Mapping and identifying land cover/land use and its change is the most important, as well as the most widely researched, topic in remote sensing. Land cover/land use has been used extensively to derive a number of biophysical variables, such as vegetation index, biomass, and carbon content (see other chapters). More importantly, land cover/land use pattern and its change reflect the underlying natural and/or social processes, thus providing essential information for modeling and understanding many different phenomena on the Earth. Knowledge of land cover/land use and its change is also critical to effective planning and management of natural resources.
Mapping land cover/land use accurately and efficiently via remote sensing requires good image classification methods. Unfortunately, there are numerous factors (e.g., image resolution and atmospheric condition) that could affect the effectiveness and accuracy of the classification algorithms. Different land cover/land use classification methods may be needed for different problems under different environmental conditions, making generalization and hence automation of the image classification process across time and space extremely difficult. As a result, new and sophisticated classification methods designed to improve the classification process continue to appear in the literature (e.g., Jensen, 2005; Gong, 2006). Newer approaches such as fuzzy classification, artificial neural network, and object-based classification have been developed and successfully applied (Definiens, 2004; Benz et al., 2004). However, these methods require extensive training and human supervision. We are still far from being able to develop a common framework to successfully identify a variety of features in different landscapes and to generalize and automate the classification process.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Baskent EZ, Jordan GA (1955) Characterizing spatial structure of forest landscapes. Can. J. Forest Res. 25:1830–1849
Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58:239–258
Bian L (2003) Retrieving urban objects using a wavelet transform approach. Photogramm. Eng. Remote Sens. 69(2):133–141
Briggs JM, Nellis JM (1991) Seasonal variation of heterogeneity in the tallgrass prairie: A quanti tative measure using remote sensing. Photogramm. Eng. Remote Sens. 57:407–411
Cao C, Lam NSN (1997) Understanding the scale and resolution effects in remote sensing and GIS. In: DA Quattrochi, MF Goodchild (eds), Scale in remote sensing and GIS. Lewis Publishers, Boca Raton, FL, pp 57–72
Carr JR (1999) Classification of digital image texture using variograms. In: PM Atkinson, NJ Tate (eds), Advances in remote sensing and GIS analysis. Wiley, London, pp 135–146
Carr JR, de Miranda FP (1998) The semivariogram in comparison to the co-occurrence matrix for classification of image texture. IEEE Trans. Geosci. Remote Sens. 36(6):1945–1952
Clarke KC (1986) Computation of the fractal dimension of topographic surfaces using the triangular prism surface area method. Comput. Geosci. 12(5):713–722
Clausi DA, Jobanputra R (2006) Preserving boundaries for image texture segmentation using grey level co-occurring probabilities. Pattern Recog. 39(2):234–245
Cliff AD, Ord JK (1973) Spatial autocorrelation. Methuen, New York
Coppin PR, Bauer ME (1966) Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews 13:207–234
Crews-Meyer KA (2002) Characterizing landscape dynamism using paneled-pattern metrics. Photogramm. Eng. Remote Sens. 68(10):1031–1040
Dale MRT (2000) Lacunarity analysis of spatial pattern: a comparison. Landscape Ecol. 15(5):467–478
Daubechies I (1990) The wavelet transform, time/frequency localization and signal analysis. IEEE Trans. Inf. Theory 36:961–1005
De Pietri DE (1995) The spatial configuration of vegetation as an indicator of landscape degrada tion due to livestock enterprises in Argentina. J. Appl. Ecol. 32:857–865
Definiens AG (2004) eCognition User Guide (accessed May 2006)
Dong P (2000) Test of a new lacunarity estimation method for image texture analysis. Int. J. Remote Sens. 21(17):3369–3373
Dunn CP, Sharpe DM, Guntenspergen GR, Stearns F, Yang Z (1991) Methods of analyzing temporal changes in landscape pattern. In: MG Turner, RH Gardner (eds), Quantitative methods in landscape ecology. The analysis and interpretation of landscape heterogeneity. Springer, New York
Emerson CW, Lam NSN, Quattrochi DA (1999) Multiscale fractal analysis of image texture and pattern. Photogramm. Eng. Remote Sens. 65(1):51–61
Emerson CW, Lam NSN, Quattrochi DA (2005) A comparison of local variance, fractal dimension, and Moran’s I as aids to multispectral image classification. Int. J. Remote Sens. 26(8):1575–1588
