Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain

Rullán-Silva, Cristóbal Daniel; Olthoff, Adriana E.; Pando, Valentín; Pajares, J. A.; Delgado, José A.

doi:10.1016/j.foreco.2015.03.005

Cited by 20 publications

(14 citation statements)

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“…Among five VIs tested in our study, the results with dMSI as the best predictor were consistent with recent studies on insect defoliations [ 2 , 36 , 37 , 38 ]. The SWIR band calculated in dMSI is known to be a good indicator for the plant moisture content in addition to the plant stress detected in the NIR band [ 1 , 43 ].…”

Section: Discussionsupporting

confidence: 92%

“…Other studies with MSI have been conducted in pine forests by Sangüesa-Barreda et al [ 2 ] demonstrating the highest significance on ANOVA tests and most recently by Zhu et al [ 38 ] with an R 2 of 0.982 in logistic regression. The MSI was also effectively applied to defoliation in deciduous forests by Townsend et al [ 36 ] with an R 2 of 0.844 in logistic regression and Rullán-Silva et al [ 37 ] with an R 2 of 0.632 in sigmoidal mixed-effects models. For monitoring coniferous forests in general, the MSI has been found to be more effective than the NDVI which has been mainly applied to deciduous forests [ 38 , 43 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

“…The relationships between X and Y were evaluated using the software R in various regression models including simple linear, logarithmic, exponential, polynomial, and logistic models. Provided that the range of defoliation % ( Y ) is limited as the response variable is bounded from 0 to 1 [ 36 , 37 , 38 ], logistic regression was employed to express proportion defoliated, following the equation:

where X represents the value of pixel in a selected dVI within the sample image, Y represents the percentage of defoliation interpreted within an individual UAV sample image, and a and b are the slope and intercept of the regression function.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery

Otsu

Pla

Vayreda

et al. 2018

Sensors

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The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery

Otsu

Pla

Vayreda

et al. 2018

Sensors

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“…Another relevant question when studying coniferous defoliation is the use of severity categories (i.e., trace, low, medium, strong defoliation) instead of considering defoliation as a continuous variable based on field data measures [71,77]. On this study, we used a concept of defoliation included in the ICP manual (http://icp-forests.net/page/icp-forests-manual; Part IV), in particular compared to assessing the loss in foliage within one year to estimate the severity of defoliation in terms of canopy changes.…”

Section: Vegetation Indexes and Defoliationmentioning

confidence: 99%

Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess Pinus pinaster Aiton. Forest Defoliation in South-Eastern Spain

et al. 2019

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Climate change is increasing the vulnerability of Mediterranean coniferous plantations. Here, we integrate a Landsat time series with a physically-based distributed hydrological model (Watershed Integrated Management in Mediterranean Environments—WiMMed) to examine spatially-explicit relationships between the mortality processes of Pinus pinaster plantations and the hydrological regime, using different spectral indices of vegetation and machine learning algorithms. The Normalized Burn Ratio (NBR) and Moisture Stress Index (MSI) show the highest correlations with defoliation rates. Random Forest was the most accurate model (R2 = 0.79; RMSE = 0.059), showing a high model performance and prediction. Support vector machines and neural networks also demonstrated a high performance (R2 > 0.7). The main hydrological variables selected by the model to explain defoliation were potential evapotranspiration, winter precipitation and maximum summer temperature (lower Out-of-bag error). These results show the importance of hydrological variables involved in evaporation processes, and on the change in the spatial distribution of seasonal rainfall upon the defoliation processes of P. pinaster. These results underpin the importance of integrating temporal remote sensing data and hydrological models to analyze the drivers of forest defoliation and mortality processes in the Mediterranean climate.

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“…In this article, we discuss another invasive leaf miner, the beech leafmining weevil, Orchestes fagi (L.) (formerly Rhynchaenus fagi) (Coleoptera: Curculionidae). This weevil is native to Europe and a common pest of European beech, Fagus sylvatica L. Populations occasionally reach outbreak levels and may reduce growth rates and seed production, but do not kill trees (Verkaik et al, 2009;Rullán-Silva et al, 2015). In 2012, O. fagi was discovered infesting American beech, Fagus grandifolia Ehrh., in Halifax and Cape Breton, Nova Scotia.…”

Section: Introductionmentioning

confidence: 99%

Impact of the Invasive Beech Leaf-Mining Weevil, Orchestes fagi, on American Beech in Nova Scotia, Canada

Sweeney

Hughes

Zhang³

et al. 2020

Front. For. Glob. Change

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The beech leaf-mining weevil, Orchestes fagi (L.), is native to Europe where it commonly attacks European beech. The weevil was discovered infesting American beech in Halifax and Cape Breton Island, Nova Scotia, Canada in 2012, but anecdotal reports of defoliated beech in the Halifax area as early as 2006 suggest it established 5-10 years prior to its discovery. Our objectives were to estimate the impact of O. fagi on American beech in forested sites and urban areas, as well as its economic impact on owners of residential properties with mature American beech. In 2014, we established fifteen plots in forested sites containing a total of 260 American beech at Sandy Lake, Oakfield, and Mount Uniacke (n = 5 plots per site), where weevil infestation levels were moderate, low, and nil, respectively. At the same time we recorded the degree of cankering by beech bark disease on the main stems of each tree. Plots were visited annually to record tree mortality (2014-2019) and percentage of leaves with larval mines or adult feeding (2016-2019). Between 2016 and 2019, the percentage of leaves mined by weevil larvae increased from 6 to 59% at Mount Uniacke and from 48 to 83% at Oakfield. During the same period, cumulative beech mortality increased from 35 to 48% at Mount Uniacke and from 10 to 70% at Oakfield. At Sandy Lake in 2016, 88% of the beech trees had died and there were too few living beech to collect a leaf sample in our plots so estimates of weevil damage (87% of leaves with mines) were obtained from life table plots in the same area. Tree mortality was associated with severity of cankering by beech bark disease only at Mount Uniacke, the site with the fewest years of defoliation by the leaf-mining weevil. We also surveyed residents of Halifax in 2016 and 2018 to determine the rate of beech mortality and costs of tree removal in urban residential areas in the same region (within 40 km) of the forest areas. Relative to the forested sites at Sandy lake and Oakfield, mortality rates were lower in urban areas (32% in 2016, 44% in 2018), even though signs of weevil defoliation had been apparent to residents as early as 2011-2012.

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Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain

Cited by 20 publications

References 50 publications

Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery

Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery

Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess Pinus pinaster Aiton. Forest Defoliation in South-Eastern Spain

Impact of the Invasive Beech Leaf-Mining Weevil, Orchestes fagi, on American Beech in Nova Scotia, Canada

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