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Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models

Luke Meyers, Anirudh Potlapally, Yuyan Chen, Mike Long, Tanya Berger-Wolf, Hari Subramoni, Remi Megret, Daniel Rubenstein

Abstract

Plant phenology, the study of cyclical events such as leafing out, flowering, or fruiting, has wide ecological impacts but is broadly understudied, especially in the tropics. Image analysis has greatly enhanced remote phenological monitoring, yet capturing phenology at the individual level remains challenging. In this project, we deployed low-cost, animal-triggered camera traps at the Pu'u Maka'ala Natural Area Reserve in Hawaii to simultaneously document shifts in plant phenology and flora-faunal interactions. Using a combination of foundation vision models and traditional computer vision methods, we measure phenological trends from images comparable to on-the-ground observations without relying on supervised learning techniques. These temporally fine-grained phenology measurements from camera-trap images uncover trends that coarser traditional sampling fails to detect. When combined with detailed visitation data detected from images, these trends can begin to elucidate drivers of both plant phenology and animal ecology.

Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models

Abstract

Plant phenology, the study of cyclical events such as leafing out, flowering, or fruiting, has wide ecological impacts but is broadly understudied, especially in the tropics. Image analysis has greatly enhanced remote phenological monitoring, yet capturing phenology at the individual level remains challenging. In this project, we deployed low-cost, animal-triggered camera traps at the Pu'u Maka'ala Natural Area Reserve in Hawaii to simultaneously document shifts in plant phenology and flora-faunal interactions. Using a combination of foundation vision models and traditional computer vision methods, we measure phenological trends from images comparable to on-the-ground observations without relying on supervised learning techniques. These temporally fine-grained phenology measurements from camera-trap images uncover trends that coarser traditional sampling fails to detect. When combined with detailed visitation data detected from images, these trends can begin to elucidate drivers of both plant phenology and animal ecology.
Paper Structure (12 sections, 6 figures, 1 table)

This paper contains 12 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Data Acquisition at NEON Domain 20 Pu'u Maka'ala Natural Area Reserve
  4. Data
  5. Site
  6. Approaches and Methods
  7. Leafing in Individual 'Ōhelo, Vaccinium calycinum
  8. Fruiting in Pūkiawe, Leptecophylla tameiameiae
  9. Avian Visitor Detection
  10. Results and Discussion
  11. Conclusions and Future Work
  12. Acknowledgements

Figures (6)

  • Figure 1: Top Left: Crop showing an 'amakihi (Chlorodrepanis virens) visiting an ohelo flower. Bottom Left: An 'omao (Myadestes obscurus) consuming a pukiawe berry. Right: Camera‐trap setup—tripod and cinderblock‐mounted—targeting a Rubus hawaiiensis individual along NEON’s phenology transect. Below: Mini-overview of general workflow.
  • Figure 2: Canopy Leaf Cover Estimation (a) Raw image of the focal plant against a complex background, making individual leaf counts difficult. (b) Depth map from DepthPro; applying a 2 m threshold isolates the plant foreground. (c) Extracted foreground showing only the focal plant with background removed. (d) Segmented leaves obtained by converting the foreground to LAB space and isolating green-chromaticity pixels.
  • Figure 3: Change in greenness of Ohelo (Vaccinium calycinum) canopy. Top: NEON manual canopy‐cover estimates and our method's greenness estimates for specimen 06367 on the PUUM phenology transect. Bottom: NEON observations and our greenness estimates for specimen 06371. The strong correspondence between methods demonstrates that our approach accurately captures the trends recorded by NEON scientists.
  • Figure 4: Avian Visitors and Berry Count Estimates of Pukiawe through Time. Top: Number of bird visits at specimen 06090 on the Phenology Transect. Middle: Manual berry counts by NEON scientists for specimen 06090. Bottom: Berry counts estimated by our computer vision model.
  • Figure 5: UMAP visualization of BioCLIP features of positive detection crops at Camera 02 and 26, non-bird and false positive bird are MegaDetector final predictions, species labels annotated by hand
  • ...and 1 more figures