Multi-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scale

Simple item page
dc.contributor.authorvan der Plas, Thijs L.
dc.contributor.authorGeikie, Simon T.
dc.contributor.authorAlexander, David G.
dc.contributor.authorSimms, Daniel M.
dc.date.accessioned2023-11-14T10:51:43Z
dc.date.available2023-11-14T10:51:43Z
dc.date.issued2023-11-07
dc.description.abstractLand cover (LC) maps are used extensively for nature conservation and landscape planning, but low spatial resolution and coarse LC schemas typically limit their applicability to large, broadly defined habitats. In order to target smaller and more-specific habitats, LC maps must be developed at high resolution and fine class detail using automated methods that can efficiently scale to large areas of interest. In this work, we present a Machine Learning approach that addresses this challenge. First, we developed a multi-stage semantic segmentation approach that uses Convolutional Neural Networks (CNNs) to classify LC across the Peak District National Park (PDNP, 1439 km2) in the UK using a detailed, hierarchical LC schema. High-level classes were predicted with 95% accuracy and were subsequently used as masks to predict low-level classes with 72% to 92% accuracy. Next, we used these predictions to analyse the degree and distribution of fragmentation of one specific habitat—wet grassland and rush pasture—at the landscape scale in the PDNP. We found that fragmentation varied across areas designated as primary habitat, highlighting the importance of high-resolution LC maps provided by CNN-powered analysis for nature conservation.en_UK
dc.identifier.citationvan der Plas TL , Geikie ST, Alexander DG, Simms DM.(2023) Multi-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scale. Remote Sensing, Volume 15, Issue 22, November 2023, Article number 5277en_UK
dc.identifier.issn2072-4292
dc.identifier.urihttps://doi.org/10.3390/rs15225277
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20545
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectremote sensingen_UK
dc.subjectsemantic segmentationen_UK
dc.subjectconvolutional neural networken_UK
dc.subjectland cover predictionen_UK
dc.subjecthabitat fragmentationen_UK
dc.titleMulti-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scaleen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
small_habitats_at_a_landscape_scale-2023.pdf
Size:
8.27 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)