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What's New Download the What's New in IDRISI Taiga brochure. Two major features of the Taiga upgrade include the Earth Trends Modeler and a suite of segment-based image classification tools. Earth Trends Modeler The Earth Trends Modeler (ETM) is specially designed for the analysis of image time series from earth observing systems such as the instruments on NASA’s Terra and Aqua satellites or the NASA/JAXA TRMM (Tropical Rainfall Measuring Mission) satellite. It includes a coordinated suite of data mining tools for the extraction of trends and underlying determinants of variability, and will be of special importance to scientists focused on climate change and ecosystem dynamics. The Earth Trends Modeler is a major addition to the IDRISI analytical system and has been created as a special extension in a manner paralleling the Land Change Modeler (LCM). Developed under a grant from the Gordon and Betty Moore Foundation, ETM provides an opening contribution on the tools that science requires to monitor our changing planet. 
An analysis of trends in sea surface temperature from 1982 to 2006. The strong monotonic trend of increasing temperature in the Atlantic is seen to be related to the Atlantic Multidecadal Oscillation (AMO) as determined from a temporal regression with four major climate teleconnection indices. The triangular wavelet analysis diagram shows the nature and scale of variations in sea surface temperature in the Labrador Sea. The animated globe shows variations in ocean height which are closely related with temperature variations.
Earth Trends Modeler allows you to:
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.
Figure 3: An illustration of several trend measures. The top image measures linearity in trends in sea surface temperature. As can be seen, the most linear trends include increases in the East and West Greenland currents and the Labrador Sea (all parts of the subpolar gyre), and the region at the mouth of the Amazon, most particularly, the Orinoco River.
Figure 4: A Seasonal Trend Analysis of vegetation conditions in Europe for the period 1982-2003 based on an analysis of vegetation index imagery from the AVHRR instrument on the NOAA Polar Orbiter satellites (shown in the space-time visualization cube). The colors represent different types of trends in the seasonal curve of vegetation photosynthesis. The graph shows vegetation photosynthetic activity (Y-axis) for each of the 12 months (X-axis) of 1982 (in green) and 2003 (in red) for the area circled in France. As can be seen, the red color that is found over most of Europe relates to increased photosynthetic activity through the winter and spring. Note that by looking at the graph, one can see that spring is coming about a month earlier in 2003 than it was in 1982.
Figure 5: A Principal Components Analysis of monthly precipitation imagery from 1979-2006 reveals the impact of the El Nino / La Nina phenomenon. The loading chart at the left shows its evolution over time. The chart at the top left shows a space-time plot of precipitation anomalies over time (vertical dimension) and all longitudes (horizontal dimension) at the equator. This is also known as a Hovmoller plot.
Figure 6: The area in the oceans (top, yellow through red) determined to have the most significant impact on growing conditions in Southern Africa (bottom, the area in red experiencing the greatest impact). This mapping results from an analytical procedure known as Empirical Orthogonal Teleconnection analysis. Information such as this can be used in the development of Early Warning Systems.
Figure 7: An illustration of the output from Fourier-PCA using the frequency plot view. This one is quite simple to understand. The component is associated with locations that show a strong presence of 25 sine wave cycles over the 25 years of the series. It thus represents the degree to which an annual cycle is present.
Figure 8: Another illustration of Fourier-PCA. Component 4 from the same analysis as that in Figure 7 showed a very difficult combination of frequencies to interpret. However, a switch to the loadings view (the graph) shows that the pattern is associated with a linear trend. The red areas in the top image thus indicate areas that are warming (a negative negative). Note that most of the world’s oceans show this effect. However, the region in the central Pacific, that is affected by the extraordinary variability of the ENSO cycle shows a residual cooling effect (ENSO appeared in Components 2 and 3 of this analysis). The image on the bottom right shows that while the pattern in the Atlantic shows some similarity to that of the Atlantic Multidecadal Oscillation, but that it is substantially different.
Figure 9: The partial correlation images for the Pacific Decadal Oscillation (top) and the Atlantic Multidecadal Oscillation (bottom) after removing the effects of the ENSO (El Nino / Southern Oscillation) and the North Atlantic Oscillation phenomena. In this analysis, climate indices for these four teleconnections were used as independent variables while monthly anomalies in sea surface temperature were the dependent variable. Each pixel is analyzed independently.
SegmentationIDRISI Taiga provides three new modules for classification from image segments. Segmentation is a process by which pixels are grouped that share a homogeneous spectral similarity. Across space and over all input bands, a moving window assesses this similarity and segments are defined according to a stated similarity threshold. The smaller the threshold, the more homogeneous the segments. A larger threshold will cause a more heterogeneous and generalized segmentation result. 
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.
The module SEGCLASS classifies the imagery using a majority rule algorithm to assign each segment to the majority class from the reference image. SEGCLASS can improve the accuracy of a pixel-based classification and produce a smoother map-like classification result while preserving the boundaries between the segments.
Land Change Modeler EnhancementsIDRISI now includes an interface to MARXAN within the Land Change Modeler application that facilitates its use. More information about MARXAN can be found at www.uq.edu.au/marxan/. MARXAN is a widely used conservation planning tool for reserve selection and design. Given a number of potential reserve sites (also called planning units) and the distribution of conservation features (such as biodiversity representation), MARXAN identifies a portfolio of sites which meet a particular target, such as minimal cost or compactness. 
Figure 12: 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.
Display Navigation ToolsEnhancements have been made to the pan and zoom in/zoom out functions. The stretch options on Composer have also been revised. Other FeaturesOther features of the new release include 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 (such as the ability to compute various statistics over time, a procedure to correlate a single pattern image with all members of a series, a completely revised PROFILE module and a utility to update the metadata for a whole series at once). 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, commonly 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 that 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. |
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