An evaluation of the factors affecting ‘poacher’ detection with drones and the efficacy of machine-learning for detection

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years

Addressing environmental and atmospheric challenges for capturing high-precision thermal infrared data in the field of astro-ecology

Using thermal infrared detectors mounted on drones, and applying techniques from astrophysics, we hope to support the field of conservation ecology by creating an automated pipeline for the detection and identification of certain endangered species and poachers from thermal infrared data. We test part of our system by attempting to detect simulated poachers in the field. Whilst we find that we can detect humans hiding in the field in some types of terrain, we also find several environmental factors that prevent accurate detection, such as ambient heat from the ground, absorption of infrared emission by the atmosphere, obscuring vegetation and spurious sources from the terrain. We discuss the effect of these issues, and potential solutions which will be required for our future vision for a fully automated drone-based global conservation monitoring system

Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography

Conservation of the Sumatran orangutans’ (Pongo abelii) habitat is threatened by change in land use/land cover (LULCC), due to the logging of its native primary forest habitat, and the primary forest conversion to oil palm, rubber tree, and coffee plantations. Frequent LULCC monitoring is vital to rapid conservation interventions. Due to the costs of high-resolution satellite imagery, researchers are forced to rely on cost-free sources (e.g. Landsat), those, however, provide images at a moderate-to-low resolution (e.g. 15–250 m), permitting identification only general LULC classes, and limit the detection of small-scale deforestation or degradation. Here, we combine Landsat imagery with very high-resolution imagery obtained from an unmanned aircraft system (UAS). ​The UAS imagery was used as ‘drone truthing’ data to train image classification algorithms. Our results show that UAS data can successfully be used to help discriminate similar land-cover/use classes (oil palm plantation vs. reforestation vs. logged forest) with consistently high identification of over 75% on the generated thematic map, where the oil palm detection rate was as high as 89%. Because UAS is employed increasingly in conservation projects, this approach can be used in a large variety of them to improve land-cover classification or aid-specific mapping needs

A global risk assessment of primates under climate and land use/cover scenarios

Primates are facing an impending extinction crisis, driven by extensive habitat loss, land use change, and hunting. Climate change is an additional threat, which alone or in combination with other drivers, may severely impact those taxa unable to track suitable environmental conditions. Here, we investigate the extent of climate and land use/cover (LUC) change-related risks for primates. We employed an analytical approach to objectively select a subset of climate scenarios, for which we then calculated changes in climatic and LUC conditions for 2050 across primate ranges (N=426 species) under a best- and a worst-case scenario. Generalised linear models were used to examine whether these changes varied according to region, conservation status, range extent, and dominant habitat. Finally, we reclassified primate ranges based on different magnitudes of maximum temperature change, and quantified the proportion of ranges overall and of primate hotspots in particular that are likely to be exposed to extreme temperature increases. We found that, under the worst-case scenario, 74% of Neotropical forest-dwelling primates are likely to be exposed to maximum temperature increases up to 7°C. In contrast, 38% of Malagasy savanna primates will experience less pronounced warming of up to 3.5°C. About one quarter of Asian and African primates will face up to 50% crop expansion within their range. Primary land (undisturbed habitat) is expected to disappear across species’ ranges, whereas secondary land (disturbed habitat) will increase by up to 98%. With 86% of primate ranges likely to be exposed to maximum temperature increases >3°C, primate hotspots in the Neotropics are expected to be particularly vulnerable. Our study highlights the fundamental exposure risk of a large percentage of primate ranges to predicted climate and LUC changes. Importantly, our findings underscore the urgency with which climate change mitigation measures need to be implemented to avert primate extinctions on an unprecedented scale

Thermal-Drones as a Safe and Reliable Method for Detecting Subterranean Peat Fires

