Multidimensional Land-use Information for Local Planning and Land Resources Assessment in Indonesia: Classification Scheme for Information Extraction from High-Spatial Resolution Imagery
Projo Danoedoro(1*)
(1) Faculty Of Geography, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author
Abstract
Keywords
Full Text:
PDFReferences
Anderson, J.R., Hardy, E., Roach, J. and Witmer, R. (1976). A Land-use and Land-cover Classification System for Use with Remote Sensor Data. Washington, DC: US Geological Survey Professional Paper 964.
Baghdadi, N., and Zribi, M. (2016) Optical Remote Sensing of Land Surfaces -- Techniques and Methods. London: ISTE Press Ltd
Blaschke, T., Kelly, M., and Merschdorf , H. (2016). Object-Based Image Analysis: Evolution, History, State of the Art, and Future Vision. In: Thenkabail, P.S. (ed.) Remote Sensing Handbook Volume I -- Remotely Sensed Data Characterization, Classification, and Accuracies. Boca Raton CRC Press.
Cord, A., Conrad, C. , Schmidt, M., and Dech, S. (2010). Standardized FAO-LCCS land cover mapping in heterogeneous tree savannas of West Africa. Journal of Arid Environments 74 (2010): pp 1083-1091.
Danoedoro, P. (2003). Multisource Classification for Land-use Mapping based on Spectral, Textural, and Terrain Information using Landsat Thematic Mapper Imagery: A Case Study of Semarang-Ungaran Area, Central Java. Indonesian Journal of Geography (52):2. pp 81-106.
Danoedoro, P., Phinn, S., and McDonald, G. (2004). Developing a Versatile Land-use Information System based on Satellite Imagery for Local Planning in Indonesia Phase I: Establishment of Classification Scheme. Proceedings. 7th International Seminar on GIS for Developing Countries (GISDECO-7)“GIS Capacity Building and Infrastructure”, Universiti Teknologi Malaysia, Skudai, Johor, pp 10-12
Danoedoro, P., and Phinn , S. (2005). Detailed Land-cover Mapping by Introducing Higher-spatial Resolution panchromatic Bands in Multispectral Classification: Examples using Landsat ETM+ and Quickbird Imagery. Proceedings, MapAsia 2005 Conference: Empowering People Through Geospatial Information, Jakarta 22-25 August 2005.
Danoedoro, P. (2006). Extracting Land-Use Information Related to Socio-Economic Function from Quickbird Imagery: A Case Study Of Semarang Area, Indonesia. Proceedings of the Map Asia Conference, Bangkok
Danoedoro, P. (2007). Versatile Land-use Information for Local Planning in Indonesia: Contents, Extraction Methods and Integration based on Moderate-and High-spatial Resolution Imagery. PhD thesis, The University of Queensland, StLucia, Australia.
Danoedoro, P. (2015). Pengaruh Jumlah dan Metode Pengambilan Titik Sampel Penguji Terhadap Tingkat Akurasi Klasifikasi Citra Digital Penginderaan Jauh. Prosiding. Simposium Sains Geoinformasi ke-4, 27-28 Oktober 2015 di Yogyakarta
Eastman, J. R. (2016). TerrSet Tutorial. Worchester, MA: Clark University
Ehlers, M., Gahler, M., and Janowsky, R. (2003). Automated Analysis of Ultrahigh Resolution Remote Sensing Data for Biotope Type Mapping: New Possibilities and Challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 1252, pp 1-12.
Feng, C.C., and , Flewelling, D.M. (2004). Assessment of Semantic Similarity between Land Use/Land Cover Classification Systems Feature Based Approach, Relative Importance Approach. Computers, Environment and Urban Systems 28 (2004): pp 229–246.
Feranec, J. , Solin, L., Kopecka, M., Otahel, J., Kupkova, L., Stych, P., Bicik, I., Kolar, J., Cerba, O., Soukup, T., and Brodsky, L. (2014). Analysis and Expert Assessment of the Semantic Similarity Between Land Cover Classes. Progress in Physical Geography (38) 3: pp 301-327.
