The use of remote sensing techniques and subsequent analysis by means of geographical information system (GIS) offers an effective method for monitoring temporal and spatial changes of landscapes. This work studies the urbanization processes and associated threats to natural ecosystems and resources in the metropolitan areas of Berlin and Erlangen-Fürth-Nürnber？Schwabach (EFNS). To compute the land use/cover (LULC) of the study areas, a supervised classification of “maximum likelihood” using Landsat data for the years of 1972, 1985, 1998, 2003, and 2015 is used. Results show that the built-up area is the dominant land use in both regions throughout the study period. This land use has increased at the expense of green and open areas in EFNS and at the expense of agricultural land in Berlin. Likewise, 5% of forest in EFNS is replaced with urban infrastructure. However, the amount of forest in Berlin increased by 3%. While EFNS experienced relatively big changes in its water bodies from 1972 to 1985, changes in water bodies in Berlin were rather slight during the last 40 years. The overall accuracy of our remotely sensed LULC maps was between 88% and 94% in Berlin and between 85.87% and 87.4% for EFNS. The combination of remote sensing and GIS appears to be an indispensable tool for monitoring changes in LULC in urban areas and help improving LU planning to avoid environmental and ecological problems.
References
[1]
Prakasam, C. (2010) LU and LC Change Detection through Remote Sensing Approach: A Case Study of Kodaikanal Taluk, Tamil Nadu.
http://www.ipublishing.co.in/jggsvol1no22010.html
[2]
Stefanov, W.L., Ramsey, M.S. and Christensen, P.R. (2001) Monitoring Urban LC Change: An Expert System Approach to LC Classification of Semiarid to Arid Urban Centers. Remote Sensing of Environment, 77, 173-185.
[3]
Yildirim, H., Ozel, M.E., Radberger, R. and Akca, A. (2002) Monitoring von Land- nutzungsanderungen über einen langen Zeitraum mit Hilfe von Satellitenferner-kundung und GIS. Allgemeine Forst und Jagdzeitung, 173, 15-20.
[4]
Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E. (1976) A LU and LC Classification System for Use with Remote Sensor Data.
http://www.pbcgis.com/data_basics/anderson.pdf
[5]
Wu, J., Vincent, B., Yang, J., Bouarfa, S. and Vidal, A. (2008) Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China. Sensors, 8, 7035-7049. https://doi.org/10.3390/s8117035
[6]
Foody, G.M. (2000) Estimation of Sub-Pixel LC Composition in the Presence of Untrained Classes. Computers & Geosciences, 26, 469-478.
[7]
Ridd, M.K. (1995) Exploring a V-I-S (Vegetation-Impervious Surface-Soil) Model for Urban Ecosystem Analysis through Remote Sensing: Comparative Anatomy for Cities. International Journal of Remote Sensing, 16, 2165-2185.
https://doi.org/10.1080/01431169508954549
[8]
Cibula, W.G. and Nyquist, M.O. (1987) Use of Topographic and Climatological Models in a Geographical Data Base to Improve Landsat MSS Classification for Olympic National Park. Photogrammetric Engineering and Remote Sensing, 53, 67- 75.
[9]
Franklin, S.E. (1994) Discrimination of Subalpine Forest Species and Canopy Density Using Digital CASI, SPOT PLA and Landsat TM Data. Photogrammetric Engineering and Remote Sensing, 60, 1233-1241.
[10]
Harris, P.M. and Ventura, S.J. (1995) The Integration of Geographic Data with Remotely Sensed Imagery to Improve Classification in an Urban Area. Photogrammetric Engineering and Remote Sensing, 61, 993-998.
[11]
Vogelmann, J.E., Sohl, T. and Howard, S.M. (1998) Regional Characterization of LC Using Multiple Sources of Data. Photogrammetric Engineering and Remote Sensing, 64, 45-57.
[12]
Greenberg, J.D. and Bradly, G.A. (1997) Analyzing the Urban-Wildland Interface with GIS: Two Case Studies. Journal of Forestry, 95, 18-22.
[13]
Stuckens, J., Coppin, P.R. and Bauer, M.E. (200) Integrating Contextual Information with Per-Pixel Classification for Improved LC Classification. Remote Sensing of Environment, 71, 282-296.
[14]
Beyer, W., Krappweis, S., Maciuga, T., Rader, J. and Sinz, M. (2002) Nationalatlas Bundesrepublik Deutschland-Dorfer und Stadte, die Metropolregion Berlin-Bran- denburg, Band 5, Spektrum-Akademischer Vlg, Heidelberg, 166-167.
[15]
Lillesand, T.M. and Kiefer, R.W. (2015) Remote Sensing and Image Interpretation. 7th Edition, Wiley, New York.
[16]
Shalaby, A. and Tateishi, R. (2007) Remote Sensing and GIS for Mapping and Monitoring LC and Land-Use Changes in the Northwestern Coastal Zone of Egypt. Applied Geography, 27, 28-41.
[17]
Campbell, J.B. and Wynne, J.H. (2011) Introduction to Remote Sensing. 5th Edition, The Guilford Press, New York.
[18]
Thomas, I.L., Benning, V.M. and Ching, N.P. (1987) Classification of Remotely Sensed Images. Geocarto International, 2, 77-77.
https://doi.org/10.1080/10106048709354113
[19]
Foody, G.M., Campbell, N.A., Trood, N.M. and Wood, T.F. (1992) Derivation and Application of Probabilistic Measures of Class Membership from the Maximum- Likelihood Classification. Photogrammetric Engineering and Remote Sensing, 58, 1335-1341.
[20]
Congalton, R.G. (1991) A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37, 35-46.
[21]
Lins, K.S. and Kleckner, R.L. (1996) LC Mapping: An Overview and History of the Concepts. In: Scott, J.M., Tear, T.H. and Davis, W.F., Eds., Gap Analysis: A Landscape Approach to Biodiversity Planning, American Society for Photogrammetry and Remote Sensing, Bethesda, MD, 57-65.
