Accession Number : ADA231692


Title :   The Effect of Remote Sensor Spatial Resolution in Monitoring U.S. Army Training Maneuver Sites


Descriptive Note : Final rept.


Corporate Author : COLORADO STATE UNIV FORT COLLINS DEPT OF FOREST AND WOOD SCIENCES


Personal Author(s) : Cunningham, Harry L.


Full Text : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA231692


Report Date : DEC 1990


Pagination or Media Count : 151


Abstract : Nine sets of remote sensor data consisting of digitized aerial photography, Aircraft MSS, spot and landsat TM were obtained over a portion of a U.S. Army training maneuver site (Pinon Canyon) in southeastern Colorado. These were processed using 19 different line and edge enhancement techniques to aid in the detection of off-road vehicular damage (Tank Trails). In addition, a classification accuracy assessment was conducted between LANDSAT TM and the airborne MSS with 6.5 meter spatial resolution. A weighted laplacian filter was the most effective and time efficient enhancement technique. Geoscan's Mk11 Airborne Mss was the most effective digital data set for enhancing tank trails. However, it only resolved 65-70% of the tank trails present in high-quality aerial photography. Therefore, high-quality aerial photography will provide the most information regarding off-road vehicular damage. LANDSAT TM classified forest cover types with a high degree of accuracy as compared to the higher resolution Airborne MSS. This is due to the high spectral variability present in a forest canopy. There was no significant difference in classification accuracy for grass cover types due to the lack of spectral variance form pixel to pixel.


Descriptors :   *SPATIAL DISTRIBUTION , AERIAL PHOTOGRAPHY , OPTIMIZATION , DETECTION , HIGH RATE , AIRCRAFT , DAMAGE , ARMY TRAINING , EDGES , SITES , RESOLUTION , ACCURACY , EFFICIENCY , HIGH RESOLUTION , TIME , VARIATIONS , SPECTRA , REMOTE DETECTORS , CLASSIFICATION , MANEUVERS , VEHICLES , OFFROAD TRAFFIC , FORESTS , TREE CANOPY , GRASSES , DATA BASES , DIGITAL SYSTEMS


Subject Categories : PERSONNEL MANAGEMENT AND LABOR RELATIONS
      MILITARY INTELLIGENCE


Distribution Statement : APPROVED FOR PUBLIC RELEASE