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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 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

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  1. Remote Sensing for the Evaluation of forest's health during phosphitetreatments JulyGaleano, Jan Kotlarz Institute of Aviation December 18th 2012

  2. Remote Sensing for the Evaluation of forest's health during phosphite treatments INTRODUCTION: 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

  3. Remote Sensing for the Evaluation of forest's health during phosphite treatments OBJECTIVES 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.)

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

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

  6. 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

  7. Remote Sensing for the Evaluation of forest's health during phosphite treatments MATERIALS: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

  8. Remote Sensing for the Evaluation of forest's health during phosphite treatments MATERIALS • Multispectral camera: TetracamMiniMCA 12 Channels. A. Laliberte et al., AgriculturalResearchService. USA J.A.J Berni et al., QuantalabSpain

  9. Remote Sensing for the Evaluation of forest's health during phosphite treatments MATERIALS • 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

  10. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS 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%

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

  12. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS • 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.

  13. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS • Atmospheric Corrections: total upwelling radiance atmospheric transmittance spectral radiance from the surface entering the atmosphere path radiance Atmosphere

  14. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS • Atmospheric Corrections: Atmospheric Transmittance path radiance: Dependent in aerosol concentration Extinction optical Thickness. : visibility : - aerosol scattering - ozone absorption Solar Zenith

  15. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS • 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)

  16. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS 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??

  17. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS: VegetationIndeces

  18. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS: VegetationIndeces

  19. 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

  20. Remote Sensing for the Evaluation of forest's health during phosphite treatments 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: Estimated Chlorophyll Concentration Map (cm2.microg-1) ROI (Region of Interest) of an Area at 700 nm PixelNumber PixelNumber

  21. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS: 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

  22. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS: 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

  23. Remote Sensing for the Evaluation of forest's health during phosphite treatments METHODS: Statistical Healthy Path Symptomatic Death Plant Ref. Giuseppina, Vannini et al. Institute for Technology Development 2008

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

  25. Remote Sensing for the Evaluation of forest's health during phosphite treatments THANK YOU FOR YOUR ATTENTION!!!Questions….Remarks….

  26. Remote Sensing for the Evaluation of forest's health during phosphite treatments BIBLIOGRAPHY • 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.

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