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This work presents the potential for high-resolution remote sensing data (LiDAR digital terrain models) to determine the spatial heterogeneity of terraced landscapes. The study objective is achieved through the identification of a new parameter that distinguishes this unique landscape form from more natural land formations. The morphological indicator proposed is called the Slope Local Length of Auto-Correlation (SLLAC), and it is derived from the local analysis of slope self-similarity. The SLACC is obtained over two steps: (i) calculating the correlation between a slope patch and a defined surrounding area and (ii) identifying the characteristic length of correlation for each neighbourhood. The SLLAC map texture can be measured using a surface metrology metric called the second derivative of peaks, or Spc. For the present study, we tested the algorithm for two types of landscapes: a Mediterranean and an Alpine …