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Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview

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Advances in Watershed Science and Assessment

Part of the book series: The Handbook of Environmental Chemistry ((HEC,volume 33))

Abstract

Accurate and detailed land use and land cover information forms an important resource for hydrologic analysis; remote sensing forms a critical resource for acquiring and analyzing broad-scale land use information. Although aerial photography is an important resource for land use information, it was the availability of multispectral satellite data beginning in 1972 that significantly advanced the ability of remote sensing researchers to systematically monitor and evaluate land use/land cover changes and their impacts on water quality and quantity. In that context, practitioners developed classification schemes specifically tailored for use with remotely sensed imagery and for systematic assessment of land use change. Since then, land observation technologies have evolved to allow extensive and intricate land use monitoring techniques, and now, in the twenty-first century, include the use of lasers for 3-D analyses and unmanned aerial systems. Such technologies have enabled land use assessment to contribute not only to its original focus in urban and regional planning but to a broad range of environmental and social issues. This chapter provides an overview of remote sensing, its technological evolution, and remote sensing applications in land use and land cover mapping and monitoring, with a focus upon implications for watershed assessment and management.

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Notes

  1. 1.

    All wavelength ranges discussed within this chapter are approximations. Different disciplines define the specific divisions of the electromagnetic spectrum in various wavelengths. Most definitions are extremely close in value.

  2. 2.

    The number of bands of an image refers to how many divisions of the electromagnetic spectrum were used to create that image. For the exact electromagnetic spectral divisions for each band, you must refer to the metadata that accompanies the image.

  3. 3.

    Georeferencing means to define a specific location on the surface of the Earth for an image, usually with a specific geographic coordinate system.

  4. 4.

    A mixed pixel means that more than one land use/land cover type is present within the spatial extent of the pixel; as such the spectral value cannot be matched to one specific feature.

References

  1. Campbell JB (1997) Land use and cover inventory. In: Philipson WR (ed) Manual of photographic interpretation, 2nd edn. American Society of Photogrammetry and Remote Sensing, Bethesda, pp 335–364

    Google Scholar 

  2. Hooke RL, Martin-Duque JF (2012) Land transformation by humans: a review. GSA Today 22(12):4–10

    Article  Google Scholar 

  3. United Nations (2005) Millennium ecosystem assessment. Island Press, Washington, DC, p 917

    Google Scholar 

  4. Younos T, Parece TE (2012) Water use and conservation. In: Stoltman J (ed) 21st century geography: a reference handbook. Sage, Los Angeles, pp 447–456

    Google Scholar 

  5. Deelstra T, Girardet H (2000) Urban agriculture and sustainable cities. In: Bakker N, Dubbeling M, Gündel S, Sabel-Koschella U, deZeeuw H (eds) Growing cities, growing food: urban agriculture on the policy agenda, a reader on urban agriculture, Deutsche Stiftung für Internationale Entwicklung, Zentralstelle für Ernährung und Landwirtschaft. Feldafing, Germany, pp 43–66

    Google Scholar 

  6. Pickett STA, Cadenasso ML, Grove JM, Nilon CH, Pouyat RV et al (2001) Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annu Rev Ecol Syst 32:127–157

    Article  Google Scholar 

  7. DeBusk K, Hunt WF, Hatch U, Sydorovych O (2010) Watershed retrofit and management evaluation for urban stormwater management systems in North Carolina. J Contemp Water Res Educ 146:64–74

    Article  Google Scholar 

  8. Welker AL, Wadzuk BM, Traver RG (2010) Integration of education, scholarship, and service through stormwater management. J Contemp Water Res Educ 146:83–91

    Article  Google Scholar 

  9. Slonecker ET, Jennings DB, Garofalo D (2001) Remote sensing of impervious surfaces: a review. Remote Sens Rev 20(3):227–255

    Article  Google Scholar 

  10. Burton GA Jr, Pitt RE (2002) Stormwater effects handbook: a toolbox for watershed managers, scientists and engineers. Lewis, Washington, DC, p 929

    Google Scholar 

  11. Civco DJ, Hurd JD, Wilson EH, Arnold CL, Prisloe MP Jr (2002) Quantifying and describing urbanizing landscapes in the Northeast United States. Photogramm Eng Remote Sens 68(10):1083–1090

    Google Scholar 

  12. Davis AP, Traver RG, Hunt WF (2010) Improving urban stormwater quality: applying fundamental principles. J Contemp Water Res Educ 146:3–10

