This investigation examined the changing surface conditions of a study area near Toolik Lake, Alaska (see Figure 1) and sought to differentiate the surficial geology and geomorphology, largely influenced by glacial activity, as well as ecology of the region, in order to characterize the state of the permafrost table. This study was conducted utilizing remotely sensed images and datasets; as well as, field data, in order to conduct analysis of the landscape over multiple years. The information was subsequently used as proxy data to make observations of the state of the permafrost table which underlies this landscape. This type of study yielded continuous estimates of the ground conditions without the need for a lengthy ground campaign that could have proved difficult in a region such as this.

Data implemented for this project included an Advanced Land Imager and Landsat ETM+ scene; as well as, a Digital Elevation Model with its derivative data. Hyperion hyperspectral data was also utilized for this project to obtain spectral characteristics of the ground surface along with field data collected with an ASD spectral radiometer. Destriping of the Hyperion image was conducted within ENVI and the Eclipse development platform utilizing the Java IDE. All images were atmospherically corrected to retrieve at-surface-reflectance values using Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes or Empirical Line Calibration where appropriate in order to facilitate cross scene analysis of the images.

Using an object oriented multi-scale segmentation approach, this study employed Definiens Professional, an image analysis application that, among other things, allowed for fuzzy analysis of data and integration of multiple data types within the same project. Working in conjunction with ENVI (Environment for Visualizing Images), a model based on spectral properties of the surface materials yielded more robust results than a standard pixel based classification derived from a training set.

Previous studies conducted have utilized datasets that were largely moderate spatial and low spectra resolution. This study employed datasets that are also moderate spatial resolution but were reinforced with high spectral resolution data provided by Hyperion, resulting in a more accurate assessment of the surface materials and increased confidence in the model. Additionally, by first segmenting the datasets it was possible to utilize textual and contextual information that is typically lost in pixel based classifications. This type of processing also allowed for automated processing of other datasets which facilitated an efficient temporal study and produced datasets that have undergone the same processing steps. This reduced the possibility of processing mistakes, increased confidence in the resulting surface classifications and subsequently, increased confidence in the resulting subsurface characterization of the permafrost table located, in most cases, at shallow depths below.