Time-Lag Transiograms and Their Implications for Landscape Change Characterization

Xinba Li, Weidong Li () and Chuanrong Zhang
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Xinba Li: Department of Economics, University of Connecticut, Storrs, CT 06269, USA
Weidong Li: Department of Geography, University of Connecticut, Storrs, CT 06269, USA
Chuanrong Zhang: Department of Geography, University of Connecticut, Storrs, CT 06269, USA

Stats, 2024, vol. 7, issue 4, 1-19

Abstract: Markov chain transition probability matrices (TPMs) have traditionally been used to characterize land use and land cover (LULC) changes and species succession. However, previous studies relied solely on TPMs or transition area matrices to describe overall class area/proportion changes, overlooking important time correlation features. This study introduces the concept of idealized time-lag transiograms and demonstrates how they can be computed from temporal TPMs, using illustrative examples. The primary objective is to explore the potential value and utility of idealized time-lag transiograms in revealing additional characteristics of landscape change. Specifically, we focus on computing idealized time-lag transiograms with a fixed starting point and highlighting their fundamental features, such as sills, practical correlation ranges, and curve shapes, along with peak positions and peak height ratios of peaked cross-transiograms. These features are identified and discussed in terms of their potential implications for characterizing LULC changes. While idealized time-lag transiograms with a fixed starting point may not precisely predict future LULC changes due to the assumptions of the Markov property and time homogeneity (i.e., stationarity), they provide new insights into future LULC dynamics, revealing aspects that traditional Markov chain analysis has overlooked.

Keywords: transition probability matrix; Markov chain; time auto-correlation; time cross-correlation; land use and land cover change (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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