remote sensing for the evaluation of forest's health during phosphite treatments

REMOTE SENSING FOR THE EVALUATION OF FOREST'S

HEALTH DURING PHOSPHITE TREATMENTS

July Galeano, Jan KotlarzInstitute of Aviation

December 18th 2012

Remote Sensing for the Evaluation of forest's health during phosphite treatments

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INTRODUCTION: Phytophthora in Oaks:

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First symptoms are in roots (Ulrika Jönsson, et al 2003).

In leaves just appear late symptoms: - Changes in color:

The crowns turn yellow and then brown due to stem cankers (Ulceration). Then, the crown turns grey as the foliage is lost (California Oak Mortality Task Force).

- Changes in the crown transparency (defoliation.)

Phytophtora: a pathogen that has caused enormous economic losses on crops worldwide, as well as environmental damage in natural ecosystems

Oak: kind of tree which wood in widespread used. In Poland, oak is the tree specie that is the most attacked by Phytophtora

+

Healthy Unhealthy


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OBJECTIVES

1. Estimation of phosphites (chemical compoud) effectiveness as elicitors of trees resistance against invasive Phytophthora.

2. Implementation and introduction into practice new methods of assessment of forest healthiness through Imageries from UAV (Unmaned Aerial Vehicles). (Those methods will be correlated with manual techniques.)

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MATERIALS AND METHODS

• Areas of Evaluation:

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• Krotoszyn and Karczma Borowa forest district : 5 ha each.

• Piaski forest district: 50 ha.

30 trees in different vitality classes will be chosen to test the phosphites effectiveness.

Actions:- Estimation of healthiness of oak

before Phosphites treatment.- Chemical Treatment: air spraying.- Annual monitoring


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MATERIALS

Remote Sensing: UAV + CAMERA • Trade-off between:

• Area Coverage • Level of detail (Spatial Resolution Res)

Spixel: pixel sizeHf: flying heightf: lens focal length

(Paine & Kiser, 2002)

• Spectral Detail (the number of spectral bands of information captured for each pixel.)

The Food and Environmental Research Agency (FERA),UK.

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6Spectral Detail: RGB Cameras VS

Multispectral Cameras

- Only 3 color channels- Just a color perception- No spectral details! - Limited spectral Information!!!

- Several spectral channels in VIS-NIR- Truth Color perception - Spectral information at the given channels!!

540

450

B

G

R

650

730

Wavelength (nm)

Ref. Images: Foster et al. 2004

Ref. Images: Foste

r et al. 2004

Reflectance Spectrum

in one pixel

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MATERIALS: RGB Vs. Multispectral Cameras

RGB CAMERAS:Wide Band Filters only RGB

MULTI SPECTRAL CAMERAS:Narrow Band Filters

Refelctance Spectrum for Plants Refelctance Spectrum for Plants

Fang Qiu et al.

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MATERIALS• Multispectral camera: Tetracam MiniMCA 12 Channels.Camera Dimensions

(mm3) Weight Power

consumption Output (Video

Quality)

Spectral Resolution

Spatial Resolution

Tetracam Mini-MCA12: first choice (1280 x 1024) 1.3 megapixel CMOS sensor, 1 per channel (12 chanels). http://www.tetracam.com/Products-Mini_MCA.htm

154,43 93,218 117,062

Mini-MCA12: 1300 g.

12 VDC @ 9W NTSC or PAL 10 – 20 nm wide band.

Object Distance (Altitude Above Ground

Level in meters) (m)

Ground Resolution

in mm per pixel (mm)

FOV (width x height) in meters

122 66 84x67 213,4 116 148x118 365,8 198 254x203 915 496 635x508

aisaEaglET (from Specim) (Optional camera) http://www.specim.fi/index.php/products/airborne/aisaeaglet

290 140 120

3,5 Kg With CPU: +8Kg. With GPS/INS sensor: +2,2Kg

Complete system: <100 W Typical, DC 10-30 V

12 bits digital Range of: 400-1000 nm Resolution of: 3.3 nm

0,4m @ 1000m altitude.

