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

Remote Sensing

Ian Olthof¹  Robert H. Fraser²  Santonu Goswami²  Josef Kellndorfer² 

[1]Canada Centre for Mapping and Earth Observation (CCMEO), Natural Resources Canada, 560 Rochester, Ottawa, ON K1A OE4, Canada

[2] E-Mail

关键词: Landsat;    arctic;    time-series;    change;    profile matching;    trend;    regression;   

DOI  :  10.3390/rs61111558

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.