ASSESSMENT OF SOIL SALINITY USING REMOTE SENSING

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ASSESSMENT OF SOIL SALINITY USING REMOTE SENSING

  1. 1. ASSESSMENT OF SOIL SALINITY USING REMOTE SENSING Presented by T.Ram Reddy 131874 1
  2. 2. CONTENTS  Introduction  Literature Review  Case study  Summary  References 2
  3. 3. INTRODUCTION  Soil salinity - The amount of salt contained in the soil  Sources of salinity - Release of salts from weathering of primary minerals - High doses of chemical fertilizers - Irrigation water 3
  4. 4.  Human-induced salinization is the process of increasing the original status of salt content in the soil.  Salt is the savor of foods but the scourge of agriculture.  Soil salinity can be remotely sensed by several airborne/space-borne techniques such as multi-/hyper- spectral imagery, and active/passive microwave sensing. 4
  5. 5. LITERATURE REVIEW  Nicolas et al. (2006) detected salinity by means of combining soil and remote-sensing data. He demonstrated that his method has given rise to significant improvements in salinity estimations, as compared to purely-regressive approaches.  Lounis et al. (2012) exploited the multi-spectral optical data from the LANDSAT ETM + (Enhanced Thematic Mapper) to map salinity . 5
  6. 6.  Goldshleger et al. (2013) explored whether spectroscopy could quantitatively assess salinity. It was concluded that reflectance spectroscopy is useful for characterizing the key properties of salinity in growing vegetation and assessing its salt quality.  Lalit Kumar et al. (2014) Modelled Spatial Variation in Soil Salinity based on Remote Sensing Indicators. 6
  7. 7. CASE STUDY - 1 Title: Monitoring of salinity in the area using multi-temporal satellite images. Authors: Koshal, A. K. Journal: International Journal of Remote Sensing and GIS Study Area  The study area lies between geo-coordinates 30 00 to 30 15` N & 76 30` to 76 45`E, Covering 577.86 sq km area of south west Punjab (Bhatinda and Muktsar districts). 7
  8. 8. DATA USED AND METHODS  Remote sensing data  IRS 1D images of the study area, were procured from the National Remote Sensing Agency (NRSA) Hyderabad. Table 1 -Details of satellite data 8
  9. 9. 9  Ancillary data  The information of contour, administrative boundaries such as sand dunes, canals, important towns, villages and roads and highway were digitized to prepare the base map.  The published soil survey reports, soil maps, water quality reports for the study area were collected and utilized during interpretation and field work
  10. 10. Fig 1 Overview of Methodology 10
  11. 11. PRE FIELD INTERPRETATION  Standard FCC was visually interpreted for salt affected soils.  The salt affected soils usually appear in tones of bright white to dull white with medium to coarse texture on Standard FCC due to the presence of salts, on soil surface.  The obstructions to natural drainage like roads, railway lines, distributaries, etc. can easily be identified on the FCC images.  The waterlogged/ pond areas appear on the FCC image in dark blue to a black tone with a smooth texture. 11
  12. 12. Fig 2 A map showing preliminary interpreted units on FCC with base details was generated before going into the field. 12
  13. 13. FIELD INVESTIGATIONS  In total, the ground truth was collected from 24 villages, 120 samples were taken from salt affected areas and non-salt affected areas.  A reconnaissance survey of the study area was done using satellite images (FCC).  Salt affected lands and affected crops were identified on the ground and ascertained on the satellite image by characterizing image characteristics.  Satellite image of IRS 1D LISS III of March & May 2000 were used for the purpose 13
  14. 14. POST FIELD WORK  The tentative legends were prepared during the pre- fieldwork were also finalized.  Using GIS database a final map showing visual salt affected soils was prepared. 14
  15. 15. RESULTS  IRS –1D Image data have been used to assess the salt affected land.  During ground verification salt accumulation was also found to be associated with salt grass and salt tolerant wild vegetation. The area mapped in the classes of moderate and severe salt affected soil was 1.72 % and 7.90% of the total area. 15
  16. 16. CASE STUDY - 2 Title: To delineate surface soil salinity in the prime rice-wheat cropping area. Authors: Iqbal. F. Journal: African Journal of Agricultural Research, 2011 Study Area  The study area, district Gujranwala in Central Punjab province, is located in Rachna Doab, which lies between longitudes 73°38’52”, 74°34’55” East and latitude 31°47’36”, 34°34’2” North. 16
  17. 17. METHODOLOGY  Satellite imagery of Landsat and published map by SSP (Soil Survey of Pakistan) were used for detection of salt affected soils.  The raw images were geo-referenced to a common UTM (Universal Transverse Mercator) coordinate system. 17
  18. 18. Fig 3 Overview of Methodology 18
  19. 19. Image preprocessing  A self-adaptive filter method was used to remove non-periodic noise and the FFT (Fast Fourier Transform) method was used to remove periodic noise.  To analyze the pattern of salinity in the study area, the maps must be co-registered in the same coordinate system (for example, UTM). Image processing  For salt affected soil detection, NDVI, NDSI, SI, MSI and SR indices were applied.  The normalized difference vegetation index (NDVI), simple ratio (SR), normalized difference salinity index (NDSI), moisture stress index (MSI) and normalized difference built-up area index (NDBI) were computed using the satellite images. 19
  20. 20. GIS analysis  The extracted soil through satellite imagery was superimposed with the salinity maps extracted through soil association map.  Finally the overlay of both NDSI was performed to extract the common saline areas.  The vegetation area was masked by NDVI and MSI and overlapped with built-up area to prepare the final map of land cover. 20
  21. 21. RESULTS  The salt prone soil showed significant reflection in thermal IR band and minimum in near infrared band.  About 70% of salt affected area is computed through satellite imagery.  Results showed that 19% of the rice-wheat cropping area of Gujranwala district in Rachna Doab of central Punjab province of Pakistan is salt affected. 21
  22. 22. Fig 4 Salt Affected Soils In Study Area 22
  23. 23. SUMMARY  Soil salinity is a major environmental hazard, that impacts the growth of many crops.  Satellite imagery and false colour composites were visually interpreted to identify salt affected lands.  Advantage of using remote sensing technology include wide coverage (the only source when data is required over large areas or regions), faster than ground methods, and facilitate long term monitoring. 23
  24. 24. REFERENCES  Goldshleger, N., Chudnovsky, A & Binyamin, R. B., (2013), Predicting salinity in tomato using soil reflectance spectra, Int. J. Remote Sens. 2013, 34, 6079–6093.  Iqbal. F., (2011), Detection of salt affected soil in rice-wheat area using satellite image, Afr. J. Agric. Res. 2011, 6, 4973–4982.  Koshal, A. K., (2012), Satellite image analysis of Soil Salinity Areas in Parts of South-West Punjab through Remote Sensing and GIS, International Journal of Remote Sensing and GIS, Vol. 1, No. 2, 2012, pp. 84-89. 24
  25. 25.  Lalit Kumar, Priyakant Sinha and Allbed. A., (2014), Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques, International Journal of Remote Sensing, 2014, 6, 1137-1157.  Lounis, M and Dehni, A., (2012), Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria, Procedia Engineering, Vol. 33, 2012, pp. 188-198.  Minasny, B., Taghizadeh, R., Sarmadianc, F and Malone, B. P., (2014), Digital Mapping Of Soil Salinity In Ardakan Region, Central Iran, Geoderma 2014, 213, 15–28. 25
  26. 26. THANK YOU 26

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