Environmental Earth Sciences

, Volume 64, Issue 3, pp 731–741 | Cite as

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

Original Article

Abstract

Since the launch of the first satellite in 1972, ecologists have been equipped with new tools to address the degradation of tropical forests, previously limited by field-based methods. This article is a review of the state of remote sensing technology in characterizing the degradation of tropical forest. The factors responsible for the structural and functional degradation of the tropical forest and its likely impacts are described in view of generating remote sensing based inputs. In order to assess the degradation and utility of geo-informatics tools, 32 parameters are identified. The research developments at different levels of information extraction from the historic to recent periods are elaborated, and future challenges are predicted. The article concludes that an additional momentum of research is required to answer many unresolved questions of tropical forest degradation.

Keywords

Remote sensing Tropical forest Degradation Scale Multispectral Hyperspectral 

References

  1. Anaya JA, Chuvieco E, Palacios-Orueta A (2009) Aboveground biomass assessment in Colombia: a remote sensing approach. For Ecol Manag 257(4):1237–1246Google Scholar
  2. Anitha K, Balasubramanian P, Prasad SN (2007) Tree Community structure and regeneration in Anaikatty hills, Western Ghats. Indian J For 30:315–324Google Scholar
  3. Anitha K, Joseph S, Ramasamy EV, Prasad SN (2009) Changes in structural attributes of plant communities along disturbance gradients in a dry deciduous forest of Southern India. Environ Monit Assess 155(1):393–405CrossRefGoogle Scholar
  4. Anitha K, Joseph S, Chandran RJ, Ramasamy EV, Prasad SN (2010) Tree species diversity and community composition in a human-dominated tropical forest of Western Ghats biodiversity hotspot, India. Ecol Complex 7(2):217–224Google Scholar
  5. Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance—the SAIL model. Rem Sen Environ 64(3):234–253CrossRefGoogle Scholar
  6. Asner GP, Martin RE (2008) Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels. Rem Sen Environ 112:3958–3970CrossRefGoogle Scholar
  7. Asner GP, Martin RE (2009) Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front Ecol Environ 7:269–276CrossRefGoogle Scholar
  8. Asner GP, Vitousek PM (2005) Remote analysis of biological invasion and biogeochemical change. Proc Natl Acad Sci USA 102:4383–4386CrossRefGoogle Scholar
  9. Asner GP, Michael P, Michael K, Rodrigo P Jr, Jose NMS, Johan CZ (2002) Estimating canopy structure in an Amazon forest from laser range finder and IKONOS satellite observations. Biotropica 34:483–492Google Scholar
  10. Asner GP, Jones MO, Martin RE et al (2008) Remote sensing of native and invasive species in Hawaiian forests. Rem Sen Environ 112:1912–1926CrossRefGoogle Scholar
  11. Baccini A et al (2008) A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environ Res Lett 3:045011CrossRefGoogle Scholar
  12. Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Nino F, Weiss M, Samain O, Roujean JL, Leroy M (2007) LAI, FAPAR and FCOVER CYCLOPES global products derived from VEGETATION. Rem Sen Environ 110:275–286CrossRefGoogle Scholar
  13. Bawa KS, Dayanandan S (1997) Socioeconomic factors and tropical deforestation. Nature 386:562–563CrossRefGoogle Scholar
  14. Biesmeijer JC, Roberts SPM, Reemer M, Ohlemüller R, Edwards M, Peeters T (2006) Parallel declines in pollinators and insect-pollinated in Britain and the Netherlands. Science 313:351–354CrossRefGoogle Scholar
  15. Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:1444–1449CrossRefGoogle Scholar
  16. Bourgeau-Chavez LL, Kasischke ES, Brunzell S, Mudd JP, Tukman M (2002) Mapping fire scars in global boreal forests using imaging radar data. Int J Remote Sens 23:4211–4234CrossRefGoogle Scholar
