Improving capacity for large-area monitoring of forest disturbance and recovery

Abstract

Information needs associated with forest monitoring have become increasingly complex. Data to support these information needs are required to be systematically generated, spatially exhaustive, spatially explicit, and to capture changes at a spatial and temporal resolution that is commensurate with both natural and anthropogenic impacts. Moreover, reporting obligations impose additional expectations of transparency, repeatability, and data provenance. The overall objective of this dissertation was to address these needs and improve capacity for large-area monitoring of forest disturbance and subsequent recovery. Landsat time series (LTS) enhance opportunities for forest monitoring, particularly for post-disturbance recovery assessments, while best-available pixel (BAP) compositing approaches allow LTS approaches to be applied over large forest extents. In substudies I and IV, forest monitoring information needs were identified and linked to image compositing criteria and data availability in Canada and Finland. In substudy II, methods were developed and demonstrated for generating large-area, gap-filled Landsat BAP image composites that preserve detected changes, generate continuous change metrics, and provide foundational, annual data to support forest monitoring. In substudy III a national monitoring framework was prototyped at scale over the 650 Mha of Canada’s forest ecosystems, providing a detailed analysis of areas disturbed by wildfire and harvest for a 25-year period (1985–2010), as well as characterizing shortand long-term recovery. New insights on spectral recovery metrics were provided by substudies V and VI. In substudies V, the utility of spectral measures of recovery were evaluated and confirmed against benchmarks of forest cover and height derived from airborne laser scanning data. In substudy VI the influence of field-measured structure and composition on spectral recovery were examined and quantified. By focusing on four key aspects of forest monitoring systems: information needs, data availability, methods development, and information outcomes, the component studies demonstrated that combining BAP compositing and LTS analysis approaches provides data with the requisite characteristics to support large-area forest monitoring, while also enabling a more comprehensive assessment of forest disturbance and recovery.