You are here:
IMPACTS OF VEGETATION DYNAMICS ON THE IDENTIFICATION OF LAND COVER CHANGE IN A BIOLOGICALLY COMPLEX COMMUNITY IN NORTH CAROLINA, USA
Citation:
Lunetta, R S., J. Ediriwickrema, D. M. Johnson, J G. Lyon, AND A. McKerrow. IMPACTS OF VEGETATION DYNAMICS ON THE IDENTIFICATION OF LAND COVER CHANGE IN A BIOLOGICALLY COMPLEX COMMUNITY IN NORTH CAROLINA, USA. REMOTE SENSING OF ENVIRONMENT 82(2/3):258-270, (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:
A land-cover (LC) change detection experiment was performed in the biologically complex landscape of the Neuse Rive Basin (NRB), NC using Landsat 5 and 7 imagery collected in May of 1993 and 2000. Methods included pixel-wise Normalized Difference Vegetation Index (NDVI) and Multiband Image Difference (MID) techniques. The NDVI method utilized non-normalized (raw) imagery data, while the MID) method required normalized imagery. Image normalization techniques included both Automatic Scattergram-Controlled Regression (ASCR) and Localized Relative Radiometric Normalization(LRRN) techniques. Change/no-change thresholds for each method were optimized using calibration curves developed from reference data and a series of method specific binary change masks. Cover class specific thresholds were derived fro each of the four methods using a previously developed NRB-LC classification (1998-1999) to support data stratification. An independent set of accuracy assessment points was selected using a disproportionate stratified sampling strategy to support the develpment of error matrices. Area weighted conditional probability accuracy statistics were calculated based on the areal extent of change and no change for each cover class. All methods tested exhibited acceptable accuracies, ranging between 84% and 92%. However, change omission errors for woody cover types were unacceptably high for all methods, with values ranging between 60% and 79%. Overall commission errors in the change category were high as well (42% to 51%) and strongly affected by the agriculture class. There were no significant differences in overall Kappa coefficient between the NDVI, MID ASCR AND LRRN normalization methods. The MID non-normalized method was inferior to both the NDVI and MID ASCR methods. Stratification by major LC type had no effect on overall accuracies, regardless of method.