What's New in IDRISI Taiga
What's New in IDRISI Taiga

IDRISI Taiga, released in February, 2009, is the 16th version of the IDRISI GIS and Image Processing software. The new version includes a variety of enhancements as well as a significant application for image time series analysis called Earth Trends Modeler and a suite of segment-based classification tools.

Download the What's New in IDRISI Taiga Brochure (PDF) for more details on added and enhanced functionality.

Earth Trends Modeler

The Earth Trends Modeler (ETM) is specially designed for the analysis and exploration of trends in image time series. It includes a coordinated suite of data mining tools for the extraction of trends and underlying determinants of variability. These tools are of special importance to scientists focused on climate change and ecosystem dynamics.

  • Fourier-PCA analysis to explore for the presence of cycles in a time series

  • Tools to explore profiles of a series over time.

  • A newly developed procedure for Seasonal Trend Analysis to examine trends in seasonality, such as phenological change in plant species.

  • A linear modeling (multiple regression) tool to examine relationships between series.

  • A variety of techniques for trend analysis.

  • Principal Components Analysis (also known as Empirical Orthogonal Function Analysis) for the decomposition of a series into its underlying constituents.

  • Empirical Orthogonal Teleconnection (EOT) method to uncover characteristic patterns of variability over space-time.

Segmentation and Segment-Based Classification

  • A new module for the creation of image segments, homogeneous pixels with spectral similarity.

  • New modules to develop training sites and signatures and classify the imagery utilizing a majority rule algorithm.

Land Change Modeler Enhancements

  • An interface to MARXAN that facilitates its use. MARXAN is a widely used conservation planning tool for reserve selection and design.

  • A new tool for validation, allowing the user to determine the quality of the prediction land use map in relation to a map of reality. A 3-way crosstabulation can be run between the later landcover map, the prediction map, and a map of reality in order to evaluate the result of a possible prediction outcome.

Display Navigation Tools    

  • Enhancements to the pan and zoom in/zoom out functions.

  • Revisions to the stretch options on Composer.

Other Features     

  • An extension of the Multi-Layer Perceptron neural network classifier to support multiple regression applications, an ISODATA unsupervised classification procedure, and some additional time series utilities.

  • A wide range of new import procedures. Many of these are designed to support the varied earth observing system image time series, including NetCDF (a format popular with the climatological community) among others.

  • Support for the creation of KML layers, both as single layers and as pyramid structures for streaming over the Internet.

  • An interface to the open source GDAL raster translation software.


Explore Trends with Earth Trends Modeler in IDRISI Taiga

An example of temporal profiling (of NDVI anomalies in southeast Massachusetts) followed by subsequent analysis of its relationship with global sea surface temperatures using the linear modeling tool.

Image Segmentation Analysis using Aerial Imagery (.5 m) with IDRISI Taiga

The SEGMENTATION module creates an image of segments that have spectral similarity across many input bands. The image on the left uses a larger similarity threshold than the one on the right, resulting in more generalized, less homogeneous segments. Using this threshold, the image allows for segments that wholly contain building objects.

Land Change Model Validation with Land Change Modeler

Validation allows you to assess the quality of your prediction model. In this example, a model was developed to predict forest cover loss to 2004 based on historical patterns. We predicted from a known state in 2001 to 2004 and validated the prediction map to a known state in 2004. The validation map shows the hits (green), misses (red), and false alarms (yellow) of our model.   

Why Taiga?     

Taiga is the name of the world’s largest biome – a vast circumpolar region south of the tundra zone in the northern hemisphere. Also known as the Boreal Forest, the Taiga is predominantly covered by coniferous forest, and commonly marked with poorly drained glacial depressions that form bogs (muskeg). We chose the name Taiga for Release 16 of the IDRISI system because it is emblematic of the risk we are now facing from climate change. Present trends exhibit a rate of temperature increase that exceeds the ability of the forest to adapt by relocation. The Taiga is thus on the frontline of the impact of climate change.

A major scientific and political problem in the climate change debate has been the lack of reliable observational data that could allay the criticisms of skeptics and opportunists. That era is now over. We are witnessing an explosion of earth observation data of an unprecedented quality. However, these data don’t reveal their gold easily. There are numerous distractions and contaminants such as clouds and unwanted sources of noise and variability.

With this release, we introduce the Earth Trends Modeler – the culmination of three years of intense research and development funded, in part, by the Gordon and Betty Moore Foundation and Google.org. The Earth Trends Modeler, like the previously developed Land Change Modeler, is another vertical application within IDRISI also directed to the major issues of human/environment relations. The Earth Trends Modeler is focused on the dynamics of the earth system and the problem of extracting information about its nature and evolution. It therefore lies at the forefront of what might be called Earth System Information Science.

This version is dedicated to the world-wide community of visionary earth system and remote sensing scientists who conceived of and developed the extraordinary constellation of satellite platforms, instruments and primary processing algorithms that now form our new earth observing system. Our task now is to build upon that base.

Source: http://www.clarklabs.org