Remote Sensing of Vegetation

Properties

K. Tansey, H. Balzter, S. Johnson, Paul Arellano and

many others

Department of Geography

University of Leicester

kevin.tansey@le.ac.uk

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