Farming from Space: Current and future opportunities for remote sensing to boost productivity for grain growers

Transcription

1 Farming from Space: Current and future opportunities for remote sensing to boost productivity for grain growers Hamlyn G Jones Division of Plant Sciences and University of Dundee, UK School of Plant Biology University of WA + Contributions from R Furbank et al.csiro HRPPC, Canberra

2 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

3 1. Sensor Technologies RGB cameras Spectral reflectance Thermal infrared (evaporation; water stress; soil water) Microwave (radar) (water content) Lidar (canopy structure) Fluorescence (photosynthesis)

4 1. Spectral reflectance vegetation indices vis infra-red NDVI map soil plant R N Normalised difference vegetation index: NDVI = (N R) (N + R) Amount of leaf material Green Cover

5 1. Vegetation indices Infinite no. of 2-band VIs:

6 1. Spectral reflectance vegetation indices Water bands (IR) info on water content Visible

7 1. Spectral reflectance vegetation indices Principle RGB cameras Conventional vegetation indices (R/ NIR) Mid-Infrared (water indices) Hyperspectral sensing Multiangular What it estimates Canopy cover, health, disease, deficiencies, etc. Canopy cover, biomass, chlorophyll, N-status, etc. Canopy water content Pigments, nutrient status, stress diagnosis, crop mapping Canopy structure

8 1. Sensor Technologies RGB cameras Spectral reflectance Thermal infrared (evaporation; water stress; soil water) Microwave (radar) (water content) Lidar (canopy structure) Fluorescence (photosynthesis)

9 1. Thermal sensing Based on the cooling effect of evaporation Cool, rapid transpiration, open stomata Hot, non-transpiring Measures water useor drought stress(actually stomatal closure) - water balance (regional) - irrigation scheduling - breeding for drought tolerance

10 How do we apply thermal sensing? Satellite Glasshouse

11 1. Sensor Technologies RGB cameras Spectral reflectance Thermal infrared (evaporation; water stress; soil water) Microwave (radar) (water content) Lidar (canopy structure) Fluorescence (photosynthesis)

12 2. Platforms Considerations in choice: Satellite Cost of data Airborne (manned) Timeliness Balloon Frequency of availability UAVs Spatial resolution UGVs/Autonomous robots Flexibility of timing Tractor-mounted Labour requirement Fixed sensor networks (e.g. WSNetworks) Trade-off

13 2. Platforms -Satellite Geostationary (MTSAT km) Medium Res. (NOAH-AVHRR, MODIS km) High Res. (Landsat m) V. High Res. (Ikonos, QuickBird, World View m)

14 2. Platforms -Airborne The Airframe Create mosaic image (using AutoPano Giga) Thanks to David Deery and colleagues

15 2. Platforms -Airborne Geocorrected thermal mosaic for plot extraction Thanks to David Deery and colleagues

16 2. Platforms -Airborne e.g. NASA Airborne imager Lidar Imaging spectrometer Thermal camera Spatial resolution m (Rem Sens 5: 2013)

17 2. Platforms -balloons

18 Thanks to Ashley Wheaton, Dookie College and Oxford Landing

19 2. Platforms -UAVs/drones Honkavaara et al 2013, RemSens 5 H

20 2. Platforms -Field platforms Osnabrucke robots e.g. Boni-rob and BreedVision Maricopa Avignon

21 2. Platforms -Field platforms The Canberra system Thanks to: Bob Furbank, Dave Deery, Xav Sirault, J. Jiminez- Berni, et al.

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23 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

24 Platform comparison Platform Satellite Airborne Balloon UAV Tractormounted Features Cloud limited (excl. radar), low spatial resolution (tradeoff with frequency), good for weather/mapping less so for crop management Below cloud, higher resolution, potential high frequency Rarely usable because of wind v. high resolution, very flexible, low cost, low payload Readily incorporated into on-farm management

25 3. Software critical for image analysis flight Original scan Geocorrected map (NERC flight a140103a, Tarquinia 2005)

26 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)

27 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

28 4. Applications -Weather Low res. geostationary satellites MTSAT 145 o E (Japan) Frequent every 10 min! but low resolution (5 km) Major contribution to services e.g. weather forecasts

29 4. Applications -Weather Frost Watch (NOAH-AVHRR) Land Surface Temperature (LST) image over South Western Australia. (Frostwatch - NOAA :02 29/08/2013 WST)

30 4. Applications Crop inventories Multi-spectral or hyperspectral imagery (satellite/air) Can be classified to delineate vegetation or crops Cover estimation (NDVI) Crop inventory (hyperspectral classification) Oak canopy Cistus Soil Grass Water Jones & Vaughan 2010, OUP

31 4. Applications -Yield prediction Yield kg/ha Yield kg/ha NDVI NDVI (from Rembold et al 2013 Rem Sens 5)

32 4. Applications stem carbohydrate Dreccer et al 2014 (Field Crop Res)

33 4. Applications -Stress diagnosis? e.g. Nitrogen deficiency (Images -Randall Pearson, S Illinois University, Ames Iowa)

34 4. Applications -Stress diagnosis SENSORS Temperature Spectral reflectance Fluorescence Multiangular LiDAR RESPONSES STRESSES Abiotic Drought/salinity Stomata Flooding Pigments, canopy cover Frost/chill Biochemistry Pollutant Canopy structure Nutrient defic./toxicity Biotic Canopy structure Disease Pest

35 4. Applications -Stress diagnosis SENSORS STRESSES Abiotic Drought/salinity Temperature Flooding Spectral reflectance Frost/chill Fluorescence Pollutant Multiangular Nutrient defic./toxicity Biotic LiDAR Disease Pest Characteristic pattern of response is diagnostic - multisensor

36 4. Applications Precision agriculture Remote sensing Tractor-mounted sensors Soil mapping Yield mapping Ad hoc sampling Define Management zones

37 4. Applications -Weed management Weed mapping Need to target optimal window (e.g. early post emergence) Photos John Heap (SARDI)

38 5. Caveatsand Conclusions Information depends on Scale Gala contemplating the Mediterranean sea Salvador Dali (St Petersburg) becomes: Portrait of Lincoln Salvador Dali (St Petersburg)

39 5. Caveatsand Conclusions Scale (pixel resolution) affects results e.g. Low resolution images may give misleading information: T mean = 34 o C T mean = 36 o C T mean = 38 o C

40 5. Caveatsand Conclusions Beware error in estimates Prediction (e.g. of N) can be very weak even if good correlation - So need good calibrations Remote sensing estimate NDVI Predicted N content (%) (from Miphokasap et al 2012 Rem Sens 4)

41 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 userfriendly software (smart phones etc.)

42 Conclusions But..... much developmental work still needed for fully practical systems, esp. with UAVs

43 CONTACT: Professor Hamlyn Jones 2010 Published by: 2014 Oxford University Press, July 2010

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