Estreguil C, Lambin E (1996) Mapping forest disturbances in Papua New Guinea with AVHRR data. J. Biogeogr. 23:757–773
Falconer K (1988) Fractal geometry: mathematical foundations and applications. Wiley, New York
Frank TD (1984) The effect of change in vegetation cover and erosion patterns on albedo and texture of Landsat images in a semiarid environment. Ann. Assoc. Am. Geogr. 74:393–407
Franklin SE, Hall RJ, Moskal LM, Maudie AJ, Lavigne MB (2000) Incorporating texture into classification of forest species composition form airborne multispectral images. Int. J. Remote Sens. 21(1):61–79
Gong P (2006) Information extraction. In: M Ridd, JD Hipple (eds), Remote sensing of human settlements. ASPRS, Bethesda, MD, pp 275–334
Gong P, Marceau DJ, Howarth PJ (1992) A comparison of spatial feature extraction algorithms for land use classification with SPOT HRV data. Remote Sens. Environ. 40:137–151
Goodchild MF (1980) Fractals and the accuracy of geographical measures. Mathematical Geology 12:85–98
Goodchild MF (1986) Spatial Autocorrelation. CATMOG (Concepts and Techniques in Modern Geography) No. 47. Geo Books, Norwich, England
Gopal S, Woodcock C (1996) Remote sensing of forest change using artificial neural networks. IEEE Trans. Geosci. Remote Sens. 34(2):398–404
Haralick RM (1979) Statistical and structural approaches to texture. Proc. IEEE 67(5):786–804
Haralick RM, Shanmugan K, Dinstein J (1973) Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6):610–621
Jaggi S, Quattrochi D, Lam NSN (1993) Implementation and operation of three fractal measurement algorithms for analysis of remote sensing data. Comput. Geosci. 19(6):745–767
Jensen J, Cowen D, Althausen J, Narumalani S, Weatherbee O (1993) An evaluation of the Coast-Watch change detection protocol in South Carolina. Photogrammetric Engineering and Remote Sensing 59(6):1039–1046
Jensen J, Cowen D, Narumalani S, Halls J (1997) Principles of change detection using digital remote sensor data. In: JL Star, JE Estes, KC McGwire (eds), Integration of geographic information systems and remote sensing. Cambridge University Press, Cambridge, pp 37–54
Jensen J (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice-Hall, New Jersey
Jupp DLB, Walker J, Pendridge LK (1986) Interpretation of vegetation structure in Landsat MSS imagery: a case study in disturbed semi-arid eucalypt woodland. Part 2. model-based analysis. J. Environ. Manag. 23:35–57
Kulkarni A (2004) Evaluation of the Impacts of Hurricane Hugo on the Land Cover of Francis Marion National Forest, South Carolina Using Remote Sensing. M.S. thesis, Louisiana State University, Baton Rouge, Louisiana
Lam NSN (1990) Description and measurement of Landsat TM images using fractals. Photogramm. Eng. Remote Sens. 56(2):187–195
Lam NSN (2004) Fractals and scale in environmental assessment and monitoring. In: E Sheppard, R McMaster R (eds), Scale and Geographic Inquiry: Nature, Society, and Method. Blackwell, Oxford, pp 23–40
Lam NSN, Quattrochi DA (1992) On the issues of scale, resolution, and fractal analysis in the mapping sciences. Prof. Geogr. 44(1):89–99
Lam NSN, De Cola L (eds) (1993) Fractals in geography. Prentice-Hall, Englewood Cliffs, NJ, 308p
Lam NSN, Quattrochi DA, Qiu HL, Zhao W (1998) Environmental assessment and monitoring with image characterization and modeling system using multiscale remote sensing data. Appl. Geogr. Stud. 2(2):77–93
Lam NSN, Qiu HL, Quattrochi DA, Emerson CW (2002) An evaluation of fractal methods for measuring image complexity. Cartogr. Geogr. Inform. Sci. 29:25–35
Lam NSN, Catts C, Quattrochi DA, Brown D, McMaster R (2004) Scale. In: R McMaster, L Usery (eds), A Research Agenda for Geographic Information Science. CRC Press, Bacon Raton, FL, Chapter 4, pp 93–128
Lambin EF (1996) Change detection at multiple temporal scales: seasonal and annual variations in landscape variables. Photogramm. Eng. Remote Sens. 62:931–938
Lambin EF, Strahler AH (1994) Indicators of land-cover change for change-vector analysis in multitemporal space at coarse spatial scales. Int. J. Remote Sens. 15:2099–2119
Lo CP, Quattrochi DA, Luvall JC (1997) Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International Journal of Remote Sensing 18 (2):287–304
Lu D, Mausel P, Brondizio E, Moran E (2005) Land-cover binary change detection methods for use in the moist tropical region of the Amazon: a comparative study. Int. J. Remote Sens. 26 (1):101–114
Lunetta R, Elvidge C (1998) Remote sensing change detection: environmental monitoring methods and applications. Sleeping Bear Press, Ann Arbor, MI
Mallat SG (1989) A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 11:674–693
Mandelbrot B (1982) The fractal geometry of nature. Freeman, New York
Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Machine Intell. 18(8):837–842
Mark DM, Aronson PB (1984) Scale-dependent fractal dimensions of topographic surfaces: an empirical investigation, with applications in geomorphology and computer mapping. Math. Geol. 11:671–684
Mas JF (1999) Monitoring land-cover changes: a comparison of change detection techniques. Int. J. Remote Sens. 20:139–152
McGarigal K (2002) Landscape pattern metrics. In AH El-Shaarawi, WW Piegorsch (eds), Encyclopedia of environmentrics, vol 2. Wiley, Sussex, England, pp 1135–1142
McGarigal K, Mark BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying land-scape structure. USDA Forest Service General Technical Report PNW-351, Portland, Oregon
Mallat S (1989) A theory of multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Intel. 11:674–693
Moller-Jensen L (1990) Knowledge-based classification of an urban area using texture and context information in Landsat-TM imagery. Photogramm. Eng. Remote Sens. 56(6):899–904
Muneeswaran K, Ganesan L, Arumugam S, Sounda KR (2005) Texture classification with combined rotation and scale invariant wavelet features. Pattern Recogn. 38(10):1495–1506
Myint S, Lam NSN (2005a) A study of lacunarity based texture analysis approaches to improve urban image classification. Comput. Environ. Urban Syst. 29:501–523
Myint S, Lam NSN (2005b) Examining lacunarity approaches in comparison with fractal and spatial autocorrelation techniques for urban mapping. Photogramm. Eng. Remote Sens. 71(8):927-937
Myint S, Lam NSN, Tyler J (2002) An evaluation of four different wavelet decomposition procedures for spatial feature discrimination in urban areas. Trans. GIS 6(4):403–429
Myint S, Lam NSN, Tyler J (2004) Wavelets for urban spatial feature discrimination: Comparisons with fractals, spatial autocorrelation, and spatial co-occurrence approaches. Photogramm. Eng. Remote Sens. 70(8):803–812
Nackaerts K, Vaesen K, Muys B, Coppin P (2005) Comparative performance of a modified change vector analysis in forest change detection. Int. J. Remote Sens. 26(5):839–852
O’Neill RV, Johnson AR, King AW (1989) A hierarchical framework for the analysis of scale. Landscape Ecol. 7(1):55–61
O’Neill RV, Krummel JR, Gardner RH, Sugihara G, Jackson B, DeAngelis DL, Milne BT, Turner MG, et al. (1998) Indices of landscape pattern. Landscape Ecol. 1:153–162
Openshaw S (1989) Automating the search for cancer clusters. Prof. Statistician 8:7–8
Pickup G, Foran BD (1987) The use of spectral and spatial variability to monitor cover change on inert landscapes. Remote Sens. Environ. 23:351–363
Plotnick RE, Gardner RH, O’Neill RV (1993) Lacunarity indices as measures of landscape texture. Landscape Ecol. 8:201–211
Quattrochi DA, Goodchild MF (1997) Scale in remote sensing and GIS. CRC/Lewis Publishers, Boca Raton, FL
Quattrochi DA, Lam NSN, Qiu HL, Zhao W (1997) Image characterization and modeling system (ICAMS): a geographic information system for the characterization and modeling of multiscale remote sensing data. In: D Quattrochi, M Goodchild (eds), Scaling in remote sensing and GIS. CRC/Lewis Publishers, Boca Raton, FL, pp 295–307
Read JM, Lam NSN (2002) Spatial methods for characterizing land-cover changes for the tropics. Int. J. Remote Sens. 23(12):2457–2474
Smits PC, Annoni A (2000) Towards specification-driven change detection. IEEE Trans. Geosci. Remote Sens. 38:1484–1488
Tate N, Atkinsons P (eds) (2001) Modelling scale in geographical information science. Wiley, New York
Turner MG, Dale VH, Gardner RH (1989) Predicting across scales: theory development and testing. Landscape Ecol. 3(3/4):245–252
Voss R (1986) Random fractals: characterization and measurement. In: R Pynn, A Skjeltorp (eds), Scaling phenomena in disordered systems. Plenum, New York
Woodcock CE, Strahler (1987) The factor of scale in remote sensing. Remote Sens. Environ. 21:311–332
Yuan D, Elvidge C, Lunetta R (1998) Survey of multispectral methods for land cover change analysis. In: R Lunetta, D Elvidge (eds), Remote sensing change detection. Sleeping Bear Press, Ann Arbor, MI
Zhu C, Yang X (1998) Study of remote sensing image texture analysis and classification using wavelet. Int. J. Remote Sens. 19(16):3197–3203
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V
About this chapter
Cite this chapter
Lam, N.SN. (2008). Methodologies for Mapping Land Cover/Land Use and its Change. In: Liang, S. (eds) Advances in Land Remote Sensing. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6450-0_13
Download citation
DOI: https://doi.org/10.1007/978-1-4020-6450-0_13
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-6449-4
Online ISBN: 978-1-4020-6450-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)