canada centre for remote sensing - ess mapping land cover change and terrestrial dynamics over...

Canada Centre for Remote Sensing - ESS

Mapping land cover change and Mapping land cover change and terrestrial dynamics over northern terrestrial dynamics over northern

Canada using multi-temporal Landsat Canada using multi-temporal Landsat imageryimagery

Christopher Butson†Robert Fraser‡

†Prologic Consulting, 75 Albert Street, Suite 206, Ottawa, Ontario, Canada. K1P 5E7

‡Natural Resources Canada, Canada Centre for Remote Sensing, 588 Booth St. Ottawa,

Ontario, Canada. K1A 0Y7


Presentation OutlinePresentation Outline

1) Introduction2) Research Objectives3) Data & Materials4) Methodology

• Cross-Correlation Analysis• Change Vector Analysis• Theil-Sen Regression Analysis

5) Results6) Conclusions7) Future Work


IntroductionIntroduction

Northern areas are characterized by:•Low air and soil temperatures•Permafrost•Short growing season and limited productivity•Climate data indicate large relative warming at high latitudes.

Intergovernmental Panel on Climate Change (IPCC) projects an increase in global mean surface temperature of 1º to 3.5º C by 2100 and an increase in sea level by 15-95cm.


Goal:Develop automated methods for detecting past and future land cover changes in the north and use this information to report on carbon fluxes for UNFCCC and track indicators of climate change in Canada.Where: Four pilot sites have been setup along the forest-tundra boundary (tree line) in northern Canada. Yukon-NWT, Manitoba, Ontario, Quebec.How: Use various change methods to monitor; I) Natural disturbances (tundra fires, vegetation) and II) Human induced changes (mining and settlements).


Research ObjectivesResearch Objectives

The main objective of this research is to develop an automated change detection technique for use with Landsat imagery to quantify past and present land cover changes in northern areas. More specifically, we aim to:

1) Test three change detection approaches for

quantifying land cover changes in Landsat imagery.2) Quantify total changed area, and land cover

changes throughout the specified time periods using circa 2000 imagery as the base-year over four pilot areas located in the forest-tundra transition zone of northern Canada.


Data & MaterialsData & Materials

Map of Canada highlighting the locations of the four pilot sites.


Study Site Province Sensor Date Path/Row

#1 North-West Terr. ETM+ July 16,2000 61/12-13

TM July 18,1992 61/12-13

#2 Manitoba ETM+ Aug 18,2001 31/19-20

TM June 28,1991 31/19-20

TM Aug 14,1985 31/19-20

MSS July 4,1979 34/19-20

#3 Ontario ETM+ July 16,2001 24/22-23

TM July 13,1991 24/22-23

#4 Quebec ETM+ July 29,2001 19/20

TM Aug 8,1990 19/20

MSS July 30,1975 19/20

Overlap scenes Province Sensor Date Path/Row

#5 Manitoba ETM+ July 3,2000 34/20

June 10,2000 33/20

#6 Ontario ETM+ July 29,2000 24/22

July 22,2000 23/22

#7 Ontario ETM+ Aug. 26,2000 28/24

Sept. 4,2000 27/24

Landsat Scene Selection:

Study sites #1-4, represent the multi-temporal sites under investigation.

Sites #5-7 represent the overlap image pairs that the change methods were tested on.


MethodologyMethodology


Cross-Correlation Analysis Cross-Correlation Analysis (CCA)(CCA)

Cross-Correlation Analysis (CCA) uses a land cover map to delineate spectral cluster statistics between the baseline image year (Time 1) and each scene in the temporal sequence (Time 2). Calculating the Z-statistic deviations from the cluster mean identifies change pixels within each land cover cluster.


Change Vector Analysis (CVA)Change Vector Analysis (CVA)

Change Vector Analysis (CVA) uses two spectral channels to map both the: 1) magnitude of change and, 2) the direction of change between the two (spectral) input images for each date.


