Information Technology in Plant Protection

Information Technology in Plant Protection

Presentation

• Prepared by:

– Dr. János Busznyák

GIS tools for Plant Protection

• Methods of Obtaining Spatial Data – Manual

– Geodesy

– With the help of Global Positioning – Photogrammetry

– Remote Sensing

– Manual Map Digitalisation – Scanning Maps

– From Digital Files

Digital Mapping Tools for Plant Protection

• Not only the digital form of the contents of a map ready to be used with a computer.

• No need for segmentation, the elements are of real size, has accurate fitting, has topology, often uses layers and objects.

• Primary Data Obtaining Methods – Measurements (GPS)

– Existing Reports

Mostly vector data are obtained from primary data obtaining methods.

• From Secondary Sources

– By digitalization, adding automatic or manual vectorization.

In the case of georeferencing and vectorization in secondary methods, the result is also a vector map. If a secondary data

collection (scanning) is not followed by vectorization, the result is a

Digital Map

• Aim

– New level of GPS analysis (vector) – New publication possibilities

– Lower storage and transfer capacity needs

• Preparatory steps

– Digitalization of map sheets

– Georeferencing, eliminating distortions, projection convertion (lots of work)

• Pre-processing

• Vectorization – Of areas

– Of line-like objects – Of objects

• Post-processing

Raster-Vector Transformation

• Vectorization

– Manual

– Semi-automatic – Automatic

Vectorization II.

• Automatic vectorization of a soil map

– Single bit

– Low data density

• Automatic vectorization of a topographic map

– 8-bit

– High data density

Black: convert to line Blue: segmented pixels

Application of the Automatic Method

• Coordinates of the shape file vertex points

– site,lat,long,name,HOTLINK – 1,38.889,-77.035,Washington

Monument,http://www.nps.g ov/wamo

– 2,38.889,-77.050,Lincoln

Memorial,c:/ESRI/AEJEE/DATA /WASHDC/linc.jpg

– 3,38.898,-77.036,White

House,c:/ESRI/AEJEE/DATA/W ASHDC/whse.txt

– 4,38.889,-

77.009,Capitol,c:/ESRI/AEJEE/

DATA/WASHDC/cap.pdf

ESRI Arc Explorer JEE tutorial

Data Input from Text File

• With the help of hybrid systems, raster and vector data can be used together.

– Vector, raster and attribute data are stored separately, in the most suitable way for the model.

– The operations are carried out by these systems in the model that is most suitable for the operation in question.

– The systems apply a wide variety of vector-raster transformations before and after the operations.

– The GoogleMaps service is based on a hybrid data model.

Hybrid Data Model, Mashup Map

• Facts that mostly influence data quality:

– Origin of data

– Geometric accuracy

– Accuracy of attribute data – Consistency of attribute data – Topologic consistency

– Completeness and validity of data

Data Quality

• Georeferencing is the process of scaling, rotating, translating and deskewing the image to match a particular size and

position.

The word was originally used to describe the process of referencing a map image to a geographic location. Source:

http://wintopo.com/help/html/georef.htm

• Usual ways:

– World file

– Header (GeoTiff, GeoJP2…)

Georeferencing

• Certain image formats include georeferencing information in the header of the image file:

– img, – bsq, – bil, – bip, – EXIF – ITT

– GeoTIFF – grid

Header

• Georeferencing information is stored in a separate word file:

– The word file contains 6 parameters of an affin

transformation that means a connection between the

image coordinate system and that of the world coordinate system.

– The images are stored as raster data, where each cell of the image is identified by a row and coloumn number.

– The name of the word file has to be the same as the image file and be in the same folder.

Word File

Georeferencing with the Help of 2 Reference Points

segítségével

Graphic Georeferencing - Rubber sheeting

• Projection, date

• Geoid, geoidundulation

• Uniform National Projection (UNP - EOV)

• Transformation

• Base points, base point systems

Projection Systems, Conversion

• Based on image surface shape – Cylinder projection

– Cone projection – Flat projection – Other projection

• Based on image surface axle – Polar (normal)

– Transversal (equatorial)

– Oblique (not normal difference)

• Based on the contact of the image and base surface – Tangent

– Transect

Classification of Projection

• Systems without projection

• Dual projection Hungarian systems

• Stereographic projection systems (BUDAPESTI, MAROSVÁSÁRHELYI)

• Oblique Mercator Projection

• HÉR, HKR, HDR

• EOV

• Gauss-Krüger

• UTM (Universal Transverse Mercator)

• GEOREF (World Geographic Reference System)

Important Projection Systems

• Reference ellipsoids nearing an area of the Earth surface

• The centre of the ellipsoid is that of the Earth

• The axis of rotation is that of the Earth’s

– Parameters

• Major axis (equatorial radius)

• Oblateness (connection between equatorial and polar radius)

• If the centre of the ellipsoid is moved until it fits to the

examined area with the least error, we will get the geodesic date.

