Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization

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 f...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Robert H. Fraser, Ian Olthof, Steven V. Kokelj, Trevor C. Lantz, Denis Lacelle, Alexander Brooker, Stephen Wolfe, Steve Schwarz
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2014
Subjects:
Q
Online Access:https://doi.org/10.3390/rs61111533
https://doaj.org/article/90b42c7ca23c4d64a9e962a8794635d8
Description
Summary: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.