Data and code for: A novel method for mapping high-precision animal locations using high-resolution imagery

Investigating ecological questions at the scale of individual organisms is
necessary to understand and predict the biological consequences of
changing environmental conditions. For small organisms this can be
challenging because ecologists need tools with the appropriate accuracy,
precision, and resolution to record and quantify their ecological
interactions. Unfortunately, many existing tools are only appropriate for
medium to large organisms or those that are wide ranging, inhibiting our
ability to investigate the spatial ecology of small organisms at fine
scales. Here, we tested a novel workflow for recording animal locations at
very fine (decimeter) spatial scales, which we refer to as High-resolution
Orthomosaic Location Recording (HOLR). The workflow for HOLR combined
direct observations with data collection of locations on high-resolution
uncrewed aerial vehicle (UAV) imagery loaded on smartphones. Observers
identified landscape features they recognized in the imagery and estimated
positions relative to these visual landmarks. We found HOLR was
approximately twice as accurate as consumer-grade GPS devices, with a mean
error of 0.75 m and a median error 0.30 m. We also found that performance
varied across landscape features, with the highest accuracy in areas that
had more visual landmarks for observers to use as reference points. In
addition to sub-meter accuracy, HOLR was cost-effective and practical in
the field, requiring no bulky equipment and allowing observers to easily
record locations away from their own location. This workflow can be used
to record locations in a variety of situations, but it will be
particularly cost-effective when users simultaneously utilize the
high-resolution environmental data contained within UAV imagery. Together,
these tools can expand the application of spatial ecology research to
smaller organisms than ever before.

在个体生物尺度上开展生态学问题研究，是理解并预测环境条件变化所引发的生物学后果的必要前提。对于小型生物而言，此类研究颇具挑战，因为生态学家需要具备恰当准确度、精度与分辨率的工具，来记录并量化它们的生态相互作用。遗憾的是，现有多数工具仅适用于中大型生物或活动范围较广的物种，这限制了我们在精细尺度上开展小型生物空间生态学研究的能力。在此，我们测试了一套可在极精细（分米级）空间尺度上记录动物位置的新型工作流，将其命名为高分辨率正射影像定位记录（High-resolution Orthomosaic Location Recording，HOLR）。该工作流将直接观测与基于智能手机加载的高分辨率无人机（uncrewed aerial vehicle, UAV）影像的位置数据采集相结合：观察者识别影像中可辨识的地表特征，并以此为视觉参照点估算目标位置。我们发现，HOLR的准确度约为消费级GPS设备的两倍，平均误差为0.75米，中位误差为0.30米。此外，其性能随地表特征不同存在差异：在拥有更多可供观察者作为参照的视觉地标区域，定位准确度最高。除了亚米级精度之外，HOLR还具备成本效益高、野外实用性强的优势，无需笨重设备，且允许观察者脱离自身当前位置轻松记录目标位置。该工作流可应用于多种场景，但当用户同时利用无人机影像中包含的高分辨率环境数据时，其成本效益尤为突出。综上，这套工具可将空间生态学研究的应用范围拓展至此前难以覆盖的更小体型生物。