– Bessel (stereographic)

– Kraszovszkij (Gauss-Krüger) – Hayford (UTM)

– WGS-84 (GPS), – IUGG-67 (EOV)

Important Ellipsoids

• Geographic Projection

– WGS 1984 Datum

• Ortographic Projection

– SPHERE Datum

• Eckert IV. Projection

– WGS 1984 Datum

Some Interesting Projections

• GPS measurement gives the height above the ellipsoid (h). When

calculating height above sea level(H), geoidundulation has to be taken into consideration.

• Geoidundulation is the separation

between the equipotential surface that represents a mean ocean surface and a reference ellipsoid (h=H+N, where N is the value of geoindundulation of the point).

• Geoid: the surface of oceans and seas, if connected by small canals under the land(Listing 1873)

Geoidundulation

• The starting coordinates have been placed 200km to the South and 650 km to the West. Thus, the Y coordinates are lower than 400, and the X coordinates are always higher than 400, which means they are easy to distinguish.

Uniform National Projection

• The first elevation of

Hungary was carried out based on the

Mediterranian base level from 1873-1913.

– Height of Nadap main

base point: 173,8385 m.

• Baltic base level after World War II.

– Height of Nadap main base point: 173,1638 m, which is 0,6747 m lower.

Uniform National Elevation Network(EOMA)

• ETRS89 (OGPSH) points

transformed into the Uniform National Projection (EOV) system and back

• The points for the

transformation are chosen automatically

• Local transformation based on the common points of the

OGPSH and EOV systems

• With 8 common points in Hungary

• With refined Geoidundulation data

Etrs89-Eov-Hivatalos-Helyi-Térbeli- Transzformáció

Transformation

• Database of Altitudinal Base Points

• Database of Horizontal Base Points

• Database of OGPS Base Points

• Országos GPS Hálózat pontjai (Points of the National GPS Network-OGPSH)

Base Points

• Video

– Georeferencig (graphical)

• Animation

– Georeferencing – Geoidundulation – Shape (create)

Videos and Animations for Chapter 1.

I. question

Identify the value of geoid-undulation at the Parliament Building, Budapest, Hungary with the help of EHT (or any other) software . II. question

Digitalize any map sheet with the help of a scanner. Georeferate it with 3 reference points with the help of GEOREGARCVIEW software.

The necessary coordinates can be obtained from mapservers (eg.

Googlemaps).

III. question

Digitalize another map sheet overlapping the previous one with the help of a scanner. Georeferate with 3 reference points with the help of GEOREGARCVIEW software. Open it together with the georeferated file of the previous task with ArcExplorer JEE (or any other) and check its accuracy.

The necessary coordinates can be obtained from mapservers (eg.

Googlemaps).

Tasks for Chapter 1.

• Global Positioning

– The coordinates of 3

satellites at a given time are needed.

– If time can be measured accurately, then wave spread speed and the time will help calculate how far we are from the satellite.

– In the case of 1 satellite, it will give a sphere surface.

GNSS Device System

• If there is a connection with 2 satellites, then we are on the sphere of both satellites. The section of the two spheres is a

circle.

• The section of the sphere of the third satellite and the circle will be two

points, one of which can always be excluded (eg.

Points far from the earth surface).

Global Positioning II.

Differential Correction

• GNSSNet

• NtripCaster IP address, port: 84.206.45.44:2101

Network RTK in Hungary(2010)

• Geotrade GNSS

– Host:

www.geotradegnss.hu – Port: 2101

Multi-Base System in Hungary ( 2010)

• Georgikon RTK coverage

• DGPS forthe whole country of Hungary

– http://gnss.georgikon.hu – 193.224.81.88:2101

Single-Base System (2010)

( 2009

Trimble European VRS System

• CSD (Circuit Switched Data)

– Line connected mobile internet - 9,6 kbit/s - 1G

• GPRS (General Packet Radio Service) – Package connected - 115 kbit/s - 2G

• EDGE (Enhanced Data Rates for GSM Evolution)

– GPRS reinforcement- 236 kbit/s-os (112-400) - 2,5G

• 3G

– 3G mobile network, video call 384 kbit/s - 3G

• HSPA (High-Speed Downlink/Uplink Packet Access)

– HSDPA theoretic data transfer speed depending on device and coverage: up to 21 Mbit/s – 3,5G

• 4G LTE (Long Term Evolution) – 1Gbit/s - 4G

Mobile Internet

• Video

– Trimble VRS system

• Animation

– GNSSNet service – Geotrade GNSS

– Georgikon GNSS Base

Videos and Animations for Chapter 2.

