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

Abstract

Abstract: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 OPEN ACCESSRemote Sens. 2013, 5 1221 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.

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

“…In ACS, an initial set of sampling plots is selected using a simple probability sampling procedure. Additional sampling plots from the neighborhood are added, where the variable of interest (i.e., plot-wise defoliation) satisfied a given criterion [48]. This procedure is repeated until no additional plots could be found.…”

“…ALS can be useful in projecting, detecting and monitoring forest hazards and tree defoliation due to its ability to directly measure vegetation structure [39][40][41]. Recent studies and developments in methods have achieved more accurate ALS-based biomass detection [39,[42][43][44][45][46][47][48]. Single trees biomass and defoliation level are highly correlated (e.g., [49]).…”

“…Kantola et al [39] earlier investigated how to separate healthy and severely defoliated trees using an approach similar to that used here combining ALS data with high resolution aerial imagery. Vastaranta et al [48] developed an area-based approach for mapping healthy and defoliated Scots pine stands with ALS data. In both of these studies, simple two-class defoliation classification schemes were used.…”

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

“…At the stand level, they found a number of significant relationships between plot-level indicators of infestation and LiDAR-derived structural metrics (e.g., proportion of grey-attack crowns, r 2 = 0.76). Vastaranta et al (2013) mapped a 34 km 2 Scots pine forest infested by common pine sawfly (Diprion pini (Linnaeus) (Hymenoptera: Diprionidae)) into two defoliation classes (< 20% and ⩾ 20%) using a single 20 pulses/m 2 ALS acquisition and 108 forest-inventory plots of 8-m radius, obtaining an overall accuracy of 84%. Working with the same data set but studying individual trees instead of plots, Kantola et al (2010) achieved an overall accuracy of 88% classifying individual trees as healthy or defoliated (same 20% defoliation threshold), based on 136 training trees and 135 validation trees whose degree of defoliation was assessed from the ground.…”

Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality

“…The obtained producer's accuracies vary between 86 and 89%-albeit including other land cover classes-and are based on spatially aggregated 2.4 m aerial imagery. Alternatively, overall accuracies of 84% for different levels of defoliation were obtained by [12] using first/last pulse ALS data. As long as models of the same datasets are compared, the results can be interpreted as in this study.…”