WO2010072876A1 - Méthode de caractérisation de végétation - Google Patents

Méthode de caractérisation de végétation Download PDF

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Publication number
WO2010072876A1
WO2010072876A1 PCT/ES2009/070602 ES2009070602W WO2010072876A1 WO 2010072876 A1 WO2010072876 A1 WO 2010072876A1 ES 2009070602 W ES2009070602 W ES 2009070602W WO 2010072876 A1 WO2010072876 A1 WO 2010072876A1
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WIPO (PCT)
Prior art keywords
vegetation
images
pri
index
spri
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PCT/ES2009/070602
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English (en)
Spanish (es)
Inventor
José A. JIMÉNEZ BERNI
Elías FERERES CASTIEL
Mª Dolores SUAREZ BARRANCO
Pablo J. Zarco Tejada
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Consejo Superior De Investigaciones Científicas (Csic)
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Publication of WO2010072876A1 publication Critical patent/WO2010072876A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/2823—Imaging spectrometer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/4738—Diffuse reflection, e.g. also for testing fluids, fibrous materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/2823—Imaging spectrometer
    • G01J2003/2826—Multispectral imaging, e.g. filter imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J2005/0077—Imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1793—Remote sensing
    • G01N2021/1797—Remote sensing in landscape, e.g. crops
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G06T2207/10036—Multispectral image; Hyperspectral image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture

