Examining the distribution of Dicymbe corymbosa monodominant forests in western Guyana using satellite imagery

The ectomycorrhizal canopy tree Dicymbe corymbosa (Caesalpiniaceae) forms single-species dominated (i.e. monodominant) forests in the central Pakaraima Mountains of western Guyana. Dicymbe corymbosa, like other tropical monodominant species, has important life history traits that promote conspecific clumping, the study of which may allow a better understanding of the phenomenon of high tree species diversity characterizing much of the tropics, and how it is circumvented. Dicymbe corymbosa forests, occurring in Guyana as patches within a non- ectomycorrhizal, mixed-species forest matrix, function as habitat islands for a diverse assemblage of putatively endemic ectomycorrhizal fungi. Ground-based studies have not adequately determined the regional extent of D. corymbosa forests, nor are they practical. Distribution information is critical to allow broader sampling of these forests and their ectomycorrhizal fungal constituents and ultimately to inform conservation plans for these unique habitats. The rugged, remote nature of the study site and spatially discrete occurrence of D. corymbosa stands suggest satellite image analysis as an ideal tool for determining the extent of these relatively unknown tropical forests. We assessed the suitability of Landsat satellite data for mapping regional distribution of D. corymbosa forests in Guyana's Upper Potaro River Basin. Supervised image classification (maximum-likelihood algorithm) was performed on images from 9 August 1989 (Landsat-5 TM) and 16 October 1999 (Landsat-7 ETM+). In situ forest reference data were used to quantitatively access accuracy of output classification maps. Supervised classification of Landsat-5 TM and Landsat-7 ETM+ imagery performed well in distinguishing forest types dominated by a single species from mixed-species forest. For both the 1989 and 1999 images, D. corymbosa forest class accuracy is acceptable (User's accuracy = 89.8 %, Khat = 0.74 (1989); User's accuracy = 80.7 %, Khat = 0.59 (1999). Results indicate my output classification maps will be useful in developing efficient sampling schemes for future forest ecology studies and field surveys for ectomycorrhizal fungi. Supervised classification of Landsat data may also be effective for identifying monodominant forests in other remote regions of the tropics.