Remote sensing and GIS based permafrost distribution mapping and modeling in discontinuous permafrost zone: a review

Transcription

1 December 14, 2007 Remote sensing and GIS based permafrost distribution mapping and modeling in discontinuous permafrost zone: a review Abstract Santosh K Panda Permafrost is an important factor in arctic land use planning and assessing the impact of changing climate in arctic ecosystem. Its regular mapping and monitoring is critical for the sustainable development of modern society in high latitude regions. On hand state-of-theart remote sensing based permafrost distribution mapping and models for different topographic setting and geographic region have been summarized. Review of empiricalstatistical models indicate that it requires limited input parameters that can be easily measured on the ground or computed from remotely sensed data. Models evaluation revealed that most of the models lack energy balance estimation at surface which is one of the most important parameters that determines the permafrost presence/absence at a particular place. It has also been found that potential solar radiation has greater influence on mountain permafrost distribution than mean annual air temperature. Remote sensing and GIS have considerable potential for mapping permafrost in areas with little quantitative information in a cost-effective manner. However, reliable results are expected, only if the models are well calibrated locally. Extrapolation of empirical model in space and time may be uncertain or even misleading. Factors on which future developments in empirical modeling depends have been noted. Introduction Permafrost has always been a major issue for any kind of developmental activity in polar and alpine regions. The stability of the terrain underlain by permafrost is uncertain due to the changing climate and increased human activity in these regions. In order to make an informed decision on the planning and design of any infrastructure developments, a detailed understanding of the present-day permafrost situation is imperative. As permafrost is highly sensitive to climate change, a regular mapping and monitoring of its situation is equally critical to assess the effect of changing climate in high latitude regions. Page 1 of 16

2 One of the major concerns in mapping permafrost is that, one is mapping a thermal condition rather than a substance or physical object (Heginbottom, 2002). This is due to the fact that permafrost is formally defined solely on the basis of temperature. The most recent definition of permafrost to receive broad international agreement is ground (soil or rock and included ice and organic material) that remains at or below 0 0 C for at least two years, for natural climatic reasons (van Everdingen, 1998). Due to its huge extent, extreme climate, limited fieldwork time, and accessibility of most part of arctic, sub-arctic region, direct field investigation of permafrost or detection by indirect geophysical technique is limited to small areas. In such a situation application of remotely sensed data can be well suited for permafrost mapping in a cost-effective and timely manner. The utilization of remotely sensed data can be greatly extended by integrating with field data to develop predictive permafrost models (Morrissey, 1983). It is the purpose of this paper to present a summary of on hand state-of-the-art remote sensing based mapping and distribution modeling approaches of permafrost in lowland high latitude (arctic and sub-arctic) and high-alpine environments. Remote sensing as a permafrost mapping tool A remote sensing satellite acquires information about physical objects on earth s surface, but permafrost is a subsurface phenomena. Thus, sub-surface thermal or ice conditions are hidden from satellite sensors. However, many environmental factors that reflect the permafrost condition and indicate its presence/absence (like vegetation, topography, snow cover) can be mapped fairly from remote sensing data. Brown (1969) provides a thorough review of factors which influence discontinuous permafrost. The relationship between these environmental factors and permafrost is exploited to get indirect information about subsurface permafrost (e.g. Etzelmuller et al., 2001, 2006; Frauenfelder et al., 1998; Hoelzle et al., 1993; Jorgenson and Kreig, 1988; Keller, 1992; Leverington and Duguay, 1997; Morrissey et al., 1986). The relationship might not be universal and vary depending on local topography, geology, and climate. However, regional permafrost mapping with reasonable accuracy for a geomorphic unit with little variation in terrain geology has been attempted (Nelson, 1986; Keller, 1992; Etzelmuller et al., 2001, 2006; Leverington and Duguay, 1997). Page 2 of 16

