Airborne light detection and ranging (LiDAR) data have become cost and time-efficient means for estimating the size of timid fauna populations through the identification of artefacts that evidence their occurrence in a large, hostile geographic area. The unobtrusive detection method helps conservation managers to assess the stability of a population and to design appropriate conservation programs. Here we propose a mound (nest) detection method for Australia's native iconic bird, the Malleefowl, from point cloud data, which can be manipulated to act as a surrogate for population data. Existing detection methods are largely through manual observations, and are therefore not efficient for covering large and remote areas. The proposed mound detection method can identify mound feature based on height and intensity values provided by the point cloud data. Each candidate mound point is initially selected by applying a height threshold utilising the classified ground points and their corresponding digital elevation model (DEM). Then, another threshold based on intensity range derived from ground truth mound area analysis is applied on the extracted initial mound points to find the final candidate mound points. These extracted points are then used to generate a binary mask where the potential mound points are found sparse. To connect those points, a morphological filter is applied on the binary image and found the mound separated from other remaining non-mound objects. To obtain the mound from other non-mound objects, a morphological cleaning operation and a connected component analysis are carried out on the mask. The non-mound objects are removed from the mask utilising the area property of mound derived from the empirical analysis of ground-truth observations. Finally, the effectiveness of the proposed technique is calculated based on ground truth. Although the mound shapes and structures are highly variable in nature, our height and intensity-based mound point extraction method detected 55 % of the ground-truthed mounds.