Remote Sensing of Vegetation Properties€¦ · 2 H 6) 0-20% • Others < 1% Natural Gas...
Transcript of Remote Sensing of Vegetation Properties€¦ · 2 H 6) 0-20% • Others < 1% Natural Gas...
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Remote Sensing of Vegetation
Properties
K. Tansey, H. Balzter, S. Johnson, Paul Arellano and
many others
Department of Geography
University of Leicester
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Research at the University of Leicester • Strong track record in Space and EO Science
• Home to the National Centre for Earth Observations
• Office for EMBRACE
• G-STEP (early catapult model)
• New Research Institute (LISEO) & National Space
Centre
• Enjoy the services and support of a NERC KEF
• Centre for Landscape and Climate Research
• Funding from EC, ESA, NERC, InnovateUK
• Interested in exploring Agri-Tech opportunities
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Earth Observation to See Vegetation
Earth observation methods provide a high spectral
resolution information about the canopy and vegetation
biochemical and biophysical characteristics.
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Hydrocarbons - Natural Geological Process
• Seepages
– “…Are hydrocarbons seeping vertically or near-vertically from the
reservoir to the surface” (van der Meer, 2006)
A generalised map of hydrocarbon microseepage and its surface expressions (from Yang, 1999: p. 8)
• Methane (CH4) 70-90%
• Ethane(C2H6) 0-20%
• Others < 1%
Natural Gas Composition
IPCC reported 4% of methane from Geological
Sources (IPCC, 2007).
Some authors suggest a bigger contribution.
• Bacteria in soil consume
(oxidize) the hydrocarbons
• Increase CO2 in soil
• Decrease Oxygen
concentration
• Low pH values
• Mineral are dissolved and
mobilized then increased
their concentrations
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Vegetation stress detection
• Soil-gas sampling (Geochemistry)
• Vegetation sampling:
– chlorophyll and other pigments content,
– structure,
– phenology.
• Reflectance measurements in field
• Airborne and satellite images
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Deepest, Darkest
Ecuador
Oil spills in the Amazon forest
http://dx.doi.org/10.1016/j.envpol.
2015.05.041
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Fieldwork: Sampling
Parameters at leaf level: •Chlorophyll a and b
•Water content
•Dry matter content
•Leaf internal structure
•Reflectance (Visible and near Infrared)
•Transmittance (Visible and near infrared)
Chlorophyll meter
Spectrophoto meter
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Fieldwork campaign in the Amazon – Vegetation
sampling
Vegetation sampling in the vertical profile of the forest
Biophysical and biochemical
parameters extraction
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Biophysical and biochemical alterations of the forest
Results show lower levels of chlorophyll content across the vertical profile of the forest,
increase foliar water content
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Canopy Models approach
Ground
{Di }
Canopy
{Ci }
Atmosphere
{Bi }
Sensor
{Ei }
Source
{Ai }
Ri = f (Ai , Bi , Ci , Di , Ei )
Ai : Spectral intensity, Wavelength (λ), location angles (θ, ψ)
Bi : Wavelength (λ) absorption and scattering properties of
aerosol particles, water vapour and ozone).
Ci : Optical parameters (reflectance and transmittance)
Pigments contents (chlorophyll a and b, etc.)
Structural parameters (geometrical shapes and positions) of
vegetation components (leaves, stems, etc.), LAI.
Di : Reflectance and absorption, roughness, texture, density,
moisture
Ei : Spatial and spectral resolution, calibration parameters,
location angle.
