Vegetation monitoring with

remote sensing

Dr Toby Waine, Lecturer in Applied Remote Sensing

Course Tutor MSc Geographical Information Management

Vegetation monitoring with

remote sensing, multi-scale

Scale

Regional crop inventory - Food security - Asset management - Governance (tax) - Policy

Precision Farming - Food security - Sustainable agriculture - Precision Irrigation - Environmental compliance

Magellium, DMCii Ltd.

Commodity traders UNODC, LUCAS 2012/15

Growers, ESA

Field level inventory - Food security - Asset management - Enforcement (Levy) - Environmental modelling

Contents

• What’s possible with remote sensing imagery?

• Imagery at different scales – spatial resolution and NDVI

• Case studies:

– Yield assessment

– Disease monitoring

– Crop area estimates

– Vegetation productivity indicator

What’s possible with Remote

Sensing imagery?

• Exploit an underlying physical relationship between a crop biophysical

parameter and spectral response, e.g. %cover, LAI, GAI, biomass

• Vegetation indices such as NDVI are often used to calibrate crop yield

indicators, to assess crop development, or disease

• Apply at different spatial scales (within-field to farm scale and above)

Different scales 250 to 1 m,

(satellite, aerial and ground)

Different scales <1 m

Near ground 1 m quadrat

0.1 m

0.25 m

0.6 m

0.1 m pixel

• Biomass • Pasture

• Forage

• Grain/fruit • Harvest index

• Harvest losses

• Roots/tubers • Harvest index

• Harvest losses

• Extract • Sugar

• Opium gum

Vegetation Index

Yiel

d/P

rod

uct

ion

Desired practical result

Yield/Production estimation

using vegetation indices

Vegetation Index

Cro

p C

ove

r

Photosysnthetically Active Vegetation PAV

1.

Biomass

Cro

p Y

ield

4.

3.

Crop Cover

Bio

mas

s 3.

2.

2.

Intermediate relationships implicit to

correlations with yield

NDVI: R-NIR images captured

simultaneously

(0.5 m pixels)

Near Infrared (840 nm) Visible-Red (640 nm)

Normalised difference

vegetation index (NDVI)

R

NIR

RIR

RIR

RIR

RIR

DNDN

DNDN

RIR

RIRNDVI

Reflectance values

NDVI is dimensionless with a range of -1 to 1

NDVI sensor

Ground data collection

for NDVI calibration

Lettuce monitoring with UAV

using NDVI

y = 0.0005x + 0.1303 R² = 0.8351

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.00 200.00 400.00 600.00 800.00 1000.00

ND

VI

Fresh Weight (g/m2)

Whole heads vs NDVI

F.W (g)

• NDVI values correlates with plant canopy • Plant population (> 95% accuracy)

• Mean foliar diameter (> 96% accuracy)

• Yield potential in lettuce

Derived crop information –

cauliflower (30 cm NDVI)

Predicting Harvest resource and planning marketing

Max diff of 286g

lettuce yield variation with soil

DD

DDDD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DD

DDD

D

Etude cru

Antartica cru

Etude

Glassica cru

Elsol cru

Challenge

Etude cru

Antertica cru

Yucaipa cru

Excalibur cru

Yucaipa cru

Elsol

Location: Cambs Field: Spooners 3 Date: 16/07/15 Crop: Iceberg

´ 0 140 28070

MetersCopyright of G's Growers Ltd

VEGETATION INDEX MAP (NDVI)

NDVI

High

0.689 - 0.715

0.662 - 0.688

0.635 - 0.661

0.608 - 0.634

0.581 - 0.607

0.553 - 0.58

0.526 - 0.552

0.499 - 0.525

0.472 - 0.498

0.445 - 0.471

0.418 - 0.444

0.39 - 0.417

0.363 - 0.389

0.336 - 0.362

0.309 - 0.335

0.282 - 0.308

0.255 - 0.281

Low

1

10

rodham

Disease detection and progression - Celery

1 w

eek

Crop survey using a UAV with multispectral camera enable to detect the location of areas affected by Septoria Apiicola (late blight)

Affected area can be measured from imagery and tracked back to variety, planting batch, seed lot…

False colour composite images form UAV. Evolution of an organic celery patch affected by ‘late bright’ in a week difference (Oct-2014)

Integration of UK-DMC and IKONOS

for opium and wheat cultivation

estimates in Afghanistan

Impact: Poppy cultivation estimates were used to inform UK and international counter narcotics policy

Probability of poppy:

Distribution of poppy

UK-DMC strata 2009

Statistical analysis exploiting spectral clusters in UK-DMC with VHR interpretations for poppy area estimates

MODIS NDVI profiles (250 m pixel)

Figure 1. NDVI profiles from MODIS imagery at agricultural locations in - Helmand (Altitude 741 m, Latitude 31.43° N), - Balkh (Altitude 1463 m, Latitude 35.80° N) and - Badakhshan (Altitude 2502 m, Latitude 36.31° N) Ref: Taylor, et al., 2010

Vegetation productivity indicator (VPI)

crop information across Afghanistan

4 year min

4 year max

4 year ave

2009

4 year min

4 year max

4 year ave

2009

Agro-ecosystems underpinned by

Agri-informatics – Agri EPI-Centre

©Alastair Parvin for Building Design Magazine

What is the appropriate spatial and temporal scale?