M.P Singh, IAS Chairman

MP Singh, IAS
Additional Chief Secretary, Dev.
to Govt. of Punjab

readmore
Dr. Brijendra Pateriya - Director Director

A warm greeting and welcome to the website of Punjab Remote Sensing Centre (PRSC), a Government of ...

ReadMore

Publications

  1. Major Research Papers Published in International/National Journals, Symposia & Conference Proceedings.
    Sub headings are:
    1. 2010 Onwards
    2. 2005 – 2009
    3. 2000 – 2004
    4. Older than 2000

    *(In black we have the publication details and in blue we have the abstract of the publication, abstract of only few publications are available)

  2. Reports, Atlas and Manuals

    Reports, Atlas and Manuals
    1 Sharma, P.K; Loshali, D.C; Minakshi; Singh, Narinder; Sood, Anil; Singh, Harpinder; Litoria, P.K. and Verma, V.K. (2008). Land use/land cover Atlas of Punjab. Punjab Remote Sensing Centre, Ludhiana, pp 185.
    2 Sharma, P.K; Sood, Anil; Setia, R.K; Singh, Narinder; Minakshi; Verma, V.K; Mehra Deepak; Choudhury, B.U; Kang, G.S; Litoria, P.K; Chopra, Rajiv; Bhatt, C.M; Bhardwaj, Rameshwar and Singh, Harpinder (2007). Ground water quality for irrigation in Punjab. Punjab Remote Sensing Centre, Ludhiana in collaboration with National Bureau of Soil Survey & Land Use Planning, Delhi Centre and Ground Water Cell, Deptt. of Agriculture, Punjab, pp 188.
    3 Sharma, P.K., Chopra, Rajiv, Litoria, P.K., Bhatt, C.M., Sood, Anil, Choudhury, B.U., Kumar, Rameshwar, Singh, Narinder, Setia, R.K., Verma, V.K., Mathur, Ajay, Loshali, D.C. Minakshi and Singh, Harpinder (2008). “Digital database of drinking water sources and quality parameters in Moga district, Punjab
    4 Sharma, P.K., Chopra, Rajiv, Litoria, P.K., Bhatt, C.M., Sood, Anil, Choudhury, B.U., Kumar, Rameshwar, Singh, Narinder, Setia, R.K., Verma, V.K., Mathur, Ajay, Loshali, D.C. Minakshi and Singh, Harpinder (2008). “Digital database of drinking water sources and quality parameters in Patiala district, Punjab.
    5 Sharma, P.K., Chopra, Rajiv, Litoria, P.K., Bhatt, C.M., Sood, Anil, Choudhury, B.U., Kumar, Rameshwar, Singh, Narinder, Setia, R.K., Verma, V.K., Mathur, Ajay, Loshali, D.C. Minakshi and Singh, Gurcharan (2008). “Digital database of drinking water sources and quality parameters in Sangrur district, Punjab
    6 Sharma, P.K., Verma, V.K., Loshali, D.C., Singh, Charanjit, Kumar, Ashok, Chopra, Rajiv, Bhatt, C.M., Singh, Harpinder, Anand, J.R., Kang, G.S. (2005). Resource Atlas of Hoshiarpur District under UNDP-TIFAC project on Information Technology for Sustainable Agriculture in Punjab. Punjab Remote Sensing Centre, Ludhiana
    7 Sharma, P.K., Verma, V.K., Minakshi, Singh, Charanjit, Litoria, P.K. and Coworkers (2005). Resource Atlas of Ludhiana District under UNDP-TIFAC project on Information Technology for Sustainable Agriculture in Punjab. Punjab Remote Sensing Centre, Ludhiana
    8 Sharma, P.K., Nayyar, V.K. and Project Team (2004). Diagnosing Micronutrient related constraints to productivity in Muktsar, Patiala, Hoshiarpur, Amritsar and Ludhiana districts of Punjab. Punjab Remote Sensing Centre in collaboration with Department of Soils, PAU, Ludhiana.
    9 Sharma, P.K., Bhatt, C.M., Verma, V.K., Litoria, P.K. and Sood, Anil (2004). Socio-economic Resource profile for Muktsar district, Punjab, Punjab Remote Sensing Centre, under the UNDP-TIFAC sponsored project “Information Technology for Sustainable Agriculture in Punjab”.
    10 Sharma, P.K., Minakshi, Landuse/land cover-cum-drainage mapping in selected sub watersheds in Kandi area of Punjab (2004). Punjab Remote Sensing Centre, Ludhiana. Scientific Note (PUNSEN-TR-2004).
    11 Sharma, P.K., Singh, Charanjit, Verma, V.K., Litoria, P.K. and Coworkers (2004). Soil Resource Inventory of Ludhiana district, Punjab for Perspectivie Land Use Planning, Project Report on UNDP-TIFAC, Information Technololgy for Sustainable Agriculture in Punjab, Punjab Remote Sensing Centre, Ludhiana.
