The first step in a RPM mapping project is to define the mapping context that include the following:

• Mapping focus: bedrock, surficial

• Nature of geologic terrane: sedimentary, igneous, metamorphic, tectonic setting

• Surficial conditions: degree of exposure, physiography

• Data availability: quantity and quality

The specific mapping project will determine the data that will be most useful for mapping purposes. For example, in well-exposed terrains, bedrock-mapping projects that have a thin residual till cover will benefit from the interpretation of magnetic, gamma-ray spectrometry, optical and radar-image data. In areas where sparse out-crops alternate with thick overburden, bedrock mapping will primarily benefit from the interpretation of magnetic data.

In surficial mapping, optical and radar remote-sensing techniques, together with gamma-ray spectrometry and digital terrain data, will contribute to distinguishing the various types of surficial materials, identifying and mapping geomorphic features and mapping stream-lined glacial landforms that provide information on glacial movement.

When choosing datasets for geological interpretation, geological setting and terrain physiography, in combination with the spatial and spectral resolution, penetration depth, season of image acquisition and aerial coverage of the remote sensing system (including airborne geophysics) are all important factors that need to be considered.

Much of the geoscience data provided by governments and private-sector contractors/vendors are now in digital format and can be increasingly accessed through the internet. The core data types and datasets acquired and interpreted for a RPM project are listed in Table 1 with references to a sample list of websites. Most of these datasets cover the complete Canadian landmass (Fig. 2a) with the exception of gamma-ray spectrometry data (Fig. 2b). Non-proprietary, medium- to low-resolution geophysical data including magnetic and gamma-ray data can be obtained from the Geological Survey of Canada’s Geophysical data centre. LANDSAT 7 Enhanced Thematic Mapper 180 x 180 km scenes consisting of one 60-m resolution thermal band, six 30 m multispectral bands in the visible to mid-infrared range and one 15-m panchromatic band in the visible range may be obtained, free, from the Geogratis website [http://geogratis.gc.ca/]. RADARSAT data can be obtained from the Canadian Space Agency (CSA) for government users, and MDA Geospatial Services (formerly Radarsat International) for non-government users. Digital elevation data (DEM) (CDED at 1:50,000 and /or 1:250,000 scale) can be downloaded from [http://www.geobase.ca/]. The internet providers of optical remotely sensed data often include a quick look download service which allows inspecting cloud cover of the scenes before download.

There are also a number of other specialized remote-sensing systems included in Table 1 that do not yet provide complete coverage of the Canadian land mass. Optical sensors including ASTER, SPOT, IKONOS, QUICKBIRD and airborne hyperspectral sensors can provide a wealth of geological information but these data are not available for all of Canada. However, if available, their use should be considered because they offer imagery at either higher spectral resolution (ASTER, 14 spectral bands) or higher spatial resolution (SPOT 5, IKONOS, QUICKBIRD). The higher spatial resolution of the latter sensor systems with 4.0 to 2.4 m multispectral and 1.0 to 0.40 m panchromatic data acquisition is not only useful for mapping and logistical planning but also as a navigational guide in hand-held field computers.

Existing field and laboratory data and published geological maps can be integrated into the RPM process to guide, calibrate and test interpretations. This is accomplished by overlaying the field observations (lithologic unit, strike and dip measurements) on enhanced image layers in a GIS environment to assist in geologically calibrating the image interpretation of units or structures. Field data can also be used in training computer classification algorithms. The statistical relationships between the numerical values of image data (representing spectral reflectance, magnetic-field intensity, radar backscatter, etc) and lithological units can be computed at field stations and then used to predict other areas that have similar signatures.

Geological mapping is increasingly being supported by digital field data capture technology using hand-held computers and GPS. This is a revolutionary development with respect to RPM because it allows validating remote predictive maps on the out-crop. Simultaneous display of remote predictive maps and GPS position in real time, may lead the field geologist to make small deviations from planned traverses to inspect subtle anomalous patterns that appear to be geologically significant, when analyzed in context of the immediate surroundings of an outcrop. This may apply, for example, to confirming the presence of a dike, when short wavelength linear magnetic anomalies from near surface magnetic bodies appear to be in close proximity to the field site.

