Identification of Land Cover Alterations in the Alta Murgia National Park (italy) with Vhr Satellite Imagery

Identification of Land Cover Alterations in the Alta Murgia National Park (italy) with Vhr Satellite Imagery

M. Caprioli E. Tarantino

Polytechnic University of Bari, Italy.

Page: 
261-270
|
DOI: 
https://doi.org/10.2495/SDP-V1-N3-261-270
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

Land cover exerts a great influence on many basic environmental processes and consequently any transformation in it may have a marked impact on the environment from the local to the global scales. In multidisciplinary research contexts, satellite remote sensing offers opportunities both to evaluate the effects of these processes and to provide one of the information layers needed for designing national strategies oriented to protection and sustainable use of our resources. The advent of recent satellite imagery has increased the possibility to investigate large-scale areas in great detail. Together with an increase in spatial and radiometric resolution, there is, usually, an increase in the variability within land parcels, generating a decrease in the accuracy of land use classification on a per-pixel basis. In order to avoid such negative impacts, an object-oriented classification methodology on IKONOS multispectral data has been implemented on the test area of the Alta Murgia National Park, in the Apulia region (Italy), where soil adaptation for agricultural practices, through rock breaking, has taken place over the last 20 years. The analysis has been conducted with a classification strategy that is able to distinguish land use functions on the basis of differences in spatial distribution and pattern of land cover forms. It consists of two phases: segmentation of the image into meaningful multipixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels; then, assignment of the segments (objects) to classes using fuzzy logic and a hierarchical decision key.

Keywords: 

land cover transformation, object-oriented classification, VHR satellite imagery

  References

[1] Basso, F., Bove, E., Dumontet, S., Ferrara, A., Pisante, M., Quaranta, G. & Taberner, M., Evaluating environmental sensitivity at the basin scale through the use of geographic information systems and remotely sensed data: an example covering the Agri basin Southern Italy. Catena, 40, pp. 19–35, 2000.

[2] Thornes, J.B., Mediterranean desertification and the vegetation cover. EUR 15415 – Desertification in a European Context: Physical and Socio-economic Aspects, eds. R. Fantechi, D. Peter, P. Balabanis & J.L. Rubio, Office for Official Publications of the European Communities: Brussels and Luxembourg, pp. 169–194, 1995.

[3] UNEP (United Nations Environmental Programme), Draft plan of actions to combat desertification. UN Conference on Desertification, Nairobi, 1977.

[4] UNEP (United Nations Environmental Programme), World Atlas of Desertification, Edward Arnold: Seven Oaks, UK, 1992.

[5] Polytechnic University of Bari, Italy (scientific coordination by Borri D.),Alta Murgia National Park, Final Report, 2002.

[6] DeFries, R.S. & Townshend, J.R.G., Global land cover characterisation from satellite data: from research to operational implementation. Global Ecology and Biogeography, 8, pp. 367–379, 1999.

[7] Franklin, S.E. & Wulder, M.A., Remote sensing methods in medium spatial resolution satellite: land cover classification of larges areas. Progress in Physical Geography, 26, pp. 173–205, 2002.

[8] Goetz, S.J., Wright, R.K., Smith, A.J., Zinecker, E. & Schaubb, E., IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the midAtlantic region. Remote Sensing of Environment, 88, pp. 194–208, 2003.

[9] Pohl, C. & Van Genderen, J.L., Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19(5), pp. 823–854, 1998.

[10] Fisher, J. & Goetz, S.J., Considerations in the use of high spatial resolution imagery: an applications research assessment. ASPRS Conference Proceedings, St. Louis, MO, Available from ASPRS, http://www.asprs.org and at http://www.geog.umd.edu/resac, 2001.

[11] Fritz, L.W., High resolution commercial remote sensing satellites and spatial information.

http://www.isprs.org/publications/highlights/highlights0402/fritz.html, 1999.

[12] Verstraete, M., The contribution of remote sensing to monitor vegetation and to evaluate its dynamic aspects. Vegetation, Modeling and Climatic Change Effects, eds. F. Veroustraete, R. Ceulemans, SPB Academic Publishing: The Hague, Netherlands, pp. 207–212, 1994.

[13] Ehlers, M., Jadkowski, M.A., Howard, R.R. & Brostuen, D.E., Application of SPOT data for regional growth analysis and local planning. Photogrammetric Engineering and Remote Sensing, 56(2), pp. 175–180, 1990.

[14] Lillesand,T.M.&Kiefer,R.W.(eds.),RemoteSensingandImageInterpretation,4thedn.,Wiley & Sons: New York, 2000.

[15] Woodcock, C. & Strahler, A., The factor of scale in remote sensing. Remote Sensing of Environment, 21, pp. 311–322, 1987.

[16] Townshend, J.R.G., The enhancement of computer classification by logical smoothing. Photogrammetric Engineering and Remote Sensing, 52, pp. 213–221, 1986.

[17] Townshend, J.R.G., Land cover, International Journal of Remote Sensing, 13, pp. 1319–1328, 1992.

[18] Donnay, J.P., Use of remote sensing information in planning. Geographical Information and Planning, eds. J. Stillwell, S. Geertman & S. Openshaw, Springer-Verlag: Berlin, pp. 242–260, 1999.

[19] Aplin, P., Atkinson, P. & Curran, P., Per-field classification of land use using the forthcoming very fine resolution satellite sensors: problems and potential solutions. Advances in remote sensing and GIS analysis, eds. P. Atkinson & N. Tate, Wiley & Son: Chichester, pp. 219–239, 1999.

[20] Caprioli, M. & Tarantino, E., Accuracy assessment of per-field classification integrating very fine spatial resolution satellite sensors imagery with topographic data. Journal of Geospatial Engineering, 3(2), pp. 127–134, 2001.

[21] Blaschke, T., Lang, S., Lorup, E., Strobl, J. & Zeil, P., Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental Information for Planning, Politics and the Public, Vol. 2, eds. A. Cremers, K. Greve, Metropolis Verlag: Marburg, pp. 555–570, 2000.

[22] Baatz,M.&Schäpe,A.,Multiresolutionsegmentation–anoptimisationapproachforhighquality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung, Vol. XII, eds. J. Strobl, Blaschke, Griesebner, Wichmann-Verlag: Heidelberg, pp. 12–23, 2000.

[23] Gorte, B., Probabilistic Segmentation of Remotely Sensed Images. ITC Publication Series, 63, 1998.

[24] Schiewe,J.,Tufte,L.&Ehlers,M.,Potentialandproblemsofmulti-scalesegmentationmethods in remote sensing. Geo-Informations-Systeme, 6, pp. 34–39, 2001.

[25] Baatz, M., Heynen, M., Hofmann, P., Lingenfelder, I., Mimler, M., Schäpe, A., Weber, M. & Willhauck, G., eCognition User Guide, Definiens AG: München, 2000.

[26] Antunes, A.F.B., Lingnau, C. & Da Silva, J.C., Object oriented analysis and semantic network for high resolution image classification. Proc. of Anais XI SBSR, Belo Horizonte, Brasil, INPE, pp. 273–279, April 05–10, 2003.

[27] Dial, G.F., Bowen, H., Gerlach, B., Grodecki, J. & Oleszczuk, R., IKONOS satellite sensor, imagery and products. Remote Sensing of Environment, 88, pp. 23–36, 2003.