I. question

Find the data of the accessible satellites of the Galileo and BEIDOU systems at a given time.

II. question

Find the terrain control stations of the Navstar GPS system at a given time.

III. question

Find the worst measurement site on the Earth’s surface

concerning ionosphere state at a topical time. Use the ‘space weather forecast’ of Australia (or any other information

source).

Tasks for Chapter 2.

http://www.ips.gov.au/Space_Weather

• GNSS Measurement – Planning (almanach)

– Realization (online correction: procession too) – Data transfer(exchange formats, RINEX - Receiver

Independent Exchange Format)

– Processing (vectors, transformation, error correction) – Network equalization (OGPSH – National GPS Network)

Terrain GNSS Measurement and Processing

• Guarantee of integrity – GNSS

– Way of correction

• Guarantee of nedded accuracy – Accuracy of the Rover device – Way of correction

– Satellite constellation

– Minimalization of other disturbing facts

Aim of GNSS Measurement Planning

• GNSS satellite data – Almanach

• Trimble Planning

• Leica Satellite Availability

• Topcon Occupation Planning

• Receiving correction data – Mobile internet

• Gprs coverage

• Style, devices, realization

Devices for Planning

• Timing

– Further in time – Back in time

• General

– YUMA formátum,USA Coast Guard Navigációs Központ (YUMA format, USA Coast Guard Navigation Center)

– A dátum és a GPS-hét kapcsolata a GPS-naptárban (the connection between date and GPS-week in the GPS

calendar)

• Trimble

• Leica

• Topcon

Almanach

Trimble Planning

• Relative

– Real time – Radio – Satellite – Internet

• Post-processed

– Digital data transfer

Channels of Correction Data

• Connection to satellites, controller

• Connection to correction service

• Setting measurement style

• Starting measurement

• Recording data

Realisation of Measurement

• Obtain, check and converse existing spatial data

• Set up a measurement plan

– Need for accuracy

– Available devices and services – Specialities of the area

– Select measurement method

• Places of measurement

• Conversion to the format of the terrain device

• Upload data to the terrain device

Preparation of Measurement

• Check measurement data

• Inspection

• Delete, edit

• New recording

• Data

• Export in needed formats

• Turn off terrain device

End of Measurement

• Load data from terrain device – Formats

– Give coordinate system and date – Examine data load mistakes

– inspection – Delete, edit

• Export to the format of procession

Processing Data

• Upload data to GIS system – Conversions

– Analyses

– Interpolations – Model building – Simulation

– Statistical analysis – Publication

• Online correction – Procession

• Offline correction

– Time of measurement – Obtain correction data – Correction

– Check

GIS procession and Analysis of Data

• EEHHTT software – Data input

• From file

• Via keyboard

– Set format of data input

– Set data conversion direction – Give coordinates

Checking Transformation

• Adatgyűjtő

– Navigation accuracy

• ArcPad / palmtop with GPS antenna

– GPS accuracy

• GPS Pathfinder office / Trimble GeoXH

– Geodesic accuracy

• Trimble Survey Controller / Trimble 5800

• Data procession – GPS Analyst

– GPS Pathfinder Office

– Trimble Geomatics Office – ArcGIS

Typical Terrain Device System

Sample

• Aim of survey: automatic data collection for 3D relief model

• Place of survey: the island of Kányavári, Hungary

• Time of survey: 21. December, 2008. 0920h-1530h

• Type of survey: RTK; Format of message transfer: CMR+

• PDOP mask: 6, elevation cutoff: 10 degrees, antenna: Trimble 5800, h_ant: 2 m

• Coordinate System Hungary Zone Hungarian EOV

• Project Datum HD72 (Hungary)

• Vertical Datum Geoid Model EGM96 (Global)

• Coordinate Units Meters; Distance Units Meters;Height Units Meters

• Name of point DeltaX DeltaY DeltaZ Slope Distance RMS

• 25001 13189,539m 1880,080m 11396,001m 17531,898m 0,002m

• Name of point X Y H

• 25001 142686.277 505893.164 109.042

Description of Continuous Topographic GPS Survey

• Take sample

• Yield mapping

• Sensors

• Auto pilot system

• Mass flow or sprayer control

• Row control

• Seeder control

Basic GPS Elements of Precision Farming

• 1. GPS survey of field blocks, soil sample taking plan

• 2. Take soil sample according to plan every 3-5 acres

• 3. Soil examination (extended and holistic)

