EROS – Surrounded By Corn Fields
EROS (Earth Resources Observation & Science) USGS Remote Sensing CenterSatellite radar remote sensing: applications to thestudy of Earth sciences and natural resourcesZhong LuSDSAIC, USGS/EROSSioux Falls, SD 57198lu@usgs.govhttp://edc.usgs.gov/Geo AppsAcknowledgements: Contributions by many colleagues/friends. Funding from NASA, USGS Land Remote Sensing Program, USGS Director VentureCapital Funds, USGS Volcano Hazards Program, USGS Earthquake HazardsProgram, and NSF. World-class service and support from ASF.AVHRR-Derived NDVI Image for June 2002 SAR imagery copyrighted by ESA, CSA, and JAXA.EROS – surrounded by corn fieldsOutline The very basics of radar remote sensing and InSAR Radar remote sensing of Earth sciences & natural deVolcanoAquiferSurface Water and WetlandSoil MoistureLand CoverAgriculture Emerging SAR/InSAR technologies Emerging L-Band Capabilities A Road Map1-Meter KONOS image1
In its simplest form, a radar operates by broadcasting apulse of electromagnetic energy into space – if that pulseencounters an object then some of that energy isredirected back to the radar antenna. Precise timing of the echo delays allows determination ofthe distance, or “range”, while measuring the Dopplerfrequency tells the velocity of the target. The electromagnetic wave is transmitted from the satellite. The wavepropagates through the atmosphere, interacts with the Earth surface.Part of the energy is returned back and recorded by the satellite. By sophisticated image processing technique, both the intensity andphase of the reflected (or backscattered) signal can be calculated. So,essentially, a complex-valued SAR image represents the reflectivity ofthe ground surface. The amplitude or intensity of the SAR image is primarily controlledby terrain slope, surface roughness, and dielectric constants,while the phase of the SAR image is primarily controlled by thedistance from satellite antenna to ground targets and partiallycontrolled by the atmospheric delays as well as the interaction ofmicrowave with ground surface.1. Interferometric deformation analysisw156 30′2.83 cmN57 50′N57 45′.214.016.811 The interferogram, depicting range changesbetween the radar and the ground, can befurther processed with a digital elevation model(DEM) to image ground deformation at ahorizontal resolution of tens of meters overareas of 100 km x 100 km with centimeter tosub-centimeter precision under favorableconditions.0N57 40′ Interferometric synthetic aperture radar(InSAR) combines phase information from twoor more radar images of the same areaacquired from similar vantage points atdifferent times to produce an interferogram.w156 10′w156 20′0.0 cm2.85.68.45 km2
3. Polarimetric SAR image analysis2. Interferometric coherence analysis- A measure of changes in backscattering -HV data will offer better potential for detection anddelineation of clearcuts than C-HH and C-VV dataLu et al., 20004. SAR polarimetric phase analysis5. Analysis of SAR images at different frequenciesPPD 0 X-bandDifferent types oftargets show differentPPD behavioursL-bandP-bandPPD /-180 PPD: 0 for odd refl. # 180 for even refl. #PPD distributedbetween 0 and180 Van Zyl, 19893
