Application of Remote Sensing And GIS in crop growth monitoring and Sustainable Agricultural development under changing Climatic scenarios

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Application of Remote Sensing
And GIS in crop growth monitoring and
Sustainable Agricultural development
under changing Climatic scenarios
Regional
Symposium on Geospatial Technology in Natural


Resource Management
Organi
Organized by Punjab Remote Sensing
Centre


Presented by
Debjyoti Majumder

M.sc Student
School Of climate
Change And Agricultural Meteorology

Punjab Agricultural University. Ludhiana
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Earths climate system Greenhouse Effect
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Observed surface temperature trend
Trends significant at the 5
level indicated with a . Grey insufficient
data
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Annual maximum and minimum temperature at
Ludhiana
Maximum Temperature
Minimum Temperature
Jalota and Kaur (2013)
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Recent vagaries /incidences
DROUGHT HITS KARNATAKA 2008
COLD WAVE IN NORTH 2006
HEAT WAVE IN NORTHERN INDIA 2007
NILAM CYCLONE 2012
Uttarakhand flood 2013
Hud Hud 2014
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Impacts on Indian Agriculture - Literature

• Sinha and Swaminathan (1991) showed that an
  increase of 2º C in temperature could Decrease
  in rice yield by about 0.75º ton/ha in the high
  yield areas and a 0.56º C increase in winter
  temperature would reduce wheat yiled by 0.45
  ton/ha.
• Rao and Sinha (1994) Showed that wheat yields
  could decrease between 28-68 without considering
  the CO2 fertilization effects and would range
  between 4- -34after considering CO2
  Fertilization effects.
• Aggarwal and Sinha (1991) using WTGROWS model
  showed that a 2º C temperature rise would
  Decrease Wheat yields in most places.
• Lat et al. (1996) concluded that carbon
  fertilization effect would not be able to offset
  the negative impacts of high temperature on rice
  yields.
• Saseendran et al. (2000) showed that for every
  1º C rise in temperature the decline in rice
  yield would be 6 .
• Aggarwal et al. (2002) using WTGROWS and
  recent Climate Change scenarios estimated impacts
  on Wheat and other Cereal Crops.
• ALL these studies focused ony on Agronomic
  impacts Climate Change.

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WHAT IS REMOTE SENSING?

• Remote sensing is the acquisition of information
  about an object or phenomenon without making
  physical contact with the object.
• In modern usage, the term generally refers to
  the use of aerial sensor technologies to detect
  and classify objects on Earth (both on the
  surface, and in the atmosphere and oceans) by
  means of propagated signals (e.g. electromagnetic
  radiation emitted from aircraft or satellites).

EARLY REMOTE SENSING FOCUS
Global Change
Hazards
Global Weather
Bio-Geo-Chemical Cycling
Atmospheric Models
GRI
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Elements of Remote Sensing

• Energy Source or Illumination (A)
• Radiation and Atmosphere (B)
• Interaction with Target (C)
• Recording of Energy by the Sensor (D)
• Transmission, Reception and Processing (E)
• Interpretation and Analysis (F)
• Application (G)

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• Green plant leaves display very low reflectance
  and transmittance in visible regions of the
  spectrum (i.e., 400 to 700 nm) due to strong
  absorptance by photosynthetic and accessory plant
  pigments.
• Reflectance and transmittance are both usually
  high in the near-infrared regions (NIR, 700 to
  1300 nm) because there is very little absorptance
  by subcellular particles or pigments and also
  because there is considerable scattering at
  mesophyll cell wall interfaces
• This sharp dissimilarity in reflectance
  properties between visible and NIR wavelengths
  underpins a majority of remote approaches for
  monitoring and managing crop and natural
  vegetation communities

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Crop Monitoring using Remote Sensing and GIS

• Analysis of crop health using remotely sensed
  images. (aerial photography, satellite imagery).
• Extensive use of high resolution panchromatic
  (black and white) and multispectral (colored)
  images.
• Extensive use of Synthetic Aperture Radar (SAR)
  microwave data.
• Microwaves are sensitive to the structure of
  crops (size and geometry of the leaves, stalks
  and fruit) and to crop moisture levels. As a
  result, radar imagery is an important component
  of a crop monitoring system.

