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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa53703
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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 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.
Keywords: forest mapping; non-forest woody vegetation; LiDAR; NDVI; high-resolution imagery
Funders: NERC, NE/T001194/1
Issue: 2
Start Page: 198