Malleefowl is listed as one of the vulnerable birds in Australia. To track the pattern of presence and abundance of Malleefowl, surveying the egg-incubator (a.k.a. nest or mound) is an extensively used technique. However, on large conservation areas, Malleefowl mound detection by manually inspection on land or from air is challenging for various environmental and technical reasons. Usually, mounds are built on the ground, and they are widely scattered over the large areas. Hence, in recent years, airborne Light Detection and Ranging (LiDAR) techniques have been used for data acquisition and analysis. However, such existing methods are still limited in terms of detection accuracy and system automation. In this paper, we propose a novel method to address these limitations. We have designed robust features which effectively represent the key visual characteristics of candidate mounds captured in LiDAR point cloud data. These features include: (1) differences of elevation between original ground points and the corresponding feet of these ground points fitted plane, and (2) convex-hull measurements. Using these features, we then use machine learning methods, i.e., clustering to differentiate the true mounds among the candidate mounds, and bagged-tree classifier to learn a model for classifying whether a patch contains a mound or not. Our training and testing datasets contain LiDAR point cloud data captured from the Tarawi Nature Reserve, and are provided by the New South Wales Government Department of Planning and Environment, Australia. They comprise a total of 1,060 patches (each 20 m×20 m) - half of which contain mounds, and the remaining half contain no mound. Our experimental results show that our proposed method has more than 84% accuracy in detecting patches with mounds