Synthetic Aperture Radar Satellites Current and Past Sensors European ERS-1, European ERS-2,1991-2000, C-band, 35-day repeat cycle1995-now, C-band, 35-day repeat cycle(experiencing malfunctions since early 2001) Japanese JERS-1,1992-1998, L-band, 44-day repeat cycle Canadian Radarsat-1, 1995-now, C-band, 24-day repeat cycle European Envisat,2002-now, C-band, 35-day repeat cycle U.S. SIR-C Mission,InSAR study of EarthquakesApril (10 days) and Oct (10 days), 1994X/C/L-band, Fully Polarized Measuring spatial and temporal patterns of surface deformation in seismicallyactive regions are extraordinarily useful for estimating seismic risks andimproving earthquake predictions. Future Sensors Japanese ALOS,2006,L-band, 46-day repeat cycleCanadian Radarsat-2, 2006(?),C-bandGerman TerraSAR-X, 2006(?),X-bandWavelength (λ)U.S. DOD Space-based Radar Constellations X-band: λ 3 cmU.S. ECHO , forever? C-band: λ 5.7 cm L-band: λ 24 cm 2002 Denali Fault EarthquakesOct. 23 and Nov 3, 2002 Denali EarthquakesAEICCourtesy ofP. Haeussler4
Slip Distribution of Oct 23, 2002 EarthquakeObservedModeled Lu, Wright, Wicks, EOS, 2003 Wright, Lu, Wicks, GRL, 2003Nov. 3, 2002 EarthquakeNov. 3, 2002 EarthquakeAscending, look angle 47 Ascending, look angle 40 11 October – 4 NovemberB29 October – 22 NovemberBInSAR GISDistance from Fault (km)Oct. 23, 2002 EarthquakeInSAR images: observed and modeledAA Lu, Wright, Wicks, EOS, 2003Lu, Wright and Wicks, EOS 84 (41), p425,430-431, 20030150300Right-lateral Displacement (cm)5
Slip DistributionNov 3, 2002 EarthquakeJointInSAR monitoring of landslidesGPS Measuring and documenting how landslides develop and are activated areprerequisites to minimize the hazards they pose in areas of rapid urbangrowth.InSAR Wright, Lu, Wicks, BSSA, 2004Slumgullion Landslide, COInSAR image of Slumgullion landslideJuly – August, 200402.83 cmLu and Coe, in prep, 20056
Radar remote sensing of volcanicprocesses Measuring how a volcano’s surface deforms before, during, and aftereruptions, provides the essential information about magma dynamics and abasis for mitigating volcanic hazards.5 km7
post-eruption InSAR images (several examples)pre-eruption InSAR imageJune 1992 - Sept. 1993Oct. 1993 - Aug. 1998Aug. 1999 - Aug. 2000Sept. 1992 - Sept. 1993Oct. 1995 – Oct. 1998Oct. 1992 - June 1997Sept. 1993 - Aug. 1995Oct. 1997 - Aug. 1999Sept. 1993 - Oct. 19987 Sept. 1991 – 28 Oct. 1991co-eruption InSAR image21 Nov. 1991 – 30 Nov. 19911 color cycle 2.8 cm deformationLu et al., GRL, 2000Lu et al., JGR, 2003Deformation history of Westdahl VolcanoInSAR images can characterize transient deformation ofWestdahl volcano before, during, and after the 1991 eruptionLu et al.,JGR, 2003InSAR constrains the quantity and timing of magma intrusionMagma plumbing system for Westdahl volcano,inferred from InSAR and modelingSea level 7 kmShallow ReservoirLu et al.,JGR, 2003 100 - 200 kmLu et al.,JGR, 20038
Land Subsidence Mapping – radarremote sensing of aquifer andhydrogeologyInSAR GISSubsidence of Al Ain, United Arab Emirates, from InSAR, 1993-1999 Mapping surface subsidence and uplift related to extraction and injection offluids in groundwater aquifers and petroleum reservoirs provides fundamentaldata on reservoir/aquifer properties and processes and improves our ability toassess and mitigate undesired consequences.L-band JERS-1 InSARA coastal area over southeastern China10 kmLandsat-7 image, Oct 2000Lu et al., in prep., 20059
Satellite Radar Image of San Bernardino, CASubsidence was up to 8 cm/yearLand subsidence GIS data layers over cities provide criticalinformation for decision making: Is my house sinking?Lu., Z., and W. Danskin, InSAR analysis of natural recharge to define structure of a ground-waterbasin, San Bernardino, California, Geophysical Research Letters, vol. 28, 2661-2664, 2001.Mapping of Land Surface Deformation by InSARRadar remote sensing of hydrology Monitoring dynamic water-level changes beneath wetlands can improvehydrological modeling predictions and enhance the assessment of future floodevents over wetlands.Range Change02.8 cm10
