Remote Sensing of Vegetation
Properties
K. Tansey, H. Balzter, S. Johnson, Paul Arellano and
many others
Department of Geography
University of Leicester
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
Earth Observation to See Vegetation
Earth observation methods provide a high spectral
resolution information about the canopy and vegetation
biochemical and biophysical characteristics.
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
Vegetation stress detection
• Soil-gas sampling (Geochemistry)
• Vegetation sampling:
– chlorophyll and other pigments content,
– structure,
– phenology.
• Reflectance measurements in field
• Airborne and satellite images
Deepest, Darkest
Ecuador
Oil spills in the Amazon forest
http://dx.doi.org/10.1016/j.envpol.
2015.05.041
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
Fieldwork campaign in the Amazon – Vegetation
sampling
Vegetation sampling in the vertical profile of the forest
Biophysical and biochemical
parameters extraction
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
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 )
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)
EO-1 Hyperion hyperspectral image pre-
processing
RAW RADIANCE
CORRECTED
REFLECTANCE
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%
Chlorophyll content at canopy level
MTCI and chlorophyll
content (μg cm-2) at
canopy level
estimated in the
study sites
Using Radars to Estimate Biomass
Monitoring and Managing Global Forest
Resources
Invasive Species Mapping
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
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
Top Related