An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

Abstract

Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a OPEN ACCESS Remote Sens. 2014, 6 2783 spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km 2 ). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

“…However, there is consensus that aerial surveys are not directly suitable as reference data for calibrating or validating remote sensing insect damage maps (de Beurs and Townsend 2008;Neigh et al 2014). Aerial survey maps are inconsistent in assigning levels of forest damage, incomplete in spatial coverage, subjective in its derivation, and are prone to overestimating damage within delineated areas (MacLean and MacKinnon 1996;Johnson and Ross 2008).…”

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

“…Land cover conversion often exhibits a strong contrast between remotely-sensed images at two or more time points. However, stress-induced changes within the same cover type (e.g., forest degradation) do not necessarily have apparent signatures in the short term, but may show trends over a long time span [73]. Hence, it has been suggested that to detect forest degradation, land cover should be characterized as continuous biophysical variables, and the method should be flexible to capture trends at the inter-annual scales [41,42].…”

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

“…A more recent study has observed multiple disturbances for the entire 30+ year AVHRR record with trend changes in vegetation productivity [42]. The use of Landsat and ancillary data to identify disturbance type in conjunction with the coarse resolution AVHRR data have also shown promise [24,43]. Disturbance information could enable more advanced terrestrial C-cycle models to capture these C-cycle dynamics.…”

“…We previously developed an automated procedure to capture harvest and insect outbreaks in the Laurentian forests of Wisconsin and Minnesota. This automated approach had 0.72 user's and 0.63 producer's accuracy for capturing insect mortality events, but had limited accuracy (0.56 user's and 0.36 producer's) in capturing insect outbreak that did not cause mortality, because of the temporally sparse Landsat Time Series Stacks (LTSS) [24]. In this study, we use a more robust time series that had limited phenology variability and a nearly complete annual anniversary record to evaluate the accuracy of this approach.…”

“…Changes in forest cover area can have large impacts on modeled carbon fluxes [50], and integrating cover type dynamics could result in the greater accuracy of post-disturbance aboveground biomass density. Our objective was to evaluate if a simple and efficient algorithm developed for another U.S. forested ecosystem [24] is sufficient for providing disturbance type information for Pacific Northwest forests. We also highlight the importance of a good practice accuracy assessment to inform mapping uncertainty.…”