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Fuzzy Land Cover Change Detection and Validation: A Comparison of Fuzzy and Boolean analyses in Tripoli City, Libya

By Abdulhakim Emhemad Khmag


This research extends fuzzy methods to consider the fuzzy validation of fuzzy land cover data at the sub-pixel level. The study analyses the relationships between fuzzy memberships generated by field survey and those generated from the classification of remotely sensed data. In so doing it examines the variations in the relationship between observed and predicted fuzzy land cover classes. This research applies three land cover classification techniques: Fuzzy sets, Fuzzy c-means and Boolean classification, and develops three models to determine fuzzy land cover change. The first model is dependent on fuzzy object change. The second model depends on the sub-pixel change through a fuzzy change matrix, for both fuzzy sets and fuzzy c-means, to compute the fuzzy change, fuzzy loss and fuzzy gain. The third model is a Boolean change model which evaluates change on a pixel-by-pixel basis. \ud The results show that using a fuzzy change analysis presents a subtle way of mapping a heterogeneous area with common mixed pixels. Furthermore, the results show that the fuzzy change matrix gives more detail and information about land cover change and is more appropriate than fuzzy object change because it deals with sub-pixel change. Finally the research has found that a fuzzy error matrix is more suitable than an error matrix for soft classification validation because it can compare the membership from the field with the classified image. \ud From this research there arise some important points: \ud • Fuzzy methodologies have the ability to define the uncertainties associated with describing the phenomenon itself and the ability to take into consideration the effect of mixed pixels. \ud • This research compared fuzzy sets and fuzzy c-means, and found the fuzzy set is more suit-able than fuzzy c-means, because the latter suffers from some disadvantages, chiefly that the sum of membership values of a data point in all the clusters must be one, so the algorithm has difficulty in handling outlying points. \ud • This research validates fuzzy classifications by determining the fuzzy memberships in the field and comparing them with the memberships derived from the classified image

Topics: Fuzzy set, Fuzzy c-means, Fuzzy change, Fuzzy validation
Publisher: University of Leicester
Year: 2013
OAI identifier:

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  1. (2005). A comparative study of accuracy assessment methods for fuzzy classification of satellite image’.
  2. (2006). A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS Environment’.
  3. (1998). A fuzzy classification of sub-urban land cover from remotely sensed imagery’.
  4. (1999). A fuzzy modelling approach to wild land mapping in
  5. (1999). A fuzzy set-based assessment of soft classification’.
  6. (1992). A fuzzy sets approach to the representation of vegetation continua from remotely sensed data: An example from lowland heath’. Photogrammetric Engineering and Remote Sensing,
  7. (2001). A generalized confusion matrix for assessing area estimates from remotely sensed data’.
  8. (2006). A generalized cross-tabulation matrix to compare softclassified maps at multiple resolutions’.
  9. (2007). A matrix-based approach to fuzzy set accuracy assessment’.
  10. (1996). A method for manual endmember selection and spectral unmixing’.
  11. (2003). A multiscale object-specific approach to digital change detection’.
  12. (2004). A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles’.
  13. (2003). A perspective on the fundamentals of fuzzy sets and their use in geographic information systems’.
  14. (1998). A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data’.
  15. (2000). A quantitative fuzzy approach to assess mapped vegetation classifications for ecological applications’.
  16. (1991). A review of assessing the accuracy of classifications of remotely sensed data’.
  17. (2007). A survey of image classification methods and techniques for improving classification performance’.
  18. (2000). Accuracy assessment curves for satellite-based change detection’. Photogrammetric Engineering and Remote Sensing,
  19. (2010). Accuracy Assessment for Fuzzy Classification in Tripoli, Libya.
  20. (2008). Accuracy assessment method for wetland object–based classification’. Photogrammetric Engineering and Remote Sensing,
  21. (1998). Accuracy assessment of a land-cover map of the Kuparuk river basin, Alaska: Considerations for remote regions’. Photogrammetric Engineering and Remote Sensing,
  22. (2004). Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data’. Remote Sensing of Environment,
  23. (2004). Advanced image processing techniques for remotely sensed hyperspectral data.
  24. (2010). ALCM : Automatic land cover mapping’.
  25. (2002). Alternative c-means clustering algorithms’.
  26. (2001). An assessment of reference data variability using a virtual field reference database’. Photogrammetric Engineering and Remote Sensing,
  27. (2004). An error matrix approach to fuzzy accuracy assessment: The NIMA geocover project’,
  28. (1997). An evaluation of fuzzy approaches to mapping land cover from aerial photographs’.
  29. (2003). An evaluation of fuzzy classifications from IRS 1C LISS III imagery: a case study’.
