A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR
Tekijät: Vastaranta, Mikko; Kantola, Tuula; Lyytikäinen-Saarenmaa, Paivi; Holopainen, Markus; Kankare, Ville; Wulder, Michael A.; Hyyppä, Juha; Hyyppä, Hannu
Kustantaja: MDPI
Kustannuspaikka: BASEL
Julkaisuvuosi: 2013
Journal: Remote Sensing
Tietokannassa oleva lehden nimi: REMOTE SENSING
Lehden akronyymi: REMOTE SENS-BASEL
Vuosikerta: 5
Numero: 3
Aloitussivu: 1220
Lopetussivu: 1234
Sivujen määrä: 15
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs5031220
Tiivistelmä
The mapping of changes in the distribution of insect-caused forest damage remains an important forest monitoring application and challenge. Efficient and accurate methods are required for mapping and monitoring changes in insect defoliation to inform forest management and reporting activities. In this research, we develop and evaluate a LiDAR-driven (Light Detection And Ranging) approach for mapping defoliation caused by the Common pine sawfly (Diprion pini L.). Our method requires plot-level training data and airborne scanning LiDAR data. The approach is predicated on a forest canopy mask created by detecting forest canopy cover using LiDAR. The LiDAR returns that are reflected from the canopy (that is, returns > half of maximum plot tree height) are used in the prediction of the defoliation. Predictions of defoliation are made at plot-level, which enables a direct integration of the method to operational forest management planning while also providing additional value-added from inventory-focused LiDAR datasets. In addition the method development, we evaluated the prediction accuracy and investigated the required pulse density for operational LiDAR-based mapping of defoliation. Our method proved to be suitable for the mapping of defoliated stands, resulting in an overall mapping accuracy of 84.3% and a Cohen's kappa coefficient of 0.68.
The mapping of changes in the distribution of insect-caused forest damage remains an important forest monitoring application and challenge. Efficient and accurate methods are required for mapping and monitoring changes in insect defoliation to inform forest management and reporting activities. In this research, we develop and evaluate a LiDAR-driven (Light Detection And Ranging) approach for mapping defoliation caused by the Common pine sawfly (Diprion pini L.). Our method requires plot-level training data and airborne scanning LiDAR data. The approach is predicated on a forest canopy mask created by detecting forest canopy cover using LiDAR. The LiDAR returns that are reflected from the canopy (that is, returns > half of maximum plot tree height) are used in the prediction of the defoliation. Predictions of defoliation are made at plot-level, which enables a direct integration of the method to operational forest management planning while also providing additional value-added from inventory-focused LiDAR datasets. In addition the method development, we evaluated the prediction accuracy and investigated the required pulse density for operational LiDAR-based mapping of defoliation. Our method proved to be suitable for the mapping of defoliated stands, resulting in an overall mapping accuracy of 84.3% and a Cohen's kappa coefficient of 0.68.
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