Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Application of remote sensing in agriculture

Remote sensing and agriculture

  • Login to see the comments

Application of remote sensing in agriculture

  1. 1. Application of Remote sensing, GIS and GPS in Agriculture Submitted by Vajinder Pal Kalra Ph.d Student
  2. 2. What is remote sensing?  “Remote” means away  Remote sensing means sensing things from a distance. Of our five senses we use 3 as a remote sensors. a) Watch a football game from a distance (sense of sight) b) Smell fleshy baked bread from the oven (sense of smell) c) Hear a telephone ring (sense of hearing) Aggarwal (2003)
  3. 3. Remote sensing is science of  acquiring,  processing, and  interpreting images and related data that are obtained from ground based, air- or space-borne instruments that record the interaction between matter (target) and electromagnetic radiation.  Remote Sensing using the electromagnetic spectrum to image the land, ocean, and atmosphere. Campbell (1987)
  4. 4. Remote sensing platforms Ground-based Airplane-based Satellite-based
  5. 5. Satellite Characteristics: Orbits and Swaths The path followed by a satellite is referred to as its orbit. As a satellite revolves around the Earth, the sensor "sees" a certain portion of the Earth's surface. The area imaged on the surface, is referred to as the swath. Swath Orbit
  6. 6. Satellite based 1. Sun-synchronous polar orbits  Most earth imaging satellites is polar-orbiting, meaning that they circle the planet in a roughly north- south ellipse while the earth revolves beneath them.  They cover each area of the world at a constant local time of day called local sun time.  Typical altitude 500-1,500 km
  7. 7. 2. Non-Sun-synchronous orbits  Tropics, mid-latitudes, or high latitude coverage, varying sampling  typical altitude 200-2,000 km  example: TRMM
  8. 8. 3. Geostationary orbits Satellites at very high altitudes, at approximately 36,000 kilometres ,which view the same portion of the earth's surface at all times. Revolve at speeds which match the rotation of the earth so they seem stationary  Weather and communications satellites
  9. 9. Types of remote sensing  Passive remote sensing systems which measure energy that is naturally available. For example : Sun  This can only take place during the time when the sun is illuminating the Earth.
  10. 10.  Active: provide their own energy source for illumination.  The sensor emits radiation which is directed toward the target to be investigated.  The radiation reflected from that target is detected and measured by the sensor.  They obtain measurements anytime, regardless of the time of day or season.  LASER, RADAR
  11. 11. Process of Remote Sensing (A) Energy source or illumination (B) Radiation and the atmosphere (C) Interaction with the target (D) Recording of energy by the sensor (E) Transmission, reception, and processing (F) Interpretation and analysis (G) Application
  12. 12. Radiation - Target Interactions There are three (3) forms of interaction that can take place when energy strikes, or is incident (I) upon the surface. 1. Absorption (A) 2. Transmission (T) 3. Reflection (R)  Specular reflection  Diffuse reflection
  13. 13. Specular reflection
  14. 14. Diffuse Reflection
  15. 15. Four types of resolution Resolution Radiometric Spectral Temporal Spatial
  16. 16. Spatial resolution It refers to the size of the smallest possible feature that can be detected. It depends upon the Instantaneous field-of-view (IFOV) which is the angular cone of visibility of the sensor. Images are composed of a matrix of picture elements, or pixels, which are the smallest units of an image.
  17. 17. Spectral resolution  Spectral resolution describes the ability of a sensor to define fine wavelength intervals.  The finer the spectral resolution, the narrower the wavelength range for a particular channel or band.
  18. 18. Radiometric resolution Sensor’s sensitivity to the magnitude of the electromagnetic energy. Sensor’s ability to discriminate very slight differences in (reflected or emitted) energy in form of bits. The finer the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in energy.
