154
B.G.H. Gorte
It was found that image fusion of micro-wave and optical sensors leads to the
identification of more land cover classes and to improved overall classification accuracies (Schistad-Scholberg et aI., 1994, Hussin and Shaker, 1996).
Acknowledgments
The author would like to thank the Earth Sciences Department of the University of
Siena, Italy, for providing the image data of the Pantanal area (see Sect. 7.2). The
analysis of the data was carried out in cooperation with Riccardo Salvini, Ph.D.
student in this Department.
Thanks are also due to Prof. A.1.M. Meijerink for his contributions to the introduction and the radar section, and for his advise and comments.
References
Angelici G., N. Brynt and S. Friendman (1997). Techniques for land use change detection
using Landsat imagery, Proc. of the American Society of Photogrammetry, Falls Church,
Virginia, pp. 217-228
Banner A. and T. Lynham (1981). Multi-temporal analysis of Landsat data for forest cut over
mapping - a trial of two procedures. Proc. of the 7th Canadian Symposium on Remote
Sensing, Winnipeg, Manitoba, Canada, pp. 233-240
Bastiaanssen, W. (1998). Remote sensing in water resources management: the state of the art.
International Water Management Institute (lWMI), Colombo, Sri Lanka
Bosch, 1.M. and J.D. Hewlett (1982). A review of catchment experiments to determine the
effects of vegetation changes on water yield and evapotranspiration. J. of Hydr. Vol 55, pp.
3-23
Bruynzeel, L.A. (1990). Hydrology of moist tropical forests and effects of conversion: a state
of knowledge review. UNESCO, 224 p.
Bunnik, N.l.J. (1978). The multispectal refe1ctance of short wave radiation by agricultural
crops in relation with their morphological and optical properties, PhD Thesis, Agricultural
University ofWageningen, Papers 78-1, 175 pp.
Calder, N. (1991). Spaceship Earth, Viking Press, London
Clevers, J.G.P.W. (1988). The application of a weigted infrared-red vegetation index for extimating leaf area index by correcting for soil moisture, Remote Sens. Environ. 29, pp.
25-37
Cross, A.M., D.C. Mason and S.l. Dury (1988). Segmentation of remotely-sensed images by
a split-and-merge process. IJRS 9(8), pp. 1329-1345
Disperati L., G. Righini, M. Bocci, P.L. Pantozzi, A.P. Fiori, S. Kozciac and A.C. Paranhos
Filho (1998). Land cover changes in the Rio Verdo de Mato Grasso region (Pantanal, Mato
Grasso do SuI, Brasil) as detected through remote sensing and GIS analysis. Technical
report, Univ. of Siena (I)
Dobson, M.C., ET. Ullaby and L.E. Pierce (1995). Land-cover classification and estimation
of terrain attributes using synthetic aperture radar. Remote Sens. Environ. 51: 199-214
Duda, R.O. and P.E. Hart (1973). Pattern classification and scene analysis. John Wiley and
Sons, New York, 465 pp.
Engheta, N. and C. Elachi (1982). Radar scattering from a diffuse vegetation layer over a
smooth surface, IEEE Trans. Geosci. Remote Sens. 33: 848-857
B.G.H. Gorte
It was found that image fusion of micro-wave and optical sensors leads to the
identification of more land cover classes and to improved overall classification accuracies (Schistad-Scholberg et aI., 1994, Hussin and Shaker, 1996).
Acknowledgments
The author would like to thank the Earth Sciences Department of the University of
Siena, Italy, for providing the image data of the Pantanal area (see Sect. 7.2). The
analysis of the data was carried out in cooperation with Riccardo Salvini, Ph.D.
student in this Department.
Thanks are also due to Prof. A.1.M. Meijerink for his contributions to the introduction and the radar section, and for his advise and comments.
References
Angelici G., N. Brynt and S. Friendman (1997). Techniques for land use change detection
using Landsat imagery, Proc. of the American Society of Photogrammetry, Falls Church,
Virginia, pp. 217-228
Banner A. and T. Lynham (1981). Multi-temporal analysis of Landsat data for forest cut over
mapping - a trial of two procedures. Proc. of the 7th Canadian Symposium on Remote
Sensing, Winnipeg, Manitoba, Canada, pp. 233-240
Bastiaanssen, W. (1998). Remote sensing in water resources management: the state of the art.
International Water Management Institute (lWMI), Colombo, Sri Lanka
Bosch, 1.M. and J.D. Hewlett (1982). A review of catchment experiments to determine the
effects of vegetation changes on water yield and evapotranspiration. J. of Hydr. Vol 55, pp.
3-23
Bruynzeel, L.A. (1990). Hydrology of moist tropical forests and effects of conversion: a state
of knowledge review. UNESCO, 224 p.
Bunnik, N.l.J. (1978). The multispectal refe1ctance of short wave radiation by agricultural
crops in relation with their morphological and optical properties, PhD Thesis, Agricultural
University ofWageningen, Papers 78-1, 175 pp.
Calder, N. (1991). Spaceship Earth, Viking Press, London
Clevers, J.G.P.W. (1988). The application of a weigted infrared-red vegetation index for extimating leaf area index by correcting for soil moisture, Remote Sens. Environ. 29, pp.
25-37
Cross, A.M., D.C. Mason and S.l. Dury (1988). Segmentation of remotely-sensed images by
a split-and-merge process. IJRS 9(8), pp. 1329-1345
Disperati L., G. Righini, M. Bocci, P.L. Pantozzi, A.P. Fiori, S. Kozciac and A.C. Paranhos
Filho (1998). Land cover changes in the Rio Verdo de Mato Grasso region (Pantanal, Mato
Grasso do SuI, Brasil) as detected through remote sensing and GIS analysis. Technical
report, Univ. of Siena (I)
Dobson, M.C., ET. Ullaby and L.E. Pierce (1995). Land-cover classification and estimation
of terrain attributes using synthetic aperture radar. Remote Sens. Environ. 51: 199-214
Duda, R.O. and P.E. Hart (1973). Pattern classification and scene analysis. John Wiley and
Sons, New York, 465 pp.
Engheta, N. and C. Elachi (1982). Radar scattering from a diffuse vegetation layer over a
smooth surface, IEEE Trans. Geosci. Remote Sens. 33: 848-857