Synergistic effects of combined Landsat-TM and SIR-B data for forest resources assessment

International Journal of Remote Sensing

Volumn 14, Issue 14, 1993, Pages 2677-2694

  Lozano Garcia, D F ^a,c   Hoffer, R M ^b  

^a PURDUE UNIVERSITY   (United States)

^b Department of Ecosystem Science and Sustainability   (United States)

^c SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

LOW PASS FILTERS; RADAR IMAGING; REMOTE SENSING; SATELLITE OBSERVATORIES; SIGNAL FILTERING AND PREDICTION;

LANDSAT 5 THEMATIC MAPPER; RADAR SMOOTH TARGETS; SHUTTLE IMAGING RADAR B (SIR-B); SYNERGISTIC EFFECT;

NATURAL RESOURCES;

CONTEXTUAL CLASSIFIER; FOREST RESOURCE; LAND COVER; LANDSAT TM; RESOURCE ASSESSMENT; SHUTTLE; SIR-B;

USA, FLORIDA;

EID: 0027790341     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431169308904301     Document Type: Article

References (24)

1. BLOM, R. G., and DAILY, M., 1982, Radar image processing for rock-type discrimination. I.E.E.E. Transactions on Geoscience and Remote Sensing, GE-20, 343-351.
2. BRISCO, B., ULABY, F. T., and DOBSON, M. C., 1983, Spaceborne SAR data for land cover classification and change detection. Digest of International Geoscience and Remote Sensing Symposium, 1, 1.1-1.8.
3. CIMINO, J., ELACHI, C., and SETTLE, M., 1986, The Shuttle Imaging radar experiment. I.E.E.E. Transactions on Geoscience and Remote Sensing, GE-24, 445-452.
4. DAILY, M. T., FARR, T., ELACHI, C., and SCHABER, G., 1979, Geologic interpretation from composite radar and Landsat imagery. Photogrammetric Engineering and Remote Sensing, 45, 1109-1116.
5. EVANS, D. L., 1986, Geological applications of multi-polarised SAR data. Proceedings of the 2nd Spaceborne Imaging Radar Symposium Jet Propulsion Laboratory (Pasadena, CA: JPL publication), 86-26, pp. 36-41
6. FLEMING, M. D., BERKEBILE, J. S., and HOFFER, R. M., 1975, Computer-aided analysis of Landsat-1 MSS data: a comparison of three approaches including a ‘Modified Clustering’ approach. LARS-Purdue University, LARS Information Note 072475, West Lafayette, Indiana.
7. FUNG, A. K., and ULABY, F. T., 1983, Matter-energy interactions in the microwave region. In Manual of Remote Sensing, 2nd edition, edited by R. N. Colwell (Falls Church, VA: American Society of Photogrammetry), pp. 115-164.
8. GUINDON, B., HARRIS, J. W. E., TEILLET, P. M., GOODENOUGH, D. G., and MEUNIER, J.-F., 1980, Integration of MSS and SAR data of forested regions in mountainous terrain. Proceedings of Nth International Symposium on Remote Sensing of Environment (Ann Arbor, E.R.I.M.) Ill, pp. 1673-1690.
9. HENNINGER, D. L., and CORNEY, J. H., 1983, Shuttle imaging Radara-A (SIR-A) data as a complement to Landsat multi-spectral scanner (MSS) data. Digest of InternationaI Geoscience and Remote Sensing Symposium, 111, 7.1-7.6.
10. HOWELL, D. A., HOUSTON, T. B., ALLEN, W. J., GENTCR, E., WEATHERSPOON, R. L., and BRACY, K. C., 1984, Soil survey of Columbia County, Florida. U.S. Department of Agriculture, Soil Conservation Service, Tallahassee, Florida.
11. RETTING, R. L., and LANDGREBE, D. L., 1976, Classification of multi-spectral image data by extraction and classification of homogeneous objects. I.E.E.E. Transactions on Geoscience and Electronics, GE-14, 19-25.
12. KNOWLTON, D. J., and HOFFER, R. M., 1983, Computer analysis of X-band radar data. Proceedings of 9th International Symposium on Machine Processing of Remotely-Sensed Data (West Lafayette, IN: LARS, Purdue University), pp. 119-127.
13. Li, R. Y., ULABY, F. T., and EYTON, J. R., 1990, Crop classification with Landsat/Radar sensor combination. Proceedings of International Symposium on Machine Processing of Remotely-Sensed Data (West Lafayette, IN: LARS, Purdue University), pp. 78-87.
14. MUELLER, P. W., 1987, Spatial filtering of Shuttle Imaging Radar-B data. LARS-Purdue University, LARS Technical Report 091487, West Lafayette, Indiana.
15. MUELLER, P. W., and HOFFER, R. M., 1989, Low pass spatial filtering of satellite radar data. Photogrammetric Engineering and Remote Sensing, 55, 887-895.
16. MYERS, N., 1981, Conversion rates in tropical moist forest: review of recent survey. In Tropical Forest: Utilization and Conservation, edited by F. Mengen (New Haven, CT: Yale University Press), pp. 48-66.
17. NATIONAL RESEARCH COUNCIL, 1981, Research Priorities in Tropical Biology (Washington, D.C.; National Academy of Sciences).
18. ROSENTHAL, W. D., 1981, Development of Visible/Infrared/Microwave Agricultural Classification and Biomass Algorithms. Ph.D. Thesis, Kansas State University.
19. SWAIN, P. H., 1978, Fundamentals of pattern recognition in remote sensing. In Remote Sensing; The Quantitative Approach, edited by P. H. Swain and S. M. Davis (New York: McGraw-Hill), pp. 136-187.
20. TALBOT, J. J., 1981, What’s happening to the World’s Forest Resources. Proceedings of 7th International Symposium on Machine Processing of Remotely-Sensed Data (West Lafayette, IN: LARS, Purdue University), pp. 587-592.
21. TOLL, D., 1985, Analysis of digital Landsat-MSS and Seasat SAR data for use in discriminating land cover at the urban fringe of Denver, CO. International Journal of Remote Sensing, 6, 1209-1229.
22. ULABY, F. T., 1982, Radar signatures of terrain: useful monitors of renewable resources. Proceedings of I.E.E.E., 70, 1410-1428.
23. ULABY, F. T., Li, R. Y., and SHANMUGAN, K. S., 1982, Crop classification using airbourne radar and Landsat data. I.E.E.E. Transactions on Geoscience and Remote Sensing, GE-20, 42-51.
24. WORLD RESOURCES INSTITUTE, 1986, World Resources 1986 (New York: Basic Books Inc.).