A framework of experimental transiogram modelling for Markov chain geostatistical simulation of landscape categories. (January 2019)

• Record Type: Journal Article
• Authors: Yu, Jia; Li, Weidong; Zhang, Chuanrong
• Is Part Of: Computers, environment and urban systems. Volume 73(2019)
• Page Start: 16
• Page End: 26
• Publication Date: 2019-01
• Subjects: Transiogram -- Landscape -- Markov chain simulation -- Fitting model -- Spatial categorical variable; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2018.07.007
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Experimental transiogram modelling, especially joint modelling, is crucial for Markov chain random field (MCRF) simulation of categorical spatial variables with multiple classes. Experimental transiogram estimation of landscape categorical variables in a non-visual context and manual model fitting are tedious and time-consuming tasks if the number of classes is large. This study presented a framework which integrated the linear interpolation method and the mathematical model fitting method for transiogram joint modelling to facilitate the whole modelling procedure. The framework was developed as a tool, entitled TGRAM. The tool provides several advanced characteristics and functions which greatly improve the efficiency of transiogram joint modelling. Two case studies were provided to demonstrate the feasibility of the tool. The modelling results can directly provide parameters (i.e., transition probability values at continuous lags in the required pixel size) for further MCRF simulations in related research areas, such as land cover/land use classification, soil and lithofacies mapping, and urban growth detection. Highlights: We developed a framework for convenient experimental transiogram joint modelling. It includes mathematical model fitting and linear interpolation methods. The framework was implemented as TGRAM tool with visual windows. It provides transition probabilities at needed lags for Markov chain simulation. The functionality of TGRAM was demonstrated using two real-world cases.