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Open AccessArticle

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

1
Department of Geography, College of Science, Swansea University, Wallace Building, Singleton Park, Swansea SA2 8PP, Wales, UK
2
Forest and Wood Technology Research Centre Foundation (CETEMAS), Pumarabule s/n, 33936 Carbayín, Asturias, Spain
3
Department of Mining Exploitation and Prospecting, University of Oviedo, 33003 Oviedo, Spain
4
Department of Biosciences, College of Science, Swansea University, Wallace Building, Singleton Park, Swansea SA2 8PP, Wales, UK
*
Author to whom correspondence should be addressed.
Both authors have contributed equally to this manuscript and share first position.
Forests 2020, 11(2), 198; https://doi.org/10.3390/f11020198
Received: 8 December 2019 / Revised: 28 January 2020 / Accepted: 7 February 2020 / Published: 11 February 2020
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. View Full-Text
Keywords: forest mapping; non-forest woody vegetation; LiDAR; NDVI; high-resolution imagery forest mapping; non-forest woody vegetation; LiDAR; NDVI; high-resolution imagery
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Roces-Díaz, J.V.; Cabo, C.; Prendes, C.; Ordoñez, C.; Santín, C. Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds. Forests 2020, 11, 198.

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