Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR

Remote Sensing

Mikko Vastaranta²  Tuula Kantola²  Päivi Lyytikäinen-Saarenmaa²  Markus Holopainen²  Ville Kankare²  Michael A. Wulder³  Juha Hyyppä¹ 

[1] Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, FI-02431 Masala, Finland; E-Mail:;Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland; E-Mails:;Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC V8Z 1M5, Canada; E-Mail:

关键词: airborne laser scanning;    Diprion pini;    forest disturbances;    forest health monitoring;    forest management planning;    needle loss;   

DOI  :  10.3390/rs5031220

来源: mdpi

【 摘 要 】

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 to 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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.