3 Remote sensing as a permafrost mapping tool is under continuous development with the availability of higher spatial, spectral and temporal resolution data at different wavelength range of the electromagnetic spectrum. Understanding the interrelationships among the various environmental factors that affect the distribution and condition of permafrost is the key to developing predictive permafrost models (Morissey, 1983). During the last few decades, permafrost scientists developed several models for calculating the distribution of permafrost. Regional-scale models are primarily based on macro-climatic parameters (e.g., mean annual air temperature-maat), exposition of slopes and experience [e.g., Haeberli s (1973) Rule of thumb ], combined in an empirical and/or statistical way. At the local scale, topography and snow are major governing factors for permafrost distribution. Topography influences the potential incoming radiation, while snow thickness influences the heat transfer at the ground surface. Energy balance models combine both factors (Etzelmuller, 2001). The role of scale in permafrost distribution Earth surface processes operate in a wide range of spatial and temporal scale (De Boer, 1992). The relative importance of the factors that influence processes normally differs depending on the scale considered. In relation to permafrost distribution modeling, processes are mainly observed and measured over short time intervals and in small areas. Thus, many process theories are valid only at local scales. Etzelmuller et al., (2001) discussed the role of local factors and regional factors at both spatial and temporal scale to predict permafrost occurrences, distribution and thickness. Spatial scale Permafrost is thermally defined and its distribution and thickness are controlled by factors that influence its surface heat balance and heat flow within it. Both local factors (snow cover, wind, vegetation, precipitation, thermal properties of soil) and regional factor (mean annual air temperature, geology) controls the ground thermal regime. However, local factors commonly override the influence of regional microclimatic factors on ground thermal conditions (Smith, 1975; Vitt et al., 1994). Etzelmuller et al. (2001) and Hoelzle et al. (2001) have distinguished three principle scales with respect to permafrost distribution modeling in mountain areas. A micro-scale with a resolution of < 25 m, a meso-scale with Page 3 of 16

4 a resolution of 25 m to 250 m, and a macro-scale with a resolution of > 250 m. Importance of permafrost mapping at different spatial scale will not be repeated, and interested reader is referred to a review paper by Heginbottom (2002). Temporal scale Permafrost is a thermal system with slow reaction to climatic change. Thus, the present state of permafrost is the result of former climatic conditions and impacts future developments (Lachenbruch et al. 1988). Time scales in which permafrost responds to surface temperature changes depend on its thickness, thermal diffusivity and unfrozen water content in the permafrost (Osterkamp, 1983; Osterkamp & Romanovsky, 1999). According to Haeberli (1990) and Osterkamp and Romanovsky (1999) even relatively warm and thin permafrost has time scale for thermal response, typically in the range of decades to centuries. Scale is a major issue in permafrost mapping and modeling (Heginbottom, 2002). At regional scale simple relations between topography and climate parameters are sufficient to produce an overall picture (Nelson and Outcalt, 1987). At local scale, local parameters (snow cover, vegetation, aspect, elevation, solar radiation, and lithology etc.) override the regional parameters and their analyses can lead to more detailed and accurate permafrost maps. Modeling permafrost distribution Three types of modeling approaches are common for permafrost distribution modeling; (a) Empirical-statistical model, (b) Process-oriented model, and (c) Thermal offset model. Etzelmuller et al. (2001) offers a brief description of these modeling approaches and their relative advantages, disadvantages and limitations. This paper focuses only on the empirical-statistical model that directly relates the documented permafrost occurrences to topo-climatic factors (altitude, slope, aspect, mean air temperature, solar radiation and vegetation etc.). It requires limited input parameters that can be easily measured on the ground or computed from remotely sensed data. The results are quite reliable if well calibrated locally or regionally and primarily applicable to certain areas assuming steady Page 4 of 16

