QUALITY ANALYSIS OF SINGLE TREE OBJECT WITH OBIA AND VEGETATION INDEX FROM LAPAN SURVEILLANCE AIRCRAFT MULTISPECTRAL DATA IN URBAN AREA

DOI: https://doi.org/10.14710/geoplanning.3.2.93-106
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Article Metrics: (Click on the Metric tab below to see the detail)

Article Info
Published: 25-10-2016
Section: Articles
Fulltext PDF Tell your colleagues Email the author

High-resolution remote sensing data as the acquisition result of LAPAN Surveillance Aircraft (LSA) has the potential to analyze urban areas. The purpose of this study was to develop a method of LSA multispectral data utilization with an analysis of the single tree object in urban areas with OBIA and vegetation index. The method proposed in this study is a hierarchical classification to obtain the specific tree object that will be used further to analyze the quality of vegetation. In particular, analysis of the vegetation quality on the tree object was carried out by calculating the value of vegetation index NDVI. As a result, the overall accuracy of the hierarchical classification of objects in urban areas reached 88 %. In conclusion, the analysis of the quality of vegetation NDVI has been able to perceive the condition of trees in the urban area.

Keywords

Aerial Remote Sensing; LSA; OBIA; NDVI; Vegetation Urban

  1. Nurwita Mustika Sari 
    Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Indonesia
  2. Dony Kushardono 
    Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Indonesia
  1. Aburas, M. M., et al. (2015). Measuring land cover change in Seremban, Malaysia using NDVI index. Procedia Environmental Sciences, 30, 238–243. [CrossRef]

  2. Baatz, M., & Schäpe, A. (2000). Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, 58, 12–23.

  3. Belgiu, M., & Drǎgut, L. (2014). Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 67–75.

  4. Blaschke, T., et al. (2000). Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental Information for Planning, Politics and the Public, 2, 555–570.

  5. Boukhabl, M., & Alkam, D. (2012). Impact of vegetation on thermal conditions outside, Thermal modeling of urban microclimate, Case study: the street of the republic, Biskra. Energy Procedia, 18, 73–84.

  6. Buyadi, S. N. A., Mohd, W. M. N. W., & Misni, A. (2015). Vegetation’s role on modifying microclimate of urban resident. Procedia-Social and Behavioral Sciences, 202, 400–407.

  7. Chulafak, G. A., Annas, A., & Kushardono, D. (2015). Pengolahan data kamera multispektral pada pesawat LSA-01 untuk pemantauan pertanian. In Prosiding Seminar Nasional Penginderaan Jauh 2015 (84–92).

  8. Du, R. (2016). Urban growth: changes, management, and problems in large cities of Southeast China. Frontiers of Architectural Research, 5(3), 290–300. [CrossRef]

  9. Gandhi, G. M., et al. (2015). NDVI: vegetation change detection using remote sensing and GIS--A case study of Vellore District. Procedia Computer Science, 57, 1199–1210.

  10. Grundström, M., & Pleijel, H. (2014). Limited effect of urban tree vegetation on NO 2 and O 3 concentrations near a traffic route. Environmental Pollution, 189, 73–76.

  11. Janhäll, S. (2015). Review on urban vegetation and particle air pollution--Deposition and dispersion. Atmospheric Environment, 105, 130–137.

  12. Jayawardhana, W., & Chathurange, V. M. I. (2016). Extraction of agricultural phenological parameters of Sri Lanka using MODIS, NDVI time series data. Procedia Food Science, 6, 235–241.

  13. Kadir, M. A. A., & Othman, N. (2012). Towards a better tomorrow: street trees and their values in urban areas. Procedia-Social and Behavioral Sciences, 35, 267–274.

  14. Kaimaris, D., Patias, P., & Tsakiri, M. (2012). Best period for high spatial resolution satellite images for the detection of marks of buried structures. The Egyptian Journal of Remote Sensing and Space Science, 15(1), 9–18.

