“Farming from Space: Current and future … powerpoints/JonesH - Farming...“Farming from Space: Current and future opportunities for remote sensing to boost productivity for grain

“Farming from Space:

Current and future opportunities for remote sensing to

boost productivity for grain growers”

Division of Plant Sciences andUniversity of Dundee, UK

School of Plant BiologyUniversity of WA

Hamlyn G Jones + Contributions fromR Furbank et al. CSIROHRPPC, Canberra

Outline

1. Sensors – what can be measured

2. Platforms – advantages/disadvantages of different

platforms

3. Software – expert systems

4. Application examples

5. Caveats and conclusions

1. Sensor Technologies

� RGB cameras

� Spectral reflectance

� Thermal infrared (evaporation; water stress; soil water)

� Microwave (radar) – (water content)

� Lidar (canopy structure)

� Fluorescence (photosynthesis)

� RGB cameras






1. Spectral reflectance – vegetation indices

soil

plant

NDVI mapvis � infra-red �

NR

NDVI = (N – R)(N + R)

Amount of leaf material

Green Cover

Normalised difference vegetation index:

1. Vegetation indices

� Infinite no. of 2-band VIs:


Vis

ible

Water bands (IR) – info on water content


Principle What it estimatesRGB cameras Canopy cover, health, disease,

deficiencies, etc.

Conventional vegetation indices(R/ NIR)

Canopy cover, biomass, chlorophyll, N-status, etc.

Mid-Infrared (water indices) Canopy water content

Hyperspectral sensing Pigments, nutrient status, stress diagnosis, crop mapping

Multiangular Canopy structure


� RGB cameras






� Based on the cooling effect of evaporation

1. Thermal sensing

Hot, non-transpiring

Cool, rapid transpiration, open stomata

� Measures water use or drought stress (actually

stomatal closure)

- water balance (regional)

- irrigation scheduling

- breeding for drought tolerance

How do we apply thermal sensing?

Satellite

Glasshouse


� RGB cameras






2. Platforms

� Satellite

� Airborne (manned)

� Balloon

� UAVs

� UGVs/Autonomous robots

� Tractor-mounted

� Fixed sensor networks

(e.g. WSNetworks)

Considerations in choice:

� Cost of data

� Timeliness

� Frequency of availability

� Spatial resolution

� Flexibility of timing

� Labour requirement

Trade-off

2. Platforms - Satellite

� Geostationary (MTSAT 2 – 15 min @ 5 km)

� Medium Res. (NOAH-AVHRR, MODIS–daily @ 0.25-1 km)

� High Res. (Landsat – 15 days @ 30 m)

� V. High Res. (Ikonos, QuickBird, World View2 – 0.5-2 m)

The Airframe

2. Platforms - Airborne

Thanks to David Deeryand colleagues

Create mosaic image (using

AutoPano Giga)

� Geocorrected thermal mosaic for plot extraction


Thanks to David Deeryand colleagues


e.g. NASA Airborne imager

� Lidar

� Imaging spectrometer

� Thermal camera

Spatial resolution 0.5-2 m

(Rem Sens 5: 2013)

2. Platforms - balloons

Thanks to Ashley

Wheaton, Dookie

College

and

Oxford Landing

2. Platforms - UAVs/drones

HHonkavaara et al 2013, RemSens 5

2. Platforms - Field platforms

Osnabrucke robots

e.g. “Boni-rob” and

“BreedVision”

Avignon Maricopa

2. Platforms - Field platforms

Thanks to: Bob Furbank, Dave Deery, Xav Sirault, J. Jiminez-

Berni, et al.

The “Canberra” system

2. Platforms – Field platforms

Sensors on field platforms

�LiDAR – detailed 3D structure –

canopy cover, canopy height, ear size,

biomass

�Thermal – temperature and ‘stress’

�Spectral – canopy cover, N-status,

chlorophyll, photosynthesis, pigments,

carbohydrates, biomass

Platform comparison

Platform Features

Satellite Cloud limited (excl. radar), low spatial resolution (trade-off with frequency), good for weather/mapping – less so for crop management

