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Principles of Applied Remote Sensing

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Abstract

Techniques that are conventionally used to process remotely sensed data into geospatial input or output products (e.g., classified maps) for further analysis and application are discussed in this chapter. This chapter focuses on the processing of moderate-spatial-resolution, multispectral digital image data, such as the imagery captured by the Landsat Enhanced Thematic Mapper (ETM)+ or Landsat Operational Land Imager (OLI) sensors. This type of data is still utilized in a large percentage of applied remote sensing efforts, especially in terrestrial environments. In the latter portion of the chapter, we briefly cover the processing of other categories of remotely sensed data such as hyperspectral and Light Detection and Ranging (LiDAR) data, which require specialized processing approaches. Readers interested in learning more about the processing of these other data types should consult the suggested reading list at the end of the chapter.

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Authors and Affiliations

  1. Environmental Sci. Policy & Mgmt., University of California, Berkeley, 94720, Berkeley, California, USA

    Siamak Khorram & Matthew D. Potts

  2. Center for Geospatial Analytics, North Carolina State University, 27695-7106, Raleigh, North Carolina, USA

    Siamak Khorram

  3. US Environmental Protection Agency Region 4 NEPA Program Office, 109 T.W. Alexander Drive, 27711, Research Triangle Park, North Carolina, USA

    Cynthia F. van der Wiele

  4. Southern Research Station, USDA Forest Service, 27709, Research Triangle Park, North Carolina, USA

    Frank H. Koch

  5. Center for Geospatial Analytics, North Carolina State University, 27695-7106, Raleigh, North Carolina, USA

    Stacy A. C. Nelson

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  1. Siamak Khorram
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Khorram, S., van der Wiele, C.F., Koch, F.H., Nelson, S.A.C., Potts, M.D. (2016). Data Processing Tools. In: Principles of Applied Remote Sensing. Springer, Cham. https://doi.org/10.1007/978-3-319-22560-9_3

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