    12 Parihar, J.S., Sharma, P.K., Sood, Anil, Patel, L.B., Sushma Panigrahy and S.S. Ray (2003). Cropping system Analysis of Punjab state using Remote Sensing and GIS. Punjab Remote Sensing Centre, Ludhiana and Space Applications Centre, ISRO, Ahmedabad. Scientific Report. RSAM/SAC/CS/SR/ 04/2003.
    13 Sharma, P.K., Verma, V.K., Litoria, P.K., Sood, Anil, Loshali, D.C., Kumar, Ashok, Singh, Charanjit, Bhatt, C.M. and Chopra, Rajiv (2003). Resource Atlas of Muktsar District, Punjab Remote Sensing Centre, Ludhiana
    14 Panigrahy, S.; Ray, S.S., Sharma, P.K., Sood, Anil and Patel, L.B. (2002). Cropping System Analysis using Remote Sensing and GIS Bathinda District, Punjab. Scientific Note RSAM/SAC/CS/SN/01/2002. Space Applications Centre, Ahmedabad and Punjab Remote Sensing Centre, Ludhiana.
    15 Chopra, R.; Verma, V.K.; Singh, Charanjit and Sharma, P.K. (2000). Mapping of Waterlogged Area in South-Western Districts of Punjab using Remote Sensing Technology, Technical Report, Punjab Remote Sensing Centre, Ludhiana.
    16 Litoria, P.K., Mathur, A. and Sharma, P.K. and officials from PPCB and CPCB. (2000). Zoning Atlas for siting of Industries based on environmental considerations, Ludhiana district, Punjab by CPCB publication EMAPS/14/ 1999-2000 Environmental Planning and Mapping Series.
    17 Singh, Charanjit; Chopra, R.; Verma, V.K. and Sharma, P.K. (2000). Subwatersheds in Kandi Areas of Haryana (Co-ordinated by Sharma, P.K.). Atlas : Punjab Remote Sensing Centre, Ludhiana.
    18 Singh, Charanjit; Verma, V.K.; Litoria, P.K.; Mathur, A.; Sood, A. and Sharma, P.K. (2000). Site Suitability Evaluation for Mulberry Plantation in Punjab. Technical Report, Punjab Remote Sensing Centre, Ludhiana.
    19 Sharma, P.K., Verma, V.K.; Patel, L.B.; Toor, G.S.; Loshali, D.C.; Singh, Charanjit and Chopra, R. (1999). Natural Resource Survey for Integrated Development using Remote Sensing Technology in Mansa District, Punjab, Technical Report, Punjab Remote Sensing Centre, Ludhiana.
    20 Sharma, P.K.; Manchanda, M.L. and project team. (1999). Integrated resource survey for sustainable development in Talwara block, Hoshiarpur district, Punjab.
    21 Sharma, P.K.; Manchanda, M.L. and project team. (1999). Integrated resource survey for sustainable development in Lehragaga block, Sangrur district, Punjab.
    22 Verma, V.K., Singh, Charanjit; Bhangu, S.S.; Mukhopadhyay, S.S. and Sharma, P.K. (1999). Soil Resource Inventory of Bathinda District, Punjab using Remote Sensing Technology, Technical Report, Punjab Remote Sensing Centre, Ludhiana.
    23 Sharma, P.K.; Singh, Charanjit; Verma, V.K.; Chopra, R. and Minakshi. (1998). Mapping and Monitoring of Salt-affected Soils in Punjab using Remote Sensing Technology. PRSC-TR/98. Punjab Remote Sensing Centre, Ludhiana.
    24 Thomas, A., Patel, L.B., Singh, Charanjit and Sharma, P.K. (1998). Mapping of Gullied/Ravinous Lands along Right Bank of Beas River in Amritsar and Gurdaspur Districts, PRSC Publication.
    25 Chopra, R., Verma, V.K., Thomas, A., Loshali, D.C., Minakshi, Toor, G.S., Sharma, P.K., Murthy, T.V.R., Singh, T.S. and Garg, J.K. (1997). Wetlands of Punjab and Union Territory of Chandigarh. Scientific Report. PRSC/TR/97, PRSC, Ludhiana and SAC, Ahmedabad.
    26 Loshali, D.C., Sood, A. and Sharma, P.K. (1997). Landuse/land cover studies in Lehragaga block, Sangrur district, Punjab. PRSC-TR-97.
    27 Singh, Charanjit; Sharma, P.K.; Verma, V.K. and Chopra, R. (1997). Delineation and Codification of Subwatersheds in Kandi Area of Punjab. Atlas : Punjab Remote Sensing Centre, Ludhiana. PRSC Publication.
    28 Thomas, A., Loshali, D.C. and Sharma, P.K. (1997). Mapping of hydromorphogeology, drainage, and land use/land cover pattern for ground water resource evaluation in Dasuya block, Hoshiarpur district. PRSC-TR-97.
    29 Thomas, Abraham and Sharma, P.K. (1996). Shift of Ravi river and Geomorphological features along its course in Amritsar and Gurdaspur districts of Punjab. PRSC Publi-19.
    30 Litoria, P.K.; Dhaliwal, S.S.; Sharma, P.K. and Singh, Charanjit. (1996). Mapping and Monitoring of Reserved/Protected Forests in Patiala District, Punjab. PRSC Publication.