• 4. Make nutrient content maps

• 5. Information, services for professional advice, analyses

• 6. Agrochemical service

• 7. Differentiated fertiliser plan

• 8. Differentiated nutrient output, plant number plan

• 9. Seeding with base station

• 10. Precision herbicid plan (based on Hu, KA, pH map and weed uptake)

• 11. Ffertiliser quantity, upload into professional advice system

• 12. Download data from the Internet

Precision Management System (IKR)

• IKR

Precision Management System

• Spreadsheet

Evaluation of Tillage Experiments

• Spreadsheet

• GIS software (weed density)

• GIS software (weed density)

Evaluation of Tillage Experiments II.

3D Model

• Video

– GNSSNet OGPSH

• Animation

Videos and Animations for Chapter 3.

I. question

Create a forecast for tomorrow 1200hr and 1215hr above 10 degree elevation cutoff for the area of the Helikon strand, Keszthely, Hungary (Lambda = 46 degree 45

minutes, Fí = 17 degree 15 minutes, h = 150 m).

GDOP=

PDOP=

HDOP VDOP=

TDOP=

Number of GPS satellites = Number of Glonass satellites=

Number of Galileo satellites=

Number of Compass satellites=

II. question

In the IKR precision management system, which service(s) can use correction GNSS base data?

III. question

Is soil sample take in the IKR precision management system realized with a yield map or a grid?

Tasks for Chapter 3.

• Remote Sensing

– With the help of remote sensing, objects can be examined that are not in a direct connection with the sensor.

– In a narrow sense, the concept of remote sensing is usually used for aerial and space images. In a wider sense, it can also be

defined for eg. remote measurements or medical applications.

– Remote sensing is the acquisition of information about an object or phenomenon, without making physical contact with the

object. In modern usage, the term generally refers to the use of aerial sensor technologies to detect and classify objects on Earth (both on the surface, and in the atmosphere and oceans) by

means of propagated signals (e.g. electromagnetic radiation emitted from aircraft or satellites).

Remote Sensing Device System, 3D Modelling

• The measurement does not influence the examined object, or change its state.

• It can be used at wavelengths out of the visisble range. The result can be examined in the visible spectrum.

• Objective, exact data can be obtained.

• Spatial, several dimension data can be obtained.

• Lots of data can be obtained from big areas in a short time.

• Areas that can not be reached or examined with other methods can be examined.

Characteristics of Remote Sensing

• Active sensors

– sense the reflection of their own radiation

• Passive sensors

– have no emission

• One or more wavelength range

• Images with more than one band are called (depending on the number of bands) multispectral or hiperspectral.

Clasification of Sensors

• Geometric

– pixel: the space of one point of the image measurable on the earth surface, its real extension.

• Spectral

– the value of radiation from the object

• Radiometric

– characterises the colour depth of the pixels

• Temporal

– the time interval between the images

Information from Sensors

• Wavelength, frequency

– Visible light (0,4 - 0,7 µm) – Infrared (0,7 µm felett) – Ultraviolet (0,4 µm alatt)

Electromagnetic Spectrum

• Scatter - Multi path scattering

• Occlusion

– Influencing factors

• Traveled distance

• Radiation energy

• Composition of the atmosphere

• Size of particles

• Wavelength

Atmospheric Effects

• Chlorophyl absorbs the energy of the wavelengths between 0.45 and 0.67 µm,mostly blue and red colours, thus the colour of the healthy plant is green.

• In an unhealthy plant, the yellow colour together with the green can be caused by red reflection caused by chlorophyl decrease.

• Reflection within the range 0.7 and 1.3 µm highly depends on leaf structure (sort specific), and dramatically increases.

• Effect of stratification, water occlusion bands above 1.3 µm.

• Above 1.3 µm, reflection is inversely proportional to the whole water content of the leaf.

Visible and Infrared Range

• The reflection curve of plant sorts are

identifiable.

• Image correction (atmospheric

distortion)

• Sample points

• Spectrum

Visible and és Infrared Range II.

• TM 1 0.45 – 0.52 µm(blue) 30 m

• TM 2 0.52 – 0.60 µm(green) 30 m

• TM 3 0.63 – 0.69 µm (red) 30 m

• TM 4 0.76 – 0.90 µm(near infrared) 30 m

• TM 5 1.55 – 1.75 µm(medium infrared) 30 m

• TM 6 10.42 – 12.50 µm(thermal infrared) 120 m

• TM 7 2.08 – 2.35 µm(middle infrared) 30 m

Spectral Bands and Resolution of Landsat TM

• ASPRS (ASPRS satellite database)

Planned Objects of Satellite Sensing

• 2002. DLR DAIS, 79 band system

• 2006. with the help of AISA DUAL hiperspectral camera,

aerial data collection service was launched by the University of Debrecen (Hungary) and the Ministry of Rural

Development.