Flood mapping over Po Yang Lake, using JERS-1 SARWater extents over Po Yang Lake, ChinaHurricane KatrinaRadarsat-1 SAR imageSept 2, 2005Water increment in July 1998Water increment in June 1998Water in April 1998Inundation andoil slicks mappedfrom SAR imageInundation,oil slicks,floating debrisMapped fromSAR imageSept 5, 2005Rykhus, lu, & Jones,2005Rykhus, lu, & Jones,200511
SAR Lidar GISWhen radar waves interact with flooded vegetationRadarSignalDouble-bounceRadar signalWaterWater-level changes imaged by L-band InSARWater-level change imaged by L-band InSAR1993/11/29 1994/01/021996/01/20 1996/03/041993/11/29 – 1994/01/02AA1996/01/201996/03/04BBARange Change0B1996/01/20 – 1996/03/041993/11/291994/01/02Range Change12 cm012 cm12
Water-level change imaged by L-band InSARC-band Radar Can See Water-level Changes in Swamp ForestsA1996/01/201996/03/041993/11/29 – 1994/01/021993/11/291994/01/02BARange Change0B1996/01/20 – 1996/03/04AB12 cmSwamp forests near coastal New Orleans Lu et al., EOS, April 5, 2005Water-level changes imaged by C-band InSAR Lu, Crane, and other, EOS, April 5, 200513
Future Data and Technology Needs for Hydrology Rapid repeat times for interferometry. Daily imagerywould be ideal for flood and other hazard assessments. Full polarization to exploit the water-vegetation interface. C- and L-band imagery would provide the necessarycontrol to map surface water elevation changes in a widerange of location.Radar remote sensing of Soilscience Mapping soil moisture will provide an environmental descriptor that integratesmuch of the land surface hydrology and is the interface for interactionbetween the solid Earth surface and life.Basic Principles Retrieval of land surface parametersSynthetic Aperture Radar (SAR) images over Carlsbad, New Mexico– Formulate a radar backscattering model– Apply an inversion procedure Ideally, we would like to start fromMaxwell‘s onMaljamar19951227Wagner et al., 200414
Mapping of change in soil moisture, Carlsbad, New MexicoBackscattering properties of soilA Radar backscsattering model (i.e., Integral Equation Model) W n ( 2k x ,0)k2exp( 2k z2 s 2 ) s 2 n I qpn2n!n 12σ qps ( s ) I qpn (2k z ) n f qp exp( s 2 k z2 ) k zn [ Fqp ( k x ,0) Fqp ( k x ,0)]2The surface backscattering components σhh, σvv and σhv can besimulated for a wide range of incidence angle, surface dielectric androughness properties, corresponding to a rang of soil moisture values.By comparing simulated backscattering values with those observed,soil moisture can be inferred (Intense computation ARSC)Lu and Meyer,IJRS, 2002The Semi-Empirical SAR Soil Moisture Retrieval SchemeSAR ImageLand useSoil TextureSoil Moisture MapMapping soil moisture with SIR-C SAR images. The horizontalresolution (several meters) of soil moisture imagery derived fromfully polarimetric SAR data is not attainable otherwiseApril 12, 1994April 15, 1994Dielectric constant εsSemi-empirical Soil Moisture Model(land use dependent)Mauser et al., 2004Dubois et al., 199515
Mapping soil moisture with AIRSAR imagesInferring soil moisture with InSAR imagesDrying (blue) and moistening (yellow/red) between two SAR acquisitions inan arid region of Colorado inferred from InSAR. Range change wasinterpreted as being due to changes in penetration depth that results fromchange in soil moisture.Dubois et al., 1995Nolan & Fatland, 2003Future Data and Technology Needs for SoilMoisture Science Rapid repeat times for interferometry. Daily imagery wouldbe ideal to map dynamic changes in the surface watercontent. A minimum requirement would be weekly coverage. Multi-wavelength capabilities for imaging soil moisturecontent at varied penetration depths. Ideally, a multiwavelength mission(s) could image soil moisture at depthsof about a few cm and tens of cm. The depth penetrationwould produce true 4-dimensionsa soil moisture maps thatwould provide the basis for hydrology and ecology studies.Radar remote sensing of landcover characterization Full polarization.16
