Application of Remote Sensing in Agriculture
- 1. Application of Remote Sensing in Agriculture By - UTTAM KUMAR 29/09/2015 1
- 2. INTRODUCTION Remote sensing has been found to be a valuable tool in evaluation, monitoring and management of land, water and crop resources. The launching of the Indian remote sensing satellite (IRS) has enhanced the capabilities for better utilization of this technology and significant progress has been made in soil and land cover mapping, land degradation studies, monitoring of waste land, assessment of crop conditions crop acreage and production estimates (Das, 2000). SKRAU, Bikaner
- 3. HISTORY • In ancient India apparently had a clear concept of remote sensing. For instance epic ‘Maha Bharata’ Sanjaya had been endowed, presumably with some equipment which enabled him to report (in real time) all the events at the distant “Kurukshetra” battle field, whether they were open or camouflaged and occurred in day or by night. • In recent times, Frenchman Mr. Tournachen took photographs for the first time from a balloon which floated over Paris in 1858. cont… SKRAU, Bikaner
- 4. • The term “Remote sensing” was first used in 1961 when U.S. Naval project on the study of Aerial photographs was renamed as “remote sensing”. • The application of remote sensing technology to agriculture and forestry was presented in couple of papers in 1968 at the occasion of U.N. conference on peaceful uses of on the space uses and the first satellite in remote sensing technology was launched in July 1972 in U.S.A. In India the remote sensing activities SKRAU, Bikaner
- 5. •“is the measurement or acquisition of information of some property of an object or phenomena by a recording device that is not in physical or intimate contact with the object or phenomena under study” What is remote sensing: SKRAU, Bikaner
- 6. Basic principle Different objects based on their structural , chemical and physical properties reflect or emit different amount of energy in different wave length ranges of the E.M.S •The sensors measure the amount of energy reflected from that object . SKRAU, Bikaner
- 7. SCALE IN REMOTE SENSING 1. small scale : 1cm=5km or more 2. Inter mediate scale : 1cm=0.5 to 5km 3. Large scale : 1cm=0.5km or less SKRAU, Bikaner
- 8. ESSENTIAL COMPONENT OF REMOTE SENSING 1. Signals from a source/light 2. Sensors on a plate form 3. Sensing (Signal reception, storage, processing, information extraction and decision making) SKRAU, Bikaner
- 9. Components of Remote Sensing process Earth Surface Source of Energy Sensing Systems SKRAU, Bikaner
- 10. The remote sensing processThe remote sensing process Visual Digital Reference data Air photos Digital data Maps Statistics GIS data sets User Decision Maker Data products Inter- pretation Information products Target audience SKRAU, Bikaner
- 11. SOURCES OF ELECTROMAGNATIC RADIATION. 1.Natural body (sun, earth) 2.Man made source(redar, lidar system) SKRAU, Bikaner
- 12. TYPES OF REMOTE SENSING 1.Passive remote sensing 2.Active remote sensing SKRAU, Bikaner
- 13. When remote sensing work is carried out with the help of electromagnetic radiation (signals) reflected by a natural body(sun and the earth). eg.visible, near infra red and microwave remote sensing. SKRAU, Bikaner
- 14. When remote sensing work is carried out with a man made source of radiations which is used to illuminate a body and to defect the signal reflected form eg. Radar and lidar remote sensing SKRAU, Bikaner
- 15. VisibleLight Nearinfrared Midinfrared Thermalinfrared Ultraviolet(UV) Microwave TVandradio Xrays γrays blue green red UV near infrared 110-1 10-2 10-3 10-4 10-5 10-6 104 103 102 10 107 106 105 wavelength (µm) wavelength (µm) 0.4 0.5 0.6 0.7 µm SKRAU, Bikaner