With respect to the interpretation of satellite image data (i.e. LAND-SAT, ASTER, SPOT, RADARSAT), sufficient time (commonly 6 to 12 months) should be set aside for the acquisition of new data before field mapping to allow processing and interpretation of the data. Also any new airborne geophysical surveys should be planned well in advance so that the data may be flown, processed and interpreted prior to field mapping. For optical and remotely sensed radar data, a narrow window lasting typically from mid-June to mid-September exists for acquiring new data in the North. Similarly, a narrow window also exists for the collection of airborne gamma-ray spectrometry data because any snow cover will absorb the gamma-ray emission. Airborne magnetic data, however, can be acquired through all the seasons. Provided that weather and terrain conditions are favourable, it is recommended that gamma-ray spectrometry data be collected in parallel with magnetic data as this will provide a geometrically consistent airborne survey consisting of magnetic, total-count, potassium, uranium, thorium and topo-graphic elevation channels providing complementary geological information at reduced survey costs.

Dependent on the type of datasets acquired, various pre-processing steps may be required that have not been carried out by the data provider. In the analysis of optical remotely sensed data, for example, atmospheric corrections are necessary to compare multi- and hyperspectral remotely sensed signatures with field spectral measurements. This kind of pre-processing is not a trivial task and requires careful calibration routines to ensure the quality and integrity of the data is preserved. If this is not done properly then the analytical results obtained in a RPM project will be prone to error.

The data acquired for RPM may have different georeferences (map datum and projection) although most spatial data warehouses in Canada now adhere to the NAD83 datum and UTM projection for the mapping scales typically employed in RPM (1 : 50 000 to 1 : 500 000 scales). Nevertheless, if legacy data in NAD27, province or larger scale map data with different map projections, GIS datum and projection transformations have to be employed to assure the appropriate georegistration of the RPM datasets. After the necessary datum and map projection transformations have been applied, it is good practice to make an assessment of misregistration between datasets by comparing the discrepancies between the landmarks (in Canada usually characteristic shapes along lake shores or river intersections) on various map and image layers due to non-systematic digitizing and/or registration errors.

A wide range of processing and enhancement methods can be used to facilitate extraction of geological information from RPM datasets (Table 2). Examples of enhanced image data (mainly from Canada’s North) are shown here (see Figs. 3 to 9). Generally, the methods employed depend on the data type to be enhanced. Derivatives of potential-field data include: vertical derivatives, upward continuation, analytic signal, magnetic susceptibility, and pseudogravity among others. Grids of measured magnetic and gravity data as well as their derivatives are enhanced by applying contrast enhancement and relief shading algorithms or both in combination (Milligan and Gunn 1997). Spatial convolution filters and colour enhancement techniques, such as decorrelation stretch (Gillespie et al. 1986) and saturation enhancement (Kruse and Raines, 1994) apply to enhancement of optical remotely sensed, multibeam radar and gamma-ray spectrometry data while band ratios or pairwise principal component analysis (Richards and Jia 2006; Jolliffe 2004; Jensen 1986) are useful to enhance geological information on multispectral or multibeam radar imagery. Most of these enhancements are generated semi-automatically using computer algorithms available with GIS and/or image-analysis systems. User input, however, is always important to fine tune the enhancement because this is guided by insight on how the dynamic range and spatial frequency distribution of the imaged physical properties are associated to geology.