  19. 19. Basics of Bit Computer store everything in 0 or 1. Each bit records an exponent of power 2. For example: 8 bits bits Max num 2 n 1 2 2 4 3 8 6 64 8 256 11 2048 12 4096 Coverage: 0 -4095Resolution: 12 bits
  20. 20. Temporal resolution  It is the revisit period, and is the length of time for a satellite to complete one entire orbit cycle, i.e. start and back to the exact same area at the same viewing angle.  For example, Landsat needs 16 days, MODIS needs one day, NEXRAD needs 6 minutes.
  21. 21. Image Interpretation Image is a pictorial representation of an object or a scene. Image can be  Analog image  Digital image Lillesand and Keifer (1994)
  22. 22. Analog image  Produced by photographic sensors on paper based media or transparent media  Variations in scene characteristics are represented as variations in brightness ( gray shades)  Objects reflecting more energy appear brighter on the image and objects reflecting less energy appear darker.
  23. 23. Digital image  A digital image is made up of square or rectangular areas called pixels.  Each pixel has an associated pixel value known as Digital Number (DN) or Brightness value (BV) or gray level which depends on the amount reflected energy from the ground.  An object reflecting more energy records a higher digital number for itself on the digital image and vice versa. DN value
  24. 24. Analysis of remote sensing imagery involves the identification of various targets in an image.  Those targets may be environmental or artificial features, which consist of points, lines, or areas. Targets may be defined in terms of the way they reflect or emit radiation. This radiation is measured and recorded by a sensor, and ultimately is depicted as an image product such as an air photo or a satellite image by comparing different targets based on any, or all, of the visual elements of tone, shape, size, pattern, texture, shadow, and association.
  25. 25. Tone  Tone refers to the relative brightness or colour of objects in an image.
  26. 26. Shape  Shape refers to the general form, structure, or outline of individual objects.  Shape can be a very distinctive clue for interpretation.  Straight edge shapes typically represent urban or agricultural (field) targets, while natural features, such as forest edges, are generally more irregular in shape.  Farm or crop land irrigated by rotating sprinkler systems would appear as circular shapes
  27. 27. Size  Size of objects in an image is a function of scale. If an interpreter had to distinguish zones of land use, and had identified an area with a number of buildings in it.  Large buildings such as factories or warehouses would suggest commercial property, whereas small buildings would indicate residential use.
  28. 28. Pattern Pattern refers to an orderly repetition of similar tones and textures will produce a distinctive and ultimately recognizable pattern. Orchards with evenly spaced trees, and urban streets with regularly spaced houses are good examples of pattern.
  29. 29. Texture Texture refers to the arrangement and frequency of tonal variation in particular areas of an image. Smooth textures are most often the result of uniform, even surfaces, such as fields, asphalt, or grasslands.  Rough textured represent irregular structure, such as a forest canopy.
  30. 30. Shadow Shadow may provide relative height of a target.  Shadows can also reduce or eliminate interpretation in their area of influence, since targets within shadows are much less (or not at all) discernible from their surroundings.
  31. 31. Association Association takes into account the relationship between other recognizable objects or features in proximity to the target of interest. Commercial properties may be associated with proximity to major transportation routes.  Residential areas would be associated with schools, playgrounds, and sports fields.
  32. 32. What is spectral reflectance curve?  A graph of the spectral reflectance of an object as a function of wavelength.  It is very useful for choosing the wavelength regions for remotely sensed data acquisition for a certain application.
  33. 33. Spectral signatures A signature is that which gives an information about an object to its identity. Identity is whatever makes an entity recognizable.
  34. 34. Spectral signature for vegetation A general characteristic of vegetation is its green colour caused by the pigment chlorophyll.  Chlorophyll reflects green energy more than red and blue energy, which gives plants green colour.
  35. 35. The major difference in leaf reflectance between species, are dependent upon leaf thickness. Thick leaf Thin leaf
  36. 36. Needle-leaf trees canopies reflect significantly less near- infrared radiation compared to broad-leaf vegetation. Coniferous forest Deciduous forest
  37. 37. Immature leaves contain less chlorophyll than older leaves, they reflect more visible light and less infrared radiation. Mature plant Immature plant
  38. 38.  Reflectance is also affected by health of vegetation.