5 state condition. However, extrapolation in time and space may lead to uncertain or even misleading results (Etzelmuller, 2001; 2006; Morissey et al., 1986). Morrissey et al. (1986) initiated the use of remotely sensed data in permafrost mapping and evaluated models predicting the occurrence of permafrost using environmental information derived from Thematic Mapper (TM) satellite data for Caribou-Poker Creeks Research Watershed (CPCRW) located within the Yukon-Tanana uplands of central Alaska, approximately 48 km north of Fairbanks. They developed logistic discriminant functions to predict permafrost using equivalent latitude (an index of direct potential solar radiation that depends on slope, aspect and actual latitude), TM daytime thermal channel and vegetation classification derived from TM satellite data. The combination of TM derived vegetation classes and daytime thermal channel produced highest (78%) permafrost classification accuracy. Percentage of area underlain by permafrost was used to distinguish permafrost classes. Three permafrost classes were delineated: frozen (95-100% frozen), discontinuously frozen (6-94% frozen), and unfrozen (0-5% frozen). The model was evaluated by comparison with a photo-interpreted permafrost map using contingency table analysis and soil temperatures recorded at sites within the watershed. They found a high degree of correspondence between the photo-interpreted permafrost map and TM derived permafrost classification. The advantage of this method is that the independent variables can be either continuous or discrete, thereby allowing the use of ancillary data such as soils and vegetation in the discrimination process. Another advantage is that the procedure is non-parametric in the sense that coefficient estimates do not depend on the assumption of multivariate normality of the underlying distributions for each class. Recent burned areas are excluded in the study where soil temperature and related site conditions may change dramatically. Application of this technique to other regions would require the development of logistic coefficients based on the incorporation of appropriate environmental data. Jorgenson and Kreig s (1988) model mapped the permafrost distribution by calculating the heat balance at the ground surface for each pixel of the mapping area. The model takes vegetation, terrain unit, and equivalent latitude as input and assigns landscape component classes to each pixel. Thermal parameters characteristics of each landscape component class were then assigned to each pixel for the heat balance computation. The key feature of this model is that it is responsive to spatial variability of a landscape and to changes in Page 5 of 16

6 climate. Limited data for characterizing the micro-climate of a broad range of vegetation types and analysis for geographic variability of soil temperature are the major weaknesses of the model. Due to difficulty in mapping snow cover distribution, snow depth and densities were not directly incorporated in the model. The model was validated against airphoto interpreted map only and no field verification was undertaken. The initial run mapped 33.4% of the area as permafrost comparison to 32.1% mapped by photointerpretation. Keller (1992, 1998) and Hoelzle (1992; Hoelzle et al., 1993) developed PERMAKART and PERMAMAP predictive models respectively for mapping mountain permafrost. Both models need a digital terrain model (DTM) as their main input. Keller (1992) used surface analysis parameter based on elevation, slope, aspect and significance in relation to general morphology and empirical knowledge about the permafrost distribution based on geophysical measurements and direct field observation to develop PERMAKART. It produces a thematic permafrost map with three classes (a) permafrost probable (b) permafrost possible and (c) no permafrost. Keller et al. (1998) generated a permafrost map of Switzerland using PERMAKART model. The map was tested with 3943 bottom temperature of snow measurements (BTS) and the lower and upper limit of permafrost classes adjusted accordingly. However, PERMAKART lacks important physical parameters. The relation between air temperature and ground temperature depends on the heat exchange process at the surface that depends upon soil thermal properties, and local microclimatic condition. Another deficiency is poor consideration of solar radiation. The model PERMAMAP is based on the spatial relationship between BTS measurements, mean annual air temperature and potential direct solar radiation (PDSR). It distinguishes between two permafrost classes; probable permafrost and no permafrost. A special feature of this model is its capability of detecting permafrost occurrences at low altitudes (e.g. in extremely shaded areas) that are caused by radiation effects. Whereas PERMAKART consider the effects of avalanche snow deposits on permafrost distribution, by taking into account foot slope areas in the digital terrain model. The evaluation of the two models with field data revealed that both models correspond well with empirical data at high altitudes, but their accuracy diminishes towards lower altitudes where permafrost becomes increasingly scattered. Application of these two models in the Fletschhorn area showed small difference in model output, that points to the fact that transfer of the models from Page 6 of 16

7 region to region should be done carefully and that result of such simulations have to be verified with additional data. Another model called PERMAMOD was developed by Frauenfelder (1997) that combines the topo-climatic factor with bio-geographical information to predict permafrost occurrence in Fletschhorn area, Swiss Alps. It offers a synthesis of field investigated permafrost indicators (rock glacier, debris flow, BTS data etc.) and topo-climatic function of the rule of thumb. Due to high content of area specific field data PERMAMOD show most accurate estimation of the local permafrost distribution. Leverington and Duguay (1997) exploited the correlative relationship between surface properties and permafrost to determine effective combination of data sources for the prediction of the presence/absence of the permafrost table within top 1.5 m of ground surface using neural network, over two study area near Mayo, Yukon Territory. They used four input data layer (a) landcover derived from Landsat TM data (b) equivalent latitude (c) aspect and (d) TM band 6 thermal channel to train the neural network. Weight values for each parameter are determined by the iterative flow of training data through the network. Permafrost classification maps for study area 1 were generated using different combination of input parameters. The combination of landcover and equivalent latitude produced the highest median agreement of 91% between predicted and field measured permafrost classes. There study disagrees with the finding of Morrissey et al. (1986) in which TM band 6 was found to be useful in classification as a proxy for equivalent latitude. They tested the portability of training data from study area 1 to study area 2 to quantitatively evaluate the consequence of mapping a study area without a local field survey. Only 60% agreement was achieved between predicted and field measured permafrost classes for study area 2. It might be due to the spatial variability of subsurface properties between the neighboring study areas. Their study concluded that portability of permafrost-surface properties relationship from one study area to other should not be assumed valid without filed investigation and extra classification tests. Lugon and Delaloye (2001) presented the results of the application of two empiricalstatistical models (model A and model B) applied to a small test zone (6 km 2 ) near Val de Rechy, Valais Alps. In the first step, model A simulates the spatial distribution of permafrost in relations to aspect and altitude of the slopes. In a second step, the Page 7 of 16