    31 Chopra, R., Verma, V.K., Sharma, P.K., Singh, Charanjit (1996). Surface flooding and waterlogging in south-western Districts of Punjab. Technical Report. PRSC
    32 Chaurasia, R., Minakshi and Sharma, P.K. (1996). Integrated land use and drainage study in block Saroa, district Nawanshahar, Punjab. PUNSEN-TR-06-96. Punjab Remote Sensing Centre, Ludhiana.
    33 Singh, Charanjit; Chopra, R.; Sood, A.; Litoria, P.K; Verma, V.K. and Sharma, P.K. (1995). Integrated study in Guni Khad Subwatersheds (Tb2a and Tb2b) through Remote Sensing Technology. Punjab Remote Sensing Centre, Ludhiana. PRSC Publication No. 17.
    34 Verma, V.K.; Sood, A.; Singh, Charanjit; Thomas, A; Chopra, R.; Loshali, D.C.; Sharma, P.K.; Ravindran, K.V., Tiwari, A.K. and Kudrat, M. (1995). Integrated Resource Survey for Sustainable Development in Talwandi Sabo Tehsil, Bathinda District, Punjab. PRSC Publ. 18.
    35 Chopra, R., Verma, V.K., Sharma, P.K. and Mahajan, Anuj (1994). Harike Wetland Ecosystem- Its Conservation and Management. PUNSEN-TR-01-94.
    36 Chopra, R., Verma, V.K. and Sharma, P.K. (1994). Sukhna Wetland Ecosystem - Its Conservation and Management. PUNSEN TR-02-94.
    37 Verma, V.K., Chopra, R. and Sharma, P.K. (1994). Kanjli Wetland Ecosystem- Its Conservation and Management. PUNSEN TR-03-94.
    38 Verma, V.K., Chopra, R., Sharma, P.K. and Singh, Charanjit. (1994). Ropar Wetland Ecosystem- Its Conservation and Management. PUNSEN TR-04-94.
    39 Sharma, P.K., Chopra, R., Verma, V.K., Thomas, A., Litoria, P.K., Dhaliwal, S.S., Harikishore, T. and Singh, Charanjit. (1993). Floods in Punjab during July, 1993- Extent, Causes and Mitigative Measures. PUNSEN-TR-02-93.
    40 Chopra, R.; Thomas, A.; Litoria, P.K. and Sharma, P.K. (1993). Report on wastelands in Gurdaspur District, Punjab. Punjab Remote Sensing Centre, Ludhiana. Technical Report No. PUNSEN-TR-06-93.
    41 Dutta, S, Kalubarme, M.H., Ajai, Gull, G.S., Dhaliwal, S.S., Mahajan, Anuj, Sharma, P.K., Mahey, R.K. and Singh, Rajwant (1993). Scientific note on "Cotton Acreage and Production Forecast in Punjab (1992-93), prepared jointly by Punjab Remote Sensing Centre, Ludhiana, Department of Agronomy (PAU) and Space Applications Centre, Ahmedabad.
    42 Sharma, P.K., Singh, Charanjit, Verma, V.K., Chopra, R., Bajwa, S.S., Litoria, P.K. and Thomas, A. (1993). Mapping and monitoring of soil salinity associated with waterlogging using remote sensing technology. PUNSEN-TR-93-3.
    43 Sehgal, J.L.; Bajwa, M.S. and Sharma, P.K. (1992). Soils of Punjab. Res. Bull. NBSS Publ. No. 31, NBSS & LUP, Nagpur.
    44 Chopra, R.; Sharma, P.K.; Litoria, P.K.; Thomas, A. (1992). Report on Wastelands in Patiala district, Punjab. PUNSEN-TR-04-92. Punjab Remote Sensing Centre, Ludhiana.
    45 Mahey, R.K. Singh, Rajwant, Singh, S.S., Gill, G.S. Sharma, P.K., Medhavy, T., Sharma, Tara and Dubey, R.P. (1992). Scientific note on "Forecast of district-wise wheat production for Punjab (1990-91) using IRS LISS-I data", prepared jointly by Punjab Remote Sensing Centre, Ludhiana, Department of Agronomy (PAU) and Space Application Centre, Ahmedabad.
    46 Chopra, R.; Thomas, A.; Litoria, P.K. and Sharma, P.K. (1992). Report on wastelands in Jalandhar district, Punjab. Punjab Remote Sensing Centre, Ludhiana. Technical Report No. PUNSEN-TR-04-93.
    47 Mahey, R.K. Sridhar, V.N.; Pokharna, S.S.; Sidhu, S.S.; Singh, Rajwant, Dhadwal, V.K.; Sharma, P.K.; Parihar, J.S. (1990). Remote sensing based wheat acreage estimation in Punjab. In : Status Report on RSAM Project "Crop acreage and production estimation". Space Applications Centre (ISRO), Ahmedabad. Oct. 1990.
    48 Bajwa, S.S., Singh, Charanjit, Sharma, A.K. and Sharma, P.K. (1989). Inventory of the area affected by floods in Punjab during September, 1988. Technical Report No. PUNSEN-01-89.
  • Setia R., Smith P., Marschner P., Gottschalk P., Baldock J., Verma V.K., Setia D., & Smith J. (2012). Simulation of salinity effects on past, present, and future soil organic carbon stocks. Environmental Science & Technology, 46(3) p.1624.