  15. Karakiş, S., Marangoz, A. M., & Büyüksalih, G. (2006). Analysis of segmentation parameters in ecognition software using high resolution quickbird ms imagery. In ISPRS Workshop on Topographic Mapping from Space.

  16. Kerr, J. T., & Dobrowski, S. Z. (2013). Predicting the impacts of global change on species, communities and ecosystems: it takes time. Global Ecology and Biogeography, 22(3), 261–263. [CrossRef]

  17. Kim, J. Y., et al. (2015). Trends in a satellite-derived vegetation index and environmental variables in a restored brackish lagoon. Global Ecology and Conservation, 4, 614–624.

  18. Kumar, D. (2015). Remote sensing based vegetation indices analysis to improve water resources management in urban environment. Aquatic Procedia, 4, 1374–1380.

  19. Kushardono, D. (2014). Teknologi Akuisisi data pesawat tanpa awak dan pemanfaatannya untuk mendukung produksi informasi penginderaan jauh. Inderaja, v(7), 24–31.

  20. Kushardono, D., et al. (2014). Pemanfaatan Data LSA (LAPAN Surveillance Aircraft) untuk Mendukung Pemetaan Skala Rinci. In Prosiding Pertemuan Ilmiah Tahunan XX MAPIN 2014.

  21. Mondal, S., et al. (2014). Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India. The Egyptian Journal of Remote Sensing and Space Science, 17(2), 123–134.

  22. Nasir, R. A., et al. (2015). Adapting human comfort in an urban area: The role of tree shades towards urban regeneration. Procedia-Social and Behavioral Sciences, 170, 369–380.

  23. Pedersen, G. B. M. (2016). Semi-automatic classification of glaciovolcanic landforms: An object-based mapping approach based on geomorphometry. Journal of Volcanology and Geothermal Research, 311, 29–40.

  24. Rajendran, S., Al-Sayigh, A. R., & Al-Awadhi, T. (2016). Vegetation analysis study in and around Sultan Qaboos University, Oman, using Geoeye-1 satellite data. The Egyptian Journal of Remote Sensing and Space Science, in press.

  25. Said, A. E., Shandoul, H. M. O., & Yekhlef, Y. Z. (2013). Updating large scale maps using high resolution satellite image. APCBEE Procedia, 5, 435–440.

  26. Sari, N. M., & Kushardono, D. (2014). Klasifikasi penutup lahan berbasis obyek pada data foto UAV untuk mendukung penyediaan informasi penginderaan jauh skala rinci. Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital, 11(2), 114–127.

  27. Sari, N. M., & Kushardono, D. (2015). Pemanfaatan data foto LAPAN surveillance aircraft dengan kamera multispektral untuk melihat kualitas vegetasi pada ruang terbuka hijau perkotaan. In Prosiding Seminar Nasional Penginderaan Jauh 2015.

  28. Saripin, I. (2003). Identifikasi penggunaan lahan dengan menggunakan Citra Landsat Thematic Mapper. Buletin Teknik Pertanian, 8(2), 54.

  29. Takács, Á., et al. (2016). Microclimate modification by urban shade trees--An integrated approach to aid ecosystem service based decision-making. Procedia Environmental Sciences, 32, 97–109.

  30. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.

  31. Wang, X., Wang, K., & Zhou, B. (2011). Object-based classification of IKONOS data for endemic Torreya mapping. Procedia Environmental Sciences, 10, 1887–1891.

  32. Zain, A. F. M., et al. (2015). The detection of urban open space at Jakarta, Bogor, Depok, and Tangerang--Indonesia by using remote sensing technique for urban ecology analysis. Procedia Environmental Sciences, 24, 87–94.

  33. Zhang, X. X., Wu, P. F., & Chen, B. (2010). Relationship between vegetation greenness and urban heat island effect in Beijing City of China. Procedia Environmental Sciences, 2, 1438–1450.

  34. Zylshal, et al. (2016). A support vector machine object based image analysis approach on urban green space extraction using Pleiades-1A imagery. Modeling Earth Systems and Environment, 2(2), 54. [CrossRef]