People are increasingly becoming aware of environmental
issues and the interconnectedness of the Earth's dynamic systems.
Scientists, resources planners and managers, and policy makers require
an understanding of these complex systems and need spatio-temporal
information for decision making and problem solving. Climate change,
glacier fluctuations and water resources, and erosion and landscape
evolution are several examples of Earth science topics that have
scientific, resource and policy implications.
Remote sensing, geographic information technology (GIT), and the availability of commercial GISs have profoundly affected our ability to generate spatio-temporal information in the Earth sciences. It is very important, however, to recognize not only the advantages associated with new data and technology, but the inherent limitations that dictate accuracy and utility of information. The mere availability of data and software tools does not necessarily equate to accurate estimates of biophysical parameters (surface albedo, altitude, surface temperature, leaf area index) or scientifically valid information and assessments.
With the advent of new satellite and airborne optical-, microwave-, and laser-based sensors, the Earth science community is assessing the spatio-temporal variability of biophysical parameters and landscape conditions in new ways. Information extraction from multispectral and multitemporal satellite imagery is based upon knowledge of radiation-transfer theory and processes. Consequently, the data must frequently be radiometrically calibrated. Image processing and GISs provide algorithms and software tools to implement this, although the use of some software can be fraught with difficulties, as atmospheric conditions may not be known, topographic-atmospheric coupling may not be accounted for, and algorithm/model assumptions may not be valid given environmental conditions. More work on evaluating and incorporating robust radiation-transfer models into GISs is warranted.
The routine generation of digital elevation models (DEMs) is a significant development in numerous disciplines in the Earth sciences. Quantitative analysis of DEMs (geomorphometry) permits the study and modeling of surface processes and landscape evolution (Wilson and Gallant, 2000; Bishop and Shroder, 2004). The literature, however, demonstrates problems associated with not correcting DEMs for numerous kinds of errors, and the inappropriate use of DEMs, given scale-dependent issues associated with estimating topographic parameters and their use in characterizing surface processes. Nevertheless, spatial analysis and modeling has generated new understandings.
For example, remote sensing and GIS studies of the Earth's cryosphere document glacier fluctuations, as many glaciers worldwide are retreating and downwasting in response to climatic warming (Bishop, et al., 2004). Similarly, we are able to test theories on landscape evolution and can estimate river incision, glacier mass movement and soil erosion rates. GIS-based erosion estimates however, need to be tested with field-based measurements and estimates based on geological techniques such as absolute age-dating, thermochronology and cosmogenic-isotope concentrations. More work on evaluating and integrating dynamic landscape evolution models into GISs that account for more than just soil erosion and river incision processes is sorely needed.
Although numerous breakthroughs in geospatial data and analysis offer Earth scientists and resource planners and managers new opportunities to address a variety of issues, challenges still remain. Bishop and Shroder (2004) address some of these issues that relate to conceptual foundations in representation and spatial analysis, as well as the need for users to understand GIScience concepts and principles that are inherently associated with the use of algorithms and models. Unfortunately, it is frequently assumed that GIS-based software represents the solution to a problem, and multi-stage processing sequences are used to generate a product, without much concern for the choice of algorithms or programs. In academia, this problem is analogous to training students to become "button-pushers" rather than problem solvers who understand GIScience concepts and know how to utilize GIT appropriately.
When utilized correctly, remote sensing and GIS studies provide additional information that can be coupled with field-based measurements to greatly improve results. Although GIS-based analysis is predominately "spatial" in nature, Earth scientists and planners need to account for temporal variation as well. While geographic information scientists are working on this problem (temporal GIS), the multidisciplinary nature of Earth systems dictates dynamic linkage of atmospheric, surface, and subsurface processes that occur and interact at spatio-temporal operational scales. Numerical GIS-based process modeling, which is frequently implemented outside of a GIS, can greatly improve our understanding of processes and permit new mapping opportunities. When integrated into a spatio-temporal GIS, more users will be able to utilize existing physical models and their predictive capabilities.
The aforementioned challenges of representation, data quality, spatial analysis and deterministic modeling should be viewed positively, as these issues are being addressed by a variety of people interested in developing new algorithms, software tools and GIS-based physical models. Ultimately, remote sensing and GIS are providing us with new insights into the complexities of geodynamics. Further development and implementation of GIScience concepts should provide opportunities to study and differentiate natural versus anthropogenically caused variations in natural systems.
References cited
Bishop, M.P., Olsenholer, J.S., Shroder, Jr., J.F., Barry, R.G., Raup, B.H., Bush, A.B.G., Copland, L., Dwyer, J.L., Fountain, A.G., Haeberli, W., Kaeaeb, A., Paul, F., Hall, D.K., Kargel, J.S., Molnia, B.F., Trabant, D.C., and Wessels, R., 2004. Global Land Ice Measurements From Space (GLIMS): Remote Sensing and GIS Investigations of the Earth's Cryosphere, Geocarto International, V. 19, No. 2, 57-84.
Bishop, M.P., and Shroder, Jr., J.F., (eds.) 2004. Geographic Information Science and Mountain Geomorphology, Praxis-Springer-Verlag, Chichester, UK, 486 pp.
