Using annual Landsat imagery to identify harvesting over a range of intensities for non-industrial family forests

Abstract

The monitoring of forested landscapes dominated by many small private forest owners is difficult or not possible without spatially explicit and up-to-date information on land cover change. Analysis of time series multispectral data from the Landsat series of satellites have the spatial and temporal characteristics required to detect sub-hectare and non-stand replacing harvest events over large areas. We identified harvests that occurred in six western upper Michigan counties from 1985 to 2011 using Landsat best available pixel (BAP) image composites and the Composite2Change (C2C) approach. We detected a total of 7071 harvesting events with size ranging from 0.5 to 171.36 ha and average size of 6.42 ha, and analyzed their temporal trajectory. To gain confidence in our harvest mapping, we compared our findings to the overlapping decade of Global Forest Watch (GFW) data. Agreement between the datasets was high, with 94.24% of the C2C and GFW harvest pixels identified with the same change year and improving to 98.74% within ±1 year. This automated harvest detection system, which can capture small and otherwise missed harvests, is valuable to natural resource agencies responsible for monitoring and compliance with regulations over large areas, and researchers requiring estimates of harvest levels and the nature of forest cover status and trends on family forests.