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

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 OPEN ACCESSRemote Sens. 2014, 6 11534 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.

Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification

“…While coefficients from Thiel-Sen linear regression predict disturbance classes best, least-squares regression that is more sensitive to outliers performs only slightly worse. Fraser et al [5] noted that visual interpretation of composited TC linear slope images alone was highly effective for discriminating major types of landscape change in the study region. Table 5 shows average Thiel-Sen coefficients by disturbance type sorted by process.…”

“…Tundra greening is also confused with regeneration and no change depending on the magnitude of greening and the age/timing of the regeneration. Fraser et al [5] demonstrate that this confusion can be reduced by visually incorporating contextual information related to the shape and size of change patches, and their geographic setting.…”

“…A reference change database was developed using a number of ancillary datasets (Table 1 ( [17,18])) and by interpreting changes in Landsat Tasseled Cap Brightness (TCB), Wetness (TCW) and Greenness (TCG) linear trend images displayed as R-G-B [5]. Polygons were digitized on Landsat data to delineate change extent and interpreted using ancillary data before assigning them to one of 21 classes that include both natural and human-induced changes representing loss or gain of vegetation, water, and soil.…”

“…That study alludes to the potential to classify Landsat Tasseled Cap trend images in Arctic environments to generate change maps. This study builds on Fraser et al [5] by testing different methods to generate a change map, apply best methods and perform a proper assessment of the final map product.…”

“…Fraser et al [5] describe the major types of landscape changes included in the reference disturbance database used in the current study, as well as provide a legend for visually interpreting Landsat Tasseled Cap linear trends displayed as an R-G-B composite. That study alludes to the potential to classify Landsat Tasseled Cap trend images in Arctic environments to generate change maps.…”