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., and Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., and others, (2013). High-Resolution Global Maps of 21st -Century Forest Cover Change. Science. 342(6160): pp 850-853.
Hengl, T. (2017). Current global land cover maps. Available at
http://worldgrids.org/doku.php/wiki:land_cover_and_land_use. Accessed on Friday 28 April 2017.
Hutyra, L.R., Yoon, B., and Alberti, M. (2011). Terrestrial Carbon Stocks Across a Gradient of Urbanization: A Study of the Seattle, WA Region. Global Change Biology 17, pp 783–797.
Janssen, L.J.M., and Di Gregorio, A. (2003). Land-use Data Collection using “Land Cover Classification System”: Results from a case Study in Kenya. Land Use Policy 20: pp 131-148.
Jensen, J. R. (2013). Remote Sensing of the Environment: An Earth Resource Perspective. 3nd edition. Englewood Cliffs, N.J.: Prentice Hall.
Kallimanis, A.S., and Koutsias, N. (2013). Geographical Patterns of Corine Land Cover Diversity Across Europe: The Effect of Grain Size and Thematic Resolution. Progress in Physical Geography 37(2): pp 161–177.
Kindu, M., Schneider, T., Teketay, D., and Knoke, T. (2013). Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands. Remote Sensing. (2013) 5: pp 2411-2435.
Kosmidou, V., Petrou, Z., Bunce, R.G.H., Mücher, C.A., Jongman, R.H.G , Bogers, M.M.B., Lucas, R.M., Tomaselli, V., Blonda, P., Padoa-Schioppa, E., Manakos, I., and Petrou, M. (2014). Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system. Ecological Indicators 36 (2014) pp 290– 300.
Lang, S. And Tiede, D. (2016). Geospatial Data Integration in OBIA: Implications of Accuracy and Validity. In: Thenkabail, P.S. (ed.) Remote Sensing Handbook Volume I -- Remotely Sensed Data Characterization, Classification, and Accuracies. Boca Raton CRC Press.
Latham, J., Cumani, R., Rosati, I., Bloise, M. (2014). Global Land Cover SHARE (GLC-SHARE) database. Beta-Release Version 1.0. FAO, Rome.
Lee, S., Hwang, S., Lee, S., Hwang, H., and Sung, H. (2009). Landscape Ecological Approach to The Relationships of Land Use Patterns in Watersheds to Water Quality Characteristics. Landscape and Urban Planning (92) (2009): pp 80–89.
Lillesand, T.M., Kiefer, R.W., and Chipman, J. (2014). Remote Sensing and Image Interpretation (6th ed.). New York: Wiley and Sons
Loveland, T.R. and Belward, A.S. (1997) The International Geosphere Biosphere Programme Data and Information System Global Land Cover Data Set (DISCover). Acta Astronautica (Vol 41), 4-10, pp 681-689.
Martínez, S. and Mollicone, S. (2012). From Land Cover to Land Use: A Methodology to Assess Land Use from Remote Sensing Data. Remote Sensing. (2012) 4: pp 1024-1045.
McCallum, I., Obersteiner, M., Nilsson, S., and Shvidenko, A. (2010) A Spatial Comparison of Four Satellite Derived 1 Km Global Land Cover Datasets. International Journal of Applied Earth Observation and Geoinformation, 8 , pp 246–255.
Moreno, A.J.P, and De Larriva, E.M. (2012). Comparison between New Digital Image Classification Methods and Traditional Methods for Land-cover Mapping. In: Giri, C.P. (ed) Remote Sensing of Land-use and Land-cover. Principles and Applications. New York: CRC Press.
Moskal, L.M., Styers, D.M. and Halabisky, M. (2011) Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data. Remote Sensing, 3, pp 2243-2262.
