Using LiDAR and GEOBIA for automated extraction of eighteenth–late nineteenth century relict charcoal hearths in southern New England. Issue 2 (4th March 2018)

• Record Type: Journal Article
• Authors: Witharana, Chandi; Ouimet, William B.; Johnson, Katharine M.
• Is Part Of: GIScience & remote sensing. Volume 55:Issue 2(2018)
• Page Start: 183
• Page End: 204
• Publication Date: 2018-03-04
• Subjects: LiDAR -- GEOBIA -- New England -- relict charcoal hearths -- historical archaeology; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2018.1431356
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Increasing availability and advancements of aerial Light Detection and Ranging (LiDAR) data have radically been shifting the way archeological surveys are performed. Unlike optical remote sensing imagery, LiDAR pulses travel through small gaps in dense tree canopies enabling archeologists to discover "hidden" past settlements and anthropogenic landscape features. While LiDAR has been increasingly adopted in archeological studies worldwide, its full potential is still being explored in the United States. Furthermore, while hand-digitizing features in remote-sensing datasets remain a valuable method for archeological surveys, it is often time- and labor intensive. The central objective of this research is to develop a geographic object-based image analysis-driven methodological framework linking low-level features and domain knowledge to automatically extract targets of interest from LiDAR-based digital terrain models (DTMs) and to closely examine the degree of interoperability of knowledge-based rulesets across different study sites focusing on the same semantic class. We apply this framework in southern New England, a geographic region in the northeastern United States where numerous seventeenth-century to early twentieth-century features such as relict charcoal hearths (RCHs), stone walls, and building foundations lie abandoned in densely forested terrain. Focusing on RCHs in this study, our results show promising agreement between manual and automated detection of these features. Overall, we show that the use of LiDAR data augmented with object-based classification workflows provides valuable baseline data for future archeological study and reconstruction of land-use/land-cover change over the past 300 years.