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Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds

Jose Roces, Carlos Cabo Gomez, Covadonga Prendes, Celestino Ordoñez, Cristina Santin Nuno

Forests, Volume: 11, Issue: 2, Start page: 198

Swansea University Authors: Jose Roces, Carlos Cabo Gomez, Cristina Santin Nuno

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DOI (Published version): 10.3390/f11020198

Abstract

Accurate mapping of landscape features is key for natural resources management and planning. For this purpose, the use of high-resolution remote sensing data has become widespread and is increasingly freely available. However, mapping some target features, such as small forest patches, is still a ch...

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Published in: Forests
ISSN: 1999-4907
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53703
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spelling 2020-08-17T14:21:11.0436355 v2 53703 2020-03-02 Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds a9c9fafbcabf9eb97ea34d3e60fab0a1 Jose Roces Jose Roces true false 660108e8078886c3e750d803be23276b Carlos Cabo Gomez Carlos Cabo Gomez true false 993c82cbaf875c1268156360e83c4dfd Cristina Santin Nuno Cristina Santin Nuno true false 2020-03-02 FGSEN Accurate mapping of landscape features is key for natural resources management and planning. For this purpose, the use of high-resolution remote sensing data has become widespread and is increasingly freely available. However, mapping some target features, such as small forest patches, is still a challenge. Standard, easily replicable, and automatic methodologies to delineate such features are still missing. A common alternative to automated methods is manual delineation, but this is often too time and resource intensive. We developed a simple and automatic method from freely available aerial light detection and ranging (LiDAR) and aerial ortho-images that provide accurate land use mapping and overcome some of the aforementioned limitations. The input for the algorithm is a coloured point cloud, where multispectral information from the ortho-images is associated to each LiDAR point. From this, four-class segmentation and mapping were performed based on vegetation indices and the ground-elevation of the points. We tested the method in four areas in the north-western Iberian Peninsula and compared the results with existent cartography. The completeness and correctness of our algorithm ranging between 78% and 99% in most cases, and it allows for the delineation of very small patches that were previously underrepresented in the reference cartography. Journal Article Forests 11 2 198 MDPI AG 1999-4907 forest mapping; non-forest woody vegetation; LiDAR; NDVI; high-resolution imagery 11 2 2020 2020-02-11 10.3390/f11020198 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University NERC, NE/T001194/1 2020-08-17T14:21:11.0436355 2020-03-02T17:00:08.9029019 Jose Roces 1 Carlos Cabo Gomez 2 Covadonga Prendes 3 Celestino Ordoñez 4 Cristina Santin Nuno 5 53703__16826__aeac9967ac0e485db05922aa582fe7bb.pdf 53703VOR.pdf 2020-03-10T14:38:39.4816443 Output 2769389 application/pdf Version of Record true Distributed under the terms of a Creative Commons Attribution 4.0 (CC-BY) Licence. true eng https://creativecommons.org/licenses/by/4.0/
title Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds
spellingShingle Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds
Jose Roces
Carlos Cabo Gomez
Cristina Santin Nuno
title_short Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds
title_full Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds
title_fullStr Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds
title_full_unstemmed Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds
title_sort Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds
author_id_str_mv a9c9fafbcabf9eb97ea34d3e60fab0a1
660108e8078886c3e750d803be23276b
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author_id_fullname_str_mv a9c9fafbcabf9eb97ea34d3e60fab0a1_***_Jose Roces
660108e8078886c3e750d803be23276b_***_Carlos Cabo Gomez
993c82cbaf875c1268156360e83c4dfd_***_Cristina Santin Nuno
author Jose Roces
Carlos Cabo Gomez
Cristina Santin Nuno
author2 Jose Roces
Carlos Cabo Gomez
Covadonga Prendes
Celestino Ordoñez
Cristina Santin Nuno
format Journal article
container_title Forests
container_volume 11
container_issue 2
container_start_page 198
publishDate 2020
institution Swansea University
issn 1999-4907
doi_str_mv 10.3390/f11020198
publisher MDPI AG
document_store_str 1
active_str 0
description Accurate mapping of landscape features is key for natural resources management and planning. For this purpose, the use of high-resolution remote sensing data has become widespread and is increasingly freely available. However, mapping some target features, such as small forest patches, is still a challenge. Standard, easily replicable, and automatic methodologies to delineate such features are still missing. A common alternative to automated methods is manual delineation, but this is often too time and resource intensive. We developed a simple and automatic method from freely available aerial light detection and ranging (LiDAR) and aerial ortho-images that provide accurate land use mapping and overcome some of the aforementioned limitations. The input for the algorithm is a coloured point cloud, where multispectral information from the ortho-images is associated to each LiDAR point. From this, four-class segmentation and mapping were performed based on vegetation indices and the ground-elevation of the points. We tested the method in four areas in the north-western Iberian Peninsula and compared the results with existent cartography. The completeness and correctness of our algorithm ranging between 78% and 99% in most cases, and it allows for the delineation of very small patches that were previously underrepresented in the reference cartography.
published_date 2020-02-11T04:06:47Z
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score 11.036553

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