  • Setia, R., Verma, V.K., & Sharma P.K. (2012). Soil informatics for evaluating and mapping soil productivity index in an intensively cultivated area of Punjab, India. Journal of Geographic Information System, 4:71-76.

  • Setia R., Marschner P., Baldock J., Chittleborough D., Verma V.K., (2011). Relationships between carbon dioxide emission and soil properties in salt-affected landscapes. Soil Biology & Biochemistry, 43:667-674.

  • Singh Harpinder, Singh Amardeep, Litoria P.K. (2011) Creation of Wetland Inventory of Kapurthala district in Punjab using Geoinformatics, International Conference on emerging trends in Computer Science and Information Technology, 26th February, Gulzar Group of Institutes, Khanna, Ludhiana.

    Abstract - Developments in Information and Communication technology have given rise to Geoinformatics which comprise of frontier technologies like Remote Sensing, Geographic Information Systems (GIS), Photogrammetry and Global Positioning System (GPS). These tools have now come in handy to address the issues on natural resource management. The study aimed at the generation of an inventory of all the Inland Wetlands in Kapurthala district using multi date IRS-P6 LISS-III digital satellite data. By applying band algebra on the four bands of the satellite data, five indices have been generated. These indices enhance the data, and delineation of water spread, vegetation extent, turbidity information of different categories of wetlands for pre and post monsoon season can be easily done. This spatial information can be very useful for wetlands monitoring and management and go as input to various government projects. When presented in map form, it allows better perception, visualization of spatial patterns and their relationship with the neighboring areas.

  • Tur, N.S., Singh, Amardeep., Mehra, Deepak., Singh, Harpinder., Minakshi, Kumar, Rajneesh and Devaser, Virrat. (2011) Mapping of Urbal Sprawl around Sahibzada Ajit Singh Nagar. Indian J. Ecol. 38(2) : 155-162

    Abstract: Mapping of extent of urban sprawl and by monitoring the temporal changes, the impact of changing land use on land, ecology and environment system can be assessed. GIS and remote sensing data along with collateral data help in analysing the growth, pattern and extent of sprawl. IRS 1A, IRS 1D LISS III, IIRS P6 and Cartosat-1 satellite data was used for mapping of urban sprawl of Sahibzada Ajit Singh Nagar (Mohali). The built-up area in the year 1990 was commuted to be 19.88 km2 . In 1990, nearly 74.3 km2 area was cropped whereas around 4.41 km2 area was under fallow land or wastelands. The built-up area in 2001 was 30.0 km2 and crop land was decreased by 5.8 km2. Nearly, 43 per cent increase in urban area was noticed between the years 2001 to 2006 as area under built-up increase to 44.22 km2 in year 2006 which further increased by 13.5 per cent to 50.20 km2 in the year 2007.

  • Singh Harpinder, Loshali, D.C., Singh Narinder, Mehra Deepak (2010) Solid Waste dumping site selection around Barnala city in Punjab using Geoinformatics, National Conference on Smart Energy-Generation, Promotion & Conservation 2010, 15-16 January, Chitkara Institute of Engineering, Rajpura.

    Abstract- Solid waste disposal is one of the major problems for a fast growing developed city. Barnala city being the headquarter of newly created Barnala district has been selected as the study area as it is growing rapidly both in population as well as in unprecedented expansion of industrial units. Identification of suitable sites (open collection points or land filling sites) for safe disposal of solid waste is an ongoing or dynamic process as the site has to change frequently along with the changing land use pattern. It is very difficult to identify periodically the suitable dumping sites through traditional surveying as it takes a lot of time and resources. In this situation, by using Remote Sensing & GIS technology, we can identify the suitable area for disposal of solid waste to evade its perilous impact on the surrounding ecosystem, from spatial and non-spatial data analysis supplemented with the required ground truth and other ancillary information, which in turn would save a lot of time, money and labour. This paper concentrates only on households, trading and construction waste.

  • Sood Anil, Choudhury, B U. Ray, S S., Jalota, S K., Sharma, P K and Panigrahy, S. (2009) Impact of cropping pattern changes on the exploitation of water resources: A remote sensing and GIS approach. J. Indian Society of Remote Sensing.37 (3): 483-491.

  • Sood, Anil, Sharma, P K., Tur, N S and Nayyar, V K. (2009) Micronutrient status and their spatial variability in soils of Muktsar district of Punjab – A GIS approach. J Indian Soc Soil Sci. 57(3): 300-306.

  • Singh Harpinder, Krishan Kewal, Litoria, P.K. (2009) Creation of a Village Information System of Moga district in Punjab using Geoinformatics, National Conference on Recent Developments in Computing and its Applications, 12-13 August, Jamia Hamdard University, New Delhi.

    Abstract: Majority of the rural population in India is living in the pre-independence conditions and the economic and technological advances have widened the rural-urban divide. Developments in Information and Communication technology have given rise to Geoinformatics which comprise of frontier technologies like Remote Sensing, Geographic information systems (GIS), photogrammetry and Global positioning system (GPS). Geoinformatics has the potential to enrich rural lives and bring revolutionary changes. The objective is to bring information collected from diverse sources onto a common platform and subsequently generate meaningful information by integrating non-spatial data with the thematic maps, using geography as the common feature so that it can be used for making effective decisions for various planning problems. In this study spatial data regarding the Land use of Moga district has been extracted from IRS LISS III Satellite data using supervised classifier Support vector machine (SVM), other linear spatial data has been visually interpreted. Non-spatial tabular data collected from various government departments has been linked with the village boundary information. The collective spatial and non-spatial information can be very useful for natural resource management, disaster management, infrastructure development, health management and various government projects as information when presented in a map format allow a better perception, visualization of spatial patterns and their relationship with the neighboring areas.

  • Mathur.A and Foody G.M. (2008) Multiclass and Binary SVM Classification: Implications for Training and classification Users. IEEE Transactions on Geoscience and Remote Sensing, Vol. 5 (2):1-5.