In order to detect stress of vegetation affected
by pollution the Inverse canopy model
technique can be applied:
Ci = f (Ai , Bi , Ri , Di , Ei )
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Method
Scaling-up model
• Chlorophyll
• Water
content
• Organic
matter
• Internal
structure
• Tree high
• DBH
• LAI
• Crow shape and
size
• Leaf size
Canopy level
Leaf level
Leaf Model (PROSPECT)
3D canopy model
(Flight or DART)
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EO-1 Hyperion hyperspectral image pre-
processing
RAW RADIANCE
CORRECTED
REFLECTANCE
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Image processing: Vegetation indices
VI LD1 relative
contribution
SG 53.0%
NDVI 21.6%
NDVI705 8.4%
CTR2 4.6%
GNDVI 4.6%
LIC1 3.5%
VOG1 2.4%
OSAVI 0.9%
MTCI 0.3%
PSSRa 0.3%
SR 0.2%
Discriminant function analysis Analysis of variance and pairwise comparison -
Holm adjustment method
Secondary
forest vs.
INDEX
Secondary
forest
Pristine
forest
Pristine
forest
1 Simple Ratio SR *** *** ***
2 Normalized Difference Vegetation NDVI *** *** **
3 Green Normalized Difference Vegetation Index GNDVI *** *** **
4 Atmospherically Resistant Vegetation Index ARVI ns *** ***
5 Enhanced Vegetation Index EVI ** *** *
6 Sum Green SG *** *** ns
7 Pigment Specific Simple Ratio-Chla PSSRa *** *** ***
8 Red Edge Normalized Difference Index NDVI705 *** *** ***
9 Modified Red Edge Simple Ratio mSR705 ns *** ***
10 Modified Red Edge Normalized Difference Index mNDVI705 ns *** ***
11 Carter Index 2 CRT2 *** *** ***
12 Lichtenthaler Index 1/Pigment Specific Normalized Difference LIC1/PSNDa *** *** **
13 Optimized Soil-Adjusted Vegetation Index OSAVI *** *** *
14 Modified Chlorophyll Absorption Ratio Index MCARI ns ns ns
15 Ratio of derivatives at 725 and 702 nm Der725-702 ns *** ***
16 Red Edge Position REP ** ns **
17 Vogelmann Red Edge Index VOG1 *** *** ***
18 Chlorophyll Index CI590 * *** ***
19 MERIS Terrestrial Chlorophyll Index MTCI *** *** ***
20 Structure Insensitive Pigment Index SIPI * *** ***
21 Red Green Ratio RG ns ns **
22 Anthocyanin Reflectance Index 1 ARI1 * ** ***
23 Anthocyanin Reflectance Index 2 ARI2 ns *** ***
24 Water Band Index WBI ns *** ***
25 Normalized Difference Water Index NDWI . *** *
26 Moisture Stress Index MSI * *** ns
27 Normalized Difference Infrared Index NDII ns *** ***
28 Normalized Heading Index NHI ns ns ns
NARROW-BAND VEGETATION INDICES: Water Indices
BROAD-BAND VEGETATION INDICES
NARROW-BAND VEGETATION INDICES: Greenness / Chlrorophyll / REP
NARROW-BAND VEGETATION INDICES: Other Pigments
Polluted forest vs.
*** Strongly significant (0.1%)
** Higly significant (1%)
* Significant(5%)
. Lowest significant (10%)
ns No significant
75%
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Chlorophyll content at canopy level
MTCI and chlorophyll
content (μg cm-2) at
canopy level
estimated in the
study sites
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Using Radars to Estimate Biomass
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Monitoring and Managing Global Forest
Resources
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Invasive Species Mapping
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Crops monitoring using high temporal resolution satellites
Sentinel-1 and 2 & Landsat + others
VENUS satellite image
(France and Israel)
2 day revisit time
10 m resolution (3 m resolution)
Super-spectral camera
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Moving it Forward
• Very interested in Gathering and Understanding User
Requirements
• Scale, Region, Application
• Track record of working with SMEs through InnovateUK or
other business engagement models
• Amazing level of support available:
• EMBRACE -> Catapult
• NERC Knowledge Exchange Fellow (Sarah)
• Business development managers in Energy and
Environment (Maggy) and Space (Sarah)
• EC H2020 Bid managers