Abstract
Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.
Highlights
Forests provide critical ecosystem goods and services and are deeply intertwined with socioeconomic systems [1,2]
In the United States, wildfires, insect outbreaks, and land use are a few of the major disturbances that can lead to large-scale changes in forest cover [7,8,9,10,11,12,13,14]
While many efforts have advanced our ability to detect and map forest disturbance [9,21,22,23,24,25,39,67], the focus of the paper was to further our understanding of forest disturbance regimes by characterizing forest disturbance types, starting from the existing forest cover loss University of Maryland Global Forest Change Product [23]
Summary
Forests provide critical ecosystem goods and services and are deeply intertwined with socioeconomic systems [1,2]. Any disturbance-induced change in forest cover can have cascading impacts on surface energy balances, carbon dynamics, wildlife habitat, and human activities [3,4,5,6]. Disturbance regimes (frequency, intensity, spatial and temporal scale) vary significantly within and between disturbance types, making widespread, automated characterization challenging [15]. Generating a baseline assessment of forest disturbance is needed to characterize how climate change and human activity are altering these regimes [10,16,17]. Methodologies that can semi-autonomously quantify disturbance type across large spatial extents are necessary in order to increase our understanding of disturbance regimes and for producing and implementing effective land management policy [29]
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