Theil-Sen Regression Analysis Theil-Sen Regression Analysis (TSA)(TSA)

Much like typical image regression change, we use Theil-Sen as it is more robust to sample outliers than ordinary least-squares regression. •Medians are outlier resistant measures of central tendency and the method uses the median of all pairwise slopes to calculate the slope of the regression line. •The median value of the sample offsets represents the intercept.


TSA con’t…TSA con’t…•Generate mask to sample pixels in each land cover•Samples are used to build a regression equation for each cover type using the baseline circa 2000 ETM+ scene as the regressor and each scene in the temporal sequence as the response.• A change mask was created by mapping pixels characterizing large residuals away from the regression line


Results – Objective #1Results – Objective #1• The overlap scene acquired earlier in the season was used as the baseline image while the latter scene was considered the time 2 map •By analyzing only the overlap portion between the two orbital paths, we assumed that the land surface (and thus land cover) does not change between acquisition dates

CCA CVA TSA

Change pixels-mean,sd 6420,394 6394,211 98,18

% Change (commission error) 1.2 1.2 0.02

Dominant LC Change Grassland Grassland Mixed Forest


Results – Objective #1 con’t…Results – Objective #1 con’t…

Comparison of techniques for burned vegetation (high probability):

TSA

CCA

RGB=4,5,3

CVA


Results – Objective #1 con’t…Results – Objective #1 con’t…Comparison of techniques for regenerating vegetation (medium probability):

TSACCARGB=4,5,3

CVA


Study site#1: Changes 1992-2000, Inuvik, NWT

a) 1992, RGB=1,2,3 b) 2000, RGB c) Prob-Change

Results – Objective #2Results – Objective #2


Results - Objective #2 con’t…Results - Objective #2 con’t…

Study site #2: Changes 1985-2001, Churchill, MN

a) 2001, RGB b) 1985, Prob-Change c) 1991, Prob-Change


Results - Objective #2 con’t…Results - Objective #2 con’t…

Site #1 (68450km2) CCA CVA TSA

1992 Change pixels (%) 0.49 1.61 3.46

1992 Dominant LC Change Conifer, Moss Conifer, Shrub Shrub, Herb

Site #2 (59490km2) CCA CVA TSA

1979 Change pixels (%) 0.007 2.4 2.7

1979 Dominant LC Change Herb, Shrub Conifer, Moss Conifer, Herb

1985 Change pixels (%) 0.006 4.3 0.47

1985 Dominant LC Change Wetlands Conifer, lichen Regen, mixed

1991 Change pixels (%) 0.005 1.2 0.4

1991 Dominant LC Change Wetlands Conifer, lichen Herb, Shrub


ConclusionsConclusionsCVA –Does not rely on the quality/accuracy of a baseline land cover map to identify

changes. Relatively large commission errors but less noise in some cases.

CCA – Relies on the quality/accuracy of a land cover map to identify changes. May under estimate land cover changes.

TSA- Relies on the quality/accuracy of a land cover map to correctly classify changes. Although the commission errors were much lower in the overlap analysis, the change maps were still noisy. Computationally intensive.


Future workFuture work• Validate change conditions using historic

and current ancillary data• Develop interactive thresholding• Spatial aggregation of change pixels• Analyze seasonal change detection

limitations• Apply change methods to northern

mosaic of Canada• Assess land cover/land use changes for

UNFCCC reporting in the north


Baseline ClassificationBaseline ClassificationCirca 2000 Landsat ETM+ 90m landcover

of northern Canada – Version I (preliminary)

Olthof, I., Butson, C., Fernandes, R., Fraser, R., Latifovic, R. and Orazietti, J. (2004). Landsat ETM+ mosaic of northern Canada. Canadian Journal of Remote Sensing, submitted 06/04.


AcknowledgementsAcknowledgements

• Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/) for the use of the Landsat MSS imagery•Canadian Space Agency (CSA)- Government Related Initiatives Program (GRIP) funding

canada centre for remote sensing - ess mapping land cover change and terrestrial dynamics over...

Documents

canada centre

remote sensing ess goal

change pixels

direction of change

magnitude of change

land cover cluster

land cover map

various change methods