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Articles | Volume XXXIX-B8
https://doi.org/10.5194/isprsarchives-XXXIX-B8-507-2012
https://doi.org/10.5194/isprsarchives-XXXIX-B8-507-2012
30 Jul 2012
 | 30 Jul 2012

HIGH TEMPORAL FREQUENCY BIOPHYSICAL AND STRUCTURAL VEGETATION INFORMATION FROM MULTIPLE REMOTE SENSING SENSORS CAN SUPPORT MODELLING OF EVENT BASED HILLSLOPE EROSION IN QUEENSLAND

B. Schoettker, R. Searle, M. Schmidt, and S. Phinn

Keywords: vegetation, dynamic, multisensor, erosion modelling, MODIS, terrestrial, management.

Abstract. This study demonstrates the potential applicability of high temporal frequency information on the biophysical condition of the vegetation from a time series of the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from 2000 to 2006 (collection 4; 8-day composites in 1 km spatial resolution) to improve modelling of soil loss in a tropical, semi-arid catchment in Queensland.

Combining the biophysical information from the MODIS FPAR with structural vegetation information from the Geoscience Laser Altimeter System on the Ice, Cloud, and land Elevation Satellite (ICESat) for six vegetation structural categories identified from a Landsat Thematic Mapper 5 (TM) and Enhanced Thematic Mapper 7 (ETM+) woody foliage projective cover product representing floristically and structurally homogeneous areas, dynamic vegetative cover factor (vCf) estimates were calculated. The dynamic vCf were determined in accordance with standard calculation methods used in erosion models worldwide. Time series of dynamic vCf were integrated into a regionally improved version of the Universal Soil Loss Equation (USLE) to predict daily soil losses for the study area. Resulting time series of daily soil loss predictions averaged over the study area coincided well with measures of total suspended solids (TSS) (mg/l) at a gauge at the outlet of the catchment for three wet seasons (R2 of 0.96 for a TSS-event). By integrating the dynamic vCf into modified USLE, the strength of the dependence of daily soil loss predictions to the only other dynamic factor in the equation – daily rainfall erosivity – was reduced.