Airborne Below cloud, higher resolution, potential high frequency

Balloon Rarely usable because of wind

UAV v. high resolution, very flexible, low cost, low payload

Tractor-mounted

Readily incorporated into on-farm management

3. Software – critical for image analysis

Original scan

Geocorrected map

flight

(NERC flight a140103a, Tarquinia 2005)

3. Software

Transforming farm management and data handling- e.g. yield mapping, soil analyses, remote sensing data, record keeping,

monitoring, etc.; - Usable on iPhone/Tablets; - Inputs directly to precision crop

management in field

(e.g. Soil Essentials Ltd. – Cloud-based mapping software shown here)

4. Applications of Remote sensing

� Weather

� Cropping areas and mapping (Government)

� Biomass estimation and Yield forecasts (Government)

� Crop phenology

� Crop vigour & stress management (e.g. Irrigation need)

� Precision agriculture – Management Zones

� Weed detection

� Soil degradation

4. Applications - Weather

MTSAT 145oE(Japan)

Frequent – every 10 min!but low resolution (5 km)

Major contribution to services

e.g. weather forecasts

Low res. geostationary satellites

Frost Watch

(NOAH-AVHRR)

Land Surface Temperature (LST) image over South Western Australia.(Frostwatch - NOAA 15 79519 04:02 29/08/2013 WST)

4. Applications - Weather

Jones & Vaughan 2010, OUP

Cover estimation (NDVI)

Crop inventory(hyperspectral classification)

Oak canopyCistusSoilGrassWater

4. Applications – Crop inventories

� Multi-spectral or hyperspectral imagery (satellite/air)

� Can be classified to delineate vegetation or crops

4. Applications - Yield prediction

(from Rembold et al 2013 Rem Sens 5)

NDVI NDVIY

ield

–k

g/h

a

Yie

ld –

kg

/ha

4. Applications – stem carbohydrate

Dreccer et al 2014 (Field Crop Res)

4. Applications - Stress diagnosis

e.g. Nitrogen deficiency

?

(Images -Randall Pearson, S Illinois University, Ames Iowa)

SENSORS

Temperature

Spectral reflectance

Fluorescence

Multiangular

LiDAR


STRESSESAbiotic

Drought/salinity

Flooding

Frost/chill

Pollutant

Nutrient defic./toxicityBiotic

Disease

Pest

RESPONSES

Stomata

Pigments, canopy cover

Biochemistry

Canopy structure

Canopy structure

SENSORS

Temperature

Spectral reflectance

Fluorescence

Multiangular

LiDAR


STRESSESAbiotic

Drought/salinity

Flooding

Frost/chill

Pollutant

Nutrient defic./toxicityBiotic

Disease

PestCharacteristic pattern of response is diagnostic - multisensor

4. Applications – Precision agriculture

Remote sensing

Tractor-mounted sensors

Soil mapping

Yield mapping

Ad hoc sampling

Define

Management

zones

4. Applications - Weed management

Weed mapping

Need to target optimal window (e.g. early post emergence)

Photos – John Heap (SARDI)

5. Caveats and Conclusions

“Gala contemplating the Mediterranean sea”

Salvador Dali (St Petersburg)

becomes: “Portrait of Lincoln”

Salvador Dali (St Petersburg)

Information depends on Scale

e.g. Low resolution images may give

misleading information:


Tmean = 38oC

Tmean = 36oC

Tmean = 34oC

Scale (pixel resolution) affects results


Prediction (e.g. of N) can be

very weak even if ‘good’

correlation

- So need good calibrations

(from Miphokasap et al 2012 Rem Sens 4)

Beware error in estimates

ND

VI

0.2

0.5

0.8

Predicted N content (%)Predicted N content (%)

Rem

ote

sen

sin

g e

stim

ate

Conclusions

� Remote sensing (all scales) has enormous

potential for farmers

� Especially ‘tractor-mounted’ and ‘Drones’

� Beware error of ‘spurious’ accuracy

� Further development depends on new ‘user-

friendly’ software (smart phones etc.)

Conclusions

�But . . . . .

much developmental work still needed for fully

practical systems, esp. with UAVs

Published by:

Oxford University Press, July 2010

CONTACT:

Professor Hamlyn Jones

[email protected]

2010 2014

Documents

“Farming from Space: Current and future … powerpoints/JonesH - Farming...“Farming from Space: Current and future opportunities for remote sensing to boost productivity for grain