8 distribution obtained is weighted in relation to ground cover (lake, alpine meadow, talus and outcropping bedrock). According to their study lakes and alpine meadow classes are indicators of absence of permafrost where as talus and outcropping bed rock are favorable for the presence of permafrost. Model B is based on the assumption that distribution of permafrost depends upon altitude and the PDSR. The correlation between lower discontinuous permafrost limit (LDP) and PDSR is used to find out the lower limit of discontinuous permafrost. Two classes resulted: probable permafrost for the pixels at higher altitudes than LDP, and improbable permafrost for others. They used BTS measurements to check the accuracy of the simulated distribution of permafrost. A BTS measurement <-3 C is an indicator of the probable existence of permafrost at the test site. With BTS values >-2 C, permafrost is unlikely to exist at shallow depths. Tanarro et al. (2001) developed a statistical model for automated mapping of the spatial distribution of permafrost in the area of Corral del Veleta in south-east Spain. Their model uses a relationship between permafrost occurrence as indicated by BTS measurements, and variable such as altitude, solar radiation and summer snow cover. They implemented the model within a geographical information system (GIS) that determines the spatial distribution of probable permafrost in Corral del Veleta. Findings of recent fieldwork such as geomorphic mapping, geophysical sounding and ground temperature logging validated the model. Etzelmuller et al. (2006) derived an empirical permafrost distribution model using relationship between certain topo-climatic factors and permafrost existence. The study area is located on the eastern side of lake Hovsgol, Mongolia at the southern fringe of continuous permafrost zone in central Asia. They used topographic parameters (slope, aspect, curvature, potential radiation and wetness index derived from DEM) and landcover parameter (derived from satellite images) in the study. They identified vegetation cover, topographic wetness, potential radiation and partly elevation as the main factor governing the permafrost distribution in the study area. Scores were derived for each single factors based on simple logistic regression. Then the sum of the derived probabilities was used as a measure of permafrost favorability in a given location within the framework of multicriteria analyses. The model was calibrated with ground temperature observations. 2D resistivity tomography measurements were used to validate the model. The major drawback of this study is the limited number of measurement points which precludes a Page 8 of 16

9 complete statistical assessment. See table 1 for a chronological list of existing empiricalstatistical model and the area it applied to. Table 1: A chronological list of empirical-statistical model for permafrost mapping and modeling Authors Year Input Parameters Area Morrissey et 1986 equivalent latitude; TM daytime thermal CPCRW, Central al. channel; vegetation from TM data; Alaska Jorgenson 1988 Vegetation, terrain unit, equivalent Spinach creek water and Kreig latitude and heat balance at surface shed, 25 km NW of Fairbanks Keller 1992 Digital Terrain Model Swiss Alps mountain Hoelzle 1993 BTS data, MAAT and PDSR Swiss Alps mountain Frauenfelder 1997 Topographic and bio-geographical Swiss Alps mountain parameters Leverington 1997 Landcover, EQ, aspect, and TM band 6 Mayo, Yukon and Duguay Territory Lugon and 2001 Aspect, altitude and landcover Val de Rechy, Valais Delaloye Alps Tanarro et al BTS data, altitude, solar radiation, Corral del Veleta, summer snow cover Spain Etzelmuller 2006 Topographic parameter, and landcover Lake Hovsgol, et al. Mongolia Discussions Morrissey et al. (1986) initiated the use of remotely sensed data in permafrost mapping and modeling. Their model produced highest classification accuracy (78%) for the combination of vegetation classes and daytime thermal channel as model inputs. They did not incorporated energy balance estimation at surface that determines the amount of heat entering the ground in summer and amount of heat released by the ground in winter. This might be the reason for low median agreement between model predicted permafrost classes and photo-interpreted permafrost classes. Jorgenson and Kreig s model consider Page 9 of 16