  39. 39. Vegetation indices Normalized Difference Vegetation Index (NDVI) This index is the ratio of the difference of the near-infrared and red reflectance, over the sum of those. [NDVI = (NIR - Red) / (NIR + Red)] It receives values from -1 (no vegetation) to +1 (abundant vegetation).
  40. 40. Normalised Difference Water Index  It employs the near-infrared band and a band in the short- wave infrared (SWIR) Instead of using the red band, a short-wave infrared band in the region between 1500 and 1750 nm is used where water has high absorption. [ NDWI = (NIR - SWIR) / (NIR + SWIR) ]
  41. 41. Spectral Signature for Soil  The five characteristics of a soil that determine its reflectance properties are, in order of importance:  Moisture content  Organic content  Structure  Iron oxide content  Texture
  42. 42. Soil moisture content A wet soil generally appears darker.  Increasing soil moisture content lowers reflectance. Dry soil Wet soil
  43. 43. Soil organic matter  A soil with 5% or more organic matter usually appears black in color.  Less decomposed organic materials have higher reflectance and vice versa. A B C
  44. 44. Soil iron content The presence of iron especially as iron oxide affects the spectral reflectance.  Reflectance in the green region decreases with increased iron content, but increases in the red region. (a) High organic content, moderately fine texture (b) Low organic, Low iron content (c) Low organic, medium iron content (d) High organic content, moderately coarse texture (e) High iron content, fine texture b c d e a
  45. 45. Soil structure  A clay soil tends to have a strong structure, which leads to a rough surface on ploughing.  Clay soils also tend to have high moisture content and as a result have a fairly low diffuse reflectance.  Sandy soils also tend to have a low moisture content and a result have fairly high and often specular reflectance properties.
  46. 46. Spectral signature for water Reflection of Light – Wavelengths  Water Depths – Shallow , Deep Suspended material Chlorophyll Content Surface Roughness
  47. 47.  The majority of radiant flux incident upon water is either not reflected but is either absorbed or transmitted.  In visible wavelengths of EMR, little light is absorbed, a small amount, usually below 5% is reflected and the rest is transmitted.  Water absorbs NIR and MIR strongly leaving little radiation to be either reflected or transmitted. This results in sharp contrast between any water and land boundaries.
  48. 48. Spectral Reflectance of Snow  Factors governing are  Snow pack thickness: Reflectance of snow decreases as it ages.  Liquid water content: Even slightly melting snow reduces reflectance .  Contaminant present: Contaminations (soot, particles, etc.) reduce snow reflectance.
