Unmanned aerial vehicle surveys reveal unexpectedly high density of a threatened deer in a plantation forestry landscape

Pereira, Javier A.; Varela, Diego; Scarpa, Leonardo; Frutos, Antonio E.; Fracassi, Natalia; Lartigau, Bernardo; Piña, Carlos Ignácio

doi:10.1017/s0030605321001058

Cited by 8 publications

(8 citation statements)

References 57 publications

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“…Studies that report aerial surveys being more accurate than traditional survey methods often have issues with detectability in the traditional survey methods. This is the case with counting rare deer in dense forests, where ground counts are ineffective due to forest cover and low densities of deer [8], where aerial imagery may provide better overview or spatial coverage. This may also be the case when there are challenges detecting marine animals at the sea surface from boats [9,10,24].…”

Section: Discussionmentioning

confidence: 99%

“…Recent developments in uncrewed aerial vehicles (UAV) technology open new opportunities to survey animal populations as a replacement or supplement to traditional count techniques [7]. UAV offers several advantages compared to traditional aerial or ground surveys (e.g., cost effectiveness, reduced environmental impact and disturbance, and operational range [8,9]), however, whether UAV methods have improved accuracy and are more efficient than traditional survey methods are largely unknown (but see [10,11]).…”

Section: Introductionmentioning

confidence: 99%

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How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

Paulsen¹,

Pedersen²,

Hann³

et al. 2022

Preprint

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Conservation of wildlife depends on precise and unbiased knowledge on the abundance and distribution of species. It is challenging to choose appropriate methods to obtain a sufficiently high detectability and spatial coverage matching the species characteristics and spatiotemporal use of the landscape. In remote regions, such as in the Arctic, monitoring efforts are often resource-intensive and there is a need for cheap and precise alternative methods. Here, we compare an uncrewed aerial vehicle (UAV; quadcopter) pilot-survey of the non-gregarious Svalbard reindeer to traditional population abundance surveys from ground and helicopter to investigate whether UAVs can be an efficient alternative technology. We found that the UAV survey underestimated reindeer abundance compared to the traditional abundance surveys when used at management relevant spatial scales. Observer variation in reindeer detection on UAV imagery was influenced by the RGB greenness index and mean blue channel. In future studies, we suggest to test long-range fixed-wing UAVs to increase the sample size of reindeer and area coverage and incorporate detection probability in animal density models from UAV imagery. In addition, we encourage focus on more efficient post-processing techniques, including automatic animal object identification with machine learning and analytical methods that account for uncertainties.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

Paulsen¹,

Pedersen²,

Hann³

et al. 2022

Preprint

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“…Remote sensing with the use of satellites can capture extensive regions of urban * r.pierdicca@staff.univpm.it forests, where cloud cover and fog can greatly impact on data collection. Unmanned aerial vehicle (UAV)-based remote sensing, being an effective method for obtaining high-quality images, has gained wide adoption in several domains, especially in forestry tree classification due to its affordability, rapid data collection, and adaptable functionality (Pereira et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Uav4tree: Deep Learning-Based System for Automatic Classification of Tree Species Using RGB Optical Images Obtained by an Unmanned Aerial Vehicle

Pierdicca,

Nepi,

Mancini

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Automated tree classification from unmanned aerial vehicle (UAV) images is a challenging task with several applications in forest management and conservation. In this study, we propose UAV4Tree a Deep Learning based system that automatically classifies RGB optical images obtained by the UAV. In particular, we explore the use of augmented datasets and various deep learning models, including ResNet, DenseNet, InceptionV3, and Vision Transformer, for the classification of tree images obtained from UAVs. Our experiments show that the use of an augmented dataset can significantly improve the accuracy of the classification by approximately 10 points compared to the use of a non-augmented dataset. We also found that fine-tuning and the introduction of dropout were essential for improving the generalization ability of the models on the augmented dataset. Furthermore, the use of Super Resolution Generative Adversarial Network (SR-GAN) in the original dataset allowed us to increase the performance of some models. Our findings provide valuable insights into the use of deep learning models for automated tree classification from UAV imagery, which has significant implications for sustainable forest management and conservation.

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“…Therefore, the counts are often substituted with different type of indices [moose seen per hunter; 26,camera trap and dung; 27,28]. However, only recently UAVs are applied in surveys of ungulates, where restricted flight range relative to the spatial scale of interest for management and difficulties detecting and identifying deer, have been major obstacles [3,8,[29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

Paulsen¹,

Pedersen²,

Hann³

et al. 2022

Preprint

View full text Add to dashboard Cite

Conservation of wildlife depends on precise and unbiased knowledge on the abundance and distribution of species. A challenge is to choose appropriate methods to obtain a sufficiently high detectability and spatial coverage matching the species characteristics and spatiotemporal use of the landscape. In remote areas, such as in the Arctic, monitoring efforts are often resource demanding and there is a need for cheap and precise alternative methods. Here, we compare an UAV pilot-survey to traditional population abundance surveys from ground and helicopter of the non-gregarious Svalbard reindeer to investigate whether small quadcopter UAVs can be an efficient alternative technology. We find that estimates of reindeer abundances from UAV imagery have lower precision and are more time consuming than present abundance surveys when used at management relevant spatial scales. We suggest that more efficient long-range fixed-wing UAVs should be evaluated for the job to increase the sampled area by UAV. In addition, the method will depend on the availability of more efficient post-processing methods including automatic animal object identification with machine learning and analytical methods that account for uncertainties.

show abstract

Unmanned aerial vehicle surveys reveal unexpectedly high density of a threatened deer in a plantation forestry landscape

Cited by 8 publications

References 57 publications

How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

Uav4tree: Deep Learning-Based System for Automatic Classification of Tree Species Using RGB Optical Images Obtained by an Unmanned Aerial Vehicle

How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

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