    Article  Google Scholar 

  13. Center for Watershed Protection (2003) Impacts of impervious cover on aquatic systems: watershed protection research monograph. Ellicott City, p 158

    Google Scholar 

  14. Bhaduri B, Minner M (2001) Long-term hydrologic impact of urbanization: a tale of two models. J Water Resour Plann Manage 127(1):13–19

    Article  Google Scholar 

  15. United States Geological Survey (2012) The USGS. Land Cover Institute. NLCD 92 land cover class definitions. http://landcover.usgs.gov/classes.php. Accessed 5 Apr 2014

  16. Hardy EE, Shelton RL (1970) Inventorying New York’s land use and natural resources. NY Food Life Sci 3(4):4–7

    Google Scholar 

  17. Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. Department of the Interior No. 964, Washington, DC

    Google Scholar 

  18. Multi-Resolution Land Characteristics Consortium (2006) National land cover database. http://www.mrlc.gov/finddata.php. Accessed 19 Dec 2012

  19. Jin S, Yang L, Danielson P, Homer C, Fry J et al (2013) A comprehensive change detection method for updating the national land cover database to circa 2011. Remote Sens Environ 132:159–175

    Article  Google Scholar 

  20. Xian G, Homer C, Fry J (2009) Updating the 2001 National land cover database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sens Environ 113:1133–1147

    Article  Google Scholar 

  21. Fry J, Xian G, Jin S, Dewitz J, Homer C et al (2011) Completion of the 2006 national land cover database for the conterminous United States. Photogramm Eng Remote Sens 77:858–864

    Google Scholar 

  22. Government of Canada, Agriculture and Agri-Food Canada (2013) Canada land inventory. http://sis.agr.gc.ca/cansis/nsdb/cli/index.html. Accessed 19 May 2014

  23. European Environment Agency (2013) Urban Atlas classification system (CORINE). http://www.eea.europa.eu/data-and-maps/data/urban-atlas. Accessed 19 May 2014

  24. The Centre for Ecology and Hydrology (2007) United Kingdom land cover mapping. http://www.ceh.ac.uk/accessinglcmdata.html. Accessed 19 May 2014

  25. United Kingdom Government (2005) Generalized land use data. http://data.gov.uk/dataset/land_use_statistics_generalised_land_use_database. Accessed 19 May 2014

  26. United States Geological Survey (2012) Land cover institute. http://landcover.usgs.gov/landcoverdata.php. Accessed 19 May 2014

  27. United Nations Global Land Cover Network (2013) Globcover-derived national land cover databases for Africa. http://www.glcn.org/databases/lc_gc-africa_en.jsp. Accessed 19 May 2014

  28. Campbell JB, Wynne RH (2011) Introduction to remote sensing, 5th edn. Guilford, New York, 647

    Google Scholar 

  29. Kain RJP (2007) Maps and rural land management, early modern Europe. In: Woodward D (ed) The history of renaissance cartography: interpretive essays history of cartography 3 (Part 1). University of Chicago Press, Chicago, pp 705–718

    Google Scholar 

  30. Stamp LD (1948) The land of Britain: its use and misuse. Longmans, London, 507

    Google Scholar 

  31. Hudson GD (1936) The unit area method of land classification. Ann Assoc Am Geogr 26(2):99–112

    Article  Google Scholar 

  32. Boyce RR (2004) Geographers and the Tennessee valley authority. Geogr Rev 94(1):23–42

    Article  Google Scholar 

  33. Garrett J, Waters D (1996) Preserving digital information. Report of the task force on archiving of digital information commissioned by The Commission on Preservation and Access and The Research Libraries Group. http://www.clir.org/pubs/reports/pub63watersgarrett.pdf

  34. Virginia, Department of Environmental Quality (2010) GIS data sets. http://www.deqvirginiagovConnectWithDEQVEGISVEGISDatasetsaspx. Accessed 13 June 2012

  35. Thrower NJ (1970) Land use in the Southwestern United States from Gemini and Apollo Imagery. Ann Assoc Am Geogr 60(1):208–209

    Article  Google Scholar 

  36. United States Geological Survey (2014) Landsat missions. http://landsat.usgs.gov/. Accessed 1 Apr 2014

  37. United States Geological Survey (2014) Landsat missions timeline. http://landsat.usgs.gov/about_mission_history.php. Accessed 7 May 2014

  38. Satellite Imaging Corporation (2014) IKONOS satellite. http://www.satimagingcorp.com/gallery-ikonos.html. Accessed 10 May 2014

