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Remote Sensing for the Evaluation of forest's health during phosphite treatments. July Galeano , Jan Kotlarz Institute of Aviation December 18th 2012. INTRODUCTION : Phytophthora in Oaks :.

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

Remote Sensing for the Evaluation of forest's health during phosphitetreatments

JulyGaleano, Jan Kotlarz

Institute of Aviation

December 18th 2012

introduction phytophthora in oaks
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsINTRODUCTION: Phytophthora in Oaks:

Oak:kind of treewhichwood in widespreadused. In Poland, oakis the treespeciethatis the most attackedby Phytophtora

+

  • 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 pathogenthat has causedenormous economic losses on crops worldwide, as well as environmental damage in natural ecosystems

Healthy

Unhealthy

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

1. Estimation of phosphites(chemicalcompoud) effectiveness as elicitors of trees resistance against invasivePhytophthora.

2. Implementation and introduction into practice new methods of assessment of forest healthinessthrough Imageries from UAV (UnmanedAerialVehicles). (Thosemethodswill be correlated with manualtechniques.)

materials and methods
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMATERIALS AND METHODS
  • Areas of Evaluation:
  • Krotoszyn and KarczmaBorowaforestdistrict:5 haeach.
  • Piaski forestdistrict: 50 ha.
  • 30 trees in different vitality classes will be chosen to test the phosphiteseffectiveness.
  • Actions:
  • Estimation of healthiness of oak beforePhosphitestreatment.
  • ChemicalTreatment: airspraying.
  • Annual monitoring
materials
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMATERIALS

Remote Sensing: UAV + CAMERA

  • Trade-off between:
    • Area Coverage
    • Level of detail (Spatial Resolution Res)

Spixel:pixelsize

Hf:flyingheight

f:lensfocallength

(Paine& Kiser, 2002)

    • Spectral Detail (the number ofspectral

bands ofinformationcaptured for each pixel.)

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

slide6

B

Reflectance Spectrum

in one pixel

730

540

450

Wavelength (nm)

G

650

R

Ref. Images: Foster et al. 2004

Ref. Images: Foster et al. 2004

materials rgb vs multispectral cameras
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMATERIALS:RGB Vs. Multispectral Cameras

Refelctance Spectrum for Plants

Refelctance Spectrum for Plants

Fang Qiu et al.

RGB CAMERAS:

Wide Band Filtersonly RGB

MULTI SPECTRAL CAMERAS:

Narrow Band Filters

materials1
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMATERIALS
  • Multispectral camera: TetracamMiniMCA 12 Channels.

A. Laliberte et al., AgriculturalResearchService. USA

J.A.J Berni et al., QuantalabSpain

materials2
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMATERIALS
  • ExampleImagesadquired with TETRACAM Mini-MCA 6 (Imagesprovided by TETRACAM. flightaltitude: 1,5 Km. Center wavelength-bandwith in nm):

450 - 20

530 - 10

780 - 10

670 - 10

730 - 10

700 - 10

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

Strategy in flying: Mapping of the forestfield

  • The imagestakenalongeach of themultiple flight linesmust containenoughoverlaps 40-60% (HaitaoXianget al)?
  • Image overlapsareaffected by:
    • Positionat the waypoint(position of the

exposure points)

  • Task to do: lines of flight and waypointsdetermination

40%

20%

methods1
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS

Image processingstrategy.

Multi-Spectral Image

Image

Processing

ForestClassification

Geometrical Corrections

(For image registration or Image superposition)

Ref. Image: Natural Resources Canada

www.nrcan.gc.ca

Atmospheric Calibration

Ref. Images: Foster et all 2004

Image/Spectralanalysis

methods2
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS
  • GeometricalCorrections:
    • Image distortionsaregiven by the fligth dynamics of the UAV: changes in roll, pitch, yaw, and altitude.

For a given image, itisnecessary to knowthe parameters (, , ,H) for anaccurate image registration of the observedareas.

methods3
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS
  • Atmospheric Corrections:

total upwelling

radiance

atmospheric

transmittance

spectral radiance from

the surface entering

the atmosphere

path radiance

Atmosphere

methods4
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS
  • Atmospheric Corrections:

Atmospheric Transmittance

path radiance:

Dependent in aerosol concentration

Extinction optical

Thickness.

