Assessment of defoliation and subsequent growth losses caused by Lymantria dispar using terrestrial laser scanning (TLS)

Abstract

Key message TLS scans of three surveys before, during and after gypsy moth gradation, allowed high-resolution tracking of defoliation and subsequent inter-annual growth losses on an individual tree level. Abstract Foliation strongly determines all tree growth processes but can be reduced by various stress factors. Insect defoliation starts at variable times and is one stress factor that may affect photosynthetic processes and cause immediate reactions like refoliation, which are difficult to detect by surveys repeated at 1-year intervals. This study used a large-scale field experiment in German oak/mixed forests affected by gypsy moths (Lymantria dispar) to test the use of terrestrial laser scanning (TLS) for detecting inter-annual foliation and growth losses at the individual tree level caused by the gypsy moth. The experiment comprised two levels of gypsy moth defoliation risk, high (H) and low (L), as well as two pest control treatment levels: spraying with the insecticide Mimic (M) or unsprayed control (C). The factorial design consisted of four treatment combinations (HC, HM, LC, and LM), applied to 11 spatial blocks with a total of 44 plots. The TLS approach detected the defoliation caused by the gypsy moth, estimated as leaf area and crown perforation parameters. For the first time, TLS-derived tree foliation was evaluated based on inter-annual stem growth. Leaf area and crown perforation showed a correlation of + 0.6 and – 0.35, respectively, with basal area increments. Furthermore, this study revealed subsequent growth losses in the same year due to defoliation. Our results show that TLS can offer new opportunities to develop new indicators that monitor foliation at the individual tree level. The crown perforation can describe defoliation or the tree’s vitality based on one scanning campaign, whereas the leaf area needed at least two.

Growth Response of Oaks to Insect Defoliation: Immediate and Intermediate Perspectives

“…In this investigation, secondary growth loss exhibited a linear relationship with defoliation intensity, culminating in approximately 40 % to 60 % secondary growth loss in severely defoliated trees during the outbreak year. Spongy moth defoliation primarily inhibits secondary tree growth through diminished leaf biomass (Clark et al, 2010;Fajvan and Wood, 1996;Naidoo and Lechowicz, 2001), as reduced photosynthetic leaf area and carbohydrate supply constrain carbon allocation to secondary tree growth, consequently leading to decreased secondary growth (Ferretti et al, 2021;Jacobs et al, 2022;Waring, 1987).…”

Predicción de cosechas de setas silvestres en bosques mediterráneos utilizando sensores remotos activos y pasivos

“…Aparte de la estructura física de la vegetación(Pascu et al, 2019), con el TLS y en combinación con algoritmos de aprendizaje automático (machine learning) se pueden llegar a cuantificar las perturbaciones de los bosques (Ghizoni, a clasificar especies en el inventario forestal(Othmani et al, 2013), a estimar el índice de área foliar(Huang y Pretzsch, 2010), a detectar el estrés por deficiencia hídrica en los árboles(Jacobs et al, 2021) y a utilizar sus datos como indicador de defoliación de plagas(Jacobs et al, 2022), entre otras muchas aplicaciones directas al ámbito forestal.…”