The droughts since the 1970s and the widespread destruction of rainforest vegetation in South and Central America have provided dramatic illustrations of the fact that we live in a constantly changing world. Whether natural or human-induced, environmental change can have profound effects. With ever-increasing populations and the consequent need to extend agriculture into increasingly marginal environments, even fairly minor changes can have profound consequences.

Earth scientists utilize earth observation data such as image time series data to model and analyze earth trends and ecosystem dynamics. Image time series data is especially useful for global climate change research and analysis. Time series analysis is critical for exploring such global events as El Nino and related sea surface temperature anomalies and impacts.

Change is not a simple phenomenon to detect. While differences from one time to another are readily measured, the more substantial issue is that of isolating true change from normal environmental variability and artifacts of the measurement process. Additionally, the effective and efficient measurement of trends or significant departures from typical profiles in time series data sets can provide a considerable challenge to the environmental manager. Indeed, experience has shown that you need varied techniques for time series analysis.

The Earth Trends Modeler (ETM) is a GIS software solution especially designed for the analysis of image time series data. It includes a coordinated suite of data mining tools and a variety of techniques for the extraction of global trends and the impacts of climate change.

Why Use Earth Trends Modeler and IDRISI?

• IDRISI is the outcome of over 20 years of geospatial technology development.
• IDRISI is engineered by expert scientists and research practitioners.
• Earth Trends Modeler is the only software available for the full exploration of image time series data.

With Earth Trends Modeler, you can:

• Visualize variability across varying temporal scales, graphically or spatially. View animations of an image series in a space-time cube format, along with Hovmoller plots, with an animated globe or with a wavelet diagram.
• Analyze long-term trends with a variety of techniques for time series analysis, including measures of linearity, monotonicity, and trend rate. Tools include trend estimation to reduce outlier effects and trend non-parametric significance measures.
• Examine trends in seasonality, such as phenological change in plant species or any image series that exhibits a seasonal response to environmental conditions.
• Uncover characteristic patterns of variability over space and time, useful for the development of early warning systems. 
• Examine relationships between time series using multiple regression tools and measures.
• Preprocess time series data by interpolating missing data, such as cloud contamination, or deseasoning or denoising the data. Tools are provided to test for serial correlation.