Application of Remote Sensing On the Environment, Agriculture and Other Uses in Nepal

Transcription

1 Application of Remote Sensing On the Environment, Agriculture and Other Uses in Nepal Dr. Tilak B Shrestha PhD Geography/Remote Sensing (NAPA Member) A Talk Session Organized by NAPA Student Coordination Committee (SCC) January 28, 2017

2 Outline Introduction Measurement Advantages Limitations The Process Applications

3 Introduction 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.

4 Measurement

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7 Remote Sensing - Advantages 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.

8 Remote Sensing - Limitations 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.

9 The Remote Sensing Process

10 Digital Image is made of Pixel picture element

11 Visible spectrum: micro meter or nano meter 1 meter = 10 6 micro meter = 10 9 nano meter

12 Radiation from Sun and Earth black body

13 Spectral Radiance of Sun

14 Radiation Budget

15 Atmosphere Transmission \ Absorption

16 Spectral Bands and Atmospheric Transmission

17 LandSat 8 Bands Wave length - Resolution

18 Color Bands and Image

19 Remote Sensor Resolution 10 m 10 m B G Jan 15 R NIR Feb 15 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. Jensen, 2000

20 Jensen, 2000 Spatial Resolution

21 Monitor TV 3 Color Guns Band combinations

22 A Satellite gathered remote sensing image

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27 Remote Sensing - Applications Earth Resource Analysis Perspective 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.

28 Sun External Forcing Functions Volcanoes Remote Sensing Earth System Science Stratos pheric Che mistry and Dynamics Phys ical Climate Sys tem Hydrologic Cycle Bio geo chemical Cycle s Ocean dynamics Marine biogeochemistry Atmospheric physics and dynamics Global moisture Tropospheric chemistry Terrestrial energy and moisture Soil and water chemistry Terrestrial ecosystems Climate Change Carbon Dio xide and Other Trace Gas e s Water pollution Air pollution Land use Human Activities Jensen, 2000

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30 Nepal: NW 31N 80 E SE 26 N 89 E

31 Kathmandu Bagmati River

32 A LandSat Image Kathmandu area Size 185 Km Square Need 12 images to cover Nepal

33 Remote Sensing Image Kathmandu & Bagmati River Natural Color

34 Remote sensing Image False Color Green - blue, Red - green, Infra Red - red

35 Remote Sensing can be used as a tool for site-specific management of crops, by estimating characteristics of soils, crops, plant stress, and effects 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

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38 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 ) Rondeaux, Steven, & Baret, 1996 GOSAVI (NIR G)/(NIR + G ) Sripada et al., 2006 MSAVI2 0.5*[2*(NIR + 1) SQRT ((2*NIR + 1) 2 8*(NIR R))] Qi, Chehbouni, Huete, Keer, & Sorooshian, 1994

39 Innovations in remote and proximal leaf sensing in precision agriculture. (DJ Mulla) Year Innovation Citation 1992 SPAD meter (650, 940 nm) used to detect N deficiency in corn Schepers et al., Nitrogen sufficiency indices Blackmer & Schepers, Optical sensor (671, 780 nm) used for on-the-go detection of variability in plant nitrogen stress Stone et al., 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 2002 CASI hyperspectral sensor based index measurements of chlorophyll Haboudane et al., 2002, MSS remote sensing of ag fields with UAV Herwitz et al., Fluorescence sensing for N deficiencies Apostol et al., 2003

40 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) R 554 /R 677 SR1 NIR/red = R 801 /R 670 SR2 NIR/green = R 800 /R 550 SR3 R 700 /R 670 SR4 R 740 /R 720 SR5 R 675 /(R 700 *R 650 ) SR6 R 672 /(R 550 *R 708 ) SR7 R 860 /(R 550 *R 708 ) DI1 R 800 R 550

41 NDVI (R 800 R 680 )/(R R 680 ) Green NDVI (GNDVI) (R 801 R 550 )/(R R 550 ) PSSRa R 800 /R 680 PSSRb R 800 /R 635 NDI1 (R 780 R 710 )/(R 780 R 680 ) NDI2 (R 850 R 710 )/(R 850 R 680 ) NDI3 (R 734 R 747 )/(R R 726 ) MCARI [(R 700 R 670 ) 0.2(R 700 R 550 )](R 700 /R 670 ) TCARI 3*[(R 700 R 670 ) 0.2*(R 700 R 550 )(R 700 /R 670 )] OSAVI ( )(R 800 R 670 )/(R R )

42 TCARI/OSAVI TVI 0.5*[120*(R 750 R 550 ) 200*(R 670 R 550 )] MCARI/OSAVI RDVI (R 800 R 670 )/SQRT(R R 670 ) MSR (R 800 /R 670 1)/SQRT(R 800 /R ) MSAVI 0.5[2R SQRT((2R ) 2 8(R 800 R 670 ))] MTVI 1.2*[1.2*(R 800 R 550 ) 2.5*(R 670 R 550 )] MCARI2 1.5[2.5(R800 R670) 1.3(R800 R550)](2R800+1)2 (6R800 5R67 0) 0.5

43 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

44 Thank you!!! Questions???