GIS, Remote Sensing and Machine Learning: Data Integration to Support the Management of Coastal Island Ecosystems.

David J Lieske, Stephanie Avery-Gomm, Patrick Champagne, Leah Fulton

Abstract read

In one paragraph

Article in Environmental management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

David J LieskeDepartment of Geography and Environment, Mount Allison University, Sackville, NB, Canada. dlieske@mta.ca.

Stephanie Avery-GommEnvironment and Climate Change Canada, Science and Technology Branch, Wildlife and Landscape Science Directorate, Ottawa, ON, Canada.

Patrick ChampagneEnvironment and Climate Change Canada, Science and Technology Branch, Wildlife and Landscape Science Directorate, Ottawa, ON, Canada.

Leah FultonDepartment of Geography, University of Victoria, Victoria, BC, Canada.

Funding

Environment and Climate Change Canada GCXE23S014

6 · The paper itself

Abstract

Islands are important components of many coastal areas around the world; however, by virtue of their geographical isolation, the state of these ecosystems is often poorly known. To address the knowledge gap for the province of Nova Scotia, Canada, geographic information systems (GIS), remote sensing (RS), and machine learning (ML) were used to examine the status of nearly 4000 islands. We classified islands topographically and determined, based on 1 m resolution LiDAR, that approximately 70% are <2 m average elevation and highly vulnerable to partial or complete flooding under near-term regimes of sea level rise and storm surge potential. Vegetation cover was strongly related to topographic class, with higher, more steeply-sided islands having more tree cover and less sand, rock, and wetland. Climatic changes were most pronounced in the form of sea surface temperature (SST) warming, with August changes (+0.063 °C yr

Indexed as

Conservation of Natural ResourcesEcosystemEnvironmental MonitoringGeographic Information SystemsIslandsMachine LearningRemote Sensing TechnologyClimate ChangeNova ScotiaAnthropogenic stressorsClimate changeCoastal islandsConservation decision supportEnvironmental managementSea level rise

Identifiers

PMID40544199

DOI10.1007/s00267-025-02202-5

PMCPMC12457486

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.