Spatial information about disturbance driven patterns of forest structure and ages across landscapes provide a valuable resource for all land management efforts including cross-ownership collaborative forest treatments and restoration. While disturbance events in general are known to impact stand characteristics, the agent of change may also influence recovery and the supply of ecosystem services. Our study utilizes the full extent of the Landsat archive to identify the timing, extent, magnitude, and agent, of the most recent fast disturbance event for all forested lands within Minnesota, USA. To account for the differences in the Landsat sensors through time, specifically the coarser spatial, spectral, and radiometric resolutions of the early MSS sensors, we employed a two-step approach, first harmonizing spectral indices across the Landsat sensors, then applying a segmentation algorithm to fit temporal trends to the time series to identify abrupt forest disturbance events. We further incorporated spectral, topographic, and land protection information in our classification of the agent of change for all disturbance patches. After allowing two years for the time series to stabilize, we were able to identify the most recent fast disturbance events across Minnesota from 1974-2018 with a change versus no-change validation accuracy of 97.2% ± 1.9%, and higher omission (14.9% 9.3%) than commission errors (1.6% ± 1.9%) for the identification of change patches. Our classification of the agent of change exhibited an overall accuracy of 96.5% ± 1.9% with classes including non-disturbed forest, land conversion, fire, flooding, harvest, wind/weather, and other rare natural events. Individual class errors varied, but all class user and producer accuracies were above 78%. The unmatched nature of the Landsat archive for providing comparable forest attribute and change information across more than four decades highlights the value of the totality of the Landsat program to the larger geospatial, ecological research, and forest management communities.
Bibliographical noteFunding Information:
Funding: This research was supported by the Environment and Natural Resources Trust Fund and the Minnesota Department of Natural Resources.
© 2020 by the authors.
- Agent attribution
- Change detection
- Landsat time series