Design and implementation of an expert system for updating thematic maps using satellite imagery (case study: changes of Lake Urmia)

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• 作者：Vahid Sadeghi ; Farshid Farnood Ahmadi ; Hamid Ebadi
• 关键词：Expert systems ; Change detection ; Thematic maps ; Updating
• 刊名：Arabian Journal of Geosciences
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：9
• 期：4
• 全文大小：6,408 KB
• 参考文献：Badiru AB, Cheung J (2002) Fuzzy engineering expert systems with neural network applications, John Wiley & Sons, New York
  Biday SG, Bhosle U (2010) Radiometric correction of multitemporal satellite imagery. J Comput Sci 6(9):1027–1036CrossRef
  Bouziani M et al (2010) Rule-based classification of a very high resolution image in an urban environment using multispectral segmentation guided by cartographic data. Geosci Remote Sens IEEE Trans 48(8):3198–3211CrossRef
  Cohen Y, Shoshany M (2002) A national knowledge-based crop recognition in Mediterranean environment. Int J Appl Earth Observation Geoinformation 4(1):75–87CrossRef
  Comber AJ et al (2004) Application of knowledge for automated land cover change monitoring. Int J Remote Sens 25(16):3177–3192CrossRef
  Congalton RG, Green K (1993) A practical look at the sources of confusion in error matrix generation. Photogramm Eng Remote Sens 59(5):641–644
  Coppin P et al (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25(9):1565–1596CrossRef
  de Moraes, RM (2004) An analysis of the fuzzy expert systems architecture for multispectral image classification using mathematical morphology operators. Int J Comput Cognition (http://​www.​YangSky.​com/​yangijcc.​htm ) 2(2): 35-69
  El Hajj M et al (2009) Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices—the case of sugarcane harvest on Reunion Island. Remote Sens Environ 113(10):2052–2061CrossRef
  Elvidge CD et al (1995) Relative radiometric normalization of Landsat Multispectral Scanner (MSS) data using automatic scattergram-controlled regression. Photogramm Eng Remote Sens 61(10): 1255-1260
  Enquist CA, Gori DF (2008) Application of an expert system approach for assessing grassland status in the US-Mexico borderlands: implications for conservation and management. Nat Areas J 28(4):414–428CrossRef
  Forghani A (1999) Expert system approach for detection of road from remote sensing date. ISPRS, Sensors and Mapping from Space. ISPRS, Germany, pp 27–30
  Forsyth R (1989) Expert systems principles and case studies. Chapman & Hall, London
  Giarratano J, Swher B (1994) Expert system: principles programming. PWS, Boston, USA
  Hassanzadeh E et al (2012) Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resour Manag 26(1):129–145CrossRef
  Huang C et al (2010) Automated masking of cloud and cloud shadow for forest change analysis using Landsat images. Int J Remote Sens 31(20):5449–5464CrossRef
  Jat MK et al (2008) Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int J Appl Earth Obs Geoinf 10(1):26–43CrossRef
  Kahya O et al (2010) Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon. Environ Monit Assess 160(1-4):431–438CrossRef
  Karbassi A et al (2010) Environmental impacts of desalination on the ecology of Lake Urmia. J Great Lakes Res 36(3):419–424CrossRef
  Kruse FA (2008) Expert system analysis of hyper-spectral data. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery. Orlando, Florida, USA: 16 - 20
  Kudrat M et al (2000) Discrimination of newly planted and ratoon crops of sugar cane using multidate IRS-1C liss III data: a knowledge based approach. J Indian Soc Remote Sens 28(2-3):179–185CrossRef
  Lucas R et al (2007) Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS J Photogramm Remote Sens 62(3):165–185CrossRef
  Metternicht G (2001) Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecol Model 144(2):163–179CrossRef
  Pengra B (2012) The drying of Iran’s Lake Urmia and its environmental consequences. UNEP-GRID, Sioux Falls, UNEP Global Environmental Alert Service (GEAS).http://​na.​unep.​net/​geas/​getUNEPPageWithA​rticleIDScript.​php?​article_​id=​79 . Accessed 25 February 2015
  Richards JA (2013) Remote sensing digital image analysis: an introduction. Springer, New YorkCrossRef
  Shrestha DP, Zinck JA (2001) Land use classification in mountainous areas: integration of image processing, digital elevation data and field knowledge (application to Nepal). Int J Appl Earth Obs Geoinf 3(1):78–85CrossRef
  Stefanov WL et al (2001) Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote Sens Environ 77(2):173–185CrossRef
  Tullis JA, Jensen JR (2003) Expert system house detection in high spatial resolution imagery using size, shape, and context. Geocarto Int 18(1):5–15CrossRef
  Yang X, Lo C (2000) Relative radiometric normalization performance for change detection from multi-date satellite images. Photogramm Eng Remote Sens 66(8):967–980
  Zhang R, Zhu D (2011) Study of land cover classification based on knowledge rules using high-resolution remote sensing images. Expert Syst Appl 38(4):3647–3652CrossRef
  
• 作者单位：Vahid Sadeghi (1)
  Farshid Farnood Ahmadi (2)
  Hamid Ebadi (1)
  
  1. Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, No. 1346, Vali-Asr Street, Mirdamad Cross, Tehran, Iran
  2. Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz, Iran
  
• 刊物类别：Earth and Environmental Science
• 出版者：Springer Berlin Heidelberg
• ISSN：1866-7538

文摘

Thematic maps are fundamental for decision-making in natural resources and ecosystem management and planning. Thematic information, land cover in particular, is dynamic and changeable with the passage of time. Hence, providing up-to-date and precise data in a short period of time is considered as a key factor for successful utilization of these maps. Field inspection and visual interpretation of satellite images and topographic maps are necessary for this purpose. These processes are labor intensive, time consuming, and costly. An expert system, as a computer system with the capability of an expert’s performance simulation, could be an assistant for experts involved with the thematic map updating process. In this paper, a knowledge-based expert (KBE) system was designed, implemented, and tested to automate, facilitate, and expedite the conventional thematic map updating procedure. The developed system in this research is based on using existing thematic map and satellite images together with the new acquired satellite image of the desired region. It entails the following five major steps: (1) relative radiometric normalization, (2) change detection, (3) spectral database construction, (4) identifying the content of the changes, and (5) cartographic operations. In order to assess the effectiveness of the proposed KBE system for automatic updating of thematic maps, two different case studies related to Urmia Lake and surrounding areas were considered. Near-anniversary bi-temporal Landsat TM 4,5 imagery, acquired on 30 June 1989 and 24 June 2007 over the eastern coast of the lake alongside the existing land cover map of 1989, were used in the first case study. Utilized data sets in the second case study were near-anniversary Landsat 8 satellite images (OLI sensor) taken on 20/7/2014 and 23/7/2015 over the southern coast of lake alongside the outdated land cover map of interest region in 2014. Test results of different parts of the system were confirmed by the experts, and finally the system could satisfy the assessment criteria from the experts’ point of view in this particular application. Overall accuracy of the updated thematic map in the first and second case studies was 97.18 and 87.81 %, respectively.