10 the energy balance at the surface and predicts the permafrost distribution by calculating the heat balance at the surface. It over estimated the permafrost area compared to photointerpreted map in the first run. This could be due to ignorance of snow cover data in the model. Snow depth, and initial snow timing is very critical for permafrost mapping. However, this model is responsive to the spatial variability of a landscape and changes in climate as landscape component was assigned based on vegetation, terrain unit and equivalent latitude. PERMAKART and PERMAMAP predictive models generate permafrost map based on surface analysis parameter but lacks physical parameters (like the relationship between air temperature and ground temperature) that depends upon soil thermal properties and local microclimatic condition. These models works well for high altitude areas and can be extrapolated in space provided result of such simulations have to be verified with additional data. Figure 1 shows an example of mountain permafrost map calculated with PERMAKART using digital elevation model TOPOPT of Switzerland with a cell resolution of 100 m. This model is incompetent to map permafrost present at lower altitudes (mostly in vary shady locations). Leverington and Duguay (1997) used the correlative relationship between surface properties and permafrost to predict the presence/absence of permafrost. They found combination of landcover and equivalent latitude as most suitable for permafrost mapping. However, they did not find TM band 6 useful as a proxy for equivalent latitude which is in disagreement with Morrissey et al. (1986) findings. Figure 2 shows an example of low-land permafrost map derived by using Neural-Network near Mayo region (Yukon Territory), Canada. Their study also concluded that portability of permafrost-surface properties relationship from one area to other may be uncertain and sometimes misleading without field investigations. The empirical model developed by Etzelmuller et al. (2006) is the latest permafrost model. The model was calibrated with ground temperature observations and 2D resistivity tomography data was used to validate the model. In regard to given data resolution, the model prediction of permafrost distribution is satisfactory. The major drawback of this model is limited number of measurement points that precludes a complete statistical assessment. The overall modeling approach is more realistic and satisfactory mapping can be achieved with high number of measurement points. One of the major concerns in empirical modeling approach is the hesitation to incorporate heat balance estimation at the surface and within the active layer in the model. All the above models except the Jorgenson and Kreig (1988) model overlooked heat balance Page 10 of 16

11 calculation. Though most of the models incorporate potential solar radiation that is an index of amount of heat reaching to a particular place, it can not be a replacement for heat balance calculation. The only reason why it should be given so much emphasis is that it is the amount of heat entering the ground in summer and released by the ground in winter is that influence the permafrost presence/absence most. Although, heat balance at the surface depends on vegetation cover, snow depth, precipitation, soil thermal properties and mean annual air temperature; any one or combination of these parameters can not be a surrogate for heat balance calculation. Jorgenson and Kreig (1988) presented a simple and effective approach for heat balance calculation from parameters that can be easily measured on the ground. Smith and Riseborough (1996) followed a similar approach and presented a functional model of the permafrost-climate relationship, which accommodates the geographical variations of climate, surface and soil factors that control ground thermal regime. In case of mountain permafrost mapping precise measurements of PDSR becomes more critical. Hoelzle (1992) reported a strong correlation between BTS and PDSR (r 2 = 0.86) for Swiss Alps. He also reported the existence of permafrost at low altitude sites, even at places where MAAT is positive if solar radiation is strongly reduced. He concluded that altitudinal range of mountain permafrost is a function of PDSR and MAAT. This relationship may be non-linear; however linear model approach was used for investigation. Research should be attempted to establish the precise non-linear relationship and incorporate in the model. Though existing empirical-statistical permafrost distribution models can be easily applied and require less input parameter, these are yes/no functions. The complex energy exchange process at the surface and within the active layer are not treated explicitly but rather as a grey box with topo-climatic factors being selected according to their relative influence in the energy balance equation (Etzelmuller et al., 2001). It provides fairly reliable results if well calibrated locally or regionally. Extrapolation in time and space may lead to uncertain or misleading results. Future directions and conclusions In recent years, the most important development in permafrost mapping has been the increasing reliance on computer-based models and on GIS as the principal methods for data compilation, storage and handling (Heginbottom, 2002). Page 11 of 16

