Skip all navigation and jump to content Jump to site navigation
About MODIS News Data Tools /images2 Science Team Science Team Science Team

   + Home
MODIS Publications Link
MODIS Presentations Link
MODIS Biographies Link
MODIS Science Team Meetings Link



Nour, MH, Smith, DW, El-Din, MG, Prepas, EE (2008). Effect of watershed subdivision on water-phase phosphorus modelling: An artificial neural network modelling application. JOURNAL OF ENVIRONMENTAL ENGINEERING AND SCIENCE, 7.

This study is an effort to incorporate low-cost time-variant remote sensing (RS) information in watershed-scale total phosphorus (TP) modelling. Four watershed subdivisions were delineated to assess the impact of watershed subdivision on the prediction accuracy of TP concentration in stream water. Four TP artificial neural network (ANN) models were designed to incorporate RS data into a semi-distributed approach. The remotely derived enhanced vegetation index and the normalized difference water index were successful in representing vegetation dynamics in the devised models. The models were applied to a 15.6 km(2) watershed in the Canadian Boreal Plain. Eight measures of goodness-of-fit statistics were used for model evaluation. Although statistical model evaluation did favour the finest resolution in this case study, the differences in performance indicators among the four models were insignificant for any practical application. The encouraging results from this exercise demonstrate the applicability of the ANN semi-distributed modelling approach and the usefulness of RS data in simulating TP dynamics. Such models can potentially serve as valuable tools for watershed-scale forest management.



NASA Home Page Goddard Space Flight Center Home Page