Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity

We examined the impact of temporal dependence between patterns of error in classified time-series imagery through a simulation modeling approach. This research extended the land-cover-change simulation model we previously developed to investigate: (1) the assumption of temporal independence between patterns of error in classified time-series imagery; and (2) the interaction of patterns of change and patterns of error in a post-classification change analysis. In this research, the thematic complexity of the classified land-cover maps was increased by increasing the number of simulated land-cover classes. Simulating maps with increased categorical resolution permitted the incorporation of: (1) higher-order, more complex spatial and temporal interactions between land-cover classes; and (2) patterns of error that better reproduce the complex error interactions that often occur in time-series classified imagery. The overall modeling framework was divided into two primary components: (1) generation of a map representing true change; and (2) generation of a suite of change maps that had been perturbed by specific patterns of error. All component maps in the model were produced using simulated annealing, which enabled us to create a series of map realizations with user-defined spatial and temporal patterns. Comparing the true map of change to the error-perturbed maps of change using accuracy assessment statistics showed that increasing the temporal dependence between classification errors did not improve the accuracy of resulting maps of change when the categorical scale of the land-cover classified maps was increased. The increased structural complexity within the time series of maps effectively inhibited the impact of temporal dependence. However, results demonstrated that there are interactions between patterns of error and patterns of change in a post-classification change analysis. These interactions played a major role in determining the accuracy associated with the maps of change.

[1]  Mary L. Cadenasso,et al.  Dimensions of ecosystem complexity: Heterogeneity, connectivity, and history , 2006 .

[2]  P. Aplin,et al.  On scales and dynamics in observing the environment , 2006 .

[3]  E. Lambin,et al.  Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model , 2001 .

[4]  Eric D. Kolaczyk,et al.  On the choice of spatial and categorical scale in remote sensing land cover classification , 2005 .

[5]  David J. Maguire,et al.  CHAPTER 20 GENERATING PRESCRIBED PATTERNS IN LANDSCAPE MODELS , 2005 .

[6]  William J. McConnell,et al.  Global Land Project: Science Plan and ImplementationStrategy , 2005 .

[7]  Daniel A. Griffith,et al.  Error Propagation Modelling in Raster GIS: Overlay Operations , 1998, Int. J. Geogr. Inf. Sci..

[8]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[9]  H. Liu Accuracy analysis of remote sensing change detection by rule-based rationality evaluation with post-classification comparison , 2003 .

[10]  Jacqueline Warren Mills,et al.  Geospatial Analysis: A Comprehensive Guide to Principles, Techniques, and Software Tools, Second Edition - by Michael J. de Smith, Michael F. Goodchild, and Paul A. Longley , 2008, Trans. GIS.

[11]  Thorsten Wagener,et al.  Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox , 2007, Environ. Model. Softw..

[12]  D. Dean,et al.  Combining location and classification error sources for estimating multi-temporal database accuracy , 2001 .

[13]  R. G. Pontius,et al.  Modeling land-use change in the Ipswich watershed, Massachusetts, USA , 2001 .

[14]  Gerard B. M. Heuvelink,et al.  Error Propagation in Cartographic Modelling Using Boolean Logic and Continuous Classification , 1993, Int. J. Geogr. Inf. Sci..

[15]  A. Veldkamp,et al.  CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica , 1996 .

[16]  Pierre Goovaerts,et al.  Simulating error propagation in land-cover change analysis: The implications of temporal dependence , 2007, Comput. Environ. Urban Syst..

[17]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[18]  E. Lambin,et al.  Land-Cover-Change Trajectories in Southern Cameroon , 2000 .

[19]  Danielle J. Marceau,et al.  Modeling complex ecological systems: an introduction , 2002 .

[20]  T. Loveland,et al.  The characteristics and interpretability of land surface change and implications for project design , 2004 .

[21]  Peter H. Verburg,et al.  Analysis of land use drivers at the watershed and household level: Linking two paradigms at the Philippine forest fringe , 2005, Int. J. Geogr. Inf. Sci..

[22]  Michael F. Goodchild,et al.  Development and test of an error model for categorical data , 1992, Int. J. Geogr. Inf. Sci..

[23]  Michael Batty,et al.  GIS, spatial analysis, and modeling , 2005 .

[24]  Jianguo Wu,et al.  A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications , 2002 .

[25]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[26]  R. G. Pontius,et al.  Detecting important categorical land changes while accounting for persistence , 2004 .

[27]  Yohay Carmel,et al.  Characterizing location and classification error patterns in time-series thematic maps , 2004, IEEE Geoscience and Remote Sensing Letters.

[28]  P. V. Oort Improving land cover change estimates by accounting for classification errors , 2005 .