– Senses in a maximum of 498 bands, at the wavelength of 0.45–2.45 micrometres.

Hiperspectral Imaging in Hungary

• National Aeronautics and Space Administration (NASA) and U.S. Geological Survey (USGS) (1999)

• Images in 7 bands (6 bands 30 m, termal-infra 60 m terrain resolution)

• Sun-synchronic orbit (the satellite travels above a given site at the same local time)

• Circulates at the height of 705 km

• Can take images of an area of 185x170 km every 16 days

LANDSAT 5 TM

• TM 1 0.45 – 0.52 µm differentation of land from plants, mapping of artificial surfaces.

• TM 2 0.52 – 0.60 µm mapping plant cover, identification of artificial surfaces.

• TM 3 0.63 – 0.69 µm differentation of planted surfaces from plantless surfaces, identification of artificial surfaces.

• TM 4 0.76 – 0.90 µm identification of plant sorts, definition of green mass, survey of plant vitality, mapping water surfaces,

mapping soil water content.

• TM 5 1.55 – 1.75 µm examination of soil and plant water content, differentation of cloudiness from snow blanket.

• TM 6 10.42 – 12.50 µm mapping heat emission (plant stress, heat pollution)

• TM 7 2.08 – 2.35 µm differentation between rock types, mapping plant eater content

Application of Landsat Images

• Imaging : central perspective

• Photogrammetry: defines the extention of real objects from the sizes taken from the image

– The resulting ortophoto (image data of the Earth surface obtained by a satellite or aerial data collectors with

geographic reference) can comprehensively be used with GPS systems

– During the planning and realisation of imaging, a GPS device system and adequate relief data are needed.

Ortophoto

• Photogrammetric evaluation is based on stereoscopy with perspectivic mapping between aerial and space images taken using central projection.

– The essence of stetoscopy is that given terrain objects are mapped in different ways in images from different sources.

The task of photogrammetry is to measure the difference between parallaxes, and calculate spatial coordinates.

Photogrammetry

• Differentation of types of vegetation

• Cover and yield

• Calculatio

• Productivity of biomass

• Vitality and disease of flora

• State of soil – IMG files

• View

• Select bands

• Colour bands

– Erdas ViewFinder 2.1

– http://rst.gsfc.nasa.gov/Front/overview.html

– FÖMI oktatóanyag (tutorial of the Institute of Geodesy, Cartography and Remote Sensing, Hungary)

Remote Sensing Data in Agriculture

• Model of objects

• Relief model

• Terrain model

• Elevation model

– Digital elevation model (DEM) is the topographic

visualisation of the earth surface. It is usually used for relief maps, 3D visualisation, waterflow modelling, and in the

case of aerial image correction. Applies remote sensing data or traditional land surveying data.

– Raster based elevation model – Vector based elevation model

Application of 3D Models

• Source elevation data create regular grid cells. The size of the cell is constant within the model.

The height of the relevant

geographic area can be considered constant in the same grid cell.

• Divides space into triangles not covering one another.

– Vertices of every triangle are data points, with the value of x, y, z.

– The points are connected with lines, which gives Delaunay triangles.

– A TIN (Triangulated Irregular

Network) is a complete graph, which keeps its topologic connection with the relevant element (intersection, edge and triangle).

– Input data fit directly into the model.

Raster and Vector Models

• SRTM (Shuttle Radar

Topography Mission 2000) program

– Digital relief of about 80%

of the Earth’s surface, with the help of radar system (Endeavour 11 days)

– Radar-interferometry, with two receivers 60 m from one another

– Mapped area: 60 degrees North, 57 degrees south – Resolution 3 (USA 1) arcsec

Global Relief Model

• TanDEM-X 2010, (TerraSAR-X)

– Mapping of the whole surface of the Earth

– Horizontal resolution 12 m, vertical resolution: 2m.

– Two-radar remote sensing satellite with stereo microwave radar device, at the height of 514 km

– Polar sun synchronic orbit

– Radiowaves emitted from a satellite with the help of Synthetic-aperture radar (SAR) technique and then

reflected from the surface are received with the antenna on the satellite , or the same surface is photographed from two different points.

Global Relief Model II.

• The digital relief model of Hungary, 5m

resolution

– 1:10 000 scale EOTR database was used – A GRID derived from

vectorized level lines.

