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REMOTE SENSING AND SPATIALLY EXPLICIT LANDSCAPE-BASED NITROGEN MODELING METHODS DEVELOPMENT IN THE NEUSE RIVER BASIN, NC
Citation:
Lunetta, R S. AND R. G. Greene. REMOTE SENSING AND SPATIALLY EXPLICIT LANDSCAPE-BASED NITROGEN MODELING METHODS DEVELOPMENT IN THE NEUSE RIVER BASIN, NC. Presented at PECORA Symposium, Denver, CO, November 11-15, 2002.
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:
The objective of this research was to model and map the spatial patterns of excess nitrogen (N) sources across the landscape within the Neuse River Basin (NRB) of North
Carolina. The process included an initial land cover characterization effort to map landscape "patches" at multiple resolutions throughout the NRB to provide the basis for subsequent mass balance and hydrologic transport modeling. Two complete SPOT 4 (XS) data acquisitions (20 scenes) and three complete sets of Landsat 7 Enhanced Thematic Mapper Plus (fTM@) level-IG (I 2 scenes) were collected between fall 1998 and summer 1999. Classifications were performed using a hybrid approach that combined supervised, unsupervised, and rule-based classification tnhniques. The final classification included forests (deciduous, evergreen, and mixed), agricultural land (tallow, hay and pasture, corn, cotton, soybeans, and tobacco), herbaceous vegetation (natural and maintained grasses), barren land (i.e., non-vegated), wetlands (herbaceous and woody), open water, and urban. Urban areas were defused based oil percentage of impervious surfaces corresponding to. 1) low (10-35% impervious), 2) medium (36-70"/o impervious,and3) high (_>71%impervious). Cover types were mapped at a nominal landscape 'Patch" size of 0.4 ha for the general watershed areas, while locations adjacent to streams (riparian buffer zones) were rnapped at 0.1 ha. Modeling results provided estimates of excess nitrogen on a seasonal time-step to adequately characterize the annual N budget. Excess N was transported from individual landscape "patches" to receiving streams using a modified form of the Soil Conservation Service (SCS) Runoff Curve Number method. The modifications included a soil moisture accounting procedure and components for calculating subsurface runoff and percolation. Output products included both seasonal statistics quantifying N loadings by major land cover types by 14-digit Hydrologic Unit Codes (HUCS) and spatially explicit landscape- based GIS coverages of N sources and sinks. The statistical N loadings product were developed as an input perameter for stream denitrification modeling efforts, while the spatially explicit N source-sink product was designed to support the development of Best Management Practices 03MPs) to obtain N reduction goals established for the. NRB. Spatially explicit N source-sink products were processed by individual 14-digit HUC (n--l 88), and then assembled to provide seamless data that was overlaid on panchromatic Digital Orthophoto Quarter Quads (DOQQs).