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???