3D Relief Model

• Generated from several sources

– Level-line digitalization – Digitalization of elevation

points

– Import GPS survey points – Correction (aerial photo) – Model generation

– Publication

• Generation from direct GNSS measurement

3D Relief Map

• Video

• Animation

– Elevation Model

Videos and animations for Chapter 4.

I. question

Find an aerial image of your place of living from internet sources.

II. question

Find a space image of your place of living from internet sources.

III. question

Measure the area of the Kányavári Island (Kányavári-sziget), Hungary on the photos of 1990., 1992. and 2002. Use Erdas

ViewFinder (or any other IMG viewer). The images can be found on the remote sensing tutorial website of FÖMI

Tasks for Chapter 4.

http://www.fomi.hu/taverzekeles_oktatoanyag

• Types of Mapservers – Static webmaps

– Dynamically created webmaps – Animated webmaps

– personalized webmaps – Open, reusable webmaps – Interactive webmaps

– Webmaps suitable for analysis – Collaborative webmaps

Spatial Data Databases

• Static webmaps

– No animation and interactivity

– Only created once, infrequently updated – Mostly scanned paper based maps

• Dynamically created webmaps

– Created on demand, often from dynamic data sources – Created by server (ArcIMS –ArcSDE)

– WMS protocol

Types of Webmaps II.

• Animated webmaps

– Show changes in the map over time (water currents, wind patterns, traffic info)

– Real time, data from sensors

– Updated Rregularly or on demand

• Personalized webmaps

– Allow user to apply own data filtering, selective content – Personal styling and symbolization

– OGC SLD WMS uniform system (Styled Layer Description)

Types of Webmaps III.

• Open, reusable webmaps

• Complex systems, open API(Google Maps, YahooMaps, BingMaps)

• Compatible with API „Open Geospatial and W3C Consortium” standards

• Interactive webmaps

• Chengeable parameters

• Easy navigation

• Events, descriptions, DOM-manipulations

Types of Webmaps IV.

• Analytic webmaps – Offer GIS-analysis

• Geodata uploaded by user

• Geodata provided by server

• Analysis is carried out by a serverside GIS, results of analysis are displayed by the client.

• Collaborative webmaps

– Geometric features being edited by one person can not be changed by any one else at the time.

– Quality check is needed before publication

(OpenStreetMap, Google Earth, Wiki- Mapia…).

Types of Webmaps V

• ‘Institute of Geodesy, Cartography and

Remote Sensing’, Hungary

• Földmérési és

Távérzékelési Intézet fontosabb adatbázisai (important databases of

the Institute of Geodesy, Cartography and

Remote Sensing)

‘FÖMI’

• To continuously inform farmers and experts, to provide professional background knowledge for tenders and developments.

• Its knowledge base is based on professional news, events, articles, studies, publications-published in an organised, updated system.

• A further aim of the site is to prepare for online data service (logbooks, electronic submission of data of farmers working on vulnerable areas), to give info on data in connection with agri-environmental management, to publish relevant

thematic maps and to ensure agrar forecast.

Hungarian National Rural Network (AIR)

• 1:200.000 scale

genetic soil map of Hungary

• 40 soil types, 80 sub

types, with colours and colour shades

• Physical soil kinds (9

categories) with striping

• Soil formation rock (28 categories) betűjelekkel

AIR Public Map Library

• Obtain, process and store weather data

• Apply weather data in the agrometeorology model of the crop growth monitoring system (Crop Growth Monitoring System, CGMS)

• Process NOAA-AVHRR and SPOT-VEGETATION satellite images using CORINE land coverage data (CORINE Land Cover, CLC)

• Common Research centre – Statistic analysis of data – Quantity forecast

– Short time crop yield forecast

MARS (Monitoring Agriculture by Remote Sensing)

terményhozam-előrejelző rendszer

• Monitoring Agriculture by Remote Sensing

MARS

‘FÖMI NÖVMON’ (Plant Monitoring)

IKR Precision Map Server

Soil Data Publication (Georgikon Mapserver, Hun)

• (Infrastructure for Spatial Information in the European Community-INSPIRE)

• ‘The INSPIRE Geoportal provide the means to search for spatial data sets and spatial data services, and subject to

access restrictions, view and download spatial data sets from the EU Member States within the framework of the

Infrastructure for Spatial Information in the European Community (INSPIRE) Directive.

• Aims at making available relevant, harmonised and quality geographic information to support formulation,

implementation, monitoring and evaluation of policies and activities which have a direct impact on the environment.’