  49. 49. Remote sensing applications in agriculture  Agricultural products from crops form a large part of every person´s diet. Producing food of sufficient quantity and quality is essential for the well-being of the people anywhere in the world.  Plants require water and nutrients in order to grow and are sensitive to extreme weather phenomena, diseases and pests.  Remote sensing can provide data that help identify and monitor crops.  When these data are organized in a Geographical Information System along with other types of data, they become an important tool that helps in making decisions about crops and agricultural strategies. Jones and Vaughan (2010)
  50. 50. National governments can use remote sensing data, in order to make important decisions about the policies they will adopt, or how to tackle national issues regarding agriculture.  Individual farmers can also receive useful information from remote sensing images, when dealing with their individual crops, about their health status and how to deal with any problems. India has its own satellites like Indian Remote Sensing Satellite (IRS) series - Resourcesat, Cartosat, Oceansat etc which provide required data for carrying out various projects. Jones and Vaughan (2010)
  51. 51. Monitoring of crop status The normal growth process of a plant can be disrupted when it goes through a stress period. When in stress, the plant is not functioning properly because of one or more causes. When a plant is stressed, it usually expresses certain visible symptoms, but also some that are not visible to the human eye. Stress symptoms may appear in all of the plants of the field or in some portions of the field, depending on the cause. Premalatha and Nageshwara (1994)
  52. 52. Chlorosis Development of a fungus Insect attack
  53. 53. Water content of field crops Water content of crop fields with thermal imaging. Isdo et al (1977)
  54. 54. Combating disease and pests Identifying the most probable areas where insects might attack. Fitzgerald et al (1999)
  55. 55. Estimate the loss of leaf area to study the damage caused by caterpiller on leaf Rouse et al (2000)
  56. 56. Crop yield estimation May 2005 Shanahan et al (2001) August 2005
  57. 57. Crop yield forecasting  In order to make estimates on future crop yield with remote sensing data alone, we need to know the relationship between vegetation indices at a particular growth stage of the crop and the final crop yield.  Historical data of previous growth seasons, serve this purpose, and the accuracy of the crop yield prediction increases as the amount of historical data increase.  However, no two growth seasons are the same. In order to make more accurate predictions, it is essential to consider the factors that affect crop growth and yield in the current year.  Information such as meteorological and climatic data, soil properties and farming practices are combined with the up-to-date remotely sensed data, in order to model the crop growth and make estimates on the final crop yield. Parihar and Nageswara (1997)
  58. 58. Crop identification  It is very important for a national government to know what crops the country is going to produce in the current growing season. This knowledge has financial benefits for the country, as it allows the budget planning for importing and exporting of food products.  One method is for someone to travel around the country and see what crop is grown in each field. But this takes too much time and costs a lot of money. Bauer (1985)
  59. 59.  By using multi-date data (data from different dates) from one growing period, it is possible to identify the different crop types, because the vegetation cover of each crop changes at different rates.  In addition, the planting and harvesting dates are also different.  By combining this information with remote sensing data, we can discriminate between different crops and also identify them. Bauer (1985)
  60. 60. Image classification showing the various crop types. Source: U.S. Geological Survey
  61. 61. GPS is short for Global Positioning System which is "a network of satellites that continuously transmit coded information, which makes it possible to precisely identify locations on earth by measuring distance from the satellites". GIS is short for Geographic Information System(s). "In the strictest sense, a GIS is a computer system capable of assembling, storing, manipulating, and displaying geographically referenced information , i.e. data identified according to their locations. Practitioners also regard the total GIS as including operating personnel and the data that go into the system“. GPS and GIS
  62. 62. Examples of GPS and GIS: Global Positioning System (GPS): An agricultural producer may use a handheld GPS receiver to determine the latitude and longitude coordinates of a water source next to a field or vineyard. Global Information System (GIS): Following a chemical spill, maps obtained from a GIS system can reveal environmentally-sensitive areas that should be protected during response and recovery phases. Source: Purdue University
  63. 63. GPS data gathering Depending on the make and model of the unit, the number of satellites available, and the quality of (unobstructed) signals, GPS receivers can collect information such as  Latitude and longitude coordinates (time-in-place or point location)  “Real Time” position (calculated while farm equipment is moving)  Elevation (if 4 or more satellites are used)
  64. 64. YOUR CURRENT POSITION COORDINATES (Latitude & Longitude, Utm, Mgrs etc.) ELEVATION (Approximate) DIRECTION TO SPECIFIED WAYPOINTS (“Markers”) (or Between Waypoints) DISTANCE TO SPECIFIED WAYPOINTS (or Between Waypoints) YOUR SPEED OF TRAVEL YOUR DIRECTION OF TRAVEL BASIC INFORMATION PROVIDED BY GPS RECEIVERS . . .
  65. 65. With GIS software, information from a GPS unit may be combined with data such as • USGS topographical maps • Digital elevation models • Critical infrastructure maps • Aerial photography • Cropland use • Census maps The Result: “Layered” maps can be generated by the GIS software.