  39. Airbus Defense and Space (2014) Spot satellite imagery. http://www.astrium-geo.com/en/143-spot-satellite-imagery/. Accessed 15 May 2014

  40. China-Brazil Earth Resources Satellite (2011). CBERS satellite. http://www.cbers.inpe.br/ingles/satellites/history.php. Accessed 15 May 2014

  41. Elecnor (2014) Deimos imaging. http://www.deimos-imaging.com/. Accessed 15 May 2014

  42. United States National Aeronautics and Space Administration (2014) MODIS Web. http://modis.gsfc.nasa.gov/. Accessed 15 May 2014

  43. United States Department of Agriculture (1986) Urban hydrology for small watersheds. Technical Release 55 (TR-55) 2nd edn, Natural Resources Conservation Service, Conservation Engineering Division, Washington, DC, p 164

    Google Scholar 

  44. United States Department of Agriculture (2007) Hydrologic soil groups. Chapter 7, National Engineering Handbook. (Part 630, Hydrology). Natural Resources Conservation Service, Conservation Engineering Division, Washington, DC, p 14

    Google Scholar 

  45. Carlson TN (2004) Analysis and prediction of surface runoff in an urbanizing watershed using satellite imagery. J Amer Water Resour Assoc 40(4):1087–1098

    Article  Google Scholar 

  46. Melesse A, Wang X (2007) Impervious surface area dynamics and storm runoff response. In: Weng Q (ed) Remote sensing of impervious surfaces. CRC, Boca Raton, pp 369–386

    Google Scholar 

  47. McPherson MB, Schneider WJ (1974) Problems in modeling urban watersheds. Water Resour Res 10(3):434–440

    Article  Google Scholar 

  48. Carlson TN (2007) Impervious surface area and its effect on water abundance and water quality. In: Weng Q (ed) Remote sensing of impervious surfaces. CRC, Boca Raton, pp 353–367

    Google Scholar 

  49. Brabec E, Schulte S, Richards PL (2002) Impervious surfaces and water quality: a review of current literature and its implications for watershed planning. J Plan Lit 16(4):499–514

    Article  Google Scholar 

  50. Multi-Resolution Land Characteristics Consortium (2006) National land cover database percent developed imperviousness. http://www.mrlc.gov/nlcd06_data.php. Accessed 19 Dec 2012

  51. Gillies RR, Brim Box J, Symanzik J, Rodemaker EJ (2003) Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta—a satellite perspective. Remote Sens Environ 86(3):411–422

    Article  Google Scholar 

  52. Yang F, Matsushita B, Fukushima T (2010) A pre-screened and normalized multiple endmember spectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin, Japan. ISPRS J Photogramm Remote Sens 65(5):479–490

    Article  Google Scholar 

  53. Ruddell BL, Chow WTL (2014) Microclimate analysis of observations in a master-planned residential community in Arizona. In: 94th American meteorological society annual meeting, Atlanta, 2–6 Feb 2014

    Google Scholar 

  54. Parece TE, Campbell JB, Carroll D (2014) Assessing variations in urban heat island effects within Roanoke, Virginia. In: American society of photogrammetry and remote sensing annual conference. Louisville, 23–28 Mar 2014

    Google Scholar 

  55. United States National Aeronautics and Space Administration (2014) About LVIS. http://lvis.gsfc.nasa.gov. Accessed 24 May 2014

  56. Lee DS, Shan J (2003) Combining Lidar elevation data and IKONOS multispectral imagery for coastal classification mapping. Mar Geod 26(1/2):117–127

    Article  CAS  Google Scholar 

  57. Buján S, González-Ferreiro E, Reyes-Bueno F, Barreiro-Fernández L, Crecente R et al (2012) Land use classification from Lidar data and ortho-images in a rural area. Photogramm Rec 27(140):401–422

    Article  Google Scholar 

  58. de Agirre AM, Malpica JA (2012) Detecting shadows in a segmented land use land cover image with LIDAR data. IGARSS 33rd Canadian symposium on remote sensing, Munich, 22–27 July 2012, pp 5458–5461

    Google Scholar 

  59. Meng X, Currit N, Wang L, Yang X (2010) Object-oriented residential building land-use mapping using lidar and aerial photographs. American society of photogrammetry and remote sensing 2010 annual conference, San Diego, 26–30 Apr 2010

