Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
New Directions And Challenges In Spatial Dynamic Modelling Of Ecosystem Functions In Heterogeneous Landscapes As Basis For A Better Sustainable Landscape Management, Karl-Otto Wenkel, Ralf Wieland, Wilfried Mirschel
New Directions And Challenges In Spatial Dynamic Modelling Of Ecosystem Functions In Heterogeneous Landscapes As Basis For A Better Sustainable Landscape Management, Karl-Otto Wenkel, Ralf Wieland, Wilfried Mirschel
International Congress on Environmental Modelling and Software
The aim of this paper is to discuss scientific challenges and new possibilities for a better modelling of consequences of land use changes in heterogeneous landscapes on ecosystem functions in space and time. The landscape or regional scale means an area of about 100 km2 up to some 1000 km2. Main problems on this scale are high complexity, structural diversity, ecological heterogeneity and uncertainty in data, in understanding of the process dynamic and by uncertainty in models.
New Directions And Challenges In Spatial Dynamic Modelling Of Ecosystem Functions In Heterogeneous Landscapes As Basis For A Better Sustainable Landscape Management, Karl-Otto Wenkel, Ralf Wieland, Wilfried Mirschel
New Directions And Challenges In Spatial Dynamic Modelling Of Ecosystem Functions In Heterogeneous Landscapes As Basis For A Better Sustainable Landscape Management, Karl-Otto Wenkel, Ralf Wieland, Wilfried Mirschel
International Congress on Environmental Modelling and Software
The aim of this paper is to discuss scientific challenges and new possibilities for a better modelling of consequences of land use changes in heterogeneous landscapes on ecosystem functions in space and time. The landscape or regional scale means an area of about 100 km2 up to some 1000 km2. Main problems on this scale are high complexity, structural diversity, ecological heterogeneity and uncertainty in data, in understanding of the process dynamic and by uncertainty in models.