Assessment of the use of radar data to improve land cover mapping accuracyLandsat TMLandsat TMJERS-1 SARJERS-1 SARC-Band multi-polarization SAR detects marshes anddistinguishes between different marsh species.Junco marshesHHPreliminary Results:overall accuracy improvement of 1 %;Large improvement over water, evergreenforest, and forested wetlandB. Wylie, R. Rykhus, L. Yang,and Z. LuSAR images are used to map biomass burning and tomonitor fires on a continuing basis (Kasischke et a., 1995)plata riverDelaAgriculture fieldsDelaAgriculture fieldsplata riverJunco marshesVVEnvisat ASAR images:10/03:11/03:03/04 (RGB)Gings et al., 2004Lava flow mapping using SAR and Landsat TMimages at Westdahl VolcanoPre-1964, 1964, and 1991 lava flowsdraped on a multi-temporal SAR image.NPre-1964, 1964, and 1991 lava flowsdraped over a Landsat TM image.NERS-1 SAR1991 Fire ScarTok, Alaska17
SAR Backscattering fromAgricultural FieldsRadar remote sensing ofAgriculture Soil surface roughnessSoil surface moistureSoil typeCrop speciesVegetation biomassVegetation moistureLand slopeSeed row directionothersChallenges: All of agriculture parameters are a function of Acquisition mode (geometry, frequency, polarisation) Acquisition time and interval Temporal signatureAnalysis ApproachSARASARimage(s)ENVISATMode44ENVISATASAR Mode- focusing- multi-looking (optional)- coregistrationradiometric calibrationDigital Elevation Model- multi-data filtering or- segmentationgeometric calibrationDigital Elevation Modelgeocoded σo datamulti-temporal and/orMulti-polarization and/orMulti-frequencyanalysesAgriculture FigureParallel ComputationCrop classification derived from multi-temporal C-band ERS-1 SAR images over Flevoland,the Netherland (Schotten et al, 1995). For regions with persistent clouds, SAR imageryallows frequent monitoring of crop growth.18
HH/VV and biomass temporal variation over wheat rleyP redicto rs: VV, VH and VV/VH0.027/08Logarithm of the Meas ured C rop height3Oats2.5Biomass (Kg/m2)Biomass4HH/VV (dB)Log(Measured crop height) a0 a1*VV a2*VH a3*VV/VH3.0P redicto rs: VV, VH and VV/VH554,54,5Logarithm of the Meas ured C rop height6Crop Height Estimation4R2 0,46343,532,52(Mattia et al., 2003)Rice - Philippines, Multi-temporal ENVISAT ASAR AP data13 November 200302 December 200318 December 200306 January 200422 January 200410 February 200416 March 200420 April 20043,532,522DateR2 0,481942,533,54Predicted Crop Height4,5522,533,54Predicted Crop Height4,55Holecz et al., 2004Rice - Philippines, ENVISAT ASAR AP & Radarsat-1 data13 November 2003 ASAR18 December 2003 ASAR06 January 2004 Radarsat-1Holecz et al., 2004Holecz et al., 200419
Rice Acreage based on ASAR AP & Radarsat-1South Africa - Maize, Multi-temporal Radarsat-1 dataRadarsat-1 FB04 Nov 200321 Nov 200328 Nov 2003Holecz et al., 2004Future Data and Technology Needs for LandCover/Vegetation/Agriculture SciencesHolecz et al., 2004Future Trends in RadarRemote Sensing Zero baseline L-HH InSAR for estimating temporaldecorrelation, which empirical models relate to vegetationcharacteristics. From single image to multi-temporal images Short repeat period that minimizes temporal decorrelation,useful for both vegetation and deformation. Fully polarimetric capability. 4-D spatial-temporal analysis From single polarization to dual/full polarization Intense computation and parallel processing Polarimetric InSAR for improved vertical structure accuracyand land-cover type discrimination. Multiple frequency for providing two height estimates usedto expand observation.20