J.A.J Berni et al., Quantalab Spain

A. Laliberte et al., Agricultural Research Service. USA

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MATERIALS• Example Images adquired with TETRACAM Mini-MCA 6

(Images provided by TETRACAM. flight altitude: 1,5 Km. Center wavelength-bandwith in nm):

780 - 10

450 - 20

730 - 10700 - 10

530 - 10 670 - 10

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METHODS

Strategy in flying: Mapping of the forest field

- The images taken along each of the multiple flight lines must contain enough overlaps 40-60% (Haitao Xiang et al)?

- Image overlaps are affected by:- Position at the waypoint (position of the exposure points)

- Task to do: lines of flight and waypoints determination

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40%

20%


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METHODS

ImageProcessing Forest Classification

Multi-Spectral Image

Ref. Image: Natural Resources Canadawww.nrcan.gc.ca

Ref. Images: Foster et all 2004

Atmospheric Calibration

Geometrical Corrections

(For image registration or Image superposition)

Image/Spectral analysis

Image processing strategy.

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METHODS• Geometrical Corrections:

• Image distortions are given by the fligth dynamics of the UAV: changes in roll , pitch , yaw , and altitude .

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𝝎 𝒙𝝎 𝒚

𝝎 𝒛

For a given image, it is necessary to know the parameters (, , ,H) for an accurate image registration of the observed areas.

𝝎 𝒙𝝎 𝒚

𝝎 𝒛


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METHODS• Atmospheric Corrections:

total upwelling radiance

atmospheric transmittance

spectral radiance from the surface entering

the atmosphere

path radiance

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Atmosphere

𝐿 𝜆

𝐿 𝑝(𝜆)

𝐿𝑠(𝜆)𝜏𝑎(𝜆)


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METHODS• Atmospheric Corrections:

Atmospheric Transmittance

path radiance:Dependent in aerosol concentrationExtinction optical

Thickness. : visibility : - aerosol scattering - ozone absorption Solar Zenith

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METHODS•Atmospheric Corrections:

Changes in measured radiance Vs. Altitude?:

- Dependant on the meteorological conditions of the day!!

- altitudes less than 0.45 km: - aerosols and the absorbing gases are well mixed- atmospheric correction is an easy task based on a

linearly interpolate between the atmospheric optical properties (and ) at Z = 0 km and at Z = 0.45 Km above the ground (Robert S. Fraser et al. Algorithm for Atmospheric Corrections of Aircraft and, Satellite Imagery. NASA)

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METHODSImage Processing: Spectral analysis

Vegetation Indeces

Physiological Statistical

Method for clasifying forest in:- 1 Healthy- 2 symptomatic- 3 Asymptomatic- 4 DeadClassification of Forest species

Physical Model Ligth-Forest Interaction:- PROSPECT- SUITS and SEAL Model

combinations of surface reflectance at two or more wavelengths designed to highlight a particular property of vegetation

On-line:3 ms full image

Of 1280x1024 pixels On-line??

Off-line:50 ms - 15sPer pixel!!

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METHODS: Vegetation Indeces

Spectral Index Characteristics and Functions

Definition

NDVI: Normalized Difference Vegetation Index

Respond to change in the amount of green biomass and more efficiently in vegetation with low to moderate density

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METHODS: Vegetation Indeces

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METHODS: Physiological 1

E0R

0

Z

L1

L2

L3

Light scattering depends on the wavelength and the size (d), shape, and refractive index (n) of the scattering particle.

