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

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

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


1
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
2
Earths climate system Greenhouse Effect
3
Observed surface temperature trend
Trends significant at the 5
level indicated with a . Grey insufficient
data
4
Annual maximum and minimum temperature at
Ludhiana
Maximum Temperature
Minimum Temperature
Jalota and Kaur (2013)
5
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
6
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.

7

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
8
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)

9
  • 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

10
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11
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.

12
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

13
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

14
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
15
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16
Signatures are Generated for Different Stages of
Crop Development
17
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

18
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.

19
Normalized Difference Vegetative Index
NDVI (Near IR Red)/(Near IR Red) Range
-1 to 1 NDVI (grass) (50-5)/(505) 0.82
20
Signature Analysis Allow Early Detection of Plant
Stress
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22
NITROGEN APPLICATION APPLIED ACCORDING TO NDVI
23
  • 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.

24
  • 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.

25
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
26
  • 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)

27
Precision Farming
28
Remote Sensing utilization in Precision Farming
29
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