Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands
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 rese...
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doaj-801636a97191486bbf0dfebaf7d49c072020-11-24T23:14:23ZengMDPI AGRemote Sensing2072-42922016-08-018866910.3390/rs8080669rs8080669Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary IslandsAlfonso Alonso-Benito0Lara A. Arroyo1Manuel Arbelo2Pedro Hernández-Leal3Earth and Atmospheric Observation Group (GOTA), Departamento de Física, Universidad de La Laguna (ULL), 38200 La Laguna, SpainInstitute of Economy, Geography y Demography (IEGD), Spanish Council for Scientific Research (CSIC), Calle Albasanz 26-28, 28037 Madrid, SpainEarth and Atmospheric Observation Group (GOTA), Departamento de Física, Universidad de La Laguna (ULL), 38200 La Laguna, SpainEarth and Atmospheric Observation Group (GOTA), Departamento de Física, Universidad de La Laguna (ULL), 38200 La Laguna, SpainWildland 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.http://www.mdpi.com/2072-4292/8/8/669image-fusionLiDARWorldView-2fuel typesGEOBIACanary Islands |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alfonso Alonso-Benito Lara A. Arroyo Manuel Arbelo Pedro Hernández-Leal |
spellingShingle |
Alfonso Alonso-Benito Lara A. Arroyo Manuel Arbelo Pedro Hernández-Leal Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands Remote Sensing image-fusion LiDAR WorldView-2 fuel types GEOBIA Canary Islands |
author_facet |
Alfonso Alonso-Benito Lara A. Arroyo Manuel Arbelo Pedro Hernández-Leal |
author_sort |
Alfonso Alonso-Benito |
title |
Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands |
title_short |
Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands |
title_full |
Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands |
title_fullStr |
Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands |
title_full_unstemmed |
Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands |
title_sort |
fusion of worldview-2 and lidar data to map fuel types in the canary islands |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2016-08-01 |
description |
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. |
topic |
image-fusion LiDAR WorldView-2 fuel types GEOBIA Canary Islands |
url |
http://www.mdpi.com/2072-4292/8/8/669 |
work_keys_str_mv |
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