  30. (2003). An evaluation of Lidar- and IFSARDerived digital elevation models
  31. (1999). An evaluation of the CoastWatch change detection protocol in South Carolina’,
  32. (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra,
  33. (2005). Analysis and optimization of the MODIS LAI and FPAR algorithm performance over broadleaf forests’.
  34. (2005). Analysis of land use/land cover and urban expansion of Nairobi city using remote sensing and GIS’.
  35. (2003). Analysis of the uncertainty and imprecision of the source data sets for a military terrain analysis application’,
  36. (2002). Analysis of topological relations between fuzzy regions in general fuzzy topological space’.
  37. (1996). Application of fuzzy logic to land suitability for rubber production in peninsular Thailand’.
  38. (1992). Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe: A case study’. Photogrammetric Engineering and Remote Sensing,
  39. (1996). Approaches to the production and evaluation of fuzzy land cover classification from remotely-sensed data’.
  40. (2001). Assessing polygon edge integrity’.
  41. (2010). Assessing the accuracy of land cover change with imperfect ground reference data’.
  42. (1999). Assessing the accuracy of remotely sensed data: Principles and practices, Lewis Publishers,
  43. (2004). Assessment of a semantic statistical approach to detecting land cover changed using inconsistent data sets’. Photogrammetric Engineering and Remote Sensing,
  44. (2010). Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geo-database and prior knowledge’.
  45. Biodiversity: Global biodiversity scenarios for the year 2100’.
  46. (2004). Cafcam: Crisp and Fuzzy Classification Accuracy Measurement Software. Paper presented at GeoComputation,
  47. (1999). Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision’.
  48. (2004). Change detection of informal settlements using multi temporal aerial photographs’.
  49. (2004). Change detection techniques’.
  50. (1993). Change detection using principal component analysis and fuzzy set theory’.
  51. (1991). Combining membership grades in image classification'.
  52. (1997). Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels’.
  53. (1996). Comparison of three methods for mapping tundra with Landsat digital data’. Photogrammetric Engineering and Remote Sensing,
  54. (2004). Computer processing of remotely sensed images, 3rd ed.
  55. (2006). Correspondence analysis for detecting land cover change’. Remote Sensing of Environment,
  56. (2009). Crisp classifiers vs. fuzzy classifiers: A statistical study,
  57. (1995). Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data’.
  58. (2003). Data quality and uncertainty: Ships passing in the night!’ In
  59. (1997). Definition of land cover classes in Middle East countries.
  60. (2006). Detecting change in vague interpretations of landscapes’.
  61. (2004). Detecting of deforestation and land conversion in Rondonia, Brazil, using change detection techniques’.
  62. (2006). Detection of land cover changes using Landsat
  63. (2009). Detection of land use and land cover change in the Accra Metropolitan Area (Ghana) from
  64. (2011). Determining the accuracy in image supervised classification problems.
  65. (2004). Developing a science of land change: Challenges and methodological issues’.
  66. (1999). Diachronic analysis of fuzzy objects’.
  67. (1998). Digital change detection in remotely sensed imagery using fuzzy set theory’,
  68. (2002). Digital change detection methods in ecosystem monitoring: a review’.
  69. (2010). Ecological status and change by remote sensing
  70. (2004). Economic Statistics: Agriculture & Related Industries: Agriculture & Farming.
  71. (2005). Estimating and accommodating uncertainty through the soft classification of remote sensing data’.
  72. (2010). Estimation of impervious surfaces of Beijing, China, with spectral normalized images using LSMA and ANN’.
  73. (2000). Estimation of sub-pixel land cover composition in the presence of untrained classes’.
  74. (2006). Expanding the conceptual, mathematical and practical methods for map comparison’.
  75. (1984). FCM: the fuzzy c-means clustering algorithm’.
  76. (2001). Formalizing fuzzy objects from uncertain classification results’.
  77. (2007). Four advances in handling uncertainties in spatial data and analysis’.
  78. (2001). Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical neural network approaches’.
  79. (2005). Further developments of a fuzzy set map comparison approach’.
  80. (2004). Fuzzy land element classification from DTMs based on geometry and terrain position’.
  81. (1993). Fuzzy logic for phytosociology, 1: Syntaxa as vague concepts’.
  82. (1993). Fuzzy logic for phytosociology, 2: Generalizations and prediction’.
  83. (2004). Fuzzy logic with engineering applications.
  84. (1989). Fuzzy mathematical methods for soil survey and land evaluation’.
  85. (2000). Fuzzy Modelling’,
  86. (1997). Fuzzy relational calculus in land evaluation’.
  87. (2009). Fuzzy segmentation for object-based image classification’.
  88. (1985). Fuzzy set theory and its applications.
  89. (2000). Fuzzy set theory and thematic maps: Accuracy assessment and area estimation’.
  90. (1988). Fuzzy sets, uncertainty and information.
