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Arctic landscapes are rapidly changing with climate warming. Vegetation communities are restructuring, which in turn impacts wildlife, permafrost, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but notable mismatches in scale occur between current field and satellite-based monitoring. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite imagery mapping. In this work we assess the viability of using high resolution UAV imagery (RGB and multispectral), along with UAV derived Structure from Motion (SfM) to predict cover, height and above-ground biomass of common Arctic plant functional types (PFTs) across a wide range of vegetation community types. We collected field data and UAV imagery from 45 sites across Alaska and northwest Canada. We then classified UAV imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Here we present datasets summarizing this data.
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Penguin colony georegistration using camera pose estimation and phototourism
Satellite-based remote sensing and uncrewed aerial imagery play increasingly important roles in the mapping of wildlife populations and wildlife habitat, but the availability of imagery has been limited in remote areas. At the same time, ecotourism is a rapidly growing industry and can yield a vast catalog of photographs that could be harnessed for monitoring purposes, but the inherently ad-hoc and unstructured nature of these images make them difficult to use. To help address this, a subfield of computer vision known as phototourism has been developed to leverage a diverse collection of unstructured photographs to reconstruct a georeferenced three-dimensional scene capturing the environment at that location. Here we demonstrate the use of phototourism in an application involving Antarctic penguins, sentinel species whose dynamics are closely tracked as a measure of ecosystem functioning, and introduce a semi-automated pipeline for aligning and registering ground photographs using a digital elevation model (DEM) and satellite imagery. We employ the Segment Anything Model (SAM) for the interactive identification and segmentation of penguin colonies in these photographs. By creating a textured 3D mesh from the DEM and satellite imagery, we estimate camera poses to align ground photographs with the mesh and register the segmented penguin colony area to the mesh, achieving a detailed representation of the colony. Our approach has demonstrated promising performance, though challenges persist due to variations in image quality and the dynamic nature of natural landscapes. Nevertheless, our method offers a straightforward and effective tool for the georegistration of ad-hoc photographs in natural landscapes, with additional applications such as monitoring glacial retreat.
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- Award ID(s):
- 2212046
- PAR ID:
- 10634842
- Editor(s):
- VishnuRadhan, Renjith
- Publisher / Repository:
- Plos One
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 19
- Issue:
- 10
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0311038
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pone.0311038