Underground peat fires are a major hazard to health and livelihoods in Indonesia, and are a major contributor to carbon emissions globally. Being subterranean, these fires can be difficult to detect and track, especially during periods of thick haze and in areas with limited accessibility. Thermal infrared detectors mounted on drones present a potential solution to detecting and managing underground fires, as they allow large areas to be surveyed quickly from above and can detect the heat transferred to the surface above a fire. We present a pilot study in which we show that underground peat fires can indeed be detected in this way. We also show that a simple temperature thresholding algorithm can be used to automatically detect them. We investigate how different thermal cameras and drone flying strategies may be used to reliably detect underground fires and survey fire-prone areas. We conclude that thermal equipped drones are potentially a very powerful tool for surveying for fires and firefighting. However, more investigation is still needed into their use in real-life fire detection and firefighting scenarios

Impending extinction crisis of the world's primates: why primates matter

Non-human primates, our closest biological relatives, play important roles in the livelihoods, cultures and religions of many societies, and offer unique insights into human evolution, biology, behavior and the threat of emerging diseases. They are an essential component of tropical biodiversity, contributing to forest regeneration and ecosystem health. Current information shows the existence of 504 species in 79 genera distributed in the Neotropics, mainland Africa, Madagascar, and Asia. Alarmingly, ~60% of primate species are now threatened with extinction and ~75% have declining populations. This situation is the result of escalating anthropogenic pressures on primates and their habitats – mainly global and local market demands leading to extensive habitat loss through the expansion of industrial agriculture, large-scale cattle ranching, logging, oil and gas drilling, mining, dam building, and the construction of new road networks in primate range regions. Other important drivers are increased bushmeat hunting and the illegal trade of primates as pets and primate body parts, along with emerging threats such as climate change and anthroponotic diseases. Often, these pressures act in synergy, exacerbating primate population declines. Given that primate range regions overlap extensively with a large, and rapidly growing, human population characterized by high levels of poverty, global attention is needed immediately to reverse the looming risk of primate extinctions and to attend to local human needs in sustainable ways. Raising global scientific and public awareness of the plight of the world’s primates and the costs of their loss to ecosystem health and human society is imperative

Spatial and temporal overlaps between leopards (Panthera pardus) and their competitors in the African large predator guild

Understanding the mechanisms facilitating coexistence within species assemblages is a key consideration for conservation as intact assemblages are necessary for maintaining full ecosystem function. The African large predator guild represents one of the few remaining functionally intact large predator assemblages on Earth, and as such, represents a unique study system to understand competitive interactions. Yet, relatively little is known of the coexistence mechanisms between some of its intermediately sized members, particularly leopards (Panthera pardus). Here, we use overlapping spatio‐temporal activity and GPS data on lions (Panthera leo), leopards, African wild dogs (Lycaon pictus) and cheetahs (Acinonyx jubatus) to examine spatial interactions and temporal partitioning between leopards and other guild members in northern Botswana. We found that at the population level, male leopard space use and activity patterns were largely unaffected by intraguild competitors. Leopards showed minimal movement coherence with competitors (avoidance or attraction) when moving through areas of home ranges shared with intraguild species. Moreover, we found evidence to support the hypothesis that guild species’ activity patterns are primarily driven by light availability rather than predator avoidance. Our results suggest predator avoidance has a limited impact on broad‐scale leopard spatio‐temporal niches, with aspects of the leopards’ ecology and life history likely facilitating its ability to thrive in close proximity to competitors. Considered alongside other studies, our results suggest that landscape‐level approaches to conservation may be suitable for aiding leopard conservation

Building relationships: how zoos and other partners can contribute to the conservation of wild orangutans

With both species of orangutan now listed as critically endangered, orangutan conservation needs some critical rethinking. Habitat loss, degradation and fragmentation, and hunting are continuing to push their populations towards further decline. Conservation efforts focusing on rehabilitation and habitat protection are in place but are insufficient unless we move towards a landscape approach that will aim at protecting and connecting areas rather than isolated patches of forest. Conservationists need to engage with communities and industry to really protect the species at a landscape level. This paper explores the current efforts in orangutan conservation on the ground and from the zoo community and new areas emerging to contribute to these new approaches needed to positively impact orangutan populations on the ground