𝜇𝑎=∑𝑖𝐾 (𝜆)𝑖 Absorption of the light depends on the

wavelength and, in the case of leaves, is dependent on absorption coefficients (k) such as: Water content Dry matter contentChlorophyll contentCarotenoid

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𝜎 𝑠 (𝑑 ,𝑛 ,𝜆 )

Related with ForestHealthiness


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METHODS: Physiological 1• Example using Non-Negative Matrix Factorization:

• Images from Multi-Spectral Camera with 6 bands (530 670 700 730 780) nm• Altitude 1.5 Km.• Only Absorption considered:

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ROI (Region of Interest) of an Area at 700 nm Estimated Chlorophyll Concentration Map (cm2.microg-1)

Pix

el N

umbe

r

Pixel Number


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METHODS: Physiological 2Kubelka-Munk:• The radiation field inside the material consists of fluxes propagating in opposite

directions forward I(x) and backward J(x) at depth x at any wavelength λ (Prospect and Seal (Feret et al. ))

• Solutions to the previous equations are given in terms of the diffuse reflectance (R) and diffuse transmittance (T) in terms of k (absorption), s (scattering), and the thickness L of the medium :

Ref. Figure:KUBELKA-MUNK THEORY IN DESCRIBING OPTICAL PROPERTIES OF PAPER

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METHODS: Statistical• Discriminations of scene components by

means of machine learning:

• Implies the training of the algorithm with ground truth or known data: use of spectrophotometers!!!

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Spectral signature at pixel X

Healthy orSymptomatic orInnorganic area….


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METHODS: Statistical

Institute for Technology Development 2008

Difference between healthy OAKS and ill ones: the ill Oaks present lower NIR reflectance and higher reflectance values around the green.

Big differentiation of trees species:

HealthyPathSymptomaticDeath Plant

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Ref. Giuseppina, Vannini et al.


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FINAL COMENTS

• The use of Multispectral systems implies prior radioactive calibration!!

• trees’ defoliation: change in chlorophyll content?. Change in spectral signature? (Leckie, 1988; MICOL ROSSIN 2006).

• Perspective: Cellular phone applications

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THANK YOU FOR YOUR ATTENTION!!!

Questions….Remarks….

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BIBLIOGRAPHY• Ulrika Jönsson, et all. Pathogenicity of Swedish isolates of Phytophthora quercina to Quercus robur in two different

soils. New Phytologist. (2003) 158:355-364.• T. Jung, et all. Involvement of soilborne Phytophthora species in Central European oak decline and the effect of site

factors on the disease. Plant Pathology (2000) 49, 706-718.• Matteo Garbelotto, et all. How to recognize symptoms of diseases caused by Phytophthora ramorum, causal agent

of Oak Death. • Jose A.J. Berni. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an

Unmanned Aerial Vehicle. IEEE Transactions on Geoscience and Remote Sensing. 0196-2892. 2009.• Fang Qiu. LiDARLiDAR and Hyperspectral Imagery Based Urban Tree Inventory. Remote Sensing and Geographic

Information Sciences. The University of Texas at Dallas.• MICOL ROSSIN, Assessment of oak forest condition based on leaf biochemical variables and chlorophyll

fluorescence. Tree Physiology 26, 1487–1496. 2006.• Institute for Technology Development. Detection, Mapping, and Monitoring of

Sudden Oak Death Using Hyperspectral Imagery, Final Report. April 2008.• Jean-Baptiste Feret. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic

pigments. Remote Sensing of Environment 112 (2008) 3030–3043.• Foster, D.H., Nascimento, S.M.C., & Amano, K. (2004). Information limits on neural identification of colored surfaces

in natural scenes. Visual Neurosci., 21, 331-336.• Classification of airborne multispectral scanner data for mapping current defoliation caused by the spruce budworm.

1988. Leckie, D.G.; Ostaff, D.P. Forest Science 34(2): 259-275.• Andrea S. Laliberte. Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and

Applications for Rangeland Environments . Remote Sens. 2011, 3, 2529-2551; doi:10.3390/rs3112529 .• Medcalf, K. A., Bodevin, N., Cameron, I., Webber J and Turton, N., (2011) Assessing the Potential of Using Remote

Sensing in Support of Current Phytophthora Work. Report to FERA. UK.

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remote sensing for the evaluation of forest's health during phosphite treatments

Documents

evaluation of forests

phytophtora attacks

methods areas of evaluation

phosphite treatmentsgiven

phosphite treatmentsfor

piaski forest district

kind of tree

tree specie