Definitions

  • the main object of the present invention is a method of estimating the theoretical PRI (index of Photochemical Reflectance) corresponding to situations of absence of water stress in vegetation from a simulation with inversion of simulation models generated from data obtained by remote sensing , as well as its use combined with temperature data of the vegetation to identify water stress.
  • Precision agriculture was born in the late 80s and early 90s in the US. Its objective is to adjust the use of agricultural resources and cultivation methods to adapt them to the heterogeneity present in the soil or crop. In other words, precision agriculture allows for greater profitability, productivity, sustainability, product quality, environmental protection, food security, and finally, greater rural development. To achieve all these objectives, precision agriculture needs to make use of the so-called information and communication technologies: global positioning systems (GPS), geographic information systems (GIS), Remote Sensing, data entry application technologies with doses variable (VRT), etc.
  • GPS global positioning systems
  • GIS geographic information systems
  • VRT doses variable
  • the two main methods used for the estimation of biophysical variables by remote sensing are: vegetation indices and the inversion of simulation models.
  • vegetation indices is an effective tool for the determination of the properties of the vegetation cover, since these are capable of increasing the signal of the vegetation while minimizing the side effects (and undesirable in most cases) derived from lighting and floor conditions.
  • Vegetation indices are combinations of two or more bands that can be calculated from the sensor outputs: voltage, reflectance or numerical counts. All are correct but each will produce different values of the vegetation index for the same observation conditions. It is considered that vegetation indices should be calculated from the reflectance so that the indices can be comparable between images taken on different dates. This ease of calculation has made vegetation indices widely used today as a non-destructive tool for estimating biophysical variables
  • a good index should be sensitive to the variation of the variable studied, but be resistant (or be minimally affected) to other factors such as the atmosphere, the soil, the architecture of the vegetation cover and the topography. According to the effects that an index is capable of facing, it is classified as: intrinsic, resistant to the ground or resistant to the atmosphere.
  • the use of these indexes presents some drawbacks, given that none of them have achieved to date completely eliminate unwanted influences.
  • its use does not allow estimating more than one variable at the same time, which has to be specifically calibrated by means of an empirical equation whose mathematical form and coefficients are particular for each estimate.
  • vegetation indices are valid empirical relationships for each image (as they are associated with their acquisition conditions) and, therefore, their operational use to estimate biophysical variables is not evident.
  • the inversion of simulation models consists in adjusting the values of the biophysical variables used as input data of the radiative transfer model, so that the reflectance simulated with them is as close as possible to that measured by the sensor.
  • These models of simulation of radiative transfer simulate, therefore, the so-called bidirectional reflectance function (known as BDRF), which allows the calculation of the reflectance of a surface based on the viewing and lighting angles , as well as a description of the biophysical and radiative characteristics thereof.
  • BDRF bidirectional reflectance function
  • Another advantage of the physical inversion of the simulation model is the fact that it is possible to use all the radiometric information provided by the multispectral sensor; contrary to vegetation indices that essentially use only two bands (red and near infrared).
  • the information contained in the different bands of a sensor is never completely correlated and, therefore, the use of all spectral information provides additional information.
  • this method allows working with the directional information provided by most of the new sensors. This type of study presents several problems due to the diversity existing between the different crops or the determination of the parameters necessary for carrying out the study. You also have to take into account
  • the object of this invention is a method for determining the theoretical index of water stress in vegetation by estimating the temperature of the vegetation, as well as the simulation and by using simulation models of radiative transfer and its inversion.
  • sPRI photochemical reflectance index
  • a remote sensing or remote sensing is carried out with thermal cameras and narrow band multispectral cameras that will be responsible for the acquisition of spectral and thermal images that will be used to make the model.
  • the cameras used in this method are two types, on the one hand of 6-band multispectral type, while thermal images are captured by thermal cameras.
  • the multispectral camera comprises 6 image sensors with 10nm pass filters calibrated radiometrically in the laboratory.
  • the parameters of the multispectral camera are obtained by means of the Bouguet calibration method; through this method, the intrinsic parameters of the camera are recovered, such as: focal length, coordinates of the main points and the radial distortion of the lenses.
  • a simulation model is used based on the WoIf simulation model, by which you can estimate both
  • the camera responsible for the acquisition of images thermal this is calibrated in the laboratory using a black body and stabilizing it before capturing.
  • the camera incorporates an FPA sensor with a spectral range of 7.5 -13 ⁇ m and allows working in a range of 233-393K;
  • the sensor has two internal calibrations implemented: one referred to the internal temperature calibration and the other is a non-uniformity correction calibration (NUC).
  • NUC non-uniformity correction calibration
  • the theoretical PRI is determined in situations of absence of water stress for the crop or part of the crop studied. This theoretical PRI obtained determines the value considered as the baseline for the determination of water stress, being therefore possible to estimate the water stress situation of a plantation or crop by obtaining in-situ PRI of said crop and its comparison with the sPRI or theoretical PRI using this method.
  • a PROSPECT radiative transfer model connected to a FLIGHT radiative transfer model (3D Forest Light Interaction Model), which is based on the Montecarlo method of "ray tracing" (MCRT), This is a model that refers to the interaction between light and Ia vegetation.
  • MCRT Montecarlo method of "ray tracing"
  • a radiative transfer model is made for the structure of the upper layer of the vegetation.
  • the FLIGHT radiative transfer model is used together with the PROSPECT radiative transfer model.
  • the results sought are obtained through an inversion of the PROSPECT-FLIGHT model based on independent tables for each crop and image acquisition conditions.
  • the method of inversion of the simulation model is based on the inversion of the pair of simulation model "leaves-upper layer” for the values of Cab (chlorophyll content) and LAI (Leaf Area Index or leaf index).
  • the simulation model is reversed by keeping the structural parameter (N), the water content (Cw) and the amount of dry matter (Cm) fixed, all of them obtained from the specific literature published for each type of crop (in this case Kempeneers et al. for peach trees and Zarco-Tejada et al. for olive trees), while variations in the values related to Cab (chlorophyll content) and LAI (Leaf Area Index or leaf index) are allowed both at the level of sheet as in the upper layers or canopy.
  • the rest of the parameters remain fixed, being characteristic for each crop and based on the representative data of the plantation, obtaining as a result a LUT (results table called by its acronym Look Up Table in English) simulated for each crop.
  • sPRI a theoretical or simulated PRI is obtained, called sPRI; from which a baseline is established that determines the limit for the water stress situation of a given crop.
  • the images in digital format acquired by the multispectral and thermal sensors are obtained.
  • the captured images are taken to the laboratory where the process of calibration and correction of the images begins and the images are calibrated radiometrically applying calibration coefficients previously generated in the laboratory with calibration instruments.
  • the atmospheric correction of the images is carried out by means of an atmospheric simulation model and data measured in the optical thickness field at the time of capturing.
  • a Geometric correction and mosaics are generated by joining all the images taken by the cameras.
  • the simulation model based on the pair based on the reflectance index in absorption of transformed chlorophyll TCARI (Transformed Chlorophyll Absorption in Reflectance Index) / and the vegetation index OSAVI is applied (Optimized Soil Adjusted Vegetation Index in its acronym in English) for the estimation of chlorophyll content.
  • Simulation models based on NDVI reflectance index (standardized vegetation differential index, also known as NDVI - Normalized Difference Vegetation Index for its acronym in English) are used, an index used to estimate the quantity, quality and development of Ia vegetation) for the estimation of leaf area index;
  • thermal-based simulation models are applied to estimate the temperature of the vegetation.
  • the average spectrum is used as input to the simulation model of radiative transfer for its investment, using input data for all its parameters except for the N and chlorophyll a + b foliar, and LAI cover.
  • the simulation model is inverted from said average spectrum of the scene taken with the multispectral camera (from which PRI is calculated), and the theoretical spectrum for non-stress conditions (from which sPRI is calculated) is obtained by inversion.
  • the sPRI baseline will define the spectral region above which it is considered that there will be stress.
  • stress classes are generated, specifically low, medium and high stress, therefore mapping the state of stress of the vegetation from multispectral and thermal images.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Multimedia (AREA)
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  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Astronomy & Astrophysics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Botany (AREA)
  • Ecology (AREA)
  • Forests & Forestry (AREA)
  • Environmental Sciences (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Image Processing (AREA)

