Unlocking ground-based imagery for habitat mapping

N. Morueta-Holme; L. L. Iversen; D. Corcoran; C. Rahbek; S. Normand

doi:10.1016/j.tree.2023.11.005

Unlocking ground-based imagery for habitat mapping

N. Morueta-Holme^*, L. L. Iversen, D. Corcoran, C. Rahbek, S. Normand

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Review › Forskning › peer review

1 Citationer (Scopus)

Abstract

Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.

Originalsprog	Engelsk
Tidsskrift	Trends in Ecology & Evolution
Vol/bind	39
Nummer	4
Sider (fra-til)	349-358
Antal sider	10
ISSN	0169-5347
DOI	https://doi.org/10.1016/j.tree.2023.11.005
Status	Udgivet - apr. 2024

Adgang til dokumentet

10.1016/j.tree.2023.11.005

Citationsformater

@article{3284d19b80db4d4d835c23eac13d2200,

title = "Unlocking ground-based imagery for habitat mapping",

abstract = "Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.",

keywords = "biodiversity, habitat complexity, image recognition, remote sensing, Street View",

author = "N. Morueta-Holme and Iversen, {L. L.} and D. Corcoran and C. Rahbek and S. Normand",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

year = "2024",

month = apr,

doi = "10.1016/j.tree.2023.11.005",

language = "English",

volume = "39",

pages = "349--358",

journal = "Trends in Ecology & Evolution",

issn = "0169-5347",

publisher = "Elsevier Ltd",

number = "4",

}

TY - JOUR

T1 - Unlocking ground-based imagery for habitat mapping

AU - Morueta-Holme, N.

AU - Iversen, L. L.

AU - Corcoran, D.

AU - Rahbek, C.

AU - Normand, S.

PY - 2024/4

Y1 - 2024/4

N2 - Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.

AB - Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.

KW - biodiversity

KW - habitat complexity

KW - image recognition

KW - remote sensing

KW - Street View

U2 - 10.1016/j.tree.2023.11.005

DO - 10.1016/j.tree.2023.11.005

M3 - Review

C2 - 38087707

AN - SCOPUS:85179818107

SN - 0169-5347

VL - 39

SP - 349

EP - 358

JO - Trends in Ecology & Evolution

JF - Trends in Ecology & Evolution

IS - 4

ER -

Unlocking ground-based imagery for habitat mapping

Abstract

Adgang til dokumentet

Fingeraftryk

Citationsformater