Osborne, P.I. (1999). Tropical Ecosystems and Ecological Concepts. Cambridge: Cambridge University Press
Oppelt, N., Scheiber, R., Gege, P. Wegmann, M., Taubenboeck, H., Berger, M. (2016). Fundamentals of Remote Sensing for Terrestrial Applications: Evolution, Current State of the Art, and Future Possibilities. In: Thenkabail, P.S. (ed.) Remote Sensing Handbook Volume I -- Remotely Sensed Data Characterization, Classification, and Accuracies. Boca Raton CRC Press.
Perlman, D.L., and Milder, J.C., (2005). Practical Ecology for Planners, Developers and Citizens. Washington: Island Press
Pu, R., Landry, S., and Yu, Q. (2011). Object-based Urban Detailed Land-cover Classification with High-spatial Resolution IKONOS imagery. International Journal of Remote Sensing, 32 (12), pp 3285-3308
Puissant, A., Hirsch, J., and Weber, C. (2005) The Utility of Texture Analysis to Improve Per-pixel Classification for High to Very High Spatial Resolution Imagery. International Journal of Remote Sensing 26(4): pp 733-745.
Sulistyo, B., Gunawan, T., Hartono, Danoedoro, P. And Listyaningrum. N. (2017). Absolute Accuracy of the Erosion Model of DEM-NDVI and it's Modification. International Journal of Geoinformatics (13) 2, pp 23-34.
Szuster, B.W. Chen, Q., and Borger, M. (2011). A Comparison of Classification Techniques to Support Land Cover and Land Use Analysis in Tropical Coastal Zones. Applied Geography 31 (2011): pp 525-532.
Tarantino, C., Lovergine, F.P., Adamo, M. and Pasquariello, G. (2011) Contextual Information for the Classification of High Resolution Remotely Sensed Images. Italian Journal of Remote Sensing 43 (2): pp 31-40.
Tchuente, A.T.K., Roujean, J-L. and Faroux, S. (2010). ECOCLIMAP-II: An Ecosystem Classification and Land Surface Parameters Database of Western Africa at 1 km Resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project. Remote Sensing of Environment 114 (2010) pp 961–976.
Tobler, W. (1988). Resolution, Resampling and All That. In H. Mounsey and R. Tomlinson (eds) Building Database for Global Science. New York: Taylor and Francis
van der Ploeg , J.D., Laurent, C., Blondeau, F., and Bonnafous, P. (2009). Farm Diversity, Classification Schemes and Multifunctionality. Journal of Environmental Management 90 (2009): pp 124–131.
van Gils, H., Huizing, H., Kannegieter, A., & van der Zee, D. (1991). The Evolution of the ITC System of Rural Land use and Land cover Classification (LUCC). ITC Journal (3): pp 163-167.
van Zuidam and van Zuidam Cancelado (1983). Terrain Analysis and Evaluation using Airphoto Interpretation – A Geomorphological Approach. Enschede: ITC
Weng and Larsson (2005). Satellite Remote Sensing of Urban Heat Islands: Current Practice and Prospects. In Jensen, R.R., Gatrell, J.D., and McLean D.D. (Eds) Geospatial Technologies in Urban Environments. Berlin: Springer-Verlag
Widayani, P., Gunawan, T. Danoedoro, P. and Mardihusodo, S.Y. (2016). Application of Geographically Weighted Regression for Vulnerable Area Mapping of Leptospirosis in Bantul District. Indonesian Journal of Geography (48) 2: pp 168- 177
Young, A. (1998). Land Resources: Now and for the Future. Cambridge: Cambridge University.
Zhang, R., and Zhu, D. (2011). Study of land cover classification based on knowledge rules using high-resolution remote sensing images. Expert Systems with Applications 38 (2011): pp 3647–3652.
DOI: https://doi.org/10.22146/ijg.32781
Article Metrics
Abstract views : 4996 | views : 3305Refbacks
- There are currently no refbacks.
Copyright (c) 2019 Indonesian Journal of Geography
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)
ISSN 2354-9114 (online), ISSN 0024-9521 (print)
IJG STATISTIC