12 In the future, developments in empirical modeling may depend on the following factors: (a) Better understanding of relationship between permafrost and local factors (vegetation, snow, precipitation, slope, aspect, elevation etc.). (b) Availability of high resolution DEM. Topographic parameters derived from a DEM (like slope, aspect, elevation, equivalent latitude, wetness index) are critical input parameters in empirical permafrost mapping and modeling. So, there is a need for high resolution DEM that will enable more accurate measurements of surface topography. (c) Most of the existing empirical models lack incorporation of energy balance estimation at the surface. In reality, this is one of the most important parameter that governs the presence/absence of permafrost at a given site. Readiness to incorporate energy balance estimation in the distribution model (e.g. Jorgenson and Kreig, 1988). Innovative approach to calculate energy balance at the surface and within the active layer with easily computed parameters. (d) With the availability of high resolution satellite data, more accurate landcover mapping would be possible that will enable more accurate micro-climatic zone mapping. (e) Integration of geophysical data (like shallow surface resistivity data) with topoclimatic data for more reliable prediction and model calibration with large number of ground temperature observations (e.g. Etzelmuller, 2006). (f) Coupling between regional climate models with permafrost models to predict the effect of changing climate on permafrost distribution. The empirical-statistical models discussed in this paper provide a valid permafrost distribution at local and regional scales. The input data needed for this kind of modeling can be easily measured at the ground and computed from remotely sensed data. It makes the procedure suitable and inexpensive as an initial modeling approach for an area with little quantitative information. Page 12 of 16

13 Acknowledgements I acknowledge Dr. Paul Layer and Dr. Rainer Newberry for reviewing this paper and for their constructive suggestions that helped in improving the overall structure and quality of this paper. They have been an enormous help. I would also like to thank my advisor Dr. Anupma Prakash for her guidance to accomplish this goal. Discussions with my friend Sudipta Sarkar have been very helpful in completing this paper. References Brown, R. J. E. (1969). Factors influencing discontinuous permafrost in Canada, in the Periglacial Environment, Past and Present. International Association Quaternary Research Congress, McGill Queens University Press, Montreal, De Boer, D. H. (1992). Hierarchies and spatial scale in process geomorphology: a review. Geomorphology 4, Etzelmuller, B., Hoelzle, M., Heggem, E.S.F., Isaksen, K., Mittaz, C., Vonder Muhll, D., Odegard, R. S., Haeberli, W. and Sollid, J. L. (2001). Mapping and modeling the occurrence and distribution of mountain permafrost. Norsk Geografisk Tidsskrift- Norwegian Journal of Geography 55, Etzelmuller, B., Heggem, E.S.F., Sharkhuu, N., Frauenfelder, R., Kaab, A. and Goulden, C. (2006). Mountain permafrost distribution modeling using a multi-criteria approach in the Hovsgol area, Northern Mongolia. Permafrost and Periglacial Processes 17, Frauenfelder, R. (1997). Permafrostuntersuchungen mit GIS Eine Studie im Fletschhorngebiet. Master thesis, Department of Geography, University of Zurich, 77 p. (Unpublished). Frauenfelder, R., Allgower, B., Haeberli, W. and Hoelzle, M. (1998). Permafrost investigations with GIS - A case study in the Fletschhorn area, Wallis, Swiss Alps. In the proceedings of Seventh International Permafrost Conference, Yellowknife, Canada, Haeberli, W. (1973). Die Basis-Temperature der winterlichen Schneedecke als moglicher Indikator fur die Verbreitung von Permafrost in den Alpen. Zeitschrift fur Gletscherkunde und Glazialgeologie XII (2), Haeberli, W. (1990). Glacier and permafrost signals of the 20 th -century warming. Annals of Glaciology 14, Heginbottom, J. A. (2002). Permafrost mapping: a review. Progress in Physical Geography 26 (4), Hoelzle, M. (1992). Permafrost occurrence from BTS measurements and climatic parameters in the Easter Swiss Alps. Permafrost and Periglacial Processes 3 (2), Page 13 of 16

