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TitleDetecting landscape changes in high latitude environments using Landsat trend analysis: 2. classification
AuthorOlthof, I; Fraser, R H
SourceRemote Sensing vol. 6, issue 11, 2014 p. 11558-11578, (Open Access)
Alt SeriesNatural Resources Canada, Contribution Series 20190181
PublisherMDPI AG
Mediapaper; on-line; digital
RelatedThis publication is related to Fraser, R H; Olthof, I; Kokelj, S V; Lantz, T C; Lacelle, D; Brooker, A; Wolfe, S; Schwarz, S; (2014). Detecting landscape changes in high latitude environments using Landsat trend analysis: 1. visualization, Remote Sensing vol. 6 no. 11
File formatpdf (Adobe® Reader®); html
ProvinceNorthwest Territories
NTS75; 85; 95; 86; 96; 97; 105; 106; 107; 115; 116; 117
Lat/Long WENS-140.0000 -110.0000 70.0000 60.0000
Subjectsenvironmental geology; hydrogeology; regional geology; LANDSAT; LANDSAT imagery; environmental studies; environmental impacts; fires
Illustrationslocation maps; Landsat images; schematic diagrams; graphs; photographs; aerial photos
ProgramRemote Sensing Science, Methodology
Released2014 11 20
AbstractMapping 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.
Summary(Plain Language Summary, not published)
This paper presents a comparison of methods to classify change processes in the Mackenzie Delta region, NWT for the Cumulative Impacts Monitoring Program (CIMP). Examples of change processes include erosion, fire, regeneration, succession and development, among others. Landsat time-series reflectance trajectories are classified using a number of different curve matching techniques based on distance and shape similarity. A product is generated and validated using a reference change database that was developed from ancillary information. The method and product are intended to allow historical cumulative environmental impact assessment from Landsat due to natural and anthropogenic causes.