• (www.inspire-geoportal.eu)

INSPIRE Geoportal

• Inspire should be based on the infrastructures for spatial

information that are created by the Member States and that are made compatible with common implementing rules and are supplemented with measures at Community level. These measures should ensure that the infrastructures for spatial information created by the Member States are compatible and usable in a Community and transboundary context.

Spatial Data Directive

• Member States shall ensure that metadata are created for the spatial data sets and services corresponding to the

themes listed in Annexes I, II and III, and that those metadata are kept up to date.

Inspire2008 metadata

• Online access to a collection of geographic data and services

• Does not store or maintain data

• Metadata, catalogues can be accessed with several search options

• With the help of a map server service, maps and metadata can be searched for and browsed.

• Personal maps can be created from existing data sources.

INSPIRE Geoportal

• INSPIRE Geoportal

INSPIRE Geoportal Viewer

• ArcExplorer JEE Corine Land Cover mash up map from several sources

• http://vektor.georgikon.hu kvsz

• http://geo.kvvm.hu clc (80%

transparency)

Mashup map: a map that includes another (API), made from several internet

sources.

Mashup Mapserver Service

WebMap and publication

Website

MapServer

Picture

Video

Web service HTML

• Steps of realization :

– 1. chose topic

– 2. create map, upload data

• a. Create web album, upload photos

• b. Upload video

– 3. create website, embed map

– 4. publish website

Steps of Realization

• Video

– Institute of Geodesy Cartography and Remote Sensing – Hungarian National Rural Netvork

– Inspire Geoportal – GoogleMaps service

• Animation

Videos and Animations for Chapter 5.

I. question

Measure the length of the Belső-tó (‘Inner lake’) of Tihany, Hungary with the help of the topographic map service of the Georgikon Mapserver (or any other mapserver).

II. question

Create a GoogleMaps map in any agricultural topic with at least 5 objects, inserted images and embed it into a website of the same topic.

III. question

Embed further mapserver services (Bingmaps, YahooMaps…) into the website you have created.

Tasks for Chapter 5.

• Prepared by:

– Dr. Máté Csák

Plant protection database

Plant Protection Information

Plant protection database

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Plant protection’s databases: Topics

• Database management theory

– Information, data

– Database models, databases

– Database Management Systems

• Relation model

– Base of theory

– Normalized database

– Catalog, data-dictionary

• Plant protection’s databases

– Practical problems and their solutions

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Database management - Information

• Information technology concepts, words of Latin origin, which is intelligence, news,

messages, information does.

• Definitions:

1) In general, the data information, news of which we consider relevant, and lack of knowledge has decreased.

2) Knowledge gains, the growth of knowledge, and it means reduce uncertainty.

3) The information provided is new data, news which removes uncertainty and

consequences.

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Theoretical - Information

The information is the same

physical reality of the universe as matter and energy.

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pure information

Information processing

Meaningful information

DNA-molecule

Computer data input

protein Calculation results

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Theoretical - Information

Manifestations:

• Clearly pronounced– Explicit

– When the information is completely clear to everyone, not in need of explanation.

– For example: the Balaton water at 28 °C

• Hidden – Implicit

– The data connection between a method can be displayed.

– For example: statistical calculation (average)

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Theoretical - Data

• The data of an object (any thing that

relates to the data), to a specific value (character state, completed forms) for

the variable (properties, attributes, characteristic, character).

– Therefore be considered as a specific data are defined, you define what kind of object that is variable, what value are added. The figures represented the value unit is always connected.

• For example: Name: Arvalin LR; Agent: Zinc phosphate; Volume: 4 %

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Theoretical – Data model

• A collection of concepts, which clearly describe the structure of a database.

– The structure includes the data type and their relationship to the restrictive

conditions for the data.

– The database conceptual level, logical structure description.

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Entity-Relationship-ER basic elements of data model

Plant Protection Information

ENTITIES

ATTRIBUTES

RELATIONSHIPS

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Data model – ER - Entities

• Entities :

are the principal data objects, which all other things to distinguish, and information is to be collected.

– Procedures at issue, and whom we want to store data.

– For example: Citizens, Workers, Patients,

Custumers; Plants, Agents, Phenological phase, Harmful; Cars, Goods, Accounts ...

– The entity to a specific value of the occurrence.

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Data model – ER - Attributes

Attributes:

• Internal structure of the entities

• are characteristics of entities that provide descriptive detail about them.

– Plants of the named individual characteristic such as : name, Latin name, ...

• The property values of an individual's actual value is determined.

• For example: Peach, Prunus persica, …

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Data model – ER – Attributes - Key

• If a property or properties to a group

of clearly specifies, that the value which the individual is involved, together they are

called keys.