- 16. COLLECTION OF DATA: 1.GROUND OBSERVATION PLATFORMS Eg; Tripod stand , man 2. AIR BORNE OBSERVATIONS Eg: BALLONS , AIRCRAFTS , DRONES 3. SPACE BORNE OBSERVATIONS Eg: SATELLITE SKRAU, Bikaner
- 17. 17 SATELLITE TYPES : 1. Natural : Moon 2. Artificial: GEOSTATIONARY Eg : INSAT series SUN SYNCHROUNOUS Eg : IRS, SPOT, LANDSAT.. SKRAU, Bikaner
- 18. 09/29/15 SKRAU, Bikaner 18 Factors that Influence Soil Reflectance in Remote Sensing: 1. Mineral composition, 2. Soil moisture, 3. Organic matter content and 4. Soil texture (surface)
- 19. APPLICATION OF REMOTE SENSING TECHNIQUES: 1.AGRICULTURE 2.FORESTRY 3.WATER RESOURSES 4.DETECTION OF WATER POLUTION 5. GEOLOGY AND MINERAL SOURSES 6. MAPPING OF LAND USE / LAND COVER 7.MONITORING OF ENVIRONMENTAL HAZARDS 8.WEATHER AND CLIMATIC RELATED APPLICATIONS 9. ENGINEERING APPLICATIONS 10. HUMAN INDUCED GEOLOGICAL HAZARDS SKRAU, Bikaner
- 20. Applications in agriculture 1. Crop identification 2. Crop acreage estimation 3. Crop condition assessment and stress detection 4. Identification of planting and harvesting dates 5. Crop yield modeling and estimation 6. Identification of pest and disease infestation 7. Soil moisture estimation 8. Irrigation monitoring and management 9. Soil mapping 10. Monitoring of droughts 11. Land cover and land degradation mapping 12. Identification of problematic soils SKRAU, Bikaner
- 21. Table 1 Area under wasteland in India as estimated by using Remote sensing techniques (NASA, 1985) Category Area (million hectare) (A) Cultivable wasteland I Salt affected lands 3.90 ii Gullies or ravines lands 4.32 iii Water logged land 0.89 iv Undulating land 10.79 v Shifting cultivation and forest blank 2.40 vi Sandy areas 10.53 (B) Uncultivated wasteland i Bassin hill ridge or rock out map 2.75 ii Snow covered area 17.70 Total 53.28 SKRAU, Bikaner
- 22. Forms of agricultural remote sensing:Forms of agricultural remote sensing: OperationalOperational StrategicStrategic SKRAU, Bikaner
- 23. Strategic Agricultural Remote SensingStrategic Agricultural Remote Sensing :: • Involves large areas • Concerned with overall crop estimates • Province of states and large companies SKRAU, Bikaner
- 24. Operational Agricultural Remote SensingOperational Agricultural Remote Sensing •Involves single farms or even fieldsInvolves single farms or even fields •Concerned with day to day managementConcerned with day to day management •Involves individual farmersInvolves individual farmers •Cost and timelinessCost and timeliness •Light aircraft/ videographyLight aircraft/ videography SKRAU, Bikaner
- 25. SERIES OF SATELLITES FOR REMOTE SENSING : LAND SAT, IKONOS, QUICKBIRD U.S.A SEO (Bhaskara-1), IRS INDIA SPOT FRANCE ERS-1 U.K JERS-1 JAPAN RADAR SAT-1 CANADA SKRAU, Bikaner
- 26. Table 2 IDENTIFICATION INDICATORS : (HOW TO IDENTIFY DIFFERENT OBJECTS) EARTH SURFACE FEATURE COLOUR SIGNATURE FOREST GREEN AGRICULTURE LAND PINK/ BRIGHT RED HEALTHY VEGETATION BROAD LEAF TYPE NEEDLE LEAF TYPE RED TO MAGENTA REDISH BROWN TO PURPLE STRESSED VEGETATION PRE –VISUAL STAGE VISUAL STAGE PINK – BLUE CYAN WATER DARK BLUE -BLACK WATER WITH SUSPENDED SEDIMENTS LIGHT BLUE UNCULTIVATED LAND BLUE /WHITE RED SOIL YELLOW DAMP GROUND DISTINCT DARK TONES SAND DUNES YELLOW / WHITE CITY/ TOWN BLUE CLOUD/ SNOW WHITE SHADOW BLACK WITH A FEW VISIBLE DETAILS SKRAU, Bikaner
- 27. WEST BENGAL SAC, ISRO(2002) SKRAU, Bikaner
- 28. SKRAU, Bikaner
- 29. PROBLEMS ON REMOTE SENSING FOR INDIAN CONDITION SMALL SIZE OF PLOTS DIVERSITY OF CROPS SOWN IN A PARTICULAR AREA VARIABILITY OF SOWING & HARVESTING DATES IN DIFFERENT FIELDS INTER CROPPING & MIXED CROPPING PRACTICES EXTENSIVE CLOUD COVER DURING THE RAINY SEASON SKRAU, Bikaner
