ABSTRACT. Although satellite imagery is becoming a basic component of the work of ecologists and conservationists, its potential and reliability are still relatively unknown for a large number of ecosystems. Using Landsat 7/ETM+ (Enhanced Thematic Mapper Plus) data, we tested the accuracy of two types of supervised classifications for mapping 13 peatland habitats in southern Quebec, Canada. Before classifying peatland habitats, we applied a mask procedure that revealed 629 peatlands covering a total of 18,103 ha; 26% of them were larger than 20 ha. We applied both a simple maximum likelihood (ML) function and a weighted maximum likelihood (WML) function that took into account the proportion of each habitat class within each peatland when classifying the habitats on the image. By validating 626 Global Positioning System locations within 92 peatlands, we showed that both classification procedures provided an accurate representation of the 13 peatland habitat classes. For all habitat classes except lawn with pools, the predominant classified habitat within 45 m of the center of the validation location was of the same type as the one observed in the field. There were differences in the performance of the two classification procedures: ML was a better tool for mapping rare habitats, whereas WML favored the most common habitats. Based on ordinations, peatland habitat classes were as effective as environmental variables such as humidity indicators and water chemistry components at explaining the distribution of plant species and performed 1.6 times better when it came to accounting for vegetation structure patterns. Peatland habitats with pools had the most distinct plant assemblages, and the habitats dominated by herbs were moderately distinct from those characterized by ericaceous shrubs. Habitats dominated by herbs were the most variable in terms of plant species assemblages. Because peatlands are economically valuable wetlands, the maps resulting from the new classification procedure presented here will provide useful information for land managers and conservationists.

The practical application of yield loss prediction models using relative leaf area of weeds is limited due to the lack of a quick and accurate method of leaf area estimation. Leaf cover (the vertical projection of plant canopy on the ground) can be used to approximate leaf area at early stages of plant development. An automated digital image analysis system for measuring leaf cover has been developed. The system has an operator-assisted module aimed at validating the automated functions. The objective of this research was to demonstrate the accuracy of the operator-assisted module under different weed–crop conditions. A laboratory experiment was conducted using simulated weed–crop populations. Two additional field experiments were conducted using corn in competition with: (1) common lambsquarters, barnyardgrass, or a mixture of both species, and (2) a natural weed community. In the laboratory experiment, a narrow linear relation was observed between leaf cover estimated with the operator-assisted module and leaf area measured with an optical area meter (r2> 0.98). In field experiments, the regression between corn leaf cover estimated by the operator-assisted module and corn leaf area measured with the optical area meter was not as good (r2< 0.55). The poor performance of the module was probably due to the overlapping and the architecture of corn leaves (especially unexpanded leaves). Nevertheless, the system showed high precision in estimating leaf area of both grassy weeds and broadleaf weeds (r2> 0.89). Generally, the accuracy of the estimates decreased as the growth stage became more advanced. Apart from its initial purpose as a calibration tool for the automated system, the operator-assisted module can have several potential research applications. It can be used: (1) as an alternative to destructive leaf area measurement at early stages of plant development, (2) as a tool in the study of plant competitive ability, and (3) as an objective and quantitative support to visual observations.

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