    Abstract: Support vector machines (SVMs) have considerable potential for supervised classification analyses, but their binary nature has been a constraint on their use in remote sensing. This typically requires a multiclass analysis be broken down into a series of binary classifications, following either the one-against-one or one-against-all strategies. However, the binary SVM can be extended for a one-shot multiclass classification needing a single optimization operation. Here, an approach for one-shot multi- class classification of multispectral data was evaluated against approaches based on binary SVM for a set of five-class classifications. The one-shot multiclass classification was more accurate (92.00%) than the approaches based on a series of binary classifications (89.22% and 91.33%). Additionally, the one-shot multi- class SVM had other advantages relative to the binary SVM-based approaches, notably the need to be optimized only once for the parameters C and 7 as opposed to five times for one-against-all and ten times for the one-against-one approach, respectively, and used fewer support vectors, 215 as compared to 243 and 246 for the binary based approaches. Similar trends were also apparent in results of analyses of a data set of larger dimensionality. It was also apparent that the conventional one-against-all strategy could not be guaranteed to yield a complete confusion matrix that can greatly limit the assessment and later use of a classification derived by that method.

  • Mathur A, Foody Giles (2008) Crop classification by a SVM with intelligently selected training data for an operational application. International Journal of Remote Sensing. 29 (8):

    Abstract: The accuracy of a supervised classification is dependent to a large extent on the training data used. The aim in training is often to capture a large training set to fully describe the classes spectrally, commonly with the requirements of a conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes, especially if using a support vector machine (SVM) as the classifier. An SVM seeks to fit an optimal hyperplane between the classes and uses only some of the training samples that lie at the edge of the class distributions in feature space (support vectors). This should allow the definition of the most informative training samples prior to the analysis. An approach to identify informative training samples was demonstrated for the classification of agricultural classes in south-western part of Punjab state, India. A small, intelligently selected, training dataset was acquired in the field with the aid of ancillary information. This dataset contained the data from training sites that were predicted before the classification to be amongst the most informative for an SVM classification. The intelligent training collection scheme yielded a classification of comparable accuracy, 91%, to one derived using a larger training set acquired by a conventional approach. Moreover, from inspection of the training sets it was apparent that the intelligently defined training set contained a greater proportion of support vectors (0.70), useful training sites, than that acquired by the conventional approach (0.41). By focusing on the most informative training samples, the intelligent scheme required less investment in training than the conventional approach and its adoption would have reduced the total financial outlay in classification production and evaluation by 26%. Additionally, the analysis highlighted the possibility to further reduce the training set size without any significant negative impact on classification accuracy.

  • Jalota, S K., Buttar, G S., Sood, Anil, Chahal, G B S., Ray, S S and Panigrahy, S. (2008) Effects of sowing date, tillage and residue management on productivity of cotton (Gossypium hirsuitum L.) – Wheat (Triticum aestivum L.) system in North West India. Soil Till Res. 99:76-83.

  • Tur, N. S., Minakshi, Sharma P.K., Sood A.K., Setia R.K. and Nayyar V.K. (2008) Mapping of macronutrient status and multi macronutrient deficiency in Patiala district using frontier technology. Soils and Crops 1-6,vol.18(1)

  • Bhatt, C.M.; Litoria, P.K. and Sharma, P.K. (2008). Geomorphic Signatures of Active Tectonics in Bist Doab Interfluvial Tract of Punjab, NW India. Journal of Indian Society of Remote Sensing (Accepted – Likely to be Published in December 2008 Issue).
  • Sharma, P.K.; Bhatt, C.M.; Verma, V.K.; Sood, Anil; Minakshi; Singh, Narinder; Litoria, P.K.; Choudhury, B.U. and Setia, R.K. (2008). Ground Water Resources Development in Punjab. NNRMS-Bulletin, NNRMS(B) – 32 (February 2008), pp 56-65.

  • Verma, V.K., Setia, R.K., Sharma, P.K., & Harpinder Singh (2008). Geoinformatics as a tool for the assessment of the impact of ground water quality of irrigation on soil health. Journal of Indian Society of Remote Sensing, 36(3):273-21.

  • Verma, V.K., Setia, R.K. & Sharma, P.K. (2007). Ionic composition and hazards of poor quality waters for irrigation in southwestern part of Punjab. Hydrology Journal, 30(3-4) : 91-101.

  • Dash J, Mathur A, Foody GM, Curran P, Chipman and Lillesand (2007) Land cover classification using multi-temporal MERIS vegetation indices. The International Journal of Remote Sensing 28 (6): 1137-1159

    Abstract: The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional- to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands.

  • Jalota S K., Sood, Anil, Vitale JD and Srinivasan, R. (2007) Simulated crop yields response to irrigation water and economic analysis: increasing irrigation water use efficiency in the Indian Punjab. Agron. J. 99:1073-1084.
  • Foody GM and Mathur A (2006). The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment 103(2):179-189.

    Abstract: The accuracy of a supervised image classification is a function of the training data used in its generation. It is, therefore, critical that the training stage of a supervised classification is designed to provide the necessary information. Guidance on the design of the training stage of a classification typically calls for the use of a large sample of randomly selected pure pixels in order to characterise the classes. Such guidance is generally made without regard to the specific nature of the application in-hand, including the classifier to be used. The design of the training stage should really be based on the classifier to be used since individual training cases can vary in value as can any one training set to a range of classifiers. It is argued here that the training stage can be designed on the basis of the way the classifier operates and with emphasis on the desire to separate the classes rather than describe them. An approach to the training of a support vector machine (SVM) classifier that is the opposite of that generally promoted for training set design is suggested. This approach uses a small sample of mixed spectral responses drawn from purposefully selected locations (geographical boundaries) in training. The approach is based on mixed pixels which are normally masked-out of analyses as undesirable and problematic. A sample of such data should, however, be easier and cheaper to acquire than that suggested by conventional approaches. This new approach to training set design was evaluated against conventional approaches with a set of classifications of agricultural crops from satellite sensor data. The main result was that classifications derived from the use of the mixed spectral responses and the conventional approach did not differ significantly, with the overall accuracy of classifications generally 92%.

