Monitoring landscape level processes using remote sensing of large plotsAuthor(s): Raymond L. Czaplewski
Source: In: Hegyi, F.; Hart, G. F.; Trinder, J. C., editors. ISPRS Commission VII Symposium - Global and Environmental Monitoring - Techniques and Impact; 17-21 September, 1990, Victoria, Canada. ISPRS Journal of Photogrammetry and Remote Sensing. 46(3): 176-177.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionGlobal and regional assessaents require timely information on landscape level status (e.g., areal extent of different ecosystems) and processes (e.g., changes in land use and land cover). To measure and understand these processes at the regional level, and model their impacts, remote sensing is often necessary. However, processing massive volumes of remotely sensing data can be infeasible if high resolution data are required for very large regions. Remote sensing of sample plots, rather than a census of the entire area, can solve certain problems. Statistical aspects of remote sensing for large plots are described, concentrating on methods needed to produce sample estimates, combine time series of ancillary estimates from other sources, calibrate for misclassification bias, and combine remotely sensed data with model predictions. These methods might improve spatial and temporal accuracy, and test our understanding of processes that are captured in landscape level models.
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Czaplewski, Raymond L. 1991. Monitoring landscape level processes using remote sensing of large plots. In: Hegyi, F.; Hart, G. F.; Trinder, J. C., editors. ISPRS Commission VII Symposium - Global and Environmental Monitoring - Techniques and Impact; 17-21 September, 1990, Victoria, Canada. ISPRS Journal of Photogrammetry and Remote Sensing. 46(3): 176-177.
KeywordsKalman filter, composite estimator, classification error, calibration, landscape models, spatial autocorrelation, spatial heterogeneity
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