Sustainable biodiversity and land management require a cost-effective means of forecasting landscape response to environmental change.
We present an approach for monitoring and forecasting landscape level indicators of the condition of protected area (PA) ecosystems including changes in snowcover, vegetation phenology and productivity using the Terrestrial Observation and Prediction System (TOPS).
In this paper, it is shown how a system can be created by using methods of Artificial Intelligence, designated (a) to provide the user with information about the transformations of Mediterranean-type landscapes in an interactive way, (b) to allow the modelling of causes and effects of landscape transformations (such as land degradation) and (c) to forecast future landscape changes.
Held every decade since 1961, this conference aims to pinpoint the key concerns facing a rapidly growing demographic of older people, while forecasting the aging landscape for the next 10 years.
It also allows us to forecast the future of the landscape of the composer network, where the growths of the degrees of prominent composers are accelerating and accordingly the recordings are becoming increasingly concentrated on those already well established, an effect similar to the so-called urban-scaling laws, where existing cultural centers win out over new centers [4, 14].
As an alternative, we embedded effects of debris-flow growth into an empirical/statistical approach to forecast potential inundation areas within digital landscapes in a GIS framework.
A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients.
This estimate includes the value of energy generated, value of reduced marginal CO2 emissions, cost of visual changes to the landscape, and cost of energy generation forecast errors.
Political Transformation Programme (PTP) announced by the Prime Minister of Malaysia in 2012 is forecast to have changed the political and the local media landscape.
For open landscapes, we applied the Delphi survey method to forecast their appearances in 2020 and 2035.
