Ninth International Conference on Permafrost ... - IARC Research

Ni n t h In t e r n at i o n a l Co n f e r e n c e o n Pe r m a f r o s tResultsAn example illustrating the concept, from the North Slopeof Alaska, using TM, ETM and ALI data, in conjunctionwith topographic data to develop our object-based modelhas been undertaken. This analysis was conducted by firstprocessing the data within ENVI (by ITT visual informationsolutions) to retrieve surface reflectance values and toregister the datasets into a common reference system. Theresults were then imported into Definiens Pro (by DefiniensAG) for segmentation and classification based on knownsurface units such as moist acidic tundra or moist nonacidictundra (~18 classes total). The results of this segmentationappear to produce objects that correspond well to what haspreviously been established; however, further work is neededin characterizing class definitions.By using self-organized manifold learning (SOMs, anunsupervised Artificial Neural Network paradigm), wealso clustered a 9-band multispectral image from the ALIinstrument. Details of this technique, capabilities, andformer analyses are summarized in Merényi et al. (2007)and references therein. We separated 30 different vegetative,soil, and landscape units along the Dalton Highway in theToolik Lake area. These include various water bodies withdifferent sediment loads, glacial ice, snow, a variety of soilsand vegetation. Detailed interpretation is ongoing. Thisunsupervised segmentation serves to support a detailedsupervised classification.Point-source soils (pedon) data and field spectrometry datahave also been acquired at different units to provide groundtruth for the satellite image interpretation.Conclusions and Future WorkPrevious studies conducted have utilized datasets thatwere largely moderate spatial and low spectra resolution.This study is employing datasets that are also moderatespatial resolution, but will be reinforced with high spectralresolution data, resulting in a more accurate assessment ofthe surface materials and increased confidence in the model.Additionally, by implementing a segmentation of the datasets,it is possible to utilize textual and contextual informationthat can be lost in pixel-based classifications (Blaschke &Strobl 2001). This type of processing also allows for a moreautomated processing scheme that can facilitate an efficienttemporal study and produce datasets that have undergone thesame processing steps with little room for human error. Thisdecreases the amount of time it takes to run an analysis andincreases confidence in the model. Preliminary results haveshown that image segmentation through a texture-based,object-oriented approach yields landscape unit geometriesthat appear to correlate well with previously conductedaerial photograph analysis undertaken in the region (Walker1996).Field spectral measurements, collected over major landcovertypes, indicate that the spectral differences betweendifferent landscape units are often minor. Based on highdimensionalintricate signatures, hyperspectral data couldprovide material discrimination, even in cases of many classeswith potentially subtle spectral distinctions. Exploitation ofthese types of data is a great challenge in itself, both fordiscovery (unsupervised clustering) and for mapping knownspecies (supervised classification). Fused with other data,this challenge increases. Analysis of hyperspectral imageryresides in our long-term goals, and we hope to report onthose at a later date.AcknowledgmentsEM is partially supported by the AISRP (grantNNG05GA94G) of NASA’s SMD. BB is supported byNASA GSRP graduate fellowship NNX0AR79H. BC andJR are supported by NASA’s ICESat program.ReferencesBlaschke, T. & Strobl, J. 2001. What’s wrong with pixels?Some recent developments interfacing remote sensingand GIS. GIS Zeitschrift fur Geoinformationssystme6(11): 12-17.Burnett, C. & Blaschke, T. 1994. A multi-scale segmentation/object relationship modeling methodology forlandscape analysis. Ecological Modeling 31: 737-747.Darwish, A. et al. 2003. Image Segmentation for the PurposeOf Object-Based Classification. Geoscience andRemote Sensing Symposium, 2003. IGARSS ’03.Proceedings. 2003 IEEE <strong>International</strong> 3: 2039-2041.Merényi, E., Farrand, W.H., Brown, R.H., Villmann, Th. &Fyfe, C. 2007. Information extraction and knowledgediscovery from high-dimensional and high-volumecomplex data sets through precision manifoldlearning, Proc. NASA Science Technology <strong>Conference</strong>(NSTC2007), College Park, Maryland, June 19–21,2007: 11pp. ISBN 0-9785223-2-X.Walker, D.A. 1996. GIS data from the Alaska North Slope.National Snow and Ice Data Center (Digital Media).Accessed 3-15-07.254