  91. (1965). Fuzzy sets’.
  92. (1990). Fuzzy supervised classification of remote sensing images’.
  93. (2006). Fuzzy vs. crisp land cover classification of satellite imagery for the identification of savanna plant communities of the Oak Openings Region of NW Ohio and SE Michigan’, Master’s thesis,
  94. (2007). General Authority For Information and Yearly Bulletin.
  95. (2005). Global consequences of land use’.
  96. (1998). Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers’.
  97. (2006). Global land cover validation: Recommendations for evaluation and accuracy assessment of global land cover maps,
  98. (1993). Guidelines for Land-Use Planning, FAO Development Series.
  99. (2008). Harshness in image classification accuracy assessment’.
  100. (2001). Hierarchical fuzzy pattern matching for the regional comparisons of land use maps’.
  101. (2009). High-resolution land cover change detection based on fuzzy uncertainty analysis and change reasoning’.
  102. (2007). Higher order vagueness in geographical population of type n fuzzy sets’.
  103. (1992). Human population growth and global landuse/cover change’.
  104. (2006). IDRISI 15: The Andes Edition. Worcester:
  105. (1999). Image classification with a neural network: From completely-crisp to fully-fuzzy situations’,
  106. (2009). Improving urban land cover classification using fuzzy image segmentation’.
  107. (1996). Incorporation of mixed pixels in the training, allocation and testing stages of supervised classifications,
  108. (1996). Inferring urban land use from satellite sensor images using kernel-based spatial reclassification’. Photogrammetric Engineering and Remote Sensing,
  109. (2000). Integrated approaches to long-term studies of urban ecological systems’.
  110. (2004). Integrating land-cover data with different ontologies: Identifying change from inconsistency’.
  111. (1996). Introductory digital image processing: A remote sensing perspective, 2 nd ed.
  112. (2004). Introductory digital image processing: A remote sensing perspective, 3rd edn. Upper Saddle River,
  113. (2000). Issues related to the detection of boundaries’.
  114. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms’.
  115. (2000). Land cover characterisation and change detection for environmental monitoring of pan-Europe’.
  116. (2003). Land cover characterization and mapping of continental Southwest Asia multi-resolution satellite sensor data’.
  117. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing.
  118. (2011). Land cover classification using reformed fuzzy Cmeans’.
  119. (2000). Land cover mapping of large areas from satellite status and research priorities’.
  120. (2009). Land use change detection of small scale sugarcane: A case study of Umbumbulu, South Africa’, published Master’s thesis,
  121. (2011). Landscape change
  122. (2001). Landscape cover dynamics pattern in Maghalaya’.
  123. (2004). Landscape metrics with ecotones: Pattern under uncertainty’.
  124. (2003). Linear mixture model applied to Amazonian vegetation classification’.
  125. (1999). Management Issues and Applications, second edition edn,
  126. (1998). Mapping historical forest types in Baraga County,
  127. (1999). Mapping sub-pixel proportional land cover with AVHRR imagery’.
  128. (2007). Mapping type 2 change in fuzzy land cover’, in A. Morris and S. Kokhan (eds), Geographic Uncertainty in Environmental Security,
  129. (2006). Method to compare and improve land cover datasets: Application to the GLC-2000 and MODIS land cover products’.
  130. (2006). Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part I: Algorithm development’.
  131. (2004). Modeling coastal environmental changes by fuzzy logic approach’. Remote Sensing for Environmental Monitoring,
  132. (1996). Modeling uncertainty in photointerpreted boundaries’.
  133. (2003). Modelling land cover change: A fuzzy approach’, published
  134. (2003). Modelling of fuzzy spatial objects and topological relations’.
  135. (2008). Monitoring and analysis of urban land cover changes over Stockholm Region between 1986 and 2004 using remote sensing and spatial metrics’.
  136. (2001). Monitoring the magnitude of land-cover change around the southern limits of the Sahara’. Photogrammetric Engineering and Remote Sensing,
  137. (2002). On formalization methods of describing fuzzy region in GIS’.
  138. (2002). Parameterization of shortwave ice cloud optical properties for various particle habits’.
  139. (1995). Past and present land-use and land-cover in the USA’.
  140. (2008). Predicting land cover change and avian community responses in rapidly urbanizing environments’.
  141. (1999). Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost based approach’.
  142. (2002). Quality assurance and accuracy assessment of information derived from remotely sensed data’,
  143. (2005). Reasoning about changes of land cover with fuzzy settings’.
  144. (2004). Remote sensing and image interpretation, 5 th ed.
  145. (2008). Remote sensing and image Interpretation, 6th ed.
  146. (2007). Remote sensing applications: An overview.
  147. (1999). Remote sensing change detection: Environmental monitoring method and applications.
