Next Article in Journal
Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch
Next Article in Special Issue
A Simple Method for Retrieving Understory NDVI in Sparse Needleleaf Forests in Alaska Using MODIS BRDF Data
Previous Article in Journal / Special Issue
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(11), 11558-11578; doi:10.3390/rs61111558

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

Canada Centre for Mapping and Earth Observation (CCMEO), Natural Resources Canada, 560 Rochester, Ottawa, ON K1A OE4, Canada
*
Author to whom correspondence should be addressed.
Received: 27 June 2014 / Revised: 27 October 2014 / Accepted: 27 October 2014 / Published: 20 November 2014
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
View Full-Text   |   Download PDF [3767 KB, uploaded 25 November 2014]   |  

Abstract

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. View Full-Text
Keywords: Landsat; arctic; time-series; change; profile matching; trend; regression Landsat; arctic; time-series; change; profile matching; trend; regression
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Olthof, I.; Fraser, R.H. Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification. Remote Sens. 2014, 6, 11558-11578.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top