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Overview

Project Highlights:

  • This study will look at new methods for evaluating insect defoliators in European boreal forests using remotely sensed data.
  • In partnership with colleagues at the Natural Resources Institute Finland (Luke), the study will incorporate additional factors that may attenuate the signal of insect outbreaks such as topographic position, drought, and forest management.
  • The elaboration of remotely sensed indices to detect insect outbreaks could lead to improved forest management strategies and reduced spread of infestation.

The economic value of boreal forests in Europe are affected by disturbances that reduce productivity and in extreme cases can cause tree mortality. Repeated outbreaks of defoliating insect such as the geometrid moth and pine sawfly are responsive to climate change and threaten forest resources in Scandinavia. The signal of defoliator infestations can be detected from optical remotely sensed (RS) imagery, although improvements are necessary to characterize areas affected and to account for variability from differences in species and topographic position. Furthermore, the spatial and temporal resolution of different RS data products should be explored to determine if syntheses of available datasets prove useful in detecting outbreaks and how we can make the best use of newly available data products, such as Sentinel-2.

Recent studies show that the detection of forest damage/defoliation caused by pine sawflies with coarse-resolution RS data (MODIS) fails in heterogeneous landscapes which are typical for the managed forests of Finland (Olsson et al., 2016). Even in cases where the detection of damaged areas with MODIS data may be useful (i.e., more homogenous landscapes), the estimation of the degree of damage has not been successful (Eklundh et al., 2009). These results refer to the correspondence (or lack of it) between MODIS data and observations at the scale of pixels/forest stands. Despite these poor results at this level, one might hypothesize, that because of the generally moderate to high spatial correlation in pine sawfly densities the regionally derived MODIS products focusing on properly selected (large, homogenous) pine stands should correlate better with results from regional pine sawfly monitoring programs, and smaller resolution Landsat products should improve the correlations further.

The elaboration of factors that can attenuate the signal of defoliator outbreaks such as management activities and drought will be helpful in focusing the research only on those areas that are potentially affected. It is possible that through the creation of regionally derived products could be used to create an early warning system of defoliator outbreaks that could be used to target areas for mitigation or containment strategies to reduce their impact.

Pine sawfly larvae feeding on Scots pine needles (blogs.cornell.edu)

Methodology

(1) Select suitable test areas (i.e., homogenous pine stands larger than MODIS pixels) from the areas with most frequent sawfly outbreaks.

(2) Trace possible forest management activities in potential sites to be excluded from test areas.

(3) Check different pine sawfly monitoring sources (see Further Reading) to determine which years can be considered as control, or low sawfly density years

(3b) Check other damage monitoring sources, weather data and associated drought simulations to determine which years may be unsuitable as control years because of other damages.

(4) Derive “background” indices from RS imagery for control years in each region.

(5) Derive/calculate the same indices from RS imagery for the known peak sawfly years in each region, and determine which kind of indices produces the best separation.

(6) Calculate the selected indices for all years and regions, and testing the correlations between the remotely sensed indices and pine sawfly monitoring results.

Training and Skills

CENTA students are required to complete 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to CENTA research themes.

The Centre for Landscapes and Climate Research at the University of Leicester will provide supervision and training for the student in using RS data to study forest health using optical sensors. Dr Barrett and Prof Balzter have extensive experience in remote sensing of disturbances in and characteristics of boreal forests in North America and Eurasia. The student will benefit from working in the CLCR, which has a critical mass of PhD students processing data using a range of analysis tools and programming languages for studying forest disturbance with RS data.

Timeline

Year 1: Student at Leicester preparing their literature review and working with preliminary data on sawfly outbreaks and RS imagery from MODIS, Landsat, and Sentinel-2 with Dr Barrett and Prof Balzter. Three months spent in Vantaa.

Year 2: Student based in Vantaa for 3 months refining methods and learning more about relevant available ecological data from Drs Peltoniemi and Neuvonen and perform fieldwork as necessary.

Year 3: Student based at Leicester to complete thesis and undertake viva examination.

Partners and collaboration (including CASE)

Dr Barrett is an expert in using RS data to study boreal forest natural disturbances in North America and Eurasia, and Dr Balzter has studied boreal forest biomass and ecosystem dynamics for two decades. Drs Peltoniemi and Neuvonen at Luke will oversee the ecological aspects of the PhD, and serve as hosts and supervisors to the student while they are in Finland. Dr Peltoniemi has extensive experience modelling productivity in Finnish forests, Dr Neuvonen is concerned with the topographical distribution of defoliator infestations. Given the social and economic implications of the research, it could potentially become CASE.

Further Details

Kirsten Barrett

Geography Department

University of Leicester, UK

kirsten.barrett@le.ac.uk

+44 (0)116 252 5010