: visibility :

- aerosol scattering

- ozone absorption

Solar Zenith

methods5
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS
  • AtmosphericCorrections:
  • 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 arewell mixed
    • atmosphericcorrectionisaneasytaskbased on a linearly interpolate between the atmospheric optical properties (and) at Z = 0 km and at Z = 0.45 Kmabove the ground (Robert S. Fraser et al.Algorithm for Atmospheric Corrections of Aircraft and, Satellite Imagery. NASA)
methods6
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS

Image Processing: Spectralanalysis

Physiological

Statistical

VegetationIndeces

combinations of surfacereflectance at two ormore wavelengthsdesigned to highlight aparticular property ofvegetation

  • Physical Model Ligth-Forest
  • Interaction:
  • PROSPECT
  • SUITS and SEAL Model
  • Method for clasifyingforest in:
  • 1 Healthy
  • 2 symptomatic
  • 3 Asymptomatic
  • 4Dead
  • Classification of Forest species

Off-line:

50 ms - 15s

Per pixel!!

On-line:

3 ms full image

Of 1280x1024 pixels

On-line??

methods physiological 1

L1

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

R

E0

METHODS: Physiological 1

L2

L3

Z

0

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

Absorption of the lightdepends on the wavelength and, in the case of leaves, is dependent on absorption coefficients (k) such as:

Water content

Dry matter content

Chlorophyll content

Carotenoid

Related with Forest

Healthiness

methods physiological 11
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS: 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:

Estimated Chlorophyll

Concentration Map (cm2.microg-1)

ROI (Region of Interest) of an Area at 700 nm

PixelNumber

PixelNumber

methods physiological 2
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS: Physiological 2

Kubelka-Munk:

  • The radiation field inside the materialconsists of fluxes propagating in opposite directionsforwardI(x) and backwardJ(x) atdepth x atanywavelengthλ(Prospect and Seal (Feret et al. ))
  • Solutions to the previousequationsaregiven in terms of the diffusereflectance (R) and diffusetransmittance (T) in terms of k (absorption), s (scattering), and the thicknessLof the medium :

Ref. Figure:

KUBELKA-MUNK THEORY IN DESCRIBING OPTICAL PROPERTIES OF PAPER

methods statistical
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS: Statistical
  • Discriminations of scene components by means of machine learning:
  • Implies the training of the algorithmwithgroundtruth or known data: use of spectrophotometers!!!

Healthyor

Symptomaticor

Innorganicarea….

Spectralsignature

atpixelX

methods statistical1
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsMETHODS: Statistical

Healthy

Path

Symptomatic

Death Plant

Ref. Giuseppina, Vannini et al.

Institute for Technology Development 2008

final coments
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsFINAL 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 phoneapplications
bibliography
Remote Sensing for the Evaluation of forest's health during phosphite treatmentsBIBLIOGRAPHY
  • Ulrika Jönsson, et all. Pathogenicity of Swedish isolates of Phytophthoraquercina to Quercusrobur in two different soils. New Phytologist. (2003) 158:355-364.
  • T. Jung, et all. Involvement of soilbornePhytophthora species in Central European oak decline and the effect of site factors on the disease. Plant Pathology (2000) 49, 706-718.
  • MatteoGarbelotto, et all. How to recognize symptoms of diseases caused by Phytophthoraramorum, 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. LiDARLiDARand 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-BaptisteFeret. 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 neuralidentification of coloredsurfaces in naturalscenes. Visual Neurosci., 21, 331-336.
  • Classification of airbornemultispectralscanner data for mappingcurrentdefoliationcaused by the sprucebudworm. 1988. Leckie, D.G.; Ostaff, D.P. Forest Science 34(2): 259-275.
  • Andrea S. Laliberte. Multispectral Remote Sensing from Unmanned Aircraft: ImageProcessing Workflows andApplications for RangelandEnvironments . 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 ofUsing Remote Sensing in Support of Current Phytophthora Work. Report to FERA. UK.