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The Brazilian Atlantic Forest is a global biodiversity hotspot and has been extensively mapped using satellite remote sensing. However, past mapping focused on overall forest cover without consideration of keystone plant resources such as <i>Araucaria angustifolia.</i> <i>A. angustifolia</i> is a critically endangered coniferous tree that is essential for supporting overall biodiversity in the Atlantic Forest. <i>A. angustifolia’s</i> distribution has declined dramatically because of overexploitation and land-use changes. Accurate detection and rapid assessments of the distribution and abundance of this species are urgently needed. We compared two approaches for mapping <i>Araucaria angustifolia</i> across two scales (stand vs. individual tree) at three study sites in Brazil. The first approach used Worldview-2 images and Random Forest in Google Earth Engine to detect <i>A. angustifolia</i> at the stand level, with an accuracy of >90% across all three study sites. The second approach relied on object identification using UAV-LiDAR and successfully mapped individual trees (producer’s/user’s accuracy = 94%/64%) at one study site. Both approaches can be employed in tandem to map remaining stands and to determine the exact location of <i>A. angustifolia</i> trees. Each approach has its own strengths and weaknesses, and we discuss their adoptability by managers to inform conservation of <i>A. angustifolia</i>.