  148. (2006). Remote sensing digital image analysis:
  149. (2006). Remote sensing image analysis: Including the spatial domain.
  150. (2002). Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas’.
  151. (2007). Remote sensing of ecology, biodiversity and conservation: A review from the perspective of remote sensing specialists’.
  152. (2010). Remote sensing of land cover classes as type 2 fuzzy sets’. Remote Sensing of Environment,
  153. (2000). Remote sensing of the environment: An earth resource perspective.
  154. (2003). Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development’.
  155. (2007). Remote sensing, models and methods for image processing, 3rd ed.
  156. (2006). Resolution dependent errors in remote sensing of cultivated areas’.
  157. (2005). Rough sets based measures for the attribute uncertainty of classified remotely sensed imagery’,
  158. (2009). Sampling designs for accuracy assessment of land cover’.
  159. (2000). Settlement expansion on agricultural lands: Its geographical, social and economic causes (Tripoli city)’, MSc thesis,
  160. (1998). Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution’.
  161. (2006). Soft classification based sub-pixel allocation model’. Remote Sensing of Environment,
  162. (2008). Soft classification of hyperspectral imagery based on linear mixing model and supervised fuzzy logic algorithms’.
  163. (2008). Soft classification of hyperspectral imagery based on liner mixing model and supervised fuzzy logic algorithms’,
  164. (2010). Some issues in contextual fuzzy c-Means classification of remotely sensed data for land cover mapping’.
  165. (2000). Sorities paradox and vague geographies’.
  166. (2004). Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon’. Remote Sensing of Environment,
  167. (2002). Spatial method for characterizing land cover and detecting land cover changes for tropics’.
  168. (2004). Spatial object modeling in fuzzy topological spaces paces with applications to land cover change’, published
  169. (2007). Spectral mixture analysis for monitoring and mapping desertification processes in semi-arid areas in North Kordofan State, Sudan’, published PhD thesis,
  170. (2002). Status of land cover classification accuracy assessment’.
  171. (2007). Sub-pixel confusion–uncertainty matrix for assessing soft classifications’. Remote Sensing of Environment,
  172. (1994). Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions’.
  173. (1998). Synergy in remote sensing—what’s in a pixel?’
  174. (2002). Target classification of the data fusion in multichannel using Dempster-Shafer method’. Photogrammetric Engineering and Remote Sensing,
  175. (1995). Terrain objects, their dynamics and their monitoring by integration of GIS and remote sensing’.
  176. (2005). The application of land evaluation techniques in the north-east of Libya’, Published PhD thesis,
  177. (2004). The consequences of urban land transformation on net primary productivity in the United States’. Remote Sensing of Environment,
  178. (2006). The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery’.
  179. (2007). The emergence of land change science for global environmental change and sustainability’.
  180. (1990). The evaluation of fuzzy membership of land cover classes in the suburban zone’.
  181. (1995). The integration of geographic data with remotely sensed imagery to improve classification in an urban area’. Photogrammetric Engineering and Remote Sensing,
  182. (2004). The landsat ETM+ spectral mixing space’.
  183. (1994). The worldwide extent of land-use change’.
  184. (1994). Theory and methods for accuracy assessment of thematic maps using fuzzy sets’. Photogrammetric Engineering and Remote Sensing,
  185. (2000). Towards soft classification of satellite data: A case study based upon Resurs MSU-SK satellite data and land cover classification within the Baltic Sea Region’.
  186. (2001). Uncertain rule-based fuzzy logic systems: Introduction and new directions. Prentice-Hall,
  187. (2004). Uncertainties in segmentation and their visualisation’,
  188. (2003). Uncertainty in remote sensing, classification and scale effect modeling.
  189. (2011). Unsupervised land cover change detection: Meaningful sequential time series analysis’.
  190. (1989). Urban boundary detection from Landsat imagery: a GIS and knowledge-based approach to MSS data’. Agenda for the 90s: Technical Papers,
  191. (1981). Urban change detection mapping using Landsat digital data’.
  192. (2004). Urbanization in land change science: Observing, monitoring, and understanding trajectories of change on the earth's surface’. Photogrammetric Engineering and Remote Sensing,
  193. (2003). Using a time series of satellite imagery to detect land use and land cover change in the Atlanta Georgia, metropolitan area’.
  194. (2003). Values for the Fuzzy C-Means Classifier in change detection for remote sensing’,
  195. (2007). Variability in soft classification prediction and its implications for sub-pixel scale change detection and super resolution mapping’.
  196. (2005). Wetland mapping through semivariogram guided fuzzy segmentation of multispectral satellite imagery’, published Master’s thesis,
  197. (2005). What is the land cover?’ Environment and Planning B:Planning and Design,
  198. (2004). Where is Helvellyn? Multiscale morphometry and the mountains of the English Lake District’.

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