Applications of Remote Sensing
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Remote sensing uses sensors on aircrafts and satellites to obtain spatial data about soil and crop conditions without physical contact. This document discusses potential applications of remote sensing in precision agriculture including using imagery to identify soil characteristics, predict yields, and schedule irrigation. Case studies are presented on using remote sensing to monitor crop variability and weeds. The document concludes that remote sensing techniques can provide a comprehensive soil and crop strategy but need improvements to be economically accessible to all farmers.
Applications of Remote Sensing
- 1. Applications of Remote Sensing in Site-Specific Management BHEEMLAL.R 131855 1
- 2. Contents Precision farming introduction Use of remote sensing in precision farming Potential applications Soil characteristics Yield predication Irrigation Literature review Case studies Conclusion References 2
- 3. Precision farming introduction Precision farming is an emerging agricultural technology that involves managing each crop input on a site-specific basis to reduce waste, increase profits, and maintain the quality of the environment. Remote sensing is a technology that can be used to obtain various spatial layers of information about soil and crop conditions. It allows detection and/or characterization of an object, series of objects, or landscape without having the sensor in physical contact. 3
- 4. Use of remote sensing in precision farming Typically remote sensing is conducted by positioning a sensor above the object (target) being observed. Platforms that support the sensors vary, depending on the altitude above the target. Today two main observation platforms are used to collect remote sensing data: • Aircraft-based (aerial) and satellite-based. • Ground-based sensors also have been used for certain specific applications and research studies. 4
- 5. Potential applications Most remotely sensed data can be imported into Geographic Information System (GIS) software to overlay with other layers of information such as yield maps, field boundaries, soil sampling locations, and analytical results. A hard copy of obtained imagery (like from 35 mm film) also can be produced to visually compare observed field patterns and to identify field anomalies. Each image should be scaled and oriented according to true geographic coordinates of every pixel. Once the data are in digital format, various analytical algorithms can be applied. 5
- 6. Fig 01: Background image Fig 02: USDA soil survey boundaries superimposed on bare soil image. 6
- 7. The most common indirect use of remote sensing images is as a base map onto which other information can be layered using GIS software. A georeferenced image provides a background for ownership boundary, yield, and fertility maps. Illustrates a remote sensing image used as a background for different spatial data layers. all layers must be georeferenced to the same geographic projection in order to display properly. Other applications include: Use of derived vegetation parameters in crop simulation models, defining soil and plant parameters to improve soil sampling strategies, and discovering and locating crop stresses such as weeds, insects, and diseases. 7
- 8. Soil characteristics These data can be used to identify areas of the field with similar physical soil properties. This method has not been widely accepted as an adequate method of soil mapping because the reflectance characteristics of the desired soil properties often are affected by variability in soil moisture content, crop residue coverage, surface roughness, atmospheric conditions, solar zenith angle, and view angle. Digital Orthophoto Quadrangles (DOQ) are the most popular examples of imagery that can be downloaded from various data bank web sites. 8
- 9. Yield prediction Experimentation in this area has been ongoing since before the launch of NASA’s first Earth Resources Technology Satellite (ERTS-1, now known as Landsat) in 1972. Yield in two ways: Focused on crop-growth models, uses remote sensing as a calibration tool for a particular model. Approach involves estimating yield based on vegetative indices such as the Normalized Difference Vegetation Index (NDVI)). 9
- 10. Irrigation To help reduce the time required and spatial uncertainty associated with irrigation scheduling, remote sensing can be used to help automate and perhaps more accurately schedule irrigation. Research to improve irrigation scheduling with remote sensing technologies has been conducted using on-site, airborne, and satellite sensors to accurately detect canopy temperature, pigment content and composition, vegetation indices, leaf cell structure, canopy architecture, and leaf-water content. One technique for detecting crop stress (including that due to a water deficit) is visible. 10
- 11. Literature review G. Dry dale and G. Metternich, (2000). Remote sensing for site-specific crop management: Evaluating the potential of digital multi-spectral imagery for monitoring crop variability and weeds within paddocks. Precision Agriculture (PA) has been defined as ‘observation, impact assessment and timely strategic response to fine-scale variation in causative components of an agricultural production process’, and thus may cover a range of agricultural enterprises, and can be applied to pre- and post-production aspects of agricultural enterprises. Robert, (2001). Remote Sensing for Crop Management, Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoringof plant growth and development and for the detection of many environmental stresses which limit plant productivity. 11
- 12. Adamchuk, Viacheslav I.; Perk, Richard L.; and Schepers, James, (2003). Precision Agriculture: Applications of Remote Sensing in Site-Specific Management" Precision farming is an emerging agricultural technology that involves managing each crop input on a site-specific basis to reduce waste, increase profits, and maintain the quality of the environment. Remote sensing is a technology that can be used to obtain various spatial layers of information about soil and crop conditions. It allows detection and/or characterization of an object, series of objects, or landscape without having the sensor in physical contact. David R. Shaw, (2004). Translation of remote sensing data into weed management decision remote sensing and associated spatial technologies provide tremendous opportunity to enhance weed management and improve–protect the environment through judicious use of the most efficacious control methods for a given site. Shweta Karsauliya, (2009). Department of Remote Sensing, Site suitability criteria of solid waste is mainly focused on two categories physical and socially. River Yamuna which is one of the largest tributaries of River Ganga and one of the most prominent rivers of our country is getting deteriorated due to various human activities, unfortunately certain stretches of River Yamuna are much polluted due to deposition of solid wastes from various sites. 12
- 13. Case Study – 1 TITILE -A Case Study of Surroundings of River Yamuna, India AUTHORS -Shweta Karsauliya Department of Remote Sensing, Banasthali University, Rajasthan. JOURNAL- (Ozeair Abessi at.2009). 13
- 14. Introduction Water pollution in the river Yamuna is severely caused due to the solid wastes, that is disposed into the river through various sites in the cities. Wastes being solid in state are non biodegradable which cannot be decomposed by the micro organisms. Hence these wastes lead to water pollution in the river. Waste remains in water forever until it is separated out by any physical process. 14
- 15. Study area Study area is located in the western part of Uttar Pradesh state which is the surroundings of Yamuna River. It covers some parts of three big cites Agra, Mathura, Firozabad. 15Fig 03: Study Area.