In addition to enhancement of individual data types, image fusion (Harris et al. 1999) combines image data into single images to highlight features of interest and analyze complementary geological information. Figure 3a shows an image fusion example in which gamma-ray spectrometry data have been combined with shaded-relief magnetic and topographic data. The gamma-ray data show three major geologic divisions: 1) the northwest portion of the area has variable radiometric signatures and magnetic response reflecting older pelitic and silicate gneisses interspersed with younger pegmatitic rocks, some of which have a distinct radiometric signatures (see Zone 1 on Fig. 3a), 2) a central zone, trending northeast, represents supracrustal and basement gneissic rocks (Penrhyn Group) of the Foxe Fold belt and, 3) a southern zone reflecting supracrustal marbles that are variably high in equivalent uranium (eU) and equivalent thorium (eTh). The linear magnetic anomaly patterns reflect tight upright folds comprising the fold belt. Rendering data as a 3-D surface is also instructive for studying the spatial relationships between different data types. For example, in Figure 3b total-field magnetic data is overlaid on a 3-D surface of the potassium (% K) gamma-ray spectrometry channel resembling a surface of topographic relief. This allows studying the spatial relationships between each dataset. Areas of anomalous response for both datasets can be seen at sites A and B (Fig. 3b) whereas areas of low response in the gamma ray and high magnetic response can be seen at site C. Figure 3c shows another 3-D surface visualization where the colour rendering is modulated by the Uranium (eU ppm) gamma-ray spectrometry channel and the "relief " is modulated by ground-based measurements of uranium. Areas of high uranium emission based on both the ground and airborne data are shown at locations A and B (Fig. 3b).

Once derivatives and enhanced data are available for geological feature extraction, two basic approaches can be employed for producing remote predictive maps (see Fig. 2). These include visual interpretation of enhanced and fused imagery and computer-assisted interpretation methods. The subsequent sections discuss these methods and demonstrate how they can be employed in remote predictive mapping.

Remote predictive maps offer field geologists an excellent opportunity to better plan ground-based traverses, saving time, money and resources (e.g. fuel) in the field. In addition, better integration of remotely sensed data into field programs, especially optical and radar imagery can provide a map of terrain units including outcrop and wetland areas (Fig. 10). The combined geological and terrain information provided by RPM, allows focusing field mapping, on 1) areas where surficial conditions (i.e. higher density of out-crop, non-wetlands, terrain accessibility etc) are more amenable for field work, or 2) areas where the geology, as determined from the predictive map, is more complex. Given this, field traverses would be located heterogeneously throughout one large mapping area over multiple years of fieldwork instead of preparing a layout of regularly distributed traverses per field season. This strategic approach may prove to be more efficient and less costly than the traditional approach, because it allows more effort to be expended on accessible areas with more variable or complex geology.

Like all predictions, remote predictive maps have an inherent degree of uncertainty (In fact all geological maps, no matter how they are produced have an inherent degree of uncertainty.). To assess the value and accuracy of a remote predictive map, it is important to understand map uncertainty and to have methods for assessing it. First, a qualitative assessment could be made by comparing the patterns of the remote predictive map with the patterns of the geological map compilation based on newly acquired field data. This should give some first order overall insight in the performance of the prediction. It should be noted, however, that discrepancies between mapped geology and the predictive maps are not necessarily interpretation errors, but could point to real geological features that are not or wrongly represented on the geological map. Such discrepancies could occur because units are generalized or poorly represented due to sparsely distributed field data or because there is no field control at all. One can also apply more systematic approaches by tallying the agreements and non-agreements between the units of the RPM and geological map at the visited field locations at various levels of map generalization. The results of this validation method can be represented in a confusion matrix (Fig. 11) in which the diagonal elements represent the number of field stations where RPM and geological maps are in agreement. In contrast, off-diagonal elements represent the number of field stations where RPM and geological maps are in conflict. The sum of the diagonal elements divided by the total number of field stations provides a measure of the overall accuracy of the prediction. In addition, the sums of rows and columns of the confusion matrix provide the "class accuracies" and "class reliabilities", respectively. This method of assessment assumes that the RPM and geological maps have the same unit legend. To generate a consistent legend, the RPM legend may have to be revised to a legend developed from data gathered in the field. Alternatively, the class names in both the RPM and geological maps could be strictly adhered to (and only field stations at which the RPM and geological maps have the exact same unit names will be considered correctly classified). This is likely to result in very low prediction accuracies that do not reflect the spatial agreement between the units of the RPM and geological maps. The discrepancies between the legends obtained from RPM and geological maps, as a result of the incongruence between field and image diagnostics, is an urgent issue on the RPM research agenda. Recent work in geography suggests that quantitative measures of semantic class similarity are feasible (e.g. Ahlqvist and Gahegan 2005). Such measures may be used to improve on the accuracy assessment methods for RPM as well.