Emerging SAR/InSAR technologiesEmerging SAR/InSAR technologiesPermanent Scatterer InSAR – Improve deformation measurement accuracy ofconventional InSARPermanent Scatterer InSAR – Improve deformation measurement accuracy ofconventional InSARCross-Platform InSAR – Generate high-resolution DEM by manipulating radar signalsfrom different platform/sensorsCross-Platform InSAR – Generate high-resolution DEM by manipulating radar signalsfrom different platform/sensorsOperational InSAR Processing System – Improve InSAR processing throughput and laythe foundation for routine monitoring seismic/volcanic/landslide deformationOperational InSAR Processing System – Improve InSAR processing throughput and laythe foundation for routine monitoring seismic/volcanic/landslide deformationScanSAR InSAR – Improve spatial coverage of conventional InSAR to image largescale deformationScanSAR InSAR – Improve spatial coverage of conventional InSAR to image largescale deformationPolarimetric InSAR – Mapping vegetation height through InSAR analysis of polarimetricSAR signalPolarimetric InSAR – Mapping vegetation height through InSAR analysis of polarimetricSAR signalMulti-temporal, polarimetric SAR – Improve land cover mapping and characterizationover regions where weather conditions plague optical remote sensingMulti-temporal, polarimetric SAR – Improve land cover mapping and characterizationover regions where weather conditions plague optical remote sensingPersistent Scatterer InSAR (PSInSAR)Improve InSAR technique- Permanent Scatterer InSARDifferential Phase EquationFor pixel n in interferogram i:φn,i φε,n,i φdefo,n,i φAPS,n,i φorbit,n,i σn,iDEM ErrorTermDeformationin LOSAtmosphericPhase TermBn 1200 mBn 1200 mNoiseOrbit ErrorTerm21
Improve InSAR technique- Permanent Scatterer InSARTransientDeformationEmerging SAR/InSAR technologiesPermanent Scatterer InSAR – Improve deformation measurement accuracy ofconventional InSARCross-Platform InSAR – Generate high-resolution DEM by manipulating radar signalsfrom different platform/sensorsDeformation (m)Deformation of Long Beach, CAAverageDeformationOperational InSAR Processing System – Improve InSAR processing throughput and laythe foundation for routine monitoring seismic/volcanic/landslide deformationScanSAR InSAR – Improve spatial coverage of conventional InSAR to image largescale deformationPolarimetric InSAR – Mapping vegetation height through InSAR analysis of polarimetricSAR signal01cm/yearMulti-temporal, polarimetric SAR – Improve land cover mapping and characterizationover regions where weather conditions plague optical remote sensingCPInSAR DEMTechnique development ofCross-Platform InSAR (CPInSAR)30-minute Repeat-Pass InSARPreliminary CPInSAR DEM: baseline 1.8 km ENVISAT SAR sensor (ASAR) uses a slightly different radarfrequency when compared to the ERS-2 SAR sensor. Accordingly ASAR data can not be combined with ERS-2 data viaconventional InSAR technique.New Orleans A technique, called cross-platform InSAR (CPInSAR) is beingdeveloped to manipulate SAR signals from two different sensors togenerate a DEM. Under favorable imaging geometry conditions and terrain types, theaccuracy of the CPInSAR-derived DEM can reach tens ofcentimeters - better than SRTM and comparable to Lidar.Kwoun and Lu, 200522
