Journal of Remote Sensing & GIS, Vol 4, No 1 (2013)

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Use of LANDSAT ETM + Data for Detection of Prosopis juliflora in Irrigated Zones

Vijay Shivaji Bhagat, Ramnath K. More


Prosopis juliflora (PJ) can be used as a relevant indicator for integrated management of irrigated saline soil in arid zones. Remotely Sensed Landsat Enhanced Thematic Mapper Plus (ETM+) dataset was used for detection and delineation of areas under PJ. The vegetation densities were classified using normalized difference vegetation index (NDVI) whereas soil moisture levels were estimated based on distribution of soil wetness index (SWI). The classified NDVI map was combined with classified SWI map for detection of PJ. It is considered that the available water in the soil is one of the determinants of plant species and densities. The field checks show that the soil under PJ is drier than the irrigated soils mainly in dry season. Therefore, the dense and medium vegetation distributed on drier soils are considered as PJ. About 16.17% of reviewed area is covered by dense and 11.29% by sparse PJ. Overall accuracy of the analyses is about 87%. The dense PJ is classified more precisely than the sparse. The methodology formulated in the study can be used as a rapid assessment tool prior to planning and monitoring the saline lands of arid zones.


[1]   WMO [World Meteorological Organization]. Climate and Land Degradation, 2005,

[2]   Raja. B.C., et al. Evaluation of rice (Oryza sativa L.) genotypes for salt tolerance. Journal of Food, Agriculture  and Environment, 2005, 3, 190-194.

[3]   Winslow. M.D. et al. Science for improving the monitoring and assessment of dryland degradation. Land Degradation and Development, 2011, 22,145–149.

[4]   Hunter. H. et al. Water quality in sugar cane catchments in Queensland. The State of Queensland, Department of Natural Resources and Mines, Report- 3, 2003.

[5]   Gozález-Núñez, T. et al. Integrated management for the sustainable use of salt-affected soils in cuba. Universidad Ciencia, 2004, 20, 85-102.

[6]   Gong. P. et al. Water table level in relation to EO-1 AL.I and ETM+ data over a mountainous meadow in California. Canadian Journal of Remote Sensing, 2004, 30,  691-696.

[7]  Bhagat. V.S. Use of LANDSAT ETM+ data for detection of potential areas for afforestation. International Journal of Remote Sensing, 2009, 30, 2607-2617. doi: 10.1080/01431160802552793.

[8]  Manhas. P.S. and Sharma. D.R. Salt displacement in a saline sodic and amended soil using low electrolyte water. Journal of the Indian Society of Soil Science, 1989, 37, 435-440.

[9]   Deosthali. V. Prioritization of villages for reclamation of salt-affected areas in irrigated tracts of Sangli District (M.S.) - A GIS and Remote Sensing approach, A technical report- GR 01/05, Department of Geography, University of Pune, 2005.

[10] Ramsey. R. et al. Evaluating the use of Landsat 30m Enhanced Thematic Mapper to monitor vegetation cover in shrubsteppe environments. Geocarto International, 2004, 19, 39–47.

[11] Singh. P.K. et al. Managing tree cover Yamunanagar district of Harayana: A Remote Sensing and GIS approach. Photonirvachak, Journal of the Indian society of Remote Sensing, 2005, 33, 219-225.

[12] Gonzalez-Alonso. F. et al. Forest biomass estimation through NDVI composites. The role of remotely sensed data to assess Spanish forests as carbon sinks. International Journal of Remote Sensing, 2006, 27,5409–5415.

[13] Kiage. L.M. et al. Recent land-cover/use change associated with land degradation in the Lake Baringo catchment, Kenya, East Africa: evidence from Landsat TM and ETM+. International Journal of Remote Sensing, 2007, 28, 1-25. doi: 10.1080/01431160701241753

[14] Adegoke. J.O. and Carleton. A.M. Relations between soil moisture and satellite vegetation indices in the U.S. Corn Belt. Journal of Hydrometeorology, 2002, 3, 395-405.

[15] Wang, C.Q. et al. Evaluating soil moisture status in China using the Temperature-Vegetation Dryness Index (TVDI). Canadian Journal of Remote Sensing, 2004, 30, 671-679.

[16] Crist. E.P. and Cicone. R.C. A physically based transformation to thematic mapper data - The TM Tasselled cap. Transactions on Geosciences and Remote sensing, 1984, 22, 256 – 263.

[17] Crist. E.P. A TM tasselled cap equivalent transformation for reflectance factor data. Remote sensing of Environment, 1985, 17, 301 – 306.

[18] Crist. E.P. et al. Vegetation and soil information contained in transformed Thematic Mapper data. In proceedings of IGARSS, Symposium, 1986, 1465–70.

[19] Huang. C. et al. Derivation of a tasselled cap transformation based on Landsat-7 at-satellite reflectance. International Journal of Remote Sensing, 2002, 23, 1741-1748.

[20] Patel. N.R. et al. Modelling of wheat yield using multi-temporal Terra/MODIS satellite data. Geocarto International, 2006, 21, 43-50.

[21] Oza, S.R. et al. Large area soil moisture estimation and mapping using space-borne multi-frequency passive microwave data. Photonirvachak. Journal of the Indian Society of Remote Sensing, 2006, 34, 343–350.