- 30. CONCLUSION : Useful for crop identification, crop diversification, yield estimation and yield prediction. To solve the problems through identification of pests and diseases. Sustainable utilization of land resources. To select the crops for optimum ground water utilization and increase the crop production and productivity SKRAU, Bikaner
Editor's Notes
- #6: What is remote sensing
- #18: GEOSTATIONARY 2.SUN SYNCHROUNOUS
- #19: 1. The mineral composition of soils affect the reflectance spectrum. Increasing reflectance of soils occurs from the visible to the shortwave infrared - with absorption bands around 1.4 um and 1.9 um related to the amount of moisture in the soil.2. Radar waves may not be able to penetrate soil if it is moist. On the soil reflectance spectra, this soil moisture will develop parallel curves. Moisture of soil has an equal effect over the spectrum and the ration between the spectral bands. Spectral bands of red and near-infrared bands are independent from the soil moisture. 3. The soil line of the soil reflectance spectra, characterizes the soil type, defines negetation indices, and corrects the plant canopy reflectances from the optical soil property effects. This soil line also represents the relationship between the red and the near-infrared soil reflectances. The least-square regression method will calculate the soil line:nir (soil) = a red (soil) = b (1). where: red (Soil) = soil reflectance in the red bandnir (soil) = soil reflectanc ein the near - infrared banda, b = parameters of the soil line estimated by the least - square regression methodOther visible bands - such as green or blue ones, can be used instead of red ones. 3. Organic matter is the third factor that influences soil optical properties. Organic matter may indirectly affect the spectral influence, based on the soil structure and water retention capacity. High organic matter in soil may produce spectral interferences for band characteristics of minerals like Mn and Fe.4. Soil texture (roughness) also affects soil optical properties. Light is trapped in the rough surfaces of the coarse soil particles. For example, if iron and lime are present, a stronger reflectance is received than if the soil material was fine textured and dry. Variations in soil reflectances occur where there is a change in distribution of light and shadow areas with surface roughness areas. This factor is important in the thermal infrared and microwave spectral domains.4. Soil size and shape influence the reflectance properties. If the size of a soil aggregate expands in diameter, a decrease in reflection will result. Soil temperatures and changes in structure are also recorded. The shape is related to the surface (texture); a smooth, even surface will probably result from a more spherical soil aggregate, instead of a jagged soil aggregate.5. Ground radar can also be used in combination with remote sensing, to detect changes of diagnostic soil horizons such as albic, spodic and argillic horizons or soil/rock boundaries. Limitations with ground radar include soils with high salt content/clay/silt/moisture amounts.
- #20: APPLICATION OF REMOTE SENSING TECHNIQUES:
- #21: Crop identification Crop acerage estimation Crop condition assessment and stress detection Identification of planting and harvesting dates Crop yield modeling and estimation Identification of pest and disease infestation Soil moisture estimation Irrigation monitoring and management Soil mapping Monitoring of droughts Land cover and land degradation mapping Problematic soils identification
- #23: Strategic
- #26: Landsat Spot ERS-1 JERS-1 IRS Radarsat-1 IKONOS Quickbird
- #27: IDENTIFICATION INDICATORS
- #29: PLATE :1