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

• Published in: Remote Sensing
• Main Authors: Ian Olthof, Robert H. Fraser
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2014
• Subjects: Landsat; arctic; time-series; change; profile matching; trend; regression; Science; Q; Arctic; Mackenzie Delta; Climate change
• Online Access: https://doi.org/10.3390/rs61111558; https://doaj.org/article/64ca15b60e6e4fac9b21e26c25d0301b

Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes.