Hyperspectral leaf reflectance for the non-destructive monitoring of urban tree diseases

Melanka Brackx, Shari Van Wittenberghe, Paul Scheunders and Roeland Samson

Observatree/IPSN Conference 23-24 februari 2016

Urban vegetation biomonitoring

Biomonitoring

Study the burden of environmental toxins to living organisms (plants)

Analyze plant indication of environmental health

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Problem

• Traffic related air pollution

major risk to health

• Need for air quality monitoring

• Limited number of air quality monitoring stations

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Solution?

Urban trees for biomonitoring air pollution

• Every tree can be a “monitoring station”

• Trees suffer from air pollution

Leaf physical and biochemical characteristics

Aim of my Phd research:

• Assess the potential of hyperspectral leaf reflectance as a tool for biomonitoring the urban environment

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Hyperspectral remote sensing

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Hyperspectral leaf reflectance

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Measuring leaf reflectance

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• Agrispec (ASD Inc.)

• VIS-NIR Spectroradiometer

• 350-2500nm

Monitoring of urban tree diseases

Techniques can be adapted from crop protection programs

spectral data was proposed to detect e.g. :

• blight on tomatoes

• rust on sugar cane

• feeding of ladybirds on eggplant

• sugar beet diseases

• apple scab

• citrus greening

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Challenges

• Differences between species

• Natural variation between trees of the same species

• Different stages of infection

• Multiple infections or stress factors

• Surroundings and background in urban environment

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Monitoring of urban tree diseases

Three different diseases:

• Powdery mildew

• Tar spot disease

• Horse-chestnut leaf miner

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Powdery mildew

Two tree species

• pedunculate oak (Quercus robur)

• field maple (Acer campestre)

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Powdery mildew

Two tree species

• pedunculate oak (Quercus robur)

• field maple (Acer campestre)

Characteristics

• White fungus on upper leaf side

• Affects many species

• Weakens plants and can deform young twigs

• Hyperspectral data of powdery mildew on crops have already been studied

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Powdery mildew

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Spectral reflectance

Powdery mildew

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Spectral reflectance

Tar spot disease (Rhytisma acerinum)

Sycamore maple (Acer pseudoplatanus)

Characteristics

• Clearly delineated black spots appear on leaves in late summer and autumn

• Commonly affects sycamores and maples

• No effect on the tree’s long-term health

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Tar spot disease

Spectral reflectance

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Horse-chestnut leaf miner (Cameraria ohridella)

Horse-chestnut (Aesculus hippocastanum)

Characteristics

• Leaf-mining moth of the Gracillariidae family

• Recently introduced invasive pest

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Horse-chestnut leaf miner

Spectral reflectance

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Analysis

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Vegetation indices

• Ratio of reflectance in different bands

• Related to chemical and physical leaf characteristics

• E.g. NDVI

Vegetation indices

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Statistical analysis

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Mixed model statistics

• For nested data: Multiple spectra were taken within leaves and multiple leaves were sampled within trees

• MIXED EFFECTS MODELS AND EXTENSIONS IN ECOLOGY WITH R (Zuur et al. 2009)

Within tree correletations

Intra class correlations (ICC)

High ICC lowers effective sample size Neff

e.g. : 7 uninfected trees 15 leaves per tree 105 samples?

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Conclusions

• Each diseases has clear effects on the reflectance of the leaves

• Strong within-tree correlations

Other factors are also influencing the spectra

Many different trees should be sampled

• Spectral indices are a good tool for the detection of tree diseases, however better indices could be developed

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Future perspectives

• Early stages of infection

• Discriminate multiple infections or stress factors

• Correct for surroundings and background in urban environment

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Thank you

Research funded by BOF (Special Research fund) of the University of Antwerp in the frame of the project ’Urban vegetation biomonitoring: exploring the potential of hyperspectral remote sensing’