Mapping of Fuel wood Trees using Geoinformatics

T.V. Ramachandra a,b,c,*


  1. Ramachandra TV, Vamseekrishna S and Shruthi BV. Decision support system to assess regional biomass energy potential. International Journal ofGreen Energy 2004; 1(4):1-22.
  2. Ramachandra TV, Joshi NV and Subramanian DK. Present and prospective role of bioenergy in regional energy system. Renewable and sustainable energy reviews 2000; 4: 375-430.   
  3. Vimal OP and Tyagi PD. Energy from biomass  – an Indian Experience. New Delhi, India: Gricole Publishing Academy, 1984.
  4. Vimal OP and Tyagi PD. Fuel wood from wastelands. New Delhi, India: Yatan Publication, 1986.
  5. Ramachandra TV and Shruthi BV. Spatial mapping of renewable energy potential. Renewable and Sustainable Energy Reviews2007; 11(7):1460-1480.
  6. Brown SL, Schroeder P, Kern JS. Spatial distribution of biomass in forests of the eastern USA. Forest Ecology  Management 1999;123: 81–90.
  7. Brown S. Measuring carbon in forests: current status and future challenges. Environment  Pollution  2002; 116: 363–372.
  8. Zheng D, Rademacher J, Chen J, Crow T, Bresee M, Le Moine J, Ryu SR. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment 2004; 93: 402–411.
  9. Lillessand TM and Kiefer RW. Remote Sensing and Image Interpretation. New York: John Wiley and Sons Inc., 1994.
  10. Wang L, Sousa WP, Gong P and Biging GS. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment 2004. 91:432–440.
  11. Mougin E, Proisy C, Marty G, Fromard F, Puig H,. Betoulle JL and Rudant JP. Multifrequency and multipolarisation radar backscattering from mangrove forests. IEEE Transactions on Geoscience and Remote Sensing 1999; 37(1):94–102.
  12. Kovacs JM, Wang JF, and Flores-Verdugo F.  Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific. Estuarine Coastal and Shelf Science2005; 62:377–384.
  13. Ramachandra TV and Kumar U. 2006. Relevance of Hyperspectral Data for Sustainable Management of Natural Resources, GIS and  Development 10(4):30-36 (India).
  14. Gonzalez RE and Woods RE. Digital Image Processing. New York: Addison-Wesley Publication, 1998.
  15. Ramachandra TV, Kamakshi G. and Shruthi BV. Bioresource Status in Karnataka. Renewable and Sustainable Energy Reviews 2004; 8 (1): 1-47.
  16. Brown S, Gillespie AJR and Lugo AE.. Biomass of tropical forests of south and southeast Asia. Canadian Journal of Forest Research 1991; 21: 111-117.
  17. Alexeyev V, Birdsey R, Stakanov V, and Korotkov I. Carbon in vegetation of Russian forests: methods to estimate storage and geographical distribution. Water. Air and Soil Pollution 1995; 82:271-282.
  18. Turner D P, Koepper G.J, Harmon ME and Lee JJ. A carbon budget for forests of the conterminous United States. Ecological Applications 1995; 5:421-436.
  19. Schroeder P, Brown S, Mo J, Birdsey R and Cieszewski. C. Biomass estimation for temperate broadleaf forests of the United States using inventory data. Forest Science 1997; 43: 424-434.
  20. Archer GR. Application of the geographic information system and remote sensing for national biomass energy supply studies. Pacific and Asian Journal of Energy 1995; 5(1): 29-41.
  21. Kelly M, Shaari D, Guo Q and Liu D. A Comparison of Standard and Hybrid Classifier Methods for Mapping Hardwood Mortality in Areas Affected by “Sudden Oak Death”. Photogrammetric Engineering and Remote Sensing 2004; 70(11): 1229-1240.
  22. Senay GB, Lyon JG, Ward AD and Nokes SK. Using High Spatial Resolution Multispectral data to Classify Corn and Soybean Crops. Photogrammetric Engineering and Remote Sensing 2000; 66(3): 319-328.
  23. Ransey III EW, Nelson GA, Sapkota SK, Seeger EB and Martella KD. Mapping Chinese Tallow with Colour-Infrared Photography. Photogrammetric Engineering and Remote Sensing 2002; 68(3): 251-256.
  24. Wang G, Gertner G, Xiao X, Wente S and  Anderson AB. Appropriate Plot Size and Spatial resolution for Mapping Multiple Vegetation types. Photogrammetric Engineering and Remote Sensing 2001; 67(5): 575-584.
  25. McCormick CM. Mapping Exotic Vegetation in the Everglades from Large-Scale Aerial Photographs. Photogrammetric Engineering and Remote Sensing1999; 65(2): 179-184.
  26. Kumar JS, Arockiasami DI and Britto JS. Estimates of current status of forest types in Kolli hill using remote sensing. Journal of the Indian Society of Remote Sensing 2000; 28(2 and 3): 141-151.
  27. Liu W, Gopal S and Curtis E. woodcock. Uncertainty and Confidence in Land Cover Classification Using a Hybrid Classifier Approach. Photogrammetric Engineering and Remote Sensing 2004; 70(8):  963-972.
  28. Gao J, Chen H, Zhang Y and Zha Y. Knowledge-Based Approaches to Accurate Mapping of Mangroves from Satellite Data. Photogrammetric Engineering and Remote Sensing 2004; 70(11): 1241-1248.
  29.  Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S and .Zhu YH. Accuracy comparison of various remote sensing data sources inthe retrieval of forest stand attributes. Forest Ecol. Management 2000; 128: 109–120.
  30. Tomppo E.  Satellite image-based national forest inventory of Finland,  Int. Arch. Photogramm. Remote Sensing 1991;. 28: 419–424.
  31. Poso S, Häme T and Paananen R. A method of estimating the stand characteristics of a forest compartment using satellite imagery.  SilvaFennica  1984; 18: 261–292.
  32.  Ardö J.  Volume quantification of coniferous forest compartments using spectral radiance recorded by LANDSAT Thematic Mapper. International .Journal of . Remote Sensing. 1992;  13: 1779–1786.
  33. Tokola T. and Heikkilä J. A priori site quality information in satellite image based forest inventory.  Silva Fennica 31; 67–78.
  34. Hyyppä J, Pulliainen J, Hallikainen M, and Saatsi A. Radar-derived standwise forest inventory, IEEE Trans. Geosci. Remote Sensing 1997; 35: 392–404,
  35. Garg VK. Leguminous Trees for the Rehabilitation of Sodic Wasteland in Northern India. Restoration Ecology 1999; 7(3): 281-287.
  36. Chaveg PS, Sides SC, Anderson JA. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT Panchromatic. Photogrammetric Engineering and Remote Sensing 1991; 57 (3): 295-303.
  37. Jensen JR. Merging different types of remotely sensed data for effective visual display. Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ: 2nd ed. Prentice-Hall, 1996.
  38. Pellemans AH, Jordans RW, Allewijn R. Merging multispectral and panchromatic SPOT images with respect to the radiometric properties of the sensor. Photogrammetric Engineering and Remote Sensing 1993; 59 (1): 81-87.
E-mail   |   Sahyadri   |   ENVIS   |   GRASS   |   Energy   |   CES   |   CST   |   CiSTUP   |   IISc   |   E-mail