  66. 66. Example of Map “Layers” A GIS database creates “layers” with many pieces of information visualized for the same area. Yield data – collected using GPS Topsoil Depth - collected using GPS Aerial photo of the area Source: University of Missouri
  67. 67. GPS – How it works 1. Constellation of more than 24 satellites Known positions (at any time) Each continuously transmits time and position data between two frequencies (L1-1575.42MHz and L2-1227.6MHz) Each orbits twice per day 2. Ground receiver (Your GPS receiver)  Calculates Position and Time  Times signal and calculates distance to each satellite received  Triangulates Latitude and Longitude  Calculates time  Must see a minimum of 4 satellites68 of 10
  68. 68. GPS & GIS Communication and Control
  69. 69. Different GPS Types 1. Hand Held 2. Backpack 3. Vehicle Mounted
  70. 70. GPS Antenna attached on the combine GPS Mounted Over Combine Harvester
  71. 71. Commercially Available GPS Garmin Trimble
  72. 72. GPS/GIS Applications in Agriculture Guidance  Point Guidance  Swath Guidance Control  Variable rate application  Variable depth tillage  Variable irrigation  Mapping Soil properties Chemical application Chemical prescriptions Tillage Maps Yield Mapping Pest Mapping Topographic Maps Planting Maps
  73. 73. Field Mapping Position data (georeference data) recorded at predetermined intervals. Other data recorded manually or automatically by monitor, computer, or data logger. Data displayed by geographic information system (GIS) in thematic map format.
  74. 74. Soil Sampling Georeferenced soil samples can be collected Sampling Methods Grid sampling: intensive sampling of entire field Directed sampling: intensive sampling of particular target areas
  75. 75. Semi-Automatic Soil Sampler
  76. 76. Yield Maps Record of spatial yield variability within a field or farm. GPS data coupled with yield data to produce map. Mechanically harvested Hand harvested Useful tool for decision making.
  77. 77. Field Scouting  Fields can be scouted for a variety of pests  Pest populations recorded on maps  Decision tools can be applied on a site specific basis
  78. 78. Variable Rate Control  Application rates designed for needs of small sections of a field  GPS determines position of equipment in the field  Computer controls use GPS data and prescription files to adjust rate
  79. 79. Precision Agriculture  It is a collection of agricultural practices that focus on specific areas of the field at a particular moment in time.  This is opposed to more traditional practices where the various crop treatments, such as irrigation, application of fertilizers, pesticides and herbicides were evenly applied to the entire field, ignoring any variability within the field. Agricultural tractor used in precision agriculture. Source: Agricultural Research Service, USDA
  80. 80. Advances in remote sensing technology and the reduced cost of sensors is now allowing for the more widespread use of such equipment in farming. With the use of these sensors it is possible to identify which particular areas of the field are in need of which treatment, and focus the application of chemicals to these particular locations alone.  Reducing the amount of chemicals used, and thus the cost of the application, as well as protecting the environment.
