Assessing 3D metric data of digital surface models for extracting archaeological data from archive stereo-aerial photographs.

Archaeological remains are under increasing threat of attrition from natural processes and the continued mechanisation of anthropogenic activities. This research analyses the ability of digital photogrammetry software to reconstruct extant, damaged, and destroyed archaeological earthworks from archive stereo-aerial photographs. Case studies of Flower's Barrow and Eggardon hillforts, both situated in Dorset, UK, are examined using a range of imagery dating from the 1940s to 2010. Specialist photogrammetric software SocetGXP® is used to extract digital surface models, and the results compared with airborne and terrestrial laser scanning data to assess their accuracy. Global summary statistics and spatial autocorrelation techniques are used to examine error scales and distributions. Extracted earthwork profiles are compared to both current and historical surveys of each study site. The results demonstrate that metric information relating to earthwork form can be successfully obtained from archival photography. In some instances, these data out-perform airborne laser scanning in the provision of digital surface models with minimal error. The role of archival photography in regaining metric data from upstanding archaeology and the consequent place for this approach to impact heritage management strategies is demonstrated

Drone‐based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. However, traditional population survey methods can be unreliable and labour-intensive, which complicates the effective conservation and management of many threatened species. We developed a method of using drone-acquired thermal orthomosaics to monitor the abundance of grey-headed flying-foxes (Pteropus poliocephalus) within tree roosts, an IUCN Red Listed species of bat. We assessed the accuracy and precision of this new method and evaluated the performance of four semiautomated methods for counting flying-foxes in thermal orthomosaics, including machine learning and Computer Vision (CV) methods. We found a high concordance between the number of flying-foxes manually counted in drone-acquired thermal imagery and the true abundance of flying-foxes in single roost trees, as obtained from direct on-ground observation. This indicated that the number of flying-foxes observed in thermal imagery accurately reflected the true abundance of flying-foxes. In addition, for thermal orthomosaics of whole roost sites, the number of flying-foxes manually counted was highly repeatable between the same-day drone surveys and human counters, indicating that this method produced highly precise abundance estimates independent of the identity/experience of human counters. Finally, the number of flying-foxes manually counted in drone-acquired thermal orthomosaics was highly concordant with the counts derived from CV and machine learning-enabled classification techniques. This indicated that accurate and precise measures of colony abundance can be obtained semi-automatically, thus greatly reducing the amount of human effort involved for obtaining abundance estimates. Our method is thus valuable for reliably monitoring the abundance of individuals in flying-fox roosts and will aid in the conservation and management of this globally threatened group of flying-mammals, as well as other homeothermic arboreal-roosting species