Remote imagery

Remote imagery and aerial photographs prove to be invaluable during the planning and reconnaissance stages of certain projects. The information they provide can be transposed to a base map, which is checked during fieldwork. The data also can be used in geographical information systems. 

Examples of semi-quantitative assessments include interpretive maps and informational matrices which could be utilized to relate land-use, topography and erosion or other non-point source problems. Remote sensing imagery from low, intermediate and high altitude are applicable to this approach (29). 

Huete A, Tucker C. Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. Int. I. Remote Sens. 1991 12 1223-1242. 

Wilkie, D.S. and J.T. Finn Remote Sensing Imagery for Natural Resources Monitoring A Guide for First-Time Users, Columbia University Press, New York, NY, 1996. 

Melack, J. M., S. J. Sippel, D. M. Valeriano, and T. R. Fisher. 1992. Environmental conditions and change on the Amazon floodplain analysis with remotely sensed imagery, pp 377-387. 24th International Symposium on Renxrte Sensing of the Environment. ERIM, Ann Arbor, Michigan. 

Bricaud A. and Morel A. (1987) Atmospheric corrections and interpretation of marine radiances in CZCS imagery use of a reflectance model. In Oceanography From Space Proceedings Of The Atp Symposium On Remote Sensing, Brest, France vol. 7, pp. 33-50. 

Gordon H. R. and Morel A. (1983) Remote Sensing of Ocean Color for Interpretation of Satellie Visible Imagery A Review. Springer, New York. 

Fracture Systems Identifiable from Air Photos and Remote Sensing Imagery... 

Kaolinite exhibits a very strong absorption feature centred at 2.2 pm along with a subordinate absorption feature at 2.16 pm (Fig. 7-2), forming a diagnostic doublet. This can be picked out in remote-sensing imagery and used to indicate areas emiched in kaolinite. 

Funk, C.C., Theiler, J., Roberts, D.A, and Borel, C.C., Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery, IEEE Transactions on Geoscience and Remote Sensing, 39(7), 410-1420 (2001)... 

Healey, G. and Slater, D., Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions, IEEE Transactions on Geoscience and Remote Sensing, 37(6), 2706-2717 (1999)... 

H. Kwon and N. M. Nasrabadi, Kernel RX-algorithm A nonlinear anomaly detector for hyperspeetral imagery,/EEE Trans. Geosci. Remote Sensing, 43(2) 388-397, (2005). 

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