Emerging SAR/InSAR technologiesPermanent Scatterer InSAR – Improve deformation measurement accuracy ofconventional InSARInSAR Future: from research to operationInSAR Processing & Monitoring SystemInSARProcessingCross-Platform InSAR – Generate high-resolution DEM by manipulating radar signalsfrom different platform/sensorsProcessingParametersOutputsInSAR ProductsOperational InSAR Processing System – Improve InSAR processing throughput and laythe foundation for routine monitoring seismic/volcanic/landslide deformationScanSAR InSAR – Improve spatial coverage of conventional InSAR to image largescale deformationProcessingQueueDPM,DPMPPMPolarimetric InSAR – Mapping vegetation height through InSAR analysis of polarimetricSAR signalMulti-temporal, polarimetric SAR – Improve land cover mapping and characterizationover regions where weather conditions plague optical remote g SAR/InSAR iesModelsOn-LineDiskData Processing ModuleDMM:PsInSAR Processing ModuleGUI:Data Cataloging and Archiving ModuleOff-LineDiskDeformation Modeling ModuleGraphic User InterfaceLu et alIn prep,2005ScanSAR InSARPermanent Scatterer InSAR – Improve deformation measurement accuracy ofconventional InSARCross-Platform InSAR – Generate high-resolution DEM by manipulating radar signalsfrom different platform/sensorsOperational InSAR Processing System – Improve InSAR processing throughput and laythe foundation for routine m
3. Polarimetric SAR image analysis C-HV data will offer better potential for detection and delineation of clearcuts than C-HH and C-VV data 4. SAR polarimetric phase analysis PPD 0 PPD /-180 PPD distributed between 0 and 180 Different types of targets show different PPD b
HHMI Biointeractive: Popped Secret: The Mysterious Origin of Corn Video with Activity and Other Resources How Stuff Works: Corn Video Section 2: Types of Corn Corn Types Quizlet Corn Types Card Sort Corn Types KERNEL Card Game Section 3: Corn Growth and De
maize act as a good source of minerals, dietary fiber and vitamins. There exist different types of corn for instance pop corn, dent corn, flour corn, sweat corn and flint corn. Spring season consider as the best time period for maize plantation and the corn is unable to tolerate coolness. The plant grows rapidly with the moisture soil.
Status of Resistance to Bt corn ! First Bt lepidopteran active traits registered in 1996 in U.S. (Bt corn borer). ! High dose expression (25 X the lethal concentration [LC] 99) for European corn borer. ! Field-evolved resistance documented in four lepidopteran species: Fall armyworm: Cry1F in Bt corn (Puerto Rico) (Storer et al. 2010)
was made from recycled plastic and wood flour composite through injection-moulding process [8]. Corn or maize is the most widely planted crop in the world. Corn husk is the leafy shell covering the corn. In general, corn husks are the leftovers after the corns were harvested and corn husk is the non-food part of the corn and it is usually left .
--Insect Petting Zoo with hissing cockroaches, drone bees, tarantula, Among the recipes featured at the Insect Tasting Event: Corn Borer Corn Bread: Use favorite corn bread recipe and substitute 1/2 cup ground dry roasted corn borer larvae for 1/2 cup corn meal. Any larvae without hairs or bright colors can be substituted for corn borer larvae.
Oct 06, 1994 · into the most diversified and integrated of the grain processing industries. The corn refining industry produces hundreds of products and byproducts, such as high fructose corn syrup (HFCS), corn syrup, starches, animal feed, oil, and alcohol. In the corn wet milling process, the corn kernel is (see Figure 2-1) separated
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Climate, Weather and Sweet Corn—1 Sweet corn production and a changing climate: growers’ views and priorities to manage uncertainty in production systems Lois Wright Morton, Ajay Nair, and Mark L. Gleason Sweet corn production, weather and climate risks Sweet corn is one of 25 major annual vegetable crops produced in the United States (U.S.).
