Mapping land cover change and terrestrial dynamics over northern canada using multi-temporal landsat imagery

As climate change research becomes increasingly concerned with predicting future trends in the net balance of atmosphere and biosphere CO2 , mapping land cover changes using remote sensing imagery may aid in systematic monitoring for these efforts. This is of special interest in northern areas as they may be more susceptible to rapid change, causing migrations of the tree line and altered permafrost depths. In the current study, we examine and quantify various land cover changes from 1975 to 2001 using multi-temporal Landsat imagery over four pilot sites located in northern Canada. To assess land cover change, three change detection methods were tested using a reference land cover map created by spectral clustering of the most current circa 2000 Landsat ETM+ scene. The three methods under comparison were: 1) Cross-correlation Analysis (CCA), 2) Change Vector Analysis (CVA) and 3) Theil-Sen Regression Analysis (TSA). The methods are similar in that they perform cluster-based statistical analysis going back through the historic data available for each site. To compare the change techniques, each method was applied to the overlapping region of two Landsat ETM+ data paths acquired less than 9 days apart. Assuming no change between the two Landsat acquisitions, CCA and CVA produced similar commission errors (%1.2) while the TSA commission error improved to %0.02. The dominant commission errors were found in the grassland land cover class. Extending this change analysis to the four pilot areas, each of the methods produced variable results. The maximum change recorded for Site #1 was 2368km 2 between 2000-1992. Site #2 characterized a maximum change of 2558km 2 . The maximum change calculated for Site #3 located in northern Ontario was 1983km 2 while the site in Quebec changed by 1031km