CiteULike: neteler dynamic

Seasonality and the dynamics of infectious diseases

Sonia Altizer — 2006-03-30T14:33:32-00:00

Ecology Letters, Vol. 9, No. 4. (April 2006), pp. 467-484.

Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI

Pieter Beck — 2006-01-29T15:50:05-00:00

Remote Sensing of Environment, Vol. 100, No. 3. (15 February 2006), pp. 321-334.

Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35 x 162 km2, 68[deg] N 23[deg] E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.

Dynamic environmental modelling in GIS: 2. Modelling error propagation

D Karssenberg — 2005-07-04T12:47:13-00:00

International Journal of Geographical Information Science, Vol. 19, No. 6. (July 2005), pp. 623-637.

Environmental modelling languages provide the possibility to construct models in two or three spatial dimensions. These models can be static models, without a time component, or dynamic models. Dynamic models are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a period or time step preceding t . Since inputs and parameters of environmental models are associated with errors, environmental modelling languages need to provide techniques to calculate how these errors propagate to the output(s) of the model. Since these techniques are not yet available, the paper describes concepts for extending an environmental-modelling language with functionality for error-propagation modelling. The approach models errors in inputs and parameters as stochastic variables, while the error in the model outputs is approximated with a Monte Carlo simulation. A modelling language is proposed which combines standard functions in a structured script (program) for building environmental models, and calculation of error propagation in these models. A prototype implementation of the language is used in three example models to illustrate the concepts.

Dynamic environmental modelling in GIS: 1. Modelling in three spatial dimensions

D Karssenberg — 2005-06-17T07:37:45-00:00

International Journal of Geographical Information Science, Vol. 19, No. 5. (May 2005), pp. 559-579.

Environmental modelling languages are programming languages developed for building computer models simulating environmental processes. They come with database and visualization routines for the data used in the models. Environmental modelling languages provide the possibility to construct dynamic models, also called forward models, which are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a time step preceding t. Nowadays, these modelling languages can deal with simulations in two spatial dimensions, but existing software does not support the construction of models in three dimensions. We describe concepts of an environmental modelling language supporting dynamic model construction in two and three spatial dimensions. The lateral dimension is represented by gridded maps, with a regular discretization, while the vertical dimension is represented by an irregular discretization in voxels. Universal spatial functions are described with these entities of the modelling language as input. Dynamic modelling through time is possible by combining these functions in structured script sections, providing a section, which is executed repetitively, representing the time steps. The concepts of the language are illustrated with two example models, built with a prototype of the language.