Abstract

   Change detection analysis in remote sensing often relies upon some form of radiometric consistency. Radiometric correction methods developed in previous studies often require ancillary information such as climate data, illumination geometry, ground reference data of pseudo-invariant features (PIFs), and satellite calibration data. Most studies do not have the luxury of having all of this data. To date, a radiometric correction method of consistent quality applicable to all change detection studies has not been accepted by the remote sensing community.

   A series of Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper covering 18 years was obtained for a primarily forested area in northern British Columbia. Different methods of radiometric correction that do not rely on ground reference data, climate data, or the subjective selection of PIFs were assessed on these data sets. These included an atmospheric transfer model which requires no climate data, a simple scaling function, and two scatterplot based regression functions.

   Assessment of radiometric consistency was performed qualitatively by using edge detection, and quantitatively using analysis of old-growth forests in equilibrium and measures of biomass accumulation in clearcuts. For each method of assessment, the two scatterplot based regressions functions yielded the best radiometric fidelity. These two methods can be completely automated and are equally applicable in any change detection series.

Introduction