A new directional land-change analysis method

Authors: Anthony M. Filippi*, Texas A&M University, Mingde You, Texas A&M University, İnci Güneralp, Texas A&M University, Burak Güneralp, Texas A&M University
Topics: Remote Sensing, Land Use and Land Cover Change
Keywords: land cover, land-change analysis, floodplain, maximum cross-correlation (MCC), fuzzy memberships, sensitivity analysis
Session Type: Virtual Poster
Day: 4/8/2021
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 51
Presentation File: No File Uploaded

A neglected area of land-dynamics research has been the quantitative characterization of land-change directionality. To better understand the effect of various drivers of land change, information regarding the direction of land change is highly useful. We propose a novel land-change analysis method that focuses on the directionality of change. Specifically, we employ Maximum Cross-Correlation (MCC) to estimate the directional change in land cover, as determined from multi-temporal Landsat 5 Thematic Mapper (TM) images, and where our study area is a dynamic river floodplain. In prior research, MCC has been used to detect and measure fluid and ice motions; however, it has not previously been employed for the analysis of the directionality of land-cover change. We applied MCC to land-cover class-membership data layers, derived from fuzzy classification of the Landsat TM images. We tested the sensitivity of the resultant change, or displacement, vectors to three user-defined parameters—template size, search window size, and a threshold parameter to determine valid (non-noisy) displacement vectors—that directly affect displacement-vector generation. A sensitivity analysis of this nature for MCC has not previously been conducted in detail within any domain. Results indicate that the rate of directional change in land cover in floodplains can be quantitatively measured based on MCC. Sensitivity analyses demonstrate that template size and the MCC threshold parameter have more of an impact on the displacement vectors than search window size. Also, results vary as a function of land-cover class, which suggests that land-cover-class spatial configuration should be considered when applying MCC.

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