  17. Braswell BH, Hagen SC, Frolking SE et al (2003) A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODIS: application in the Brazilian Amazon region. Rem Sen Environ 87:243–256CrossRefGoogle Scholar
  18. Broadbent EN, Asner GP, Keller M et al (2008) Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol Conserv 141:1745–1757CrossRefGoogle Scholar
  19. Brook BW, Sodhi NS, Ng PKL (2003) Catastrophic extinctions follow deforestation in Singapore. Nature 424:420–426CrossRefGoogle Scholar
  20. Carreiras JMB, Pereira JMC, Campagnolo ML et al (2006) Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data. Rem Sen Environ 101:283–298CrossRefGoogle Scholar
  21. Chambers JQ, Asner GP, Morton DC, Anderson LO, Saatchi SS, Espírito-Santo FDB (2007) Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends Ecol Evol 22:414–423CrossRefGoogle Scholar
  22. Chapin FS III, Zavaleta ES, Eviner VT (2000) Consequences of changing biodiversity. Nature 405:234–243CrossRefGoogle Scholar
  23. Clark DB, Read JM, Clark ML, Cruz AM, Dotti MF, Clark DA (2004) Application of 1-m and 4-m resolution satellite data to ecological studies of tropical rain forests. Ecol Appl 14:61–74CrossRefGoogle Scholar
  24. Clark ML, Roberts DA, Clark DB (2005) Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Rem Sen Environ 96:375–398CrossRefGoogle Scholar
  25. Cochrane MA (2000) Using vegetation reflectance variability for species level classification of hyperspectral data. Int J Remote Sens 21:2075–2087CrossRefGoogle Scholar
  26. Cochrane MA (2003) Fire science for rainforests. Nature 421:913–919CrossRefGoogle Scholar
  27. Convention on Biological Diversity (2002) Harmonization of forest-related definitions for use by various Stakeholders. In: Conference of the parties to the Convention on Biological Diversity—6th meeting, The Hague, p 16Google Scholar
  28. Couturier S, Taylor D, Siegert F, Hoffmann A, Bao MQ (2001) ERS SAR backscatter: a potential real-time indicator of the proneness of modified rainforests to fire. Rem Sen Environ 76:410–417CrossRefGoogle Scholar
  29. Crutzen PJ, Andreae MO (1990) Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science 250:1669–1678CrossRefGoogle Scholar
  30. Curran PJ (2001) Imaging spectrometry for ecological applications. Int J Appl Earth Observ Geoinf 3(4):305–312CrossRefGoogle Scholar
  31. Daily GC (1997) Nature’s services. Island Press, WashingtonGoogle Scholar
  32. Dale MRT (1999) Spatial pattern analysis in plant ecology. Cambridge University Press, New YorkCrossRefGoogle Scholar
  33. Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C (2008) Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Rem Sen Environ 112:2592–2604CrossRefGoogle Scholar
  34. Defries RS (1999) Combining satellite data and biogeochemical models to estimate global effects of human-induced land cover change on carbon emissions and primary productivity. Global Biogeochem Cycles 13:803–815CrossRefGoogle Scholar
  35. Dennis RA, Colfer CP (2006) Impacts of land use and fire on the loss and degradation of lowland forest in 1983–2000 in East Kutai District, East Kalimantan, Indonesia. Singap J Trop Geogr 27:30–48CrossRefGoogle Scholar
  36. Diaz S, Fargione J, Chapin FS, Tilman D (2006) Biodiversity loss threatens human well-being. PLoS Biol 4:e277CrossRefGoogle Scholar
  37. Drolet GG, Huemmrich KF, Hall FG, Middleton EM, Black TA, Barr AG, Margolis HA (2005) A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest. Rem Sen Environ 98(2–3):212–224Google Scholar
  38. Ewers RM, Laurence WF (2006) Scale-dependent patterns of deforestation in the Brazilian Amazon. Environ Conserv 33:203–211CrossRefGoogle Scholar
  39. Fensholt R, Sandholt I (2003) Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Rem Sen Environ 87(1):111–121Google Scholar
  40. Food and Agricultural Organization (2002) Evaluation of world forestry resources 2000. Main Report. FAO, Rome, p 466Google Scholar
