Using high-definition aerial photography to search in 3D for malleefowl mounds is a cost-effective alternative to ground searches

Author:

Thompson Scott,Thompson Graham,Sackmann Jessica,Spark Julia,Brown Tristan

Abstract

The threatened malleefowl (Leipoa ocellata) constructs a large (often >3 m) incubator mound (nest) that is considered a useful proxy for surveying its presence and abundance in the context of an environmental impact assessment. Here we report on the effectiveness and relative cost of using high-definition aerial photography to search in 3D for malleefowl mounds by comparing results to those of earlier ground-based searches. High-definition colour aerial photography was taken of an area of ~7014 ha and searched in 3D for malleefowl mounds. All 24 active (i.e. in use) malleefowl mounds known before the examination of aerial photography were detected using the new assessment technique. Of the 108 total mounds (active and inactive) known from earlier on-ground surveys, 94 (87%) were recorded using the new technique. Mounds not detected were all old and weathered, many barely above ground level and some with vegetation growing in the crater. Approximately 6.3% of the identifications considered ‘confident’ and ~35.0% considered ‘potential’ based on the aerial photography proved to be false positives. The cost of detecting malleefowl mounds using the interpretation of high-definition 3D colour aerial photography and then subsequently examining these areas on the ground is appreciably cheaper than on-ground grid searches.

Publisher

CSIRO Publishing

Subject

Nature and Landscape Conservation,Ecology

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automatic Malleefowl Mound Detection Using LiDAR-based Ground and Habitat Features with Planar Terrain Modelling;2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW);2024-07-15

2. Automatic Malleefowl Mound Detection using Robust LiDAR-based Features and Classification;2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA);2022-11-30

3. Detection of Malleefowl Mounds from Point Cloud Data;2021 Digital Image Computing: Techniques and Applications (DICTA);2021-11

4. Adaptive management informs conservation and monitoring of Australia's threatened malleefowl;Biological Conservation;2019-05

5. Semi-automated detection of eagle nests: an application of very high-resolution image data and advanced image analyses to wildlife surveys;Remote Sensing in Ecology and Conservation;2017-01-19

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