Abstract

La méthode de cette invention est utile pour déterminer des situations de stress hydrique dans la végétation. Cette méthode est fondée sur des modèles de transfert radiatif formés à partir d'images thermiques et multispectrales et sur leur inversion postérieure afin d'obtenir un indice de photorésistance chimique (PRI) théorique à partir de laquelle on peut déterminer la situation de la végétation par comparaison à la PRI.
PCT/ES2009/070602 2008-12-23 2009-12-17 Méthode de caractérisation de végétation WO2010072876A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ES200803673A ES2341696B1 (es) 2008-12-23 2008-12-23 Metodo de caracterizacion de vegetacion.
ESP200803673 2008-12-23

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106067169A (zh) * 2016-05-24 2016-11-02 北京农业信息技术研究中心 植物水分胁迫状态自动监控方法及系统
CN108254396A (zh) * 2017-12-05 2018-07-06 江苏大学 一种基于micro-CT和偏振-高光谱成像多特征融合的番茄苗期水分胁迫检测方法
RU2746690C1 (ru) * 2020-05-07 2021-04-19 Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский Нижегородский государственный университет им. Н.И. Лобачевского» Система для измерения фотохимического индекса отражения PRI у растений

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109583311A (zh) * 2018-10-31 2019-04-05 中化地质矿山总局地质研究院 采矿区周边粉尘影响评价方法及系统

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US5187754A (en) * 1991-04-30 1993-02-16 General Electric Company Forming, with the aid of an overview image, a composite image from a mosaic of images
WO2001033505A2 (fr) * 1999-11-04 2001-05-10 Monsanto Company Modele multivariable destine a identifier des zones de reaction de culture dans un champ
US6567537B1 (en) * 2000-01-13 2003-05-20 Virginia Commonwealth University Method to assess plant stress using two narrow red spectral bands
US7068816B1 (en) * 2002-01-15 2006-06-27 Digitalglobe, Inc. Method for using remotely sensed data to provide agricultural information

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5187754A (en) * 1991-04-30 1993-02-16 General Electric Company Forming, with the aid of an overview image, a composite image from a mosaic of images
WO2001033505A2 (fr) * 1999-11-04 2001-05-10 Monsanto Company Modele multivariable destine a identifier des zones de reaction de culture dans un champ
US6567537B1 (en) * 2000-01-13 2003-05-20 Virginia Commonwealth University Method to assess plant stress using two narrow red spectral bands
US7068816B1 (en) * 2002-01-15 2006-06-27 Digitalglobe, Inc. Method for using remotely sensed data to provide agricultural information

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106067169A (zh) * 2016-05-24 2016-11-02 北京农业信息技术研究中心 植物水分胁迫状态自动监控方法及系统
CN108254396A (zh) * 2017-12-05 2018-07-06 江苏大学 一种基于micro-CT和偏振-高光谱成像多特征融合的番茄苗期水分胁迫检测方法
WO2019109383A1 (fr) * 2017-12-05 2019-06-13 江苏大学 Procédé de détection de stress hydrique pour tomates au stade de semis sur la base d'une fusion de caractéristiques multiples de micro-ct et d'imagerie hyperspectrale en polarisation
US11436824B2 (en) 2017-12-05 2022-09-06 Jiangsu University Water stress detection method for tomatoes in seedling stage based on micro-CT and polarization-hyperspectral imaging multi-feature fusion
RU2746690C1 (ru) * 2020-05-07 2021-04-19 Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский Нижегородский государственный университет им. Н.И. Лобачевского» Система для измерения фотохимического индекса отражения PRI у растений

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ES2341696A1 (es) 2010-06-24

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