14 Hoelzle, M., Haeberli, W. and Keller, F. (1993). Application of BTS-measurements for modelling mountain permafrost distribution. In the proceedings of Sixth International Permafrost Conference, Beijing 1, Hoelzle, M., Mittaz, C., Etzelmuller, B. and Haeberli, W. (2001). Surface energy fluxes and distribution models relating to permafrost in European Mountain areas: an overview of current developments. Permafrost and Periglacial Processes 12, Jorgenson, M. T. and Kreig, R. A. (1988). A model for mapping permafrost distribution based on landscape component maps and climatic variables. In the proceedings of Fifth International Permafrost Conference, Trondheim, Norway 1, Keller, F. (1992). Automated mapping of mountain permafrost using the program PERMAKART within the Geographical Information System ARC/INFO. Permafrost and Periglacial Processes 3 (2), Keller, F., Frauenfelder, R., Gardaz, J., Hoelzle, M., Kneisel, C., Lugon, R., Phillips, M., Reynard, E. and Wenker, L. (1998). Permafrost map of Switzerland. In the proceedings of Seventh International Permafrost Conference, Yellowknife, Canada, Lachenbruch, A., Cladouhous, T. T. and Saltus, R. W. (1988). Permafrost temperatures and the changing climate. In the proceedings of Fifth International Permafrost Conference, Trondheim, Norway, Leverington, D. W. and Duguay, C. R. (1997). A Neural network method to determine the presence or absence of Permafrost near Mayo, Yukon Territory, Canada. Permafrost and Periglacial Processes 8, Lugon, R. and Delaloye, R. (2001). Modelling alpine permafrost distribution, Val de Rechy, Valais Alps (Switzerland). Norsk Geografisk Tidsskrift-Norwegian Journal of Geography 55, Morrissey, L. A. (1983). The utility of remotely sensed data for permafrost studies. In proceedings of the Fourth International Permafrost Conference. National Academy Press, Washington, DC, Morrissey L. A., Strong, L. and Card, D. H. (1986). Mapping Permafrost in the Boreal forest with Thematic Mapper satellite data. Photogrammetric Engineering and Remote Sensing 52, Nelson, F. E. (1986). Permafrost distribution in Central Canada: Applications of a climatebased predictive model. Annals of the Association of American Geographers 76 (4), Nelson, F. E. and Outcalt, S. I. (1987). A computational method for prediction and regionalization of permafrost. Arctic and alpine Research 19, Osterkamp, T. E. (1983). Response of Alaskan permafrost to climate. In the proceedings of Fourth International Conference on Permafrost, Page 14 of 16

15 Osterkamp, T. E. and Romanovsky, V. E. (1999). Evidence for warming and thawing of discontinuous permafrost in Alaska. Permafrost and Periglacial Processes 10, Smith, M. W. (1975). Microclimatic influences on ground temperatures and permafrost distribution, Mackenzies Delta, Northwest Territories. Canadian Journal of Earth Sciences 12, Smith, M. W. and Riseborough, D. W. (1996). Permafrost monitoring and detection of climate change. Permafrost and Periglacial Processes 7, Tanarro, L. M., Hoelzle, M., Garcia, A., Ramos, M., Gruber, S., Gomez, A., Piquer, M. and Palacios, D. (2001). Permafrost distribution modeling in the mountains of the Mediterranean: Corral del Veleta, Sierra Nevada, Spain. Norsk Geografisk Tidsskrift- Norwegian Journal of Geography 55, van Everdingen, R. O. (1998). Multi-language glossary of permafrost and related ground ice terms. International Permafrost Association, Circumpolar Active-Layer Permafrost System (CAPS). Boulder CO: NSIDC, University of Colorado at Boulder. Version 1.0. CD-ROM. Available from National Snow and Ice Data Center, Vitt, D. H., Halsey, L. A. and Zoltai, S. C. (1994). The bog landforms of continental western Canada relative to climate and permafrost patterns. Arctic and Alpine Research 26, Figure 1: Permafrost map of Switzerland calculated with PERMAKART. Both permafrost possible and probable categories are shown in black (Keller et al., 1998). Page 15 of 16

16 Figure 2: Permafrost map of study area, near Mayo (Yukon Territory). Black color represents water bodies, while two lighter shades represent areas with (gray) and without (white) a permafrost table within 1.5 m of ground surface. The agreement rate of this image, based on comparison with 120 test sites, is 94% (Leverington and Duguay, 1997). Page 16 of 16