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MULTI-TEMPORAL REMOTE SENSING ANALYTICAL APPROACHES FOR CHARACTERIZING LANDSCAPE CHANGE
Citation:
Lunetta, R S. MULTI-TEMPORAL REMOTE SENSING ANALYTICAL APPROACHES FOR CHARACTERIZING LANDSCAPE CHANGE. Presented at First International Workshop on the Anabold of Multi-Temporal Remote Sensing Images, Tremo, Italy, September 13-14, 2001.
Impact/Purpose:
Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.
Description:
Changes in landscape composition and function result from both acute land-cover conversions and chronic landscape changes. Land-cover conversions are typically mediated by human land-use activities (e.g. conversion from forest to agriculture), while more subtle chronic landscape changes can result from either natural processes (i.e., insect infestations, successional processes, and climatic changes) or human land-use activities (e.g., forest thinning). Landscape conversions are relatively rare events at regional to national scales of analysis. Over large geographic regions within the United States it has been estimated tha land-cover conversions have been estimated to occur at a rate of approximately 0.5% per annum. Numerous efforts have recently been undertaken in an attempt to document land-cover conversions using moderate resolution remote sensor data for both the United States and Mexico. However, current spectral based change detection techniques using either pre or post classification approaches have tended to be performance limited in biologically complex ecosystems, owing ( in large part) to vegetation phenology induced errors. Phenology errors are largely associated with the temporal displacement inherent with sequential satellite data collections. This temporal displacement can result in an unacceptable level of both omission (type 1) and commission (type 2) errors when the data are processed using traditional spectral based approaches Ongoing research is focusing on the development of automated land cover change detection methods based on "vegetation dynamics" to identify chaange locations and minimize errors.