Remotely-sensed Data for Natural Resource Models

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Abstract
Attempts to model ecosystems have increased in recent years through the application of systems theory and the improvement in computer capacity and speed. A major problem with these models is providing data for input or validation. A potential source of data is information collected by remote-sensing techniques. Remotely-sensed data can be used in natural resource simulation models to provide spatial and temporal measurements, data for model calibration or validation, and independent feedback to keep the model simulation on track with reality. Remote sensing can provide spatial and temporal measurements of many landscape parameters that could improve our ability to understand and model the spatial and temporal characteristics of landscapes. The challenge for remote-sensing scientists, landscape ecologists, and natural resource modellers, is to determine the most effective way to interpret and use the data from remote sensors in natural resource management. Natural resource models that can fully utilize the spatial data which remote-sensing techniques can provide, will almost certainly improve our understanding of landscapes and our ability to simulate and manage them wisely.

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