- 16. Data used and methodology Visual interpretation LANDSAT-TM imagery is done using Google earth and image interpretation elements like tone, texture, association, shape, size, pattern and different erosion characteristics. Weightage assigned for each categories: Table 1: 16
- 17. Land use land cover Land use land cover refers to the type of material present on the land space, Information of the area land use land cover can be get from the satellite image. 17 Fig 04: Land use land cover map Fig 05: Study area classification
- 18. Case Study-2 TITILE- Remote sensing for site-specific crop management: Evaluating the potential of digital multi-spectral imagery for monitoring crop variability and weeds within paddocks AUTHORS-G. Dry dale* and G. Metternich** JOURNAL- Whelan and McCartney, (2000). 18
- 19. Introduction Precision Agriculture (PA) has been defined as ‘observation, impact assessment and timely strategic response to fine-scale variation in causative components of an agricultural production process’, and thus may cover a range of agricultural enterprises, and can be applied to pre- and post-production aspects of agricultural enterprises (Australian Centre for Precision Agriculture, 2002). Site-specific crop management (SSCM) is one facet of precision agriculture and is defined as ‘matching resource application and agronomic practices with soil and crop requirements as they vary in space and time within a field’ (Whelan and McBratney, 2000). 19
- 20. Data sets and study area The DMSI system The field selected for the study falls in the northern region of the shire of Wickepin, located in the South West of Western Australia. The DMSI was captured using SpecTerra Services Digital Multi-Spectral Camera (DMSC), which is comprised of four 12 bit digital CCD cameras recording 1024 pixels of 1024 pixels per line. 20
- 21. Fig 07: Study area location. 21
- 22. Field data collection During the seeding operations for cereal crops, generally, a constant seed rate is set for the entire paddock. The number of plants established depends on factors such as soil moisture, surface crusting, seedling vigour, sowing depth, fertiliser level, disease and insect attack (Madin et al., 1993). 22
- 23. Fig 08: Quadrants of the field transect: a) Sample site C5 showing high density of canola and weeds; b) Sample site C14 showing low density of canola and weeds. 23
- 24. Fig 09: Transect field samples: a) Sample C5 showing an average canola height of 20cm; b) Sample C14 showing and average canola height of 4cm.24
- 25. Crop density methodology Table 2: 25
- 26. Conclusion Using these remote sensing techniques we are able to get the total soil strategy from external point of view as well as internal point of view. But these remote sensing techniques are not being used by many people, so there is a need to improve these techniques so that every farmer can do his work in less time. These techniques should be made economic such that every individual can use it. 26
- 27. References Adamchuk, Viacheslav I.; Perk, Richard L.; and Schepers, James S., "EC03-702 Precision Agriculture: Applications of Remote Sensing in Site- Specific Management" (2003). Historical Materials from University of Nebraska-Lincoln Extension. Paper 705. http://digitalcommons.unl.edu/extensionhist/705 David R. Shaw, (2004). Translation of remote sensing data into weed management decision remote sensing and precision agriculture: ready for harvest or still maturing? Photogram. Eng. Remote Sens 65:1118–1123. G. Drysdale and G. Metternicht (Whelan and McCartney, 2000). Remote Sensing For Site-Specific Crop Management: Evaluating the Potential of Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds within Paddocks. Department of Spatial Sciences Curtin University of Technology GPO Box U 1987, Perth, WA 6845 27
- 28. Robert, (2001). Remote Sensing for Crop Management. Photogrammetric Engineering & Remote Sensing Vol. 69, No. 6, June 2003, pp. 647–664. Shweta Karsauliya, (Ozeair Abessi at 2009). Application of Remote Sensing and GIS in Solid Waste Management: A Case Study of Surroundings of River Yamuna, India. International Journal of Environmental Engineering and Management. ISSN 2231-1319, Volume 4, Number 6 (2013), pp. 593-604. 28
- 29. THANK YOU 29