Date of Award
Honors Thesis (Open Access)
Colby College. Environmental Studies Program
In recent years, lidar has proven itself as a forestry tool capable of accurate, large- scale inventories. Lidar has even shown utility in multitemporal analysis and growth assessment, given high-resolution or small-scale point clouds. However, lidar’s efficacy as a multitemporal tool with relatively low-resolution, large-scale datasets is comparatively unknown. In this study, I compared forest in Midcoast Maine bitemporally, with publicly available datasets from the years 2007 and 2012. Specifically, I compared differences in growth characteristics of riparian, wetland, and upland forests. Although the 2007 dataset (created for geomorphological research) and the 2012 dataset (statewide, general-purpose) possess varying point densities and differ in intended use, I detected meaningful change between the two years, though not between these three classes of forest.
Between 2007 and 2012, riparian, wetland, and upland forests grew roughly 140 centimeters. However, these gains were largely balanced out by a similar rate of harvest. In maps of canopy height difference between the two years, these disturbances are particularly visible. Specific forest management styles are also discernable, from clearcuts to thinning. The level of detail and forest growth visible in this study affirm lidar’s value in multitemporal analysis, despite relatively low resolutions. As lidar point clouds grow in quality and quantity, so will their value to future research.
lidar, bitemporal lidar, forest, forest growth, Midcoast Maine
Recommended CitationDenlinger, Soren, "Measuring Differential Forest Growth in the Sheepscot River Headwaters with Bitemporal Lidar" (2020). Honors Theses. Paper 987.