Combined use of vegetation and water indices from remotely-sensed AVIRIS and MODIS data to monitor riparian and semiarid vegetation.

摘要

The objectives of dissertation were to examine vegetation and water indices from AVIRIS and MODIS data for monitoring semiarid and upland vegetation communities related with moisture condition and their spatial and temporal dependencies in estimating evapotranspiration (ET). The performance of various water indices, including the normalized difference water index (NDWI) and land surface water index (LSWI), with the chlorophyll-based vegetation indices (VIs), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) was evaluated in (1) investigating sensitivity of vegetation and land surface moisture condition, (2) finding optimal indices in detecting seasonal variations in vegetation water status at the landscape level, and (3) their spatial and temporal scale dependency on estimating ET. The analyses were accomplished through field radiometric measurement, airborne-based and satellite data processing accompanied with water flux data.; The results of these studies showed vegetation and landscape moisture condition could be identified in VI--WI scatter-plot. LSWI (2100) showed the biggest sensitivity to variation of vegetation and background soil moisture condition as well. Multi-temporal MODIS data analysis was able to show water use characteristic of riparian vegetation and upland vegetation. Results showed water use characteristics of riparian vegetation are relatively insensitive to summer monsoon pulse, while upland vegetation is highly tied to summer monsoon rain. The relationship between water flux measurement from eddy covariance tower and satellite data has shown that MODIS derived EVI and LSWI (2100) have similar merit to estimate ET rate, but better correlation was observed from the relationship between MODIS EVI and ET.; Pixel aggregation results using fine resolution AVIRIS data showed moderate resolution spatial scale 250m or 500m, best predicted ET rates over all study areas. Surface fluxes temporally aggregated to weekly or biweekly intervals showed the strongest ET versus EVI relationships. ET measured at flux towers can be scaled over heterogeneous vegetation associations by simple statistical methods that use meteorological data and flux tower data as ground input, and using the MODIS Enhanced Vegetation Index (EVI) as the only source of remote sensing data.