Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery

Abstract

The success and rate of forest regeneration has consequences for sustainable forest management, climate change mitigation, and biodiversity, among others. Systematically monitoring forest regeneration over large and often remote areas is challenging. Remotely sensed data and associated analytical approaches have demonstrated consistent and transparent options for spatially-explicit characterization of vegetation return following disturbance. Moreover, time series of satellite imagery enable the establishment of spatially meaningful recovery baselines that can provide a benchmark for identifying areas that are either under- or over-performing relative to those baselines. This information allows for the investigation and/or prioritization of areas requiring some form of management intervention, including guiding tree planting initiatives. In this research, we assess recovery following stand replacing disturbances for the 650 Mha forested ecosystems of Canada for the period 1985–2017, wherein ~51 Mha of Canada's forested ecosystems were impacted by wildfire, and ~ 21 Mha were impacted by harvesting. For quantification of forest recovery, we implement the Years to Recovery or Y2R metric using Landsat time series data based on the Normalized Burn Ratio (NBR) to relate the number of years required for a pixel to return to 80% of its pre-disturbance NBR value. By the end of the analyzed period, 76% of areas impacted by wildfire were considered spectrally recovered compared to 93% of harvested areas. On average, we found that harvest areas had more rapid spectral recovery (mean Y2R = 6.1 years) than wildfire (mean Y2R = 10.6 years) and importantly, that Y2R varied by ecozone, disturbance type, pre-disturbance land cover, and latitude. We used airborne laser scanning data to assess whether pixels that were considered spectrally recovered had attained United Nations Food and Agricultural Organization benchmarks of canopy height (>5 m) and cover (>10%) across four geographic regions representing different forest types. Overall, 87% and 97% of recovered pixels sampled in harvests and wildfires, respectively, had achieved at least one of the benchmarks, with benchmarks of height more readily achieved than benchmarks of cover. By analyzing spatial patterns of Y2R, we identified areas that had significant positive or negative spatial clustering in their rate of spectral recovery. Approximately 3.5–4% of areas disturbed by wildfire or harvest had significant positive spatial clustering, indicative of slower spectral recovery rates; these areas were also less likely to have attained benchmarks of height and cover. Conversely, we identified significant negative spatial clustering for 0.94% of areas recovering from harvest and 1.93% of areas recovering from wildfire, indicative of spectral recovery that was more rapid than the ecozonal baseline. Herein, we demonstrated that remote sensing can provide spatial intelligence on the nature of disturbance-recovery dynamics in forested ecosystems over large areas and moreover, can retrospectively quantify and characterize historic forest recovery trends within the past three decades that have implications for forest management, climate change mitigation, and restoration initiatives in the near term.