban area is green spaces.
Urban green spaces, such as parks, playgrounds, and residential greenery may
promote both mental and physical health. Besides, they contribute to ecosystem services
such as reducing heat island effect and carbon storage, aiding water regulation
etc. Therefore, mapping urban green infrastructure from a high-resolution
satellite image provides an important tool to conduct studies, researches, and
projects for sustainable development of urban areas. As the material of this
research, one of the orthophotos of Aydin urban area exemplifies the park, the
green cover in the agricultural area, the playground, and the residential
garden, was used. For classifying land cover from the orthophoto with
Object-Based Image Analysis (OBIA), eCognition Developer 9.0 software was
utilized. To combine spectral and shape features, multiresolution segmentation
was implemented. Additionally, features as brightness and ratio green were used
for the extraction of urban green areas. In this research, urban green areas
were successfully extracted from the orthophoto and accuracy assessment was
performed on the classified image. OBIA of high resolution imagery enables to
extract detailed information of various targets on urban areas. The result of
accuracy assessment of the classification achieved 84.68% overall accuracy. To
increase the accuracy via manual interventions, manual classification tool of
eCognition Developer 9.0 may be used if needed.  

Anahtar Kelimeler

Urban green space, Green infrastructure, Object-based classification

Kaynakça

• Aguilar, M. A., Aguilar, F. J., Lorca, A. G., Guirado, E., Betlej, M., Cichón, P., Nemmaoui, A., Vallario, A., Parente, C. (2016). Assessment of Multiresolution Segmentation for Extracting Greenhouses from Worldview-2 imagery. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41.
• Akpinar, A. (2016). How is quality of urban green spaces associated with physical activity and health?. Urban Forestry & Urban Greening, 16: 76-83.
• Anonymous (2017). Aydin Ili 2016 Yili Cevre Durum Raporu. Aydin Valiligi, Cevre ve Sehircilik Il Mudurlugu, 89s.
• Baltsavias, E. P. (1996). Digital ortho-images—a powerful tool for the extraction of spatial-and geo-information. ISPRS Journal of Photogrammetry and Remote sensing, 51(2): 63-77.
• Baró, F., Palomo, I., Zulian, G., Vizcaino, P., Haase, D., Gómez-Baggethun, E. (2016). Mapping ecosystem service capacity, flow and demand for landscape and urban planning: A case study in the Barcelona metropolitan region. Land Use Policy, 57: 405-417.
• Bartholome, E., Belward, A. S. (2005). GLC2000: A New Approach to Global Land Cover Mapping from Earth Observation Data. International Journal of Remote Sensing, 26(9): 1959-1977.
• Bechtel, B., Alexander, P. J., Böhner, J., Ching, J., Conrad, O., Feddema, J., Mills, G., See, L., Stewart, I. (2015). Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS International Journal of Geo-Information, 4(1): 199-219.
• Benediktsson, J. A., Pesaresi, M., Amason, K. (2003). Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, 41(9): 1940-1949.
• Blaschke, T. (2010). Object Based Image Analysis for Remote Sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16.
• Blok, A., Tschötschel, R. (2016). World port cities as cosmopolitan risk community: Mapping urban climate policy experiments in Europe and East Asia. Environment and Planning C: Government and Policy, 34(4): 717-736.
• Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1): 35-46.Derkzen, M. L., Teeffelen, A. J., Verburg, P. H. (2015). Quantifying urban ecosystem services based on high‐resolution data of urban green space: an assessment for Rotterdam, the Netherlands. Journal of Applied Ecology, 52(4): 1020-1032.
• Duro, D. C., Franklin, S. E., Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118: 259-272.
• Foody, G. M. (2002). Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment, 80(1): 185-201.
• Gao, Y and Mas, J. F. (2008). A Comparison of the Performance of Pixel Based and Object Based Classifications over Images with Various Spatial Resolutions. Online Journal of Earth Sciences, 2(1): 27–35.
• Goodin, D. G., Anibas, K. L., Bezymennyi, M. (2015). Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape. International Journal of Remote Sensing, 36(18): 4702-4723.
• Grafius, D. R., Corstanje, R., Warren, P. H., Evans, K. L., Hancock, S., Harris, J. A. (2016). The impact of land use/land cover scale on modelling urban ecosystem services. Landscape Ecology, 31(7): 1509-1522.
• Haala, N., Brenner, C. (1999). Extraction of buildings and trees in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3): 130-137.
• Herold, M., Scepan, J., Müller, A., Günther, S. (2002). Object-oriented mapping and analysis of urban land use/cover using IKONOS data. In 22nd Earsel Symposium Geoinformation for European-Wide Integration (pp. 4-6).
• Hu, T., Yang, J., Li, X., Gong, P. (2016). Mapping urban land use by using landsat images and open social data. Remote Sensing, 8(2): 151.
• Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91-106.
• Huth, J., Kuenzer, C., Wehrmann, T., Gebhardt, S., Tuan, V. Q., Dech, S. (2012). Land cover and land use classification with TWOPAC: Towards automated processing for pixel-and object-based image classification. Remote Sensing, 4(9): 2530-2553.
• Ierodiaconou, D., Schimel, A. C., Kennedy, D., Monk, J., Gaylard, G., Young, M., Diesing, M., Rattray, A. (2018). Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters. Marine Geophysical Research, 39(1-2), 271-288.
• Jia, Y. (2015). Object-based Land Cover Classification with Orthophoto and LIDAR Data. Master of Science Thesis, Royal Institute of Technology (KTH) Stockholm, Sweden.
• Jia, Y., Ge, Y., Ling, F., Guo, X., Wang, J., Wang, L., Chen, Y., Li, X. (2018). Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data. Remote Sensing, 10(3), 446.
• Khatami, R., Mountrakis, G., Stehman, S. V. (2016). A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 177: 89-100.
• Kressler, F. P., Steinnocher, K., Franzen, M. (2005). Object-oriented classification of orthophotos to support update of spatial databases. In Geoscience and Remote Sensing Symposium, 2005. IGARSS'05. Proceedings. 2005 IEEE International (Vol. 1, pp. 4-pp). IEEE.
• Lee, A. C., Maheswaran, R. (2011). The health benefits of urban green spaces: a review of the evidence. Journal of Public Health, 33(2): 212-222.
• Liu, D., Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4): 187-194.
• Lu, D., Weng, Q. (2009). Extraction of urban impervious surfaces from an IKONOS image. International Journal of Remote Sensing, 30(5): 1297-1311.
• Maas, J., Verheij, R. A., Groenewegen, P. P., De Vries, S., Spreeuwenberg, P. (2006). Green space, urbanity, and health: how strong is the relation?. Journal of Epidemiology & Community Health, 60(7): 587-592.
• MacLean, M. G and Congalton, R. G. (2012). Map accuracy assessment issues when using an object-oriented approach. In Proceedings of the American Society for Photogrammetry and Remote Sensing 2012 Annual Conference.
• Motohka, T., Nasahara, K. N., Oguma, H., & Tsuchida, S. (2010). Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2(10): 2369-2387.
• Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote sensing of environment, 115(5): 1145-1161.
• Neubert, M., Herold, H., Meinel, G. (2008). Assessing image segmentation quality–concepts, methods and application. In Object-based image analysis (pp. 769-784). Springer, Berlin, Heidelberg.
• Poursanidis, D., Chrysoulakis, N., Mitraka, Z. (2015). Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping. International Journal of Applied Earth Observation and Geoinformation, 35: 259-269.
• Qian, J., Zhou, Q and Hou, Q. (2007). Comparison of pixel-based and object-oriented classification methods for extracting built-up areas in aridzone. In ISPRS Workshop on Updating Geo-Spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs (pp. 163–171).
• Rawat, J. S., Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1): 77-84.
• Rogan, J., Chen, D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning, 61(4): 301-325.
• Sahalu, A. G. (2014). Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia (Doctoral dissertation).
• Schowengerdt, R. A. (2012). Techniques for Image Processing and Classifications in Remote Sensing. Academic Press. ISBN 0126289808, 9780126289800, 249 p.
• Shalaby, A., Tateishi, R. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1): 28-41.
• Stefanov, W. L., Ramsey, M. S., Christensen, P. R. (2001). Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment, 77(2): 173-185.
• Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150.
• Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Kaźmierczak, A., Niemela, J., James, P. (2007). Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review. Landscape and urban planning, 81(3): 167-178.
• Weih, R. C., Riggan, N. D. (2010). Object-based classification vs. pixel-based classification: Comparative importance of multi-resolution imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4), C7.
• Xiong, J., Thenkabail, P. S., Tilton, J. C., Gumma, M. K., Teluguntla, P., Oliphant, A., Congalton, R. G., Yadav, K., Gorelick, N. (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using sentinel-2 and Landsat-8 data on Google earth engine. Remote Sensing, 9(10): 1065.
• Yan, G., Mas, J. F., Maathuis, B. H. P., Xiangmin, Z., Van Dijk, P. M. (2006). Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18): 4039-4055.
• Yazgi, D., Yılmaz, K. T. (2017). The Assessment of Landscape Fragmentation in an Agricultural Environment Degradation or Contribution to Ecosystem Services? Fresenius Environmental Bulletin, 26(12A): 7941-7950.You-Shui, Z., Xue-Zhi, F., Jin-Kang, D., & Guo-Qin, G. (2004). Study on extraction of urban green space from IKONOS remote sensing images. 地理研究, 23(2): 274-280.
• Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7): 799-811.