Wilson, J.P., and Gallant, J.C., (eds.) 2000. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York, 479 pp.
Remote sensing, geographic information technology (GIT), and the availability of commercial GISs have profoundly affected our ability to generate spatio-temporal information in the Earth sciences. It is very important, however, to recognize not only the advantages associated with new data and technology, but the inherent limitations that dictate accuracy and utility of information. The mere availability of data and software tools does not necessarily equate to accurate estimates of biophysical parameters (surface albedo, altitude, surface temperature, leaf area index) or scientifically valid information and assessments.
With the advent of new satellite and airborne optical-, microwave-, and laser-based sensors, the Earth science community is assessing the spatio-temporal variability of biophysical parameters and landscape conditions in new ways. Information extraction from multispectral and multitemporal satellite imagery is based upon knowledge of radiation-transfer theory and processes. Consequently, the data must frequently be radiometrically calibrated. Image processing and GISs provide algorithms and software tools to implement this, although the use of some software can be fraught with difficulties, as atmospheric conditions may not be known, topographic-atmospheric coupling may not be accounted for, and algorithm/model assumptions may not be valid given environmental conditions. More work on evaluating and incorporating robust radiation-transfer models into GISs is warranted.
The routine generation of digital elevation models (DEMs) is a significant development in numerous disciplines in the Earth sciences. Quantitative analysis of DEMs (geomorphometry) permits the study and modeling of surface processes and landscape evolution (Wilson and Gallant, 2000; Bishop and Shroder, 2004). The literature, however, demonstrates problems associated with not correcting DEMs for numerous kinds of errors, and the inappropriate use of DEMs, given scale-dependent issues associated with estimating topographic parameters and their use in characterizing surface processes. Nevertheless, spatial analysis and modeling has generated new understandings.
For example, remote sensing and GIS studies of the Earth's cryosphere document glacier fluctuations, as many glaciers worldwide are retreating and downwasting in response to climatic warming (Bishop, et al., 2004). Similarly, we are able to test theories on landscape evolution and can estimate river incision, glacier mass movement and soil erosion rates. GIS-based erosion estimates however, need to be tested with field-based measurements and estimates based on geological techniques such as absolute age-dating, thermochronology and cosmogenic-isotope concentrations. More work on evaluating and integrating dynamic landscape evolution models into GISs that account for more than just soil erosion and river incision processes is sorely needed.
Although numerous breakthroughs in geospatial data and analysis offer Earth scientists and resource planners and managers new opportunities to address a variety of issues, challenges still remain. Bishop and Shroder (2004) address some of these issues that relate to conceptual foundations in representation and spatial analysis, as well as the need for users to understand GIScience concepts and principles that are inherently associated with the use of algorithms and models. Unfortunately, it is frequently assumed that GIS-based software represents the solution to a problem, and multi-stage processing sequences are used to generate a product, without much concern for the choice of algorithms or programs. In academia, this problem is analogous to training students to become "button-pushers" rather than problem solvers who understand GIScience concepts and know how to utilize GIT appropriately.
When utilized correctly, remote sensing and GIS studies provide additional information that can be coupled with field-based measurements to greatly improve results. Although GIS-based analysis is predominately "spatial" in nature, Earth scientists and planners need to account for temporal variation as well. While geographic information scientists are working on this problem (temporal GIS), the multidisciplinary nature of Earth systems dictates dynamic linkage of atmospheric, surface, and subsurface processes that occur and interact at spatio-temporal operational scales. Numerical GIS-based process modeling, which is frequently implemented outside of a GIS, can greatly improve our understanding of processes and permit new mapping opportunities. When integrated into a spatio-temporal GIS, more users will be able to utilize existing physical models and their predictive capabilities.
The aforementioned challenges of representation, data quality, spatial analysis and deterministic modeling should be viewed positively, as these issues are being addressed by a variety of people interested in developing new algorithms, software tools and GIS-based physical models. Ultimately, remote sensing and GIS are providing us with new insights into the complexities of geodynamics. Further development and implementation of GIScience concepts should provide opportunities to study and differentiate natural versus anthropogenically caused variations in natural systems.
References cited
Bishop, M.P., Olsenholer, J.S., Shroder, Jr., J.F., Barry, R.G., Raup, B.H., Bush, A.B.G., Copland, L., Dwyer, J.L., Fountain, A.G., Haeberli, W., Kaeaeb, A., Paul, F., Hall, D.K., Kargel, J.S., Molnia, B.F., Trabant, D.C., and Wessels, R., 2004. Global Land Ice Measurements From Space (GLIMS): Remote Sensing and GIS Investigations of the Earth's Cryosphere, Geocarto International, V. 19, No. 2, 57-84.
Bishop, M.P., and Shroder, Jr., J.F., (eds.) 2004. Geographic Information Science and Mountain Geomorphology, Praxis-Springer-Verlag, Chichester, UK, 486 pp.
Wilson, J.P., and Gallant, J.C., (eds.) 2000. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York, 479 pp.
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