Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Abstract

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R2 relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation.

Remote sensing and spectroscopy of lichens

“…Data collected by sensors onboard unoccupied aerial vehicles (UAV) have recently emerged also in lichen mapping. Lichen cover has been estimated for caribou areas in Canada and the United States based on UAV data using random forest models (Macander et al., 2020 ), various machine learning methods (He et al., 2021 ) and neural networks (Jozdani, Chen, Chen, Leblanc, Lovitt, et al., 2021a , Jozdani, Chen, Chen, Leblanc, Prevost, et al., 2021b ; Richardson et al., 2021 ). While UAV data cannot be used to cover fully the extensive areas where reindeer and caribou herd, it can serve as part of a multiscale remote sensing framework which incorporates satellite and ground reference data as well as UAV data.…”

Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery

“…While these indices provide general information about vegetation health status and forest composition, they do not directly relate to the number of trees that exist within forest stands. This issue may be addressed by adopting integrated multi-scale workflows using convolutional neural networks (CNNs) [12,13]. Advancements in deep learning for object detection and increased availability of high-quality high-resolution imagery have caused an expansion in studies focused on detect and localize (D&L) methods.…”

Deep Learning and Synthetic Imagery for Migratory Bird Species Identification Using Drones

“…In many ecological research drone missions, data is collected in the form of images and videos for the development of computer vision applications (Wilson et al, 2022;Corregidor-Castro and Valle, 2022), often employing deep learning models (Reckling et al, 2021;Rominger and Meyer, 2021;Richardson et al, 2021;Ooi, 2020). The success of deep learning applications with drone imagery is associated with the flexibility offered by lightweight aircraft carrying high resolution cameras that are capable of collecting large volumes of data from a bird's eye view perspective.…”