Using Landsat to Diagnose Trends in Disturbance Magnitude Across the National Forest System

Friday, 19 December 2014
Alexander J Hernandez1, Sean P Healey2, Stephen V. Stehman3 and R. Douglas Ramsey1, (1)Utah State University, Logan, UT, United States, (2)Rocky Mountain Research Statio, Ogden, UT, United States, (3)SUNY College of Environmental Science and Forestry, Syracuse, NY, United States
The Landsat archive is increasingly being used to detect trends in the occurrence of forest disturbance. Beyond information about the amount of area affected, forest managers need to know if and how disturbance severity is changing. For example, the United States National Forest System (NFS) has developed a comprehensive plan for carbon monitoring, which requires a detailed temporal mapping of forest disturbance magnitudes across 75 million hectares. To meet this need, we have prepared multitemporal models of percent canopy cover that were calibrated with extensive field data from the USFS Forest Inventory and Analysis Program (FIA). By applying these models to pre- and post-event Landsat images at the site of known disturbances, we develop maps showing first-order estimates of disturbance magnitude on the basis of cover removal. However, validation activities consistently show that these initial estimates under-estimate disturbance magnitude. We have developed an approach, which quantifies this under-prediction at the landscape level and uses empirical validation data to adjust change magnitude estimates derived from initial disturbance maps.

In an assessment of adjusted magnitude trends of NFS’ Northern Region from 1990 to the present, we observed significant declines since 1990 (p < .01) in harvest magnitude, likely related to known reduction of clearcutting practices in the region. Fire, conversely, did not show strongly significant trends in magnitude, despite an increase in the overall area affected. As Landsat is used to provide increasingly precise maps of the timing and location of historical forest disturbance, a logical next step is to use the archive to generate widely interpretable and objective estimates of disturbance magnitude.