  81. 81. Soil test of phosphorus, potassium and pH for a central Missouri (USA) farm. (Blue to red is low to high for the concentrations) Davis et al (1998)
  82. 82. Adjustment of ultra-low volume herbicide applicators. With this method the use of chemicals in agriculture is greatly reduced. Source: Keith Weller, USDAAgricultural Research Service
  83. 83.  By using remote sensing and GPS, it is possible to identify the exact location where the application of fertilizers or pesticides is required. The Variable Rate Treatment (VRT) is a system that regulates the rate of pesticides or fertilisers, releasing only the required amount over the areas or the field that are in need of the chemicals. Sensors (top) and variable-rate applicators (bottom) on a combine. Variable rate treatment Source: Oklahoma State University
  84. 84. Experimental Setup for Paddy field Preparation using nine tyne Cultivator Trial parameters Without Navigator With Navigator Area to be cultivated 55x36.4m=0.20ha 55x36.4m=0.20ha Location of research plot Latitude : 30.9084640 Longitude: 75.8173750 Latitude : 30.9084890 Longitude: 75.8175660 Total width of machine (m) 2.6 2.6 Position of GPS antenna from cultivator 1.7m Front 1.7m Front Visibility of navigator screen to operator Not visible Visible
  85. 85. Missed area During Operation of Cultivator Without Navigator (0.001+0.002+0.004+0.005+0.004 +0.009+0.012+0.001+0.001+0.001+ 0.007=0.047ha) With Navigator (0.001+0.001+0.003+0.001+ 0.002+0.001+0.001+0.004+ 0.001+0.001+0.001=0.017ha)
  86. 86. Overlapped Area During Operation of Cultivator Without Navigator (0.004+0.004+0.005+0.001+0.006 + 0.003+0.003+0.002+0.006+0.008 =0.042) With Navigator (0.001+0.001+0.002+0.001+ 0.001=0.006)
  87. 87. Summarized Data During Operation of Cultivator Simplified parameters Without Navigator With Navigator Area to be cultivated 55x36.4m=0.20ha 55x36.4m=0.20ha Location of research plot -Latitude : 30.9084640 -Longitude: 75.8173750 -Latitude : 30.9084890 -Longitude: 75.8175660 Calculated area 0.11 ha 0.19ha Perimeter - 175m Productivity 0.35 ha/h 0.55ha/h Total area cultivated 0.29ha 0.20ha Uncovered/missing area of the field 0.001+0.002+0.004+0.005 +0.004 +0.009+0.012+0.001+ 0.001+0.001 0.001+0.001+0.003+0.001+ 0.002+0.001+ 0.001+0.004+ 0.001+0.001+0.001 = 0.017
  88. 88. Summarized Data During Operation of Cultivator Simplified parameters Without Navigator With Navigator Total overlapped area 0.004+0.004+0.005+0.001 +0.006 + 0.003+0.003+0.002+0.006 +0.008 =0.042 0.001+0.001+0.002+0.001+ 0.001 =0.006 Out of Boundary area cultivated/covered 0.001+0.001+0.001+0.001+ 0.001 +0.001=0.006 0.002+0.001+0.001+0.001+ 0.002+0.001+0.001=0.009 Total un-useful cultivated area (overlapped + unnecessary area cultivated) 0.048ha 0.015ha Effective area 0.111ha 0.185ha
  89. 89. Fertilizer spreader GPS Antenna Electrical connections from 12V dc battery Setting chart for spreader Spreader setting machine Experimental Setup for Urea Spreading using Fertilizer Spreader
  90. 90. Missed/Uncovered Area During Urea Fertilization Without Navigator (0.003+0.001+0.002+0.141=0.147ha) With Navigator (0.001+0.004+0.002+0.010+0.003 + 0.008+0.012= 0.067ha )
  91. 91. Satellite Navigator GPS Antenna fitted on hood Zero till drill settingTractor attached with zero till drill Experimental Setup During Zero Till Drill Operation
  92. 92. Overlapping During Zero Till Drilling with Navigator
  93. 93. Zero Till Drilling Without Navigator
  94. 94. Real Time Kinematic Positioning System
  95. 95. RTK —How does it work? 1.Base station transmits corrections via radio to the mobile receivers in the field 2.RTK base stations transmit data once per second 3.Data in format called CMR (Compact Measurement Record) 4.Dual frequency data format transmits data in a more compact and robust way than other formats
  96. 96. Conclusion  Remote sensing technology can be used to assess various abiotic and biotic stresses in different crop.  The remote sensing plays an important role in detecting and management of various crop issues even at a small land holding with high resolution.  The discrimination can be made between different crops based on the reflectance characteristics for different policy making decisions  Crop yield forecast is also an important factor in decision making and therefore can accomplished by ground and satellite based remote sensing.  By using the microwave remote sensing, studies related with the nutrient and moisture assessment can be under taken on temporal and spatial scales.

×