SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom.

Slides:



Advertisements
Similar presentations
Objective: ●harmonized data sets on snow cover extent (SE), snow water equivalent (SWE), soil freeze and vegetation status from satellite information,
Advertisements

 nm)  nm) PurposeSpatial Resolution (km) Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.
NDVI Anomaly, Kenya, January 2009 Vegetation Indices Enhancing green vegetation using mathematical equations and transformations.
Crop Canopy Sensors for High Throughput Phenomic Systems
Environmental Remote Sensing GEOG 2021 Spectral information in remote sensing.
PhD remote sensing course, 2013 Lund University Understanding Vegetation indices PART 1 Understanding Vegetation indices PART 1 : General Introduction.
Calculation of Vegetation Indices with PAR and Solar Radiation Measurements David R. Cook Argonne National Laboratory.
Radar, Lidar and Vegetation Structure. Greg Asner TED Talk.
Vegetation indices and the red-edge index
The Role of Models in Remotely Sensed Primary Production Estimates
NDVI Normalized difference vegetation index Band Ratios in Remote Sensing KEY REFERENCE: Kidwell, K.B., 1990, Global Vegetation Index User's Guide, U.S.
Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.
Use of remote sensing on turfgrass Soil 4213 course presentation Xi Xiong April 18, 2003.
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison Benevento, June 2007.
MODIS Subsetting and Visualization Tool: Bringing time-series satellite-based land data to the field scientist National Aeronautics and Space Administration.
Radiometric and biophysical measures of global vegetation from multi-dimensional MODIS data Ramakrishna Nemani NTSG.
Differences b etween Red and Green NDVI, What do they predict and what they don’t predict Shambel Maru.
Two-band vegetation indices Three-band vegetation indices
Review of Statistics and Linear Algebra Mean: Variance:
Introduction To describe the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.
West Hills College Farm of the Future. West Hills College Farm of the Future Precision Agriculture – Lesson 4 Remote Sensing A group of techniques for.
Karnieli: Introduction to Remote Sensing
GreenSeeker® Handheld Crop Sensor
Electromagnetic Radiation Most remotely sensed data is derived from Electromagnetic Radiation (EMR). This includes: Visible light Infrared light (heat)
Soil-Adjusted Vegetation Index A transformation technique to minimize soil brightness from spectral vegetation indices involving red and near- infrared.
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
NDVI: What It Is and What It Measures Danielle Williams.
1 Lecture 7 Land surface reflectance in the visible and RIR regions of the EM spectrum 25 September 2008.
Satellite observations of terrestrial ecosystems and links to climate and carbon cycle Bases of remote sensing of vegetation canopies The Greening trend.
The Use of Red and Green Reflectance in the Calculation of NDVI for Wheat, Bermudagrass, and Corn Robert W. Mullen SOIL 4213 Robert W. Mullen SOIL 4213.
Spatial Model-Data Comparison Project Conclusions Forward models are very different and do not agree on timing or spatial distribution of C sources/sinks.
LAI/ fAPAR. definitions: see: MOD15 Running et al. LAI - one-sided leaf area per unit area of ground - dimensionless fAPAR - fraction of PAR (SW radiation.
#9 #118 #11 #13 #14 SOC Camera Images & Tree Samples SOC camera 09/05 11:01 am, composited by R-G-B Tag #Family 9 Vochysiaceae 11 Leguminosae-
EG2234: Earth Observation Interactions - Land Dr Mark Cresswell.
Remote Sensing and Image Processing: 4 Dr. Hassan J. Eghbali.
Beyond Spectral and Spatial data: Exploring other domains of information GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations G.P.Asner and K.B.Heidebrecht.
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
GEOG2021 Environmental Remote Sensing Lecture 3 Spectral Information in Remote Sensing.
Measuring Vegetation Characteristics
IGARSS 2011, Jul. 27, Vancouver 1 Monitoring Vegetation Water Content by Using Optical Vegetation Index and Microwave Vegetation Index: Field Experiment.
Xiangming Xiao Institute for the Study of Earth, Oceans and Space University of New Hampshire, USA The third LBA Science Conference, July 27, 2004, Brasilia,
Calculation of Vegetation Indices with PAR and Solar Radiation Measurements David R. Cook Argonne National Laboratory.
Figure 1. (A) Evapotranspiration (ET) in the equatorial Santarém forest: observed (mean ± SD across years of eddy fluxes, K67 site, blue shaded.
A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.
Estimating Cotton Defoliation with Remote Sensing Glen Ritchie 1 and Craig Bednarz 2 1 UGA Coastal Plain Experiment Station, Tifton, GA 2 Texas Tech, Lubbock,
REMOTE SENSING INDICATORS FOR CROP GROWTH MONITORING AT DIFFERENT SCALES Zongnan Li 1, 2 and Zhongxin Chen 1, 2* 1 Key Laboratory of Resources Remote Sensing.
Lecture Notes – Vegetation indices Fred Watson, ENVS 436/536, CSUMB, Fall 2010 Many of these slides are from Jianglong Zhang and Cindy Schmidt.
Interactions of EMR with the Earth’s Surface
NOTE, THIS PPT LARGELY SWIPED FROM
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
Farms, sensors and satellites. Using fertilisers Farming practice are changing Growing quality crops in good yields depends on many factors, including.
Electromagnetic Radiation
GEOG2021 Environmental Remote Sensing
Using vegetation indices (NDVI) to study vegetation
Vegetation Indices Radiometric measures of the amount, structure, and condition of vegetation, Precise monitoring tool phenology inter-annual variations.
Evolution of OSU Optical Sensor Based Variable Rate Applicator
Basics of radiation physics for remote sensing of vegetation
Vegetation Indices Radiometric measures of the amount, structure, and condition of vegetation, Precise monitoring tool phenology inter-annual variations.
Hyperspectral Remote Sensing
Radiometric Theory and Vegetative Indices
Sources of Variability in Canopy Spectra and the Convergent Properties of Plants Funding From: S.V. Ollinger, L. Lepine, H. Wicklein, F. Sullivan, M. Day.
Remote Sensing Section 3.
Vegetation.
Remote Sensing Landscape Changes Before and After King Fire 2014
Hyperspectral Remote Sensing
Presentation transcript:

SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom

1. SpectroSense2+ Meter 2. Spectral Reflectance 3. Vegetation Indices 4. SpectroSense2+ Configurations SPECTROSENSE2+

1. SpectroSense2+ Meter 2. Spectral Reflectance 3. Vegetation Indices 4. SpectroSense2+ Configurations SPECTROSENSE2+

SPECTROSENSE 2+.GPS 8 channel meter Display 8 readings at once Ratio of incident / reflected Store function Automatic datalogging VI & LAI calculations GPS for field mapping SPECTROSENSE2+

VEGETATION INDICES (VIs) “Greenness” Index Detects presence of vegetation Measures ratios of spectral bands Estimate plant biomass / cover Identify plant species Detect plant health / disease SPECTROSENSE2+

VEGETATION INDICES (VIs) Applications Crops & Horticulture, Precision Agriculture Climate Change Satellite Ground Truthing Carbon Balance SPECTROSENSE2+

VI Calculations NDVI & PRI MODIS EVI EVI2 & MSAVI2 RVI & WBI LAI fPAR Individual readings SPECTROSENSE2+

1. SpectroSense2+ Meter 2. Spectral Reflectance 3. Vegetation Indices 4. SpectroSense2+ Configurations SPECTROSENSE2+

SPECTRAL REMOTE SENSING Measuring Reflected Light Different surfaces reflect different wavelengths Easy identification of land, ocean, forest, desert etc Satellite maps Ground truth maps SPECTROSENSE2+

SPECTRAL REMOTE SENSING Measuring Reflected Light VIS, NIR and SWIR wavelengths Each surface has a “fingerprint” spectrum Low values - absorption High values - reflectance SPECTROSENSE2+

VEGETATION INDICES (VIs) Calculated from reflected radiation “Greenness” Indices Detects presence of vegetation Estimate plant biomass / cover Identify plant species Detect plant health / disease SPECTROSENSE2+

1. SpectroSense2+ Meter 2. Spectral Reflectance 3. Vegetation Indices 4. SpectroSense2+ Configurations SPECTROSENSE2+

SIMPLE RATIO VEGETATION INDICES Simple Ratio of two Wavelengths RVIRelative Vegetation Index Uses Red and NIR bands WBIWater Band Index Uses 970nm and 990 nm bands SPECTROSENSE2+

Relative Vegetation Index RVI =NIR Red SPECTROSENSE2+

RVI Ranges from 0 to infinity 0 bare soil >0vegetation SPECTROSENSE2+

Water Band Index WBI = R 900 or R 900 R 970 R 1530 Surface wetness, water vapour flux SPECTROSENSE2+

WBI For Green Vegetation Range is 0.8 to 1.2 SPECTROSENSE2+

NDVI Normalised Differential Vegetation Index The most widely used “greenness” VI Measure ratios of NIR to Red Estimates amount of Biomass & Ground Cover Health Status of plants SPECTROSENSE2+

Normalised Differential Vegetation Index NDVI = (R NIR - R Red ) (R NIR + R Red ) This has limitations in dense vegetation SPECTROSENSE2+

NDVI Ranges from -1 ice and snow 0 bare soil +1 full vegetation cover SPECTROSENSE2+