– For example: name in Plants

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Data model – ER - Relationship

The relationships:

• the external structure of entities,

• the represent real-world associations among one or more entities.

• are described in terms of degree, connectivity, and existence.

– For example: Plants-Harmful, Accounts-Goods, ...

• A particular occurrence of a relationship is called relationship instance.

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Datamodel – ER – Relationship - Types

The types of relationships:

•Independent connectivity

•1:1 connectivity

•1:N connectivity

•N:M connectivity

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Adatmodell – ER – Kapcsolatok 1.

1. Independent connectivity

– The two entities independent of each

other, if one set of instances, nothing is linked to a single element or another

entities.

• For example:

• Agent’s Id: Employe’s account

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Data model – ER – Relationship 2.

2. One –to – one connectivity (1:1):

• One of the elements of each set of

instances of another entity set exactly one element is linked.

– For example:

Agent’s Id: Agent’s name

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Data model – ER – Relationship - 1:1 Connectivity

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Adatmodell – ER – Kapcsolatok 3.

3. One-to-many connectivity:

• A set of instances of each element of the B element within the multi-set of instances.

– For example: Aetiologies : Diseases

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Data model – ER – Relationships - 1:N connectivity

Plant Protection Information

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Adatmodell – ER – Kapcsolatok 4.

4. Many-to-many connectivity:

• A set of intstances of all elements of the B element within the multi-set of instances, vice versa.

– Például: Plants : Diseases

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Data model – ER – Relationship - N:M connectivity

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Data model - ER definition

• The data model is

a finite number set of entity ,

their finite number set of properties and their set of relationship.

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Data model - Types

Depending on the core 3 is based on storing the physical data model exist.

entity property connectivity

•net, hierarchical + - +

•relation + + -

•Object oriented + + +

• + object-relational (mixid data model)

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Databases

• Database: some relation to each other in a structured set of data, stored so that

multiply users can access, typically digital form.

• The database is a finite number of entities occur, their are a finite number of property value, and the relationship of the presence data model orgonized as a combination.

• Benefit: you can use many at once. The data are stored "single" only.

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Integrated database

• Linked to all data that are used by

different users in different groupings.

• The physical placement of data,

centrally, redundancy-free or minimal, controlled redundancy occurs .

• Centrally controlled

– data protection,

– entering the new data, and – change existing data.

Plant Protection Information

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Database Management System (DBMS)

• A softvare, which provides the connection to the database.

• Allows databases

– creation,

– query the data, – modification,

– maintenance,

– large amounts of data on long-term safe storage.

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Database Management System (DBMS)

• Grouping

– According to the number of users

• Single-user

• Multi-user

– Job sharing as

• A tasking

• Client-Server

– Number of storage locations

• A stored

• Split /shared

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Database Management System (DBMS)

• The system components

– Data Definition Language (DDL)

• User level

• Conceptual level

• Physical storage level

– Data Manipulation Language (DML) – Data Control Language (DCL)

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Database Management System (DBMS) – Operating concept

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DBMS – Operating concept - Explanation

1 Request for information from the database (Application program)

2 Request the interpretation and analysis (DBMS: syntax, existence, rights)

3a Executeable→ to operating system 3b can not execute → to program

4 Contact the exterior container (operating system)

5 The transfer of the requested data (OS, from storage into buffer)

6 The passing of data, feedback for a program 7 The receipt of data into a program.

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Database Management System (DBMS)

• Two types:

– Has a autonomous languages

• Oracle (1977)

• DB/2 (1983)

• SyBase (1987)

• Informix (1981)

• Ingres (1980)

– Plug-in type

• IDMS (1983)

• SQL (1986)

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Relation database model – Theoretical basis

• In 1970 Dr. Edgar F. Codd (IBM) create the Relation Database Model.

• The data model describes the various types of data, their relation, connections, and their privacy procedures.

• The collected data are logically separate entity types, entities (table). Determine that the individual entities, whereas we can clearly identify, and also what additional features (attributes).

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VirKor program – Relation diagram

In VirKor database has seven tables and their properties and relations.

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VirKor program – Relational mode of representation

• Relation of entities

(special tables) shows.

• They describe the real world, different entities and their properties.

• Plants table

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VirKor program - Relational mode of representation

• The connection between the entities can be

depicted in relations.

• The data management

comes true with relational operations.

• Plants – Pests relation

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Relation model – Benefits and disadvantages Benefits:

• Mathematical (set theoretical) based on models

• Very close to everyday thinking,

• Most flexibly modifiable,

• Well-separable, can be made independent the three level.

Disadvantages:

• The power delivery is less effective.

– This is not so big trouble already today.

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