    Google Scholar 

  60. Antonarakis A, Richards K, Brasington J (2008) Object-based land cover classification using airborne LiDAR. Remote Sens Environ 112(6):2988–2998

    Article  Google Scholar 

  61. NASA Jet Propulsion Laboratory (2014) Airborne visible/infrared imaging spectrometer. http://aviris.jpl.nasa.gov/. Accessed 5 June 2014

  62. Amato U, Antoniadis A, Carfora MF, Colandrea P, Cuomo V et al (2013) Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use. IEEE J Sel Topics Appl Earth Observ Remote Sens 6(2):615–625

    Article  Google Scholar 

  63. United States Geological Survey (2011) Earth observing, sensors—hyperion. http://eo1.usgs.gov/sensors/hyperion. Accessed 6 June 2014

  64. Petropoulos GP, Arvanitis K, Sigrimis N (2012) Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping. Exp Syst Appl 39(3):3800–3809

    Article  Google Scholar 

  65. Petropoulos GP, Kalaitzidis C, Prasad Vadrevu K (2012) Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Comput Geosci 41:99–107

    Article  Google Scholar 

  66. Ampe EM, Vanhamel I, Salvadore E, Jef D, Bashir I et al (2012) Impact of urban land-cover classification on groundwater recharge uncertainty. IEEE J Sel Topics Appl Earth Observ Remote Sens 5(6):1859–1867

    Article  Google Scholar 

  67. European Space Agency (2014) Earth online. https://earth.esa.int/web/guest/-/proba-chris-level-1a-1488. Accessed 18 June 2014

  68. Berni J, Zarco-Tejada PJ, Suarez L, Fereres E (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47(3):722–738

    Article  Google Scholar 

  69. Hodgson A, Kelly N, Peel D (2013) Unmanned aerial vehicles (UAVs) for surveying Marine Fauna: a Dugong case study. PLoS One 8:1–15

    Google Scholar 

  70. Herwitz SR, Johnson LF, Dunagan SE, Higgins RG, Sullivan DV et al (2004) Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Comput Electron Agric 44(1):49–61

    Article  Google Scholar 

  71. Ahmad A, Tahar KN, Udin WS, Hashim KA, Darwin N, et al (2013) Digital aerial imagery of unmanned aerial vehicle for various applications, Penang, 29 Nov–1 Dec 2013, pp 535–540

    Google Scholar 

  72. Primicerio J, Di Gennaro S, Fiorillo E, Genesio L, Lugato E et al (2012) A flexible unmanned aerial vehicle for precision agriculture. Precis Agric 13:517–523

    Article  Google Scholar 

  73. Shim DH, Han J, Yeo H-T (2009) A development of unmanned helicopters for industrial applications. In: Oh P, Piegl L, Valavanis K (eds) Unmanned aircraft systems. Springer, The Netherlands, pp 407–421

    Chapter  Google Scholar 

  74. Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11(3):138–146

    Article  Google Scholar 

  75. Astuti G, Giudice G, Longo D, Melita CD, Muscato G et al (2009) An overview of the “Volcan Project”: an UAS for exploration of volcanic environments. In: Oh P, Piegl L, Valavanis K (eds) Unmanned aircraft systems. Springer, The Netherlands, pp 471–494

    Chapter  Google Scholar 

  76. Lu H, Li Y-S, Lin X-C (2011) Classification of high resolution imagery by unmanned aerial vehicle. Sci Surv Mapp 36(6):106–108

    Google Scholar 

  77. Vermeulen C, Lejeune P, Lisein J, Sawadogo P, Bouché P (2013) Unmanned aerial survey of elephants. PLoS One 8:1–7

    Google Scholar 

  78. Diaz-Varela RA, Zarco-Tejada PJ, Angileri V, Loudjani P (2014) Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. J Environ Manage 134:117–126

    Article  CAS  Google Scholar 

  79. Laliberte AS, Goforth MA, Steele CM, Rango A (2011) Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments. Remote Sens 3:2529–2551

    Article  Google Scholar 

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Acknowledgement

We thank Dr. Valerie Thomas, Virginia Tech, Department of Forest Resources and Environmental Conservation, Blacksburg, Virginia, for providing the Lidar data mentioned in Sect. 6.1 Lidar.

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Correspondence to Tammy E. Parece .

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Parece, T.E., Campbell, J.B. (2015). Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview. In: Younos, T., Parece, T. (eds) Advances in Watershed Science and Assessment. The Handbook of Environmental Chemistry, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-319-14212-8_1

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