Environmental data are often spatial in nature. In this chapter, we will examine image processing techniques which play a key role in artificial applications operating on spatial data. These AI applications often seek to extract information from the spatial data and use that information to aid decision makers.
Consider for example, land cover data. Since different locations have different types and amounts of forestry, land cover information has to be explicitly tied to geographic location. Such spatial data may be collected either through in-situ (in place) measurements or by remote sensing over large areas. An insitu measurement of land cover, for example, would involve visiting, observing and cataloging the type of land cover at a particular location. A remotely sensed measurement of land cover might be carried out from a satellite. The remotely-sensed measurement would cover a much larger area, but would be indirect (i.e., the land coverage would have to be inferred from the satellite channels) and would be gridded (i.e., one would get only one land cover value for one pixel of the satellite image). Users of land-cover data often wish to use the data to recover higher-level information such as determining what fraction of a particular country is wooded — AI applications help provide such an answer, building on well understood image processing methods.
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References
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698
Carvalho, L., & Jones, C. (2001). A satellite method to identify structural properties of mesoscale convective systems based on the Maximum Spatial Correlation Tracking Technique (MASCOTTE). Journal of Applied Meteorology 40(10), 1683–1701
Cressman, G. P. (1959). An operational objective analysis system. Monthly Weather Review 87367–374
Dixon, M., & Wiener, G. (1993). TITAN: Thunderstorm identification, tracking, analysis and nowcasting: A radar-based methodology. Journal of Atmospheric and Oceanic Technology 106
Johnson, J. T., MacKeen, P. L., Witt, A., Mitchell, E. D., Stumpf, G. J., Eilts, M. D., et al. (1998). The Storm Cell Identification and Tracking (SCIT) algorithm: An enhanced WSR-88D algorithm. Weather and Forecasting 13263–276
Koch, S. E., DesJardins, M., & Kocin, P. J. (1983). An interactive Barnes objective map analysis scheme for use with satellite and conventional data. Journal of Climate and Applied Meteorology 221487–1503
Lakshmanan, V. (2000). Speeding up a large scale filter. Journal of Oceanic and Atmospheric Technology 17468–473
Lakshmanan, V. (2004). A separable filter for directional smoothing. IEEE Geoscience and Remote Sensing Letters 1192–195
Lakshmanan, V., Rabin, R., & DeBrunner, V. (2003). Multiscale storm identification and forecast. Journal of Atmospheric Research367–380
Lakshmanan, V., Fritz, A., Smith, T., Hondl, K., & Stumpf, G. J. (2007). An automated technique to quality control radar reflectivity data. Journal of Applied Meteorology 46288–305
Oliver, M. A., & Webster, R. (1990). Kriging: A method of interpolation for geographical information system, Int'l. Journal of Geographical Information Systems 4(3), 313–332
Turner, B., Zawadzki, I., & Germann, U. (2004). Predictability of precipitation from continental radar images. Part III: Operational nowcasting implementation (MAPLE). Journal of Applied Meteorology 43(2), 231–248
Tuttle, J., & Gall, R. (1999). A single-radar technique for estimating the winds in tropical cyclones. Bulletin of the American Meteorological Society80, 683–685
Vincent, L., & Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. PAMI (13)6, 583–598
Wolfson, M., Forman, B. E., Hallowell, R. G., & Moore, M. P. (1999). The growth and decay storm tracker. Eighth Conference on Aviation (pp. 58–62). Dallas, TX: American Meteorological Society
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Lakshmanan, V. (2009). Automated Analysis of Spatial Grids. In: Haupt, S.E., Pasini, A., Marzban, C. (eds) Artificial Intelligence Methods in the Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9119-3_16
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