Application of Remote Sensing On
the Environment, Agriculture and Other
Uses in Nepal
A Talk Session Organized by NAPA Student Coordination Committee (SCC)
January 28, 2017
Dr. Tilak B Shrestha
PhD Geography/Remote Sensing
(NAPA Member)
Outline
• Introduction
• Measurement
• Advantages
• Limitations
• The Process
• Applications
“Remote Sensing” is the art and science of
obtaining information about an object without
being in direct physical contact with the object.
Sensors may be mounted on satellites, planes or in
vehicles. It can be used to measure and monitor
important biophysical characteristics and human
activities on Earth.
Introduction
Measurement
• Remote sensing is unobtrusive if the sensor is passively recording the electromagnetic
energy reflected from or emitted by the phenomenon of interest. This is a very
important consideration as passive remote sensing does not disturb the object or area of
interest.
• Remote sensing science can provide fundamental, new scientific data or information.
Under controlled conditions, remote sensing can provide fundamental biophysical
information, including: x, y location, z elevation or depth, bio- mass, temperature,
moisture content, etc.
• The remotely sensed data can be obtained systematically over very large geographic
areas, and it has become critical to the successful modeling of numerous natural (e.g.,
water-supply estimation; eutrophication studies; nonpoint source pollution) and cultural
(e.g., land-use conversion at the urban fringe; water-demand estimation; population
estimation; food security) processes.
Remote Sensing - Advantages
• Remote sensing science has limitations. Perhaps the greatest limitation is that it is
often oversold. Remote sensing is not a panacea that will provide all the information
needed to conduct physical, biological, or social science research. It simply provides
some spatial, spectral, and temporal information of value in a manner that is
hopefully efficient and economical.
• Powerful active remote sensor systems that emit their own electromagnetic radiation
(e.g., LiDAR, RADAR, SONAR) can be intrusive.
Remote Sensing - Limitations
The Remote Sensing Process
Digital Image is made of Pixel ‘picture element’
Visible spectrum: 0.4 – 0.7 micro meter or 400 – 700 nano meter1 meter = 106 micro meter = 109 nano meter
Radiation from Sun and Earth – black body
Spectral Radiance of Sun
Radiation Budget
Atmosphere Transmission \ Absorption
Spectral Bands and Atmospheric Transmission
LandSat 8 Bands – Wave length - Resolution
Color Bands and Image
Remote Sensor Resolution
• Spatial - the size of the field-of-view, e.g. 10 x 10 m.
• Spectral - the number and size of spectral regions the sensor
records data in, e.g. blue, green, red, near-infrared
thermal infrared, microwave (radar).
• Temporal - how often the sensor acquires data, e.g. every 30 days.
• Radiometric - the sensitivity of detectors to small differences in
electromagnetic energy.
10 m
B G R NIR
Jan
15
Feb
15
10 m
Jensen, 2000
Spatial
Resolution
Jensen, 2000
Monitor – TV – 3 Color Guns – Band combinations
A Satellite gathered remote sensing image
Such information may be useful for modeling:
• the global carbon cycle,
• biology and biochemistry of ecosystems,
• aspects of the global water and energy cycle,
• climate variability and prediction,
• atmospheric chemistry,
• characteristics of the solid Earth,
• population estimation, and
• monitoring land-use change and natural hazards.
Earth Resource Analysis Perspective
Remote Sensing - Applications
Remote Sensing Earth
System Science
Human Activities
Biogeochemical Cycle sHydrologic
CyclePhysical Climate System
External
Forcing
Functions
Water pollution Land use
Atmospheric
physics and
dynamics
Terrestrial
energy and
moisture
Ocean
dynamics
Marine
biogeochemistry
Tropospheric
chemistry
Terrestrial
ecosystems
VolcanoesSun
Soil and water
chemistry
Global moisture
Stratospheric Che mistry and Dynamics
Climate
ChangeCarbon Dioxide and Other Trace Gase s
Air pollution
Jensen, 2000
Nepal:NW 31N 80 ESE 26 N 89 E
Kathmandu Bagmati River
A LandSat ImageKathmandu areaSize – 185 Km SquareNeed 12 images to cover Nepal