  • Foody GM, Mathur A, Hernandez CS and Boyd DS (2006) Training set size requirements for the classification of a specific class. Remote Sensing of Environment 104(1):1-14.

    Abstract: The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at 95% and 97% from the user's and producer's perspectives respectively.

  • Kaur, G., Singh, J. and Sood Anil. (2006) Power generation potential from crop biomass in Punjab. Agricultural Engineering Today. 30(5-6):40-46.

  • Jalota, S K., Sood, Anil, Chahal, GBS and Choudhury, BU. (2006) Crop water productivity of cotton (Gossypium hirsuitum L.)-wheat (Triticum aestivum L.) system as influenced by deficit irrigation, soil texture and precipitation. Agric. Water Manage.84:137-146.

  • Minakshi, Mehra Deepak, Sharma. P K., Tur, N. S., Singh, Harpinder and Kang,G.S. 2006.Assessment , Management and Spatial distribution of ground water for irrigation in RupNagar District of Punjab(India).Journal of Enviornmental Science and Engineering 91-96,vol. 48(2)

  • Bhatt C.M.; Sharma P.K.; Singh R.K.; Verma V.K. and Litoria P.K. (2006). “Mapping and Monitoring of Waterlogged Areas in parts of Muktsar Block (Punjab) using Remote Sensing and GIS Approach”. Indian Journal of Ecology and Environment, 33: 35-39.

  • Verma, V.K.; Setia, R.K.; Sharma, P.K.; Singh, Harpinder and Litoria, P.K. (2006). Geoinformatics Support for Information Based Agriculture : A Case Study of Arid Tract of Punjab. Proc. CD of “Inernational Symposium on Geospatial Database for Sustainable Development”, held at Goa, India (September 27-30, 2006). Paper No. RS-A-6. (The paper was awarded Best Poster Paper Award).

  • Singh, Amarvir and Loshali, D.C. (2005) Land-use mapping in Kotla Khad using remote sensing technique. J. Envir. Ecol. 2005, 23(1), 7-12.

  • Ray, S S., Sood, Anil, Das, G., Panigrahy, S., Sharma, P K and Parihar, J S. (2005) Use of GIS and Remote Sensing for crop diversification – a case study for Punjab State. J. Indian Society of Remote Sensing. 33(1):181-188.

  • Ray, S S., Sood, Anil, Panigrahy, S. and Prihar, J S. (2005) Derivation of indices using remote sensing data to evaluate cropping systems. J. Indian Soc. Remote Sensing. 33(4):475-481.

  • Minakshi , Sharma, P.K., Kaur, Amandeep and Shelly Vanita (2005). Satellite based study of Land transformation in Ludhiana District, Punjab. Journal of Indian Society of Remote Sensing. 33 (1): 63-68.

  • Minakshi , Tur, N.S Nayyar, V.K., Sharma, P.K. and Sood,. A.K; (2005) Spatial distribution of Micronutrients as affected by soil physico-chemical properties in soils of Patiala District- A GIS Approach. Journal of Indian Society of Soil Science.

  • Verma, V.K., Setia, R.K., Sharma, P.K., Singh, Charanjit & Kumar Ashok (2005). Pedospheric variations in distribution of DTPA- extractable micronutrients in soils developed on different physiographic units in central parts of Punjab, India. International Journal of Agriculture and Biology, 7(2): 243-246.

  • Verma, V.K., Patel, L.B., Toor G.S., & Sharma P.K. (2005). Spatial distribution of macronutrients in soils of arid tract of Punjab, India. International Journal of Agriculture and Biology, 7(2):295-297.

  • GM Foody and Mathur A (2004) A Relative evaluation of multi-class classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing 42 (6):1335-1343.

    Abstract: Support vector machines (SVMs) have considerable potential as classifiers of remotely sensed data. A constraint on their application in remote sensing has been their binary nature, requiring multiclass classifications to be based upon a large number of binary analyses. Here, an approach for multiclass classification of airborne sensor data by a single SVM analysis is evaluated against a series of classifiers that are widely used in remote sensing, with particular regard to the effect of training set size on classification accuracy. In addition to the SVM, the same datasets were classified using a discriminant analysis, decision tree, and multilayer perceptron neural network. The accuracy statements of the classifications derived from the different classifiers were compared in a statistically rigorous fashion that accommodated for the related nature of the samples used in the analyses. For each classification technique, accuracy was positively related with the size of the training set. In general, the most accurate classifications were derived from the SVM approach, and with the largest training set the SVM classification was significantly (p < 0.05)more accurate (93.75%) than that derived from the discriminant analysis (90.00%) and decision tree algorithms (90.31%). Although each classifier could yield a very accurate classification, > 90% correct, the classifiers differed in the ability to correctly label individual cases and so may be suitable candidates for an ensemble-based approach to classification.

  • Foody G.M, and Mathur, A (2004) Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote sensing of Environment 93 (1-2):107-117.

    Abstract: Conventional approaches to training a supervised image classification aim to fully describe all of the classes spectrally. To achieve a complete description of each class in feature space, a large training set is typically required. It is not, however, always necessary to have training statistics that provide a complete and representative description of the classes, especially if using nonparametric classifiers. For classification by a support vector machine, only the training samples that are support vectors, which lie on part of the edge of the class distribution in feature space, are required; all other training samples provide no contribution to the classification analysis. If regions likely to furnish support vectors can be identified in advance of the classification, it may be possible to intelligently select useful training samples. The ability to target useful training samples may allow accurate classification from small training sets. This potential for intelligent training sample collection was explored for the classification of agricultural crops from multispectral satellite sensor data. With a conventional approach to training, only a quarter of the training samples acquired actually made a positive contribution to the analysis and allowed the crops to be classified to a high accuracy (92.5%). The majority of the training set, therefore, was unnecessary as it made no contribution to the analysis. Using ancillary information on soil type, however, it would be possible to constrain the training sample acquisition process. By limiting training sample acquisition only to regions with a specific soil type, it was possible to use a small training set to classify the data without loss of accuracy. Thus, a small number of intelligently selected training samples may be used to classify a data set as accurately as a larger training set derived in a conventional manner. The results illustrate the potential to direct training data acquisition strategies to target the most useful training samples to allow efficient and accurate image classification.

  • Mathur A, Foody G.M (2004) Land cover classification by support vector machine: towards efficient training IGARSS '04 held at Anchorage, Alaska (USA). In proceeding of: Geoscience and Remote Sensing Symposium, 2004: 10/2004; DOI: 10.1109/IGARSS.2004.1368508ISBN: 0-7803-8742

    Abstract: The accuracy of supervised classification is dependent to a large extent on the input training data. In general, the analyst aims to capture a large training set to fully describe the classes spectrally with the conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes if using a support vector machine (SVM) as the classifier. A key attraction of the SVM based approach to classification is that it seeks to fit an optimal hyperplane between the classes and since it uses only the training samples that lie at the edge of the class distributions in feature space (support vectors) it may require only a small training sample. The paper shows the potential of SVM of using only a fraction of the training data (support vectors) collected by the usual random scheme for a study carried in the south western part of Punjab state of India.

  • Minakshi , Tur, N.S, Setia, R.K. and Sharma, P.K. (2004). An appraisal of quality of under ground water in Fatehgarh Sahib District of Punjab. Indian Journal of Ecology 31 (2):133-136.

  • Bhatt, C.M.; Litoria, P.K.; Sharma, P.K. and Sidhu, Namdev (2004). Geographical Information System (GIS) Application in Parliamentary Elections-2004 in Muktsar District, Punjab (India). Poster Paper Presented at 24th ISRS National Convention and National Symposium on Converging Space Technologies for National Development, Organised by RRSSC, Jodhpur and B.M. Birla Science & Technology Centre, Jaipur at Jaipur from November 3-5, 2004.

  • Litoria, P.K.; Verma, V.K.; Loshali, D.C.; Sharma, P.K.; Sood, Anil; Patel, L.B. and Manchanda, M.L. (2003). Resource Constraints Mapping in part of Siwalik Hills (Punjab) Using Remote Sensing and GIS. Proc. CD of National Symposium on Resource Management with Spatial Reference to Geoformalizing and Decentralized Planning ISPRS WC/11/3 workshop on integrated Monitoring System at CESS, Thiruvanthapuram, December 9-12, 2003.

  • Chopra, Rajiv; Litoria, P.K.; Verma, V.K.; Sharma, P.K. and Sharma, K.P. (2003). Remote Sensing and GIS Applications for Urban Landuse Mapping - A case study of Chandigarh. Proc. CD of National Symposium on Resource Management with Spatial Reference to Geoformalizing and Decentralized Planning ISPRS WC/11/3 workshop on integrated Monitoring System at CESS, Thiruvanthapuram, December 9-12, 2003.

  • Bhatt, C.M.; Singh, R.K., Litoria, P.K. and Sharma, P.K. (2003). Use of Remotely Sensed Data and GIS Techniques for Assessment of Waterlogged and Salt-Affected Area tehsilwise in Muktsar District of Punjab. Proc. CD of 7th GSDI Conference, Bangalore.

  • Sharma, P.K., Verma, V.K., Loshali, D.C., and Litoria, P.K. (2003). Land use planning for sustainable land management – Role of remote sensing and GIS. SLUB News 1: 7-9.

  • Sood, Anil, Chhibba, I M., Verma, V K, Sharma, P K and Nayyar, V K. (2002) Soil Fertility in Different Physiographic Units in Arid Track of Punjab. Indian J. Agricultural Sciences. 72(6):329-33.

  • Chopra, Rajiv, Verma V.K. & Sharma P.K. (2001). Mapping, monitoring and conservation of Harike wetland (Punjab), India using remote sensing technology. International Journal of Remote Sensing, 22/1:89-98.

  • Verma,V.K., Sharma,P.K., Patel, L.B., Loshali,D.C. and Toor,G.S. (2000) Natural Resource Management for Sustainable Development using Remote sensing Technology- a case study. NNRMS(B) 24:41-46.

  • Dhaliwal, S.S.; Litoria, P.K.; Singh, Charanjit and Sharma, P.K. (2000) Degradation of Reserved/Protected Forests in District Patiala (Punjab) : A Vegetation Change Detection Study Using Remote Sensing Technology. Quarterly International Journal of Ecology, Environment and Conservation, Vol. 6(2), pp. 153-157.

  • Verma, V.K.; Litoria, P.K.; Loshali, D.C. and Sharma, P.K. (2000). Land Degradation in Siwaliks : Its Assessment and Management Using Remote Sensing Technology. In : Fifty Years of Research on Sustainable Resource Management in Shivaliks (Editors : Mittal, S.P.; Aggarwal, R.K. and Samra, J.S.). Central Soil and Water Conservation Research & Training Institute, Research Centre, Chandigarh, India. pp. 35-46.

  • Minakshi, Chaurasia, R. and Sharma, P.K. (1999). Landuse/land cover mapping and change detection study of block Dehlon, district, Ludhiana using Satellite data. J. Indian Soc. Rem. Sensing 27 (2) : 115-122.

  • Mathur A, Litoria PK, Sharma PK and Kalubarme MH (1997) Database Management and Analysis for CAPE project using Remote Sensing and GIS: An innovative approach. Photonirvachak: journal of Indian Society of Remote Sensing.

    Abstract: The present study attempts to conceptualise an approach to integrate the remotely sensed spatial and non-spatial data generated over the years under Crop Acreage and Production Estimation (CAPE) project through GIS for their easy retrieval and comparison; and to develop a program in dBASE to calculate crop acreage using non-spatial attributes imported from GIS. The “Crop Information System” thus developed would help the planners in analysis/comparison of the database related to crops over the years.

  • Litoria PK, Mathur A, Singh CK and Sharma PK (1997). Site Suitability Evaluation for Sewage Treatment Plants in Phillaur and Phagwara Townships (Punjab) using Remote Sensing and GIS. Photonirvachak: journal of Indian Society of Remote Sensing, Dehradun, Vol. 25(4).

    Abstract: Level-II urban land use information available in this Town and Country Planning maps, Survey of India toposteets for Phillaur and Phagwara towns and the land use information generated through visual interpretation of satellite data was digitized, integrated and analysed using PAMAP GIS. The land use map of the two towns suggest that the wastelands located near the point of present disposal can be utilised for siting sewage treatment plants in both the towns. The STP sites suggested were away from the thickly habitated area. It was observed that some of the areas earmarked for locating STP’s, were partially brought under habitation before the execution of the project. Hence, it is necessary that planning and execution of such projects should be done on a real time basis so that the sites identified for locating STP’s are not brought under other land uses.

  • Sood, Anil, Verma, V.K., Thomas, A., Sharma, P.K. and Brar, J.S. (1997) Assessment and Management of Underground Water Quality in Talwandi Sabo Tehsil of Bathinda district, Punjab. J. Indian Soc. Soil Sci. 46: 421-426.

  • Chaurasia, R. Minakshi, Singh Charanjit and Loshali, D.C. (1996) Watershed landuse study in siwaliks - A case study of block Saroa district Hoshiarpur. Nat. Geog. J. of india. 42 (1 & 2) : 47-52.

  • Chaurasia, R., Minakshi, Singh, Charanjit and Loshali, D.C. (1996) Watershed landuse study in Siwaliks - a case study of block Saroa distt. Hoshiarpur. Nat. Geog. J. India. 42 (1 & 2) 47-52.

  • Chaurasia, R., Loshali, D.C., Dhaliwal, S.S., Minakshi, Sharma, P.K., Kudrat, M. and Tiwari, A.K. (1996) Landuse change analysis for Agricultural Management - A case study of Tehsil Talwandi Sabo. J. Indian Soc. Rem. Sens. 24 (2) : 115-123.

  • Sharma, P.K., Chopra, R., Verma, V.K., & Thomas, A. (1996). Flood management using remote sensing technology:The Punjab (India) Experience. International Journal of Remote Sensing, 17(17):3511-21.

  • Chaurasia, R., Loshali, D.C., Dhaliwal, S.S. and Sharma, P.K. (1995) Assessing the change in agricultural land in Bathinda district, Punjab through Remote Sensing Techniques. Nat. Geog. J. India. 41 (1) : 39-44.

  • Chaurasia, R., Sharma, P.K., Loshali, D.C. and Minakshi. (1995) The science of Remote Sensing and its application in Natural Resource Management. Science and Culture. 61 (1-3) : 11-15.

  • Minakshi, Chaurasia, R., Loshali, D.C. and Sharma, P.K. (1995) Drainage characteristics of some of the streams of block Saroa, District Hoshiarpur, Punjab. Indian J. Soil and Water Conservation. 39 (1 & 2) : 32-36.

  • Chaurasia,R. Minakshi and Sharma,P.K. (1995) Climate and its variation in Ludhianan. Indian J.Soil. Cons. 23 (1) : 1-5.

  • Minakshi, Chaurasia,R. Loshali,D.C and Sharma,P.K. (1995) Drainage characteristics of some of the streams of block Saroa District Hoshiarpur, Punjab.J.Soil and Water Conserv. 39 (1 & 2) : 32-36.

  • Loshali, D.C. and Singh, R.P. (1992) Partitioning of rainfall by three Central Himalayan forests. For Ecol. Manage. 53 : 99-105.

  • Loshali, D.C., Upadhyay, V.P., Singh, R.P. and Singh, J.S. (1990) Overland flow and soil movement from forests in Kumaun Himalaya. Can. J. For. Res. 20 : 606-608.

Visitor No. (01-01-2016 onwards) :-