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Geographic Information System

• A geographic information system (GIS) is a system
  designed to capture, store, manipulate, analyze,
  manage, and present all types of geographical
  data.
• GIS- a type of system, which digitally makes and
  "manipulates" spatial areas that may be
  jurisdictional, purpose, or application-oriented.
• GIS is a spatial data infrastructure

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Specific Areas of GIS Applications in Agriculture

• Maintaining accurate records of a broad range of
  field characteristics
• Field and spatial feature mapping
• Remote Sensing Application in Agro-Ecological
  Zoning
• Crop Type Classification
• Crop monitoring using remote sensing and GIS.
• Yield mapping and forecasting
• Mapping soil sample results
• Creating Variable Rate Fertilizer Application
  maps
• Statistical analysis of yield and soil sample
  tabular data
• Analysis of interpolated yield and soil test
  maps.
• Soil Carbon Dynamics and land Productivity
  Assessment

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Retrieval of agrometeorological parameters using
satellite remote sensing data
surface albedo is estimated by remote sensing
measurements covering optical spectral bands.
Surface Albedo
Surface temperature image generated by processing
of Landsat TM
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Signatures are Generated for Different Stages of
Crop Development
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Crop Condition Assessment

• Healthy vegetation contains large quantities of
  chlorophyll
• Reflectance in the blue and red parts of the
  spectrum is low since chlorophyll absorbs this
  energy.
• In contrast, reflectance in the green and
  near-infrared spectral regions is high.
• Stressed or damaged crops experience a decrease
  in chlorophyll content and changes to the
  internal leaf structure.
• The reduction in chlorophyll content results in a
  decrease in reflectance in the green region and
  internal leaf damage results in a decrease in
  near-infrared reflectance.
• These reductions in green and infrared
  reflectance provide early detection of crop stress

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Contd..

• Examining the ratio of reflected infrared to red
  wavelengths (NDVI) is an excellent measure of
  vegetation health
• Healthy plants have a high NDVI value because of
  their high reflectance of infrared light, and
  relatively low reflectance of red light.
• The irrigated crops appear bright green in a
  real-colour simulated image.
• In a CIR (colour infrared simulated) image, where
  infrared reflectance is displayed in red, the
  healthy vegetation appears bright red
• Areas of consistently healthy and vigorous crop
  would appear uniformly bright.
• Stressed vegetation would appear dark amongst the
  brighter, healthier crop areas.

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Normalized Difference Vegetative Index
NDVI (Near IR Red)/(Near IR Red) Range
-1 to 1 NDVI (grass) (50-5)/(505) 0.82
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Signature Analysis Allow Early Detection of Plant
Stress
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NITROGEN APPLICATION APPLIED ACCORDING TO NDVI
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• SALINITY STRESS
• Salts in soils and irrigation water are important
  factors limiting productivity in many croplands .
  
• Remedial solutions require mapping of affected
  areas in space and time. This can be accomplished
  using remote sensing measurements which identify
  contaminated soils by their unusually high
  surface re?ectance factors or by detecting
  reduced biomass or changes in spectral properties
  of plants growing in affected areas.
• An increase in canopy temperature of plants
  exposed to excessive salts in irrigation water,
  suggesting the possibility of previsual detection
  of stress which could be remedied by increasing
  the leaching fraction or switching to a higher
  quality of water.

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• An integrated Remote Sensing and GIS based
  methodology was developed for studying carbon
  dynamics such as annual crop Net Primary
  Productivity (NPP), soil organic matter
  decomposition and the annual soil carbon balance.

• NPP f NPPi, Fs, F (CO2)
• Where NPPi is Climatic ( Rainfall, Temperature)
  potential NPP Fs- Soil factor and F (CO2) -
  nutrition factor of atmospheric CO2 content to
  NPP.
• The century model estimates decomposition loss
  (DL) of soil humus carbon as DL f(Ki,T,Md, Td,
  Ci)
• Where Ki is Maximum decomposition rate, T is
  Soil (Silt Clay) content Md and Td are
  Rainfall and temperature factors, respectively,
  and Ci is initial soil humus Carbon content.
• Land productivity data provide information about
  the inherent fertility status of soilscapes,
  which is a useful guideline for supplementing
  soil nutrients from external sources, such as
  fertilizers/ manure
• According to the Storie Index model, land
  productivity (LP) is expressed as - LP
  f(A,B,C,X)
• Where, A is a rating based on soil development B
  is a rating based on soil texture C is a rating
  based on terrain slope and X is a composite
  rating based on soil fertility, pH, drainage,
  erosion etc.

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Pest Monitoring
Comparisons between hyperspectral reflectance
factors of a normal green cotton leaf and a
cotton leaf covered with honeydew produced by
whiteflies (Bemesia tabaci), a leaf covered with
a secondary mold Aspergillus sp. growing on the
whitefly honeydew, and a chlorotic leaf without
honeydew. Data were acquired with a Spectron
SE-590 spectroradiometer. Solar incidence angle
was 45 degrees to the leaf surface and viewing
angle was normal to leaf surface
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• Multispectral imagery of an 81-ha Mississippi
  cotton field in which spatial variation in plant
  growth is represented by different colours. Areas
  with more vigorous plant growth (green) are more
  likely to attract and support high populations of
  tarnished plant bugs (Lygus lineolaris). (Image
  courtesy of ITD Spectral Visions, Stennis Space
  Center, Mississippi and ARS, Genetics and
  Precision Agriculture Research Unit, Mississippi
  State University)

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Precision Farming
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Remote Sensing utilization in Precision Farming
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