Remote Sensing ImageKathmandu & Bagmati River
Natural Color
Remote sensing Image False Color – Green - blue,
Red - green, Infra Red - red
Remote Sensing can be used as a tool for site-specific management ofcrops, by estimating characteristics of soils, crops, plant stress, andeffects of fertilizer, tillage etc. (W Casady & HL Palm)
+ Soil brightness - Construct soil maps or direct soil sampling+ Crop vigor or health - Several uses+ Vegetation cover - Replant decisions+ Chlorophyll content - Nitrogen management+ Yield prediction - General management+ Weed escapes - Weed management+ Stress due to canopy - Irrigation management moisture deficits+ Crop residue - Compliance with erosion prevention guidelines
Multi-spectral broad-band vegetation indices available for
use in precision agriculture. (DJ Mulla)
Index Definition Reference
NG G/(NIR + R + G) Sripada et al., 2006
NR R/(NIR + R + G) Sripada et al., 2006
RVI NIR/R Jordan, 1969
GRVI NIR/G Sripada et al., 2006
DVI NIR − R Tucker, 1979
GDVI NIR − G Tucker, 1979
NDVI (NIR − R)/(NIR + R) Rouse et al., 1973
GNDVI (NIR − G)/(NIR + G) Gitelson et al., 1996
SAVI 1.5*[(NIR − R)/(NIR + R + 0.5)] Huete, 1988
GSAVI 1.5*[(NIR − G)/(NIR + G + 0.5)] Sripada et al., 2006
OSAVI (NIR − R)/(NIR + R + 0.16) Rondeaux, Steven, & Baret, 1996
GOSAVI (NIR − G)/(NIR + G + 0.16) Sripada et al., 2006
MSAVI2 0.5*[2*(NIR + 1) – SQRT ((2*NIR + 1)2 − 8*(NIR − R))]
Qi, Chehbouni, Huete, Keer, & Sorooshian, 1994
Innovations in remote and proximal leaf sensing in
precision agriculture. (DJ Mulla)
Year Innovation Citation
1992SPAD meter (650, 940 nm) used to detect N deficiency in
cornSchepers et al., 1992
1995 Nitrogen sufficiency indices Blackmer & Schepers, 1995
1996Optical sensor (671, 780 nm) used for on-the-go detection
of variability in plant nitrogen stressStone et al., 1996
2002 Yara N sensor Link et al., 2002, TopCon industries
2002 GreenSeeker (650, 770 nm) Raun et al., 2002, NTech industries
2004 Crop Circle (590, 880 nm or 670, 730, 780 nm) Holland et al., 2004, Holland scientific
2002CASI hyperspectral sensor based index measurements of
chlorophyllHaboudane et al., 2002, 2004
2002 MSS remote sensing of ag fields with UAV Herwitz et al., 2004
2003 Fluorescence sensing for N deficiencies Apostol et al., 2003
Narrow band Hyperspectral Vegetation Indices: These indices variously respond to canopy or leaf scale effects of leaf area index, chlorophyll, specific pigments, or nitrogen stress. Aerial hyperspectral imagery has revolutionized the ability to distinguish multiple crop characteristics, including nutrients, water, pests, diseases, weeds, biomass and canopy structure. Ground-based sensors have been developed for on-the-go monitoring of crop and soil characteristics such as N stress, water stress, soil organic matter and moisture content. (DJ Mulla)
Index Definition
Greenness index (G) R554/R677
SR1 NIR/red = R801/R670
SR2 NIR/green = R800/R550
SR3 R700/R670
SR4 R740/R720
SR5 R675/(R700*R650)
SR6 R672/(R550*R708)
SR7 R860/(R550*R708)
DI1 R800 − R550
NDVI(R800 − R680)/(R800 + R680)
Green NDVI (GNDVI) (R801 − R550)/(R800 + R550)
PSSRa R800/R680
PSSRb R800/R635
NDI1 (R780 − R710)/(R780 − R680)
NDI2 (R850 − R710)/(R850 − R680)
NDI3 (R734 − R747)/(R715 + R726)
MCARI [(R700 − R670) − 0.2(R700 − R550)](R700/R670)
TCARI 3*[(R700 − R670) − 0.2*(R700 − R550)(R700/R670)]
OSAVI (1 + 0.16)(R800 − R670)/(R800 + R670 + 0.16)
TCARI/OSAVI
TVI 0.5*[120*(R750 − R550) − 200*(R670 − R550)]
MCARI/OSAVI
RDVI (R800 − R670)/SQRT(R800 + R670)
MSR (R800/R670 − 1)/SQRT(R800/R670 + 1)
MSAVI 0.5[2R800 + 1 − SQRT((2R800 + 1)2 − 8(R800 − R670))]
MTVI 1.2*[1.2*(R800 − R550) − 2.5*(R670 − R550)]
MCARI21.5[2.5(R800−R670)−1.3(R800−R550)](2R800+1)2−(6R800−5R67
0)−0.5
In Nepal, it will be good to have a remote sensing program,
with following issues put together.
Applications: Agriculture, Forestry, Meteorology, Geology,
Planning
Satellite Imagery: USA – LandSat etc., Indian and others
Overlapping need based multiuse imagery by season &
location
Developing network of sample ‘plots’ for various uses &
locations
Continuous process of application, evaluation and
innovation
Thank you!!!
Questions???