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Remote Sens. 2016, 8(8), 669;

Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands

Earth and Atmospheric Observation Group (GOTA), Departamento de Física, Universidad de La Laguna (ULL), 38200 La Laguna, Spain
Institute of Economy, Geography y Demography (IEGD), Spanish Council for Scientific Research (CSIC), Calle Albasanz 26-28, 28037 Madrid, Spain
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou, Xiaofeng Li and Prasad S. Thenkabail
Received: 6 June 2016 / Revised: 19 July 2016 / Accepted: 16 August 2016 / Published: 18 August 2016
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Wildland fires are one of the factors causing the deepest disturbances on the natural environment and severely threatening many ecosystems, as well as economic welfare and public health. Having accurate and up-to-date fuel type maps is essential to properly manage wildland fire risk areas. This research aims to assess the viability of combining Geographic Object-Based Image Analysis (GEOBIA) and the fusion of a WorldView-2 (WV2) image and low density Light Detection and Ranging (LiDAR) data in order to produce fuel type maps within an area of complex orography and vegetation distribution located in the island of Tenerife (Spain). Independent GEOBIAs were applied to four datasets to create four fuel type maps according to the Prometheus classification. The following fusion methods were compared: Image Stack (IS), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), as well as the WV2 image alone. Accuracy assessment of the maps was conducted by comparison against the fuel types assessed in the field. Besides global agreement, disagreement measures due to allocation and quantity were estimated, both globally and by fuel type. This made it possible to better understand the nature of disagreements linked to each map. The global agreement of the obtained maps varied from 76.23% to 85.43%. Maps obtained through data fusion reached a significantly higher global agreement than the map derived from the WV2 image alone. By integrating LiDAR information with the GEOBIAs, global agreement improvements by over 10% were attained in all cases. No significant differences in global agreement were found among the three classifications performed on WV2 and LiDAR fusion data (IS, PCA, MNF). These study’s findings show the validity of the combined use of GEOBIA, high-spatial resolution multispectral data and low density LiDAR data in order to generate fuel type maps in the Canary Islands. View Full-Text
Keywords: image-fusion; LiDAR; WorldView-2; fuel types; GEOBIA; Canary Islands image-fusion; LiDAR; WorldView-2; fuel types; GEOBIA; Canary Islands

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Alonso-Benito, A.; Arroyo, L.A.; Arbelo, M.; Hernández-Leal, P. Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands. Remote Sens. 2016, 8, 669.

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