Wildlife conservation

Summary

Wildlife conservation refers to the practice of protecting wild species and their habitats in order to maintain healthy wildlife species or populations and to restore, protect or enhance natural ecosystems. Major threats to wildlife include habitat destruction, degradation, fragmentation, overexploitation, poaching, pollution, climate change, and the illegal wildlife trade. The IUCN estimates that 42,100 species of the ones assessed are at risk for extinction. Expanding to all existing species, a 2019 UN report on biodiversity put this estimate even higher at a million species. It is also being acknowledged that an increasing number of ecosystems on Earth containing endangered species are disappearing. To address these issues, there have been both national and international governmental efforts to preserve Earth's wildlife. Prominent conservation agreements include the 1973 Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the

Official source

https://en.wikipedia.org/wiki/Wildlife_conservation

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Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission’s Joint Research Center ( JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be imple- mented as a module (i.e., Aerial Clicker) to extend an existing platform called Artiﬁcial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epﬂ.ch/savmap). The results suggest that the platform we have developed to combine crowdsourcing and machine learning to make sense of large volumes of aerial images can be used for disaster response.

Mary Ann Liebert, Inc2016

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Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process. In this project, we present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining. In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels. We show that a combination of MoCo, CLD, and geometric augmentations outperforms conventional models pretrained on ImageNet by a large margin. Crucially, our method still yields favorable results even if we reduce the number of training animals to just 10%, at which point our best model scores double the recall of the baseline at similar precision. This effectively allows reducing the number of required annotations to a fraction while still being able to train highaccuracy models in such highly challenging settings.

IEEE2021

Identifying Conservation and Restoration Priorities for Saproxylic and Old-Growth Forest Species: A Case Study in Switzerland

Saproxylic (dead-wood-associated) and old-growth species are among the most threatened species in European forest ecosystems, as they are susceptible to intensive forest management. Identifying areas with particular relevant features of biodiversity is of prime concern when developing species conservation and habitat restoration strategies and in optimizing resource investments. We present an approach to identify regional conservation and restoration priorities even if knowledge on species distribution is weak, such as for saproxylic and old-growth species in Switzerland. Habitat suitability maps were modeled for an expert-based selection of 55 focal species, using an ecological niche factor analyses (ENFA). All the maps were then overlaid, in order to identify potential species' hotspots for different species groups of the 55 focal species (e.g., birds, fungi, red-listed species). We found that hotspots for various species groups did not correspond. Our results indicate that an approach based on "richness hotspots" may fail to conserve specific species groups. We hence recommend defining a biodiversity conservation strategy prior to implementing conservation/restoration efforts in specific regions. The conservation priority setting of the five biogeographical regions in Switzerland, however, did not differ when different hotspot definitions were applied. This observation emphasizes that the chosen method is robust. Since the ENFA needs only presence data, this species prediction method seems to be useful for any situation where the species distribution is poorly known and/or absence data are lacking. In order to identify priorities for either conservation or restoration efforts, we recommend a method based on presence data only, because absence data may reflect factors unrelated to species presence.

Springer-Verlag2009

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Mary Ann Liebert, Inc2016