Fraction of PAR Absorbed by the plant canopy fPAR = 1.24 * NDVI Strongly linked to LAI Leaf Area Index SPECTROSENSE2+

fPAR Ranges from -1 ice and snow 0 bare soil +1 full vegetation cover SPECTROSENSE2+

LAI Leaf Area Index LAI = Total upper leaf surface Ground surface area 0 = bare soil 6 = dense forest Can be estimated using NDVI SPECTROSENSE2+

From NDVI LAI = a * e (b * NDVI) where a = , e = exponential, b = LAI Leaf Area Index SPECTROSENSE2+

PRI Photochemical Reflective Index Measure ratios of Green to Yellow Rate of photosynthesis Light Use Efficiency LUE Health Status of plants Productivity of plants SPECTROSENSE2+

PRI = Photochemical Reflective Index PRI = (R R 531 ) (R R 531 ) corresponds with Xanthophyll Cycle activity & LUE Light Use Efficiency SPECTROSENSE2+

PRI Ranges from -1 to +1 Healthy Vegetation -0.2 to +0.2 SPECTROSENSE2+

MODIS EVI Enhanced Vegetation Index Uses MODIS bands 1, 2 and 3 Red, NIR and Blue Better sensitivity for high biomass Responsive to canopy structure SPECTROSENSE2+

MODIS EVI EVI = (R NIR - R Red ) * G (R NIR + C 1 *R Red - C 2 *R Blue + L ) where C 1 =6, C 2 =7.5, L=1, G=2.5 SPECTROSENSE2+

MODIS EVI Ranges from -1 to +1 Healthy Vegetation 0.2 to 0.8 SPECTROSENSE2+

EVI2 2 band Enhanced Vegetation Index Uses just Red and NIR bands Blue band not required Good correlation with MODIS EVI SPECTROSENSE2+

EVI2 EVI2 = 2.5 * (R NIR - R Red ) (R NIR * R Red + 1) 2 band Enhanced Vegetation Index SPECTROSENSE2+

EVI2 Ranges from -1 to +1 Healthy Vegetation 0.2 to 0.8 SPECTROSENSE2+

MSAVI2 Modified Soil Adjusted Vegetation Index Uses Red and NIR bands Compensates for exposed soil areas Best where vegetation cover is <40% SPECTROSENSE2+

MSAVI2 MSAVI2 = (2*R NIR + 1) -  [(2 * R NIR + 1) 2 - 8*(R NIR - R Red )] 2 Modified Soil Adjusted Vegetation Index SPECTROSENSE2+

MSAVI2 Ranges from -1 ice and snow 0 bare soil +1 full vegetation cover SPECTROSENSE2+

LAI Leaf Area Index LAI = Total upper leaf surface Ground surface area 0 = bare soil 6 = dense forest Can be estimated using PAR Sensors SPECTROSENSE2+

Using a pair of PAR Sensors An Incident PAR sensor above the canopy A Line PAR sensor below the canopy LAI = -2 * ln (PAR LQ / PAR I ) LAI Leaf Area Index SPECTROSENSE2+

1. SpectroSense2+ Meter 2. Spectral Reflectance 3. Vegetation Indices 4. SpectroSense2+ Configurations SPECTROSENSE2+

SPECTROSENSE 2+.GPS One SpectoSense2+ meter Two identical sensors GPS module for mapping Hand held pole Carry case SPECTROSENSE2+

TWO IDENTICAL SENSORS Cosine corrected for incident light measurements Narrow angle FOV sensor for reflected light measurements Simultaneous readings allow use in any light conditions SPECTROSENSE2+

TWO OR FOUR BAND SENSORS Specially designed geometry of narrow angle light acceptance Changing the height above the ground changes the area of measurement E.g. 1.8m height = 0.5m 2 area r h 25° Sensor 1 Sensor 2 SPECTROSENSE2+

HAND HELD NDVI SYSTEM SpectroSense2+ meter Two 2 channel sensors Red and NIR bands NDVI & RVI fPAR & LAI EVI2 & MSAVI SPECTROSENSE2+

MODIS EVI SYSTEM SPECTROSENSE2+ SpectoSense2+ meter Two 3 or 4 channel sensors Blue, Red and NIR bands NDVI & RVI fPAR & LAI MODIS EVI EVI2 & MSAVI

NDVI / PRI SYSTEM SpectroSense2+ meter Two 4 channel sensors Red, NIR, 531 & 570nm bands NDVI & RVI fPAR & LAI EVI2 & MSAVI PRI SPECTROSENSE2+

LEAF AREA INDEX Two PAR sensors Incident PAR sensor above canopy Line PAR sensor below canopy SPECTROSENSE2+

SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom