Classification of Needle Loss of Individual Scots Pine Trees by Means of Airborne Laser Scanning

Forests 2013, 4(2), 386-403; doi:10.3390/f4020386

Author

Tuula Kantola ^1,* , Mikko Vastaranta ¹

, Päivi Lyytikäinen-Saarenmaa ¹

, Markus Holopainen ¹

, Ville Kankare ¹

, Mervi Talvitie ¹

 and Juha Hyyppä ²

¹

Department of Forest Sciences, University of Helsinki, P.O. Box 27, Helsinki FI-00014, Finland

²

Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Masala FI-02431, Finland

^*

Author to whom correspondence should be addressed. 

Received: 1 April 2013 / Revised: 23 May 2013 / Accepted: 27 May 2013 / Published: 14 June 2013

View Full-Text   |     Download PDF [643 KB, uploaded 14 June 2013]   |    

 Browse Figures

 

Abstract 

Forest disturbances caused by pest insects are threatening ecosystem stability, sustainable forest management and economic return in boreal forests. Climate change and increased extreme weather patterns can magnify the intensity of forest disturbances, particularly at higher latitudes. Due to rapid responses to elevating temperatures, forest insect pests can flexibly change their survival, dispersal and geographic distributions. The outbreak pattern of forest pests in Finland has evidently changed during the last decade. Projection of shifts in distributions of insect-caused forest damages has become a critical issue in the field of forest research. The Common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini has resulted in severe growth loss and mortality of Scots pine (Pinus sylvestris L.) (Pinaceae) in eastern Finland. In this study, tree-wise defoliation was estimated for five different needle loss category classification schemes and for 10 different simulated airborne laser scanning (ALS) pulse densities. The nearest neighbor (NN) approach, a nonparametric estimation method, was used for estimating needle loss of 701 Scots pines, using the means of individual tree features derived from ALS data. The Random Forest (RF) method was applied in NN-search. For the full dense data (~20 pulses/m²), the overall estimation accuracies for tree-wise defoliation level varied between 71.0% and 86.5% (kappa-values of 0.56 and 0.57, respectively), depending on the classification scheme. The overall classification accuracies for two class estimation with different ALS pulse densities varied between 82.8% and 83.7% (kappa-values of 0.62 and 0.67, respectively). We conclude that ALS-based estimation of needle losses may be of acceptable accuracy for individual trees. Our method did not appear sensitive to the applied pulse densities. View Full-Text

Keywords: ALS;  defoliation;  Diprion pini;  forest disturbances;  effect of pulse density;  LiDAR;  random forest

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

For further details log on website :
http://www.mdpi.com/1999-4907/4/2/386