[22] Gessing. D. et al. Influence of mesquite on soil nitrogen and carbon development: implications for agro forestry and global carbon sequestration. Journal of Arid Environments, 2000, 46,157-180.

[23] Pasiecznik. N.M. et al. Identifying tropical Prosopis Species: A Field Guide. HDRA, Coventry, UK, 2004.

[24] Sharma. I.K. Ecological and economic importance of PJ (Sw.) DC in the Indian Thar desert. Journal of Economic and Taxonomic Botany, 1981, 2, 245-248.

[25] Bhojvaid. P.P. et al. Reclaiming sodic soils for wheat production by PJ (Swartz.) DC. Afforestation in India. Agro forestry Systems, 1996, 34,139-150.

[26] Singh. G. Effect of site preparation techniques on PJ in an alkali soil. Forest Ecology and Management, 1996, 80, 267-278.

[27] Felkar. P. et al. Variation in growth among 13 Prosopis (mesquite) species. Experimental Agriculture, 1981, 28, 209-218.

[28] Pasiecznik. N.M.  Prosopis–pest or providence, weeds or wonder tree? European Tropical Forest Research Network Newsletter, 1999, 28,12-14.

[29] Mawangi. S. and Swallow. B. Prsopis juliflora invasion and livelihoods in the Lake Baringo area of Kenya. Conservation and Society, 2008, 6, 130-140.

[30] Jones. K. et al. Effect of tree species and crown pruning on root length and soil water content in semi-arid agro forestry. Plant and Soil, 1998, 201, 197-207.

[31] Pasiecznik. N.M. et al. The PJ –Prosopis pallid Complex: A Monograph. HDRA, Coventy, UK, 2001.

[32] Khan. D. et al. Germination, growth and ion regulation in PJ (Swartz.) DC, under saline conditions. Pakistan Journal of Botany, 1987, 19, 131-138.

[32] Singh. G. et al. Effects of irrigation on PJ and soil properties of an alkali soil. International Tree Crops Journal, 1990, 6, 81-99.

[33] Pasiecznik. N.M. et al. Cassama, M. Pretreatment of Prosopis seeds to break dormancy. International Tree Crops Journal, 1998, 9, 187-193.

[34] Frayh. A.A. et al. Human sensitization to PJ antigenin Saudi Arabia. Annals of Saudi Medicine, 1999, 19 (4), 331-336.

[35] Velu. G. et al. Allelopathy: field observations and methodology. Proceedings of the International Conference on Allelopathy, 1996, 299-302.

[36] Khanna. M. et al. Mesquite gum (PJ): Potential binder in tablet dosage forms. Journal of Scientific and Industrial Research, 1997, 56, 366-368.

[37] Kijne. J.W. Salinity and sodicity in Pakistan's Punjab: A Threat to sustainability of irrigated Agriculture?, International Journal of Water Resources Development, 1995, 11, 73–86. doi: 10.1080/07900629550042470

[38] Vagen. T.G. et al. Soil carbon sequestration in sub-Saharan Africa: A review. Land Degradation and Development, 2005, 16, 53-71.

[39] Pavan-dayaker, T.K. Mapping of potential fishing zones using OCM data of Irs-P4 and Geographic Information System. Environmental Informatics Archives, 2003, 1, 475-480.

[40] Pan. Y. et al. Improved estimates of net primary production from MODIS satellite data at regional and local scales. Ecological Applications, 2006, 16, 125-132.

[41] Hui. F. et al. Modelling spatial-temporal change of Poyang Lake using multitemporal Landsat imagery. International Journal of Remote Sensing, 2008, 29, 5767–5784.

[42] Dupigny. G. et al. A Moistur Index for Surfac Characterization over a Semi arid area. Photogrammetric Engineering and Remote Sensing, 1999, 65, 937-945.

[43] Frazier. P.S. and Page. K.J. Water Body Detection and Delineation with Landsat TM Data, Photogrammetric Engineering and Remote Sensing, 2000, 66, 1461-1467.

[44] Wu. G.G. et al. Comparison of MODIS and Landsat TM5 images for mapping tempo–spatial dynamics of Secchi disk depths in Poyang Lake National NatureReserve, China, International Journal of Remote Sensing, 2008, 29, 2183–2198.

[45] Wang, G. et al. Anderson, A. Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images. International Journal of Remote Sensing, 2002, 23, 3649–3667.

[46] Ryu. J.H. et al. Waterline extraction from Landsat TM data in a tidal flat A case study in Gomso Bay, Korea. Remote Sensing of Environment, 2002, 83, 442–456.

[47] Harma. P. et al. Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland, The Science of The Total Environment, 2001, 268, 107-121

[48] Bhagat. V.S and Sonawane. K.R. Use of LANDSAT ETM+ data for delineation of water bodies in hilly zones. Journal of Hydroinformatics, 2011,13.4, 661-671, doi:10.2166/hydro.2010.018

[49] Jenson. J.R. Introducery digital image processing: A Remote Sensing Perspective. Prentice Hall, New Jersey, 1996.

[50] Rahman. R. and Saha S.K. Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data. Photonirvachak, Journal of the Indian Society of Remote Sensing, 2008, 36, 189-201.

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