Integration of multi-seasonal remotely-sensed images for improved landuse classification of a hilly watershed using geographical information systems

References

• Adinarayana , J. , Flach , J. D. , and Collins , W. G. , 1994 , Mapping landuse patterns in a river catchment using GIS. Journal of Environmental Management , 42 , 55 – 61 .
   Google Scholar
• Bocco , G. , and Valenzuela , C. R. , 1991 , Integration of GIS and remote sensing in landuse and erosion studies. In Introduction to GIS , edited by C. R. Valenzuela, ITC Publication No. LIS-22 (Enschede: ITC), pp. 192 – 205 .
   Google Scholar
• Buckland , S. T. , 1994 , Use of groundtruth data to correct landcover area estimates from remotely sensed data. International Journal of Remote Sensing , 15 , 1273 – 1282 .
   Web of Science ®Google Scholar
• Chibula , W. G. , and Nyquist , M. O. , 1987 , Use of topographic and climatological models in a geographical database to improve Landsat MSS classification for Olympic National Park. Photogrammetric Engineering and Remote Sensing , 53 , 67 – 75 .
   Google Scholar
• Corves , C. , and Place , C J. , 1994 , Mapping the reliability of satellite-derived landcover maps— an example from the Central Amazon Basin. International Journal of Remote Sensing , 15 , 1283 – 1294 .
   Web of Science ®Google Scholar
• Duane , N. , Kamlesh Lulla and Jensen , J. , 1990 , Interfacing GIS and remote sensing for rural land-use analysis. Photogrammetric Engineering and Remote Sensing , 56 , 329 – 331 .
   Google Scholar
• Jezching , T. , Jon , S. , and Jain , A. K. , 1991 , Knowledge-based segmentation of Landsat images. I.E.E.E. Transactions on Geoscience and Remote Sensing , 29 , 222 – 232 .
   Google Scholar
• Johnsson , K. , 1994 , Segment-based landuse classification from SPOT satellite data. Photogrammetric Engineering and Remote Sensing , 60 , 47 – 53 .
   Web of Science ®Google Scholar
• Kimes , D. S. , Harrison , P. R. , and Ratcliffe , P. A. , 1991 , A Knowledge-based expert syste for inferring vegetation characteristics. International Journal of Remote Sensing , 12 , 1987 – 2020 .
   Web of Science ®Google Scholar
• Parachini , M. L. , and Folving , S. , 1994 , Land use classification and regional planning inVal Malenco (Italian Alps): a study on the integration of remotely sensed data and digital terrain models for thematic mapping. In Mountain Environments and GIS , edited by M. F. Price and D. I. Heywood London : Taylor & Francis, pp. 59 – 75 .
   Google Scholar
• Skidmore , A. K. , Ryan , P. J. , Dawes , W. , Short , D. and O'Loughlin , E. , 1991 , Use of expert system to map forest soils from a GIS. International Journal of GIS , 5 , 431 – 445 .
   Web of Science ®Google Scholar
• Soil Survey Staff, 1975, Soil Taxonomy—A basic system of soil classification for making and interpreting soil survey. Soil Conservation Service, United States of AmericaDepartment of Agriculture Handbook No. 436 , Washington , D.C.
   Google Scholar
• Strahler , A. H. , 1980 , Use of prior probabilities in maximum likelihood classification of remotely-sensed data. Remote Sensing of Environment , 10 , 135 – 163 .
   Web of Science ®Google Scholar
• Venkatachalam , P. , 1993 , GRAM. A PC-based Geographical Information Systems package. Lecture series of a UNESCO-sponsored course on GIS for Natural Resources Management, 9 October-12 November 1993, Indian Institute of Technology, Bombay .
   Google Scholar