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1. Capturing long‐tailed individual tree diversity using an airborne imaging and a multi‐temporal hierarchical model

   https://doi.org/10.1002/rse2.335  

   Weinstein, Ben G.; Marconi, Sergio; Graves, Sarah J.; Zare, Alina; Singh, Aditya; Bohlman, Stephanie A.; Magee, Lukas; Johnson, Daniel J.; Townsend, Phillip A.; White, Ethan P. (May 2023, Remote Sensing in Ecology and Conservation)

   Abstract Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands of trees. To train computer vision models, ground‐based species labels are combined with airborne reflectance data. Due to the difficulty of finding rare species in a large landscape, many classification models only include the most abundant species, leading to biased predictions at broad scales. For example, if only common species are used to train the model, this assumes that these samples are representative across the entire landscape. Extending classification models to include rare species requires targeted data collection and algorithmic improvements to overcome large data imbalances between dominant and rare taxa. We use a targeted sampling workflow to the Ordway Swisher Biological Station within the US National Ecological Observatory Network (NEON), where traditional forestry plots had identified six canopy tree species with more than 10 individuals at the site. Combining iterative model development with rare species sampling, we extend a training dataset to include 14 species. Using a multi‐temporal hierarchical model, we demonstrate the ability to include species predicted at <1% frequency in landscape without losing performance on the dominant species. The final model has over 75% accuracy for 14 species with improved rare species classification compared to 61% accuracy of a baseline deep learning model. After filtering out dead trees, we generate landscape species maps of individual crowns for over 670 000 individual trees. We find distinct patches of forest composed of rarer species at the full‐site scale, highlighting the importance of capturing species diversity in training data. We estimate the relative abundance of 14 species within the landscape and provide three measures of uncertainty to generate a range of counts for each species. For example, we estimate that the dominant species, Pinus palustris accounts for c. 28% of predicted stems, with models predicting a range of counts between 160 000 and 210 000 individuals. These maps provide the first estimates of canopy tree diversity within a NEON site to include rare species and provide a blueprint for capturing tree diversity using airborne computer vision at broad scales. 

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2. Airborne Lidar and Electro-Optical Imagery along Surface Ruptures of the 2019 Ridgecrest Earthquake Sequence, Southern California

   https://doi.org/10.1785/0220190338  

   Hudnut, Kenneth W.; Brooks, Benjamin A.; Scharer, Katherine; Hernandez, Janis L.; Dawson, Timothy E.; Oskin, Michael E.; Ramon Arrowsmith, J.; Goulet, Christine A.; Blake, Kelly; Boggs, Matthew L.; et al (April 2020, Seismological Research Letters)

   Abstract Surface rupture from the 2019 Ridgecrest earthquake sequence, initially associated with the Mw 6.4 foreshock, occurred on 4 July on a ∼17  km long, northeast–southwest-oriented, left-lateral zone of faulting. Following the Mw 7.1 mainshock on 5 July (local time), extensive northwest–southeast-oriented, right-lateral faulting was then also mapped along a ∼50  km long zone of faults, including subparallel splays in several areas. The largest slip was observed in the epicentral area and crossing the dry lakebed of China Lake to the southeast. Surface fault rupture mapping by a large team, reported elsewhere, was used to guide the airborne data acquisition reported here. Rapid rupture mapping allowed for accurate and efficient flight line planning for the high-resolution light detection and ranging (lidar) and aerial photography. Flight line planning trade-offs were considered to allocate the medium (25 pulses per square meter [ppsm]) and high-resolution (80 ppsm) lidar data collection polygons. The National Center for Airborne Laser Mapping acquired the airborne imagery with a Titan multispectral lidar system and Digital Modular Aerial Camera (DiMAC) aerial digital camera, and U.S. Geological Survey acquired Global Positioning System ground control data. This effort required extensive coordination with the Navy as much of the airborne data acquisition occurred within their restricted airspace at the China Lake ranges. 

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3. A Framework for Autonomic Computing for In Situ Imageomics

   https://doi.org/10.1109/ACSOS58161.2023.00018  

   Kline, Jenna; Stewart, Christopher; Berger-Wolf, Tanya; Ramirez, Michelle; Stevens, Samuel; Babu, Reshma Ramesh; Banerji, Namrata; Sheets, Alec; Balasubramaniam, Sowbaranika; Campolongo, Elizabeth; et al (September 2023, IEEE)

   In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal social groups, species, and even whole ecosystems. Monitoring biological traits over large spaces and long periods of time could enable new, data-driven approaches to wildlife conservation, biodiversity, and sustainable ecosystem management. However, to accurately infer biological traits, machine learning methods for images require voluminous and high quality data. Adaptive, data-driven approaches are hamstrung by the speed at which data can be captured and processed. Camera traps and unmanned aerial vehicles (UAVs) produce voluminous data, but lose track of individuals over large areas, fail to capture social dynamics, and waste time and storage on images with poor lighting and view angles. In this vision paper, we make the case for a research agenda for in situ imageomics that depends on significant advances in autonomic and self-aware computing. Precisely, we seek autonomous data collection that manages camera angles, aircraft positioning, conflicting actions for multiple traits of interest, energy availability, and cost factors. Given the tools to detect object and identify individuals, we propose a research challenge: Which optimization model should the data collection system employ to accurately identify, characterize, and draw inferences from biological traits while respecting a budget? Using zebra and giraffe behavioral data collected over three weeks at the Mpala Research Centre in Laikipia County, Kenya, we quantify the volume and quality of data collected using existing approaches. Our proposed autonomic navigation policy for in situ imageomics collection has an F1 score of 82% compared to an expert pilot, and provides greater safety and consistency, suggesting great potential for state-of-the-art autonomic approaches if they can be scaled up to fully address the problem. 

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4. On avoiding Moving Objects for indoor autonomous quadrotors

   https://doi.org/10.1109/COASE.2016.7743447  

   Shin, Donghyeok; Kim, Junwhan; Yu, Byunggu; Jeong, Dong H. (August 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE),)

   A mini quadrotor can be used in many applications, such as indoor airborne surveillance, payload delivery, and warehouse monitoring. In these applications, vision-based autonomous navigation is one of the most interesting research topics because precise navigation can be implemented based on vision analysis. However, pixel-based vision analysis approaches require a high-powered computer, which is inappropriate to be attached to a small indoor quadrotor. This paper proposes a method called the Motion-vector-based Moving Objects Detection. This method detects and avoids obstacles using stereo motion vectors instead of individual pixels, thereby substantially reducing the data processing requirement. Although this method can also be used in the avoidance of stationary obstacles by taking into account the ego-motion of the quadrotor, this paper primarily focuses on providing our empirical verification on the real-time avoidance of moving objects. 

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5. Using time‐series remote sensing to identify and track individual bird nests at large scales

   https://doi.org/10.1002/rse2.70046  

   Ernest, S_K_Morgan; Garner, Lindsey A; Weinstein, Ben G; Frederick, Peter; Senyondo, Henry; Yenni, Glenda M; White, Ethan P (December 2025, Remote Sensing in Ecology and Conservation)

   The challenges of monitoring wildlife often limit the scales and intensity of the data that can be collected. New technologies—such as remote sensing using unoccupied aircraft systems (UASs)—can collect information more quickly, over larger areas, and more frequently than is feasible using ground‐based methods. While airborne imaging is increasingly used to produce data on the location and counts of individuals, its ability to produce individual‐based demographic information is less explored. Repeat airborne imagery to generate an imagery time series provides the potential to track individuals over time to collect information beyond one‐off counts, but doing so necessitates automated approaches to handle the resulting high‐frequency large‐spatial scale imagery. We developed an automated time‐series remote sensing approach to identifying wading bird nests in the Everglades ecosystem of Florida, USA to explore the feasibility and challenges of conducting time‐series based remote sensing on mobile animals at large spatial scales. We combine a computer vision model for detecting birds in weekly UAS imagery of colonies with biology‐informed algorithmic rules to generate an automated approach that identifies likely nests. Comparing the performance of these automated approaches to human review of the same imagery shows that our primary approach identifies nests with comparable performance to human review, and that a secondary approach designed to find quick‐fail nests resulted in high false‐positive rates. We also assessed the ability of both human review and our primary algorithm to find ground‐verified nests in UAS imagery and again found comparable performance, with the exception of nests that fail quickly. Our results showed that automating nest detection, a key first step toward estimating nest success, is possible in complex environments like the Everglades and we discuss a number of challenges and possible uses for these types of approaches. 

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