Agricultural potential and actualized development in Hawai’i: an airborne LiDAR survey of the leeward Kohala field system (Hawai’i Island)

Archaeological investigations of Hawaiian agriculture have relied on relatively coarse-grained data to investigate archipelago-wide processes, or on fine-grained data to examine patterning within localized zones of agricultural production. These trade-offs between spatial coverage and data resolution have inhibited understanding of both spatial patterns and temporal trends. Our analysis of 173 km2 of high-resolution airborne Light Detection and Ranging (LiDAR) data for leeward Kohala, Hawai’i Island identifies spatial and temporal patterning in regional agricultural development. Differential densities of alignments suggest variable levels of agricultural intensity. Agricultural processes of expansion, segmentation, and intensification can also be discriminated, with distinct zones of the field system having undergone different mixes of development. Areas within the field system with moderate to high levels of both average production and variability in production (determined using a climate-driven productivity model) were utilized relatively early in a highly intensified manner; these areas often underwent processes of segmentation and intensification. Less productive areas were developed later and exhibit evidence of expansion with lower amounts of segmentation and intensification, at set levels of intensity. The spatial and temporal variability in agricultural activities was influenced by the diverse environmental conditions across the landscape as well as variation in cultivars and cultivation techniques. Combining the high-resolution LiDAR data from a large area with potential productivity modeling allows for a more fine-grained understanding of agricultural development in this region of the Hawaiian archipelago.

► High-resolution LiDAR data from 173 km2 of leeward Kohala (Hawai'i) are analyzed.
► Differential agricultural activities and modes of development are identified.
► The results of a climate-driven net primary productivity model are presented.
► Agricultural variability relates to variation in potential productivity.