Ninth International Conference on Permafrost ... - IARC Research

Characterizing Polar Landscapes from Multispectral and Hyperspectral ImageryJustin L. RichState University of New York at Buffalo, Department of Geology, Buffalo, New York, USABea CsathoState University of New York at Buffalo, Department of Geology, Buffalo, New York, USAErzsébet MerényiRice University, Department of Electrical and Computer Engineering, Houston, Texas, USABrian BueRice University, Department of Electrical and Computer Engineering, Houston, Texas, USAChien-Lu PingUniversity of Alaska Fairbanks, School of Natural Resources and Agricultural Sciences, Fairbanks, Alaska, USALynn EverettThe Ohio State University, Byrd Polar Research Center, Columbus, Ohio, USAIntroductionThere is a physically based, conceptual understanding ofmany of the significant interactions that impact permafrostaffectedsoils. Our observationally based knowledgehowever, is inadequate in many cases to quantify theseinteractions or to predict their net impact. To pursue keygoals, such as understanding the response of permafrostaffectedsoil systems to global environmental changesand their role in the carbon balance, and to transform ourconceptual understanding of these processes into quantitativeknowledge, it is necessary to acquire geographically diversesets of fundamental observations at high spatial and oftentemporal resolution. The main goal of the research presentedhere is to characterize permafrost-affected landscapes byusing multispectral and hyperspectral imagery.analysis quandaries, such as the modifiable aerial unitproblem (Burnett & Blaschke 2003) or the effects of hardclassifications.Nonstandard advanced Neural Network architectures toattack tasks, such as the determination of the relevant meritsof the data components, have also been utilized. Pixel-levelfusion, where the measured values from all experiments fora given location (image pixel) are used as one stack-vectorserving as the signature of the material at that location, hasbeen applied. Here, one particular challenge is to determinethe relative contributions of the data from the variousmeasurements.ApproachThe sheer amount and the heterogeneity of datasets (e.g.,LIDAR, stereo imagery, multispectral, hyperspectral, andSAR imagery) make joint interpretation (fusion) a dauntingtask. Here remote sensing, pattern recognition, and landscapeanalysis techniques are combined for the delineation of soillandscape units and geomorphic features and for inferringthe physical properties and composition of the surface froma fused dataset consisting of an Advanced Land Imager(ALI), Landsat +ETM (ETM) or Landsat TM (TM) scene,and topographic data with its derivative products.Exploration of the relationship between the mappedsurface units and permafrost conditions on the North Slopeof Alaska (Fig. 1) has been undertaken. Since the depth of thepermafrost manifests in the active layer on a variety of scales,we apply texture-based, object-oriented multiresolution softclassifications. This allowed for integration of multiple datatypes within the same surface unit through the use of a regionbasedsegmentation algorithm based on data values andshape properties (Darwish et al. 2003). It is recognized thatthis type of analysis will lend itself to better characterizationof complex landscape units and processes than a pixelbasedapproach and can help to alleviate traditional spatialFigure 1. The area of interest in the region of Toolik Lake, Alaska.The location of the Dalton Highway and the Trans Alaska Pipelineare also included for reference.253