  41. Fraser RH, Fernandes R, Latifovic R (2003) Multi-temporal mapping of burned forest over Canada using satellite-based change metrics. Geocarto Int 18:37–47CrossRefGoogle Scholar
  42. French NHF, Bourgeau-Chavez LL, Wang Y, Kasischke ES (1999) Initial observations of Radarsat imagery at fire-disturbed sites in interior Alaska. Rem Sen Environ 68:89–94CrossRefGoogle Scholar
  43. Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH (2002) Global land cover mapping from MODIS: algorithms and early results. Rem Sen Environ 83:287–302CrossRefGoogle Scholar
  44. Fuller DO (2000) Satellite remote sensing of biomass burning with optical and thermal sensors. Prog Phys Geogr 24:543–561Google Scholar
  45. Geist HJ, Lambin EF (2002) Proximate causes and underlying driving forces of tropical deforestation. Bioscience 52:143–150CrossRefGoogle Scholar
  46. Ghiyamat A, Shafri HZM (2010) A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment. Int J Remote Sens 31:1837–1857CrossRefGoogle Scholar
  47. Goetz SJ, Prince SD (1996) Remote sensing of net primary production in boreal forest stands. Agric For Meteorol 78(3–4):149–179CrossRefGoogle Scholar
  48. Goetz S, Baccini A, Laporte N et al (2009) Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance Manag 4:2CrossRefGoogle Scholar
  49. Grace J, Nichol C, Disney M, Lewis P, Quaife T, Bowyer P (2007) Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? Global Change Biol 13:1484–1497Google Scholar
  50. Groombridge B, Martin J (2000) Global biodiversity: earth’s living resources in the 21st century. World Conservation Monitoring Centre, UKGoogle Scholar
  51. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecological Modelling 135:147–186CrossRefGoogle Scholar
  52. Gutman G, Ignatov A (1995) Global land monitoring from AVHRR: potential and limitations. Int J Remote Sens 16:2301–2309CrossRefGoogle Scholar
  53. Hansen MC, Stehman SV, Potapov PV, Loveland TR, Townshend JRG, DeFries RS, Pittman KW, Arunarwati B, Stolle F, Steininger MK, Carroll M, DiMiceli C (2008) Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data. Proceedings of the National Academy of Sciences 105(27):9439–9444Google Scholar
  54. Hubbell SP, He F, Condit R, Borda-de-Agua L, Kellner J, ter Steege H (2008) How many tree species are there in the Amazon and how many of them will go extinct? Proc Natl Acad Sci 105:11498–11504CrossRefGoogle Scholar
  55. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira G (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Rem Sen Environ 83(1-2):195–213Google Scholar
  56. Ingram JC, Dawson TP, Whittaker RJ (2005) Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Rem Sen Environ 94:491–507CrossRefGoogle Scholar
  57. International Tropical Timber Organization (2002) Guidelines for the restoration. Management and Rehabilitation of Degraded and Secondary Tropical Forests, Yokohama, JapanGoogle Scholar
  58. Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner GP, Francois C, Ustin SL (2009) PROSPECT + SAIL models: a review of use for vegetation characterization. Rem Sen Environ 113:S56–S66CrossRefGoogle Scholar
  59. Jia GJ, Burke IC, Goetz AFH, Kaufmann MR, Kindel BC (2006) Assessing spatial patterns of forest fuel using AVIRIS data. Rem Sen Environ 102:318–327CrossRefGoogle Scholar
  60. Jin S, Sader SA (2005) MODIS time-series imagery for forest disturbance detection and quantification of patch size effects. Rem Sen Environ 99:462–470CrossRefGoogle Scholar
  61. Joseph S (2008) Assessment of landcover dynamics and its conservation implications in tropical forests of Western Ghats. In: India student conference on conservation science, Cambridge University, UKGoogle Scholar
  62. Joseph S, Anitha K, Murthy M (2009a) Forest fire in India: a review of the knowledge base. J Forest Res 14(3):127–134CrossRefGoogle Scholar
  63. Joseph S, Blackburn GA, Gharai B, Sudhakar S, Thomas AP, Murthy MSR (2009b) Monitoring conservation effectiveness in a global biodiversity hotspot: the contribution of land cover change assessment. