Abstract
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here we present a simple, but effective means of visually assessing landscape disturbances in northern environments using trend analysis of Landsat satellite image stacks. Linear trends of the Tasseled Cap brightness, greenness, and wetness indices, when composited into an RGB image, effectively distinguish diverse landscape changes based on additive color logic. Using a variety of reference datasets within Northwest Territories, Canada, we show that the trend composites are effective for identifying wildfire regeneration, tundra greening, fluvial dynamics, thermokarst processes including lake surface area changes and retrogressive thaw slumps, and the footprint of resource development operations and municipal development. Interpretation of the trend composites is aided by a color wheel legend and contextual information related to the size, shape, and location of change features. A companion paper in this issue (Olthof and Fraser) focuses on quantitative methods for classifying these changes.
Highlights
Northern environments are changing rapidly in response to recent climate warming, human development, and natural disturbances [1]
As we demonstrate with the case studies discussed in this paper, a combination of the Tasselled Cap (TC) indices in RGB space and contextual information related to the shape, size, and location of change objects can be sufficient to identify the type of landscape change without the benefit of ground-based information
Wildfires are the dominant disturbance impacting the boreal forest in Northwest Territories (NWT)
Summary
Northern environments are changing rapidly in response to recent climate warming, human development, and natural disturbances [1]. Monitoring these environments to inform decision makers and land and wildlife managers poses significant challenges due to their large extent and sparse human populations. Remote sensing has successfully been used to detect and map large-area landscape changes related to Arctic lake extent [3,4], storm-surge vegetation dieback [5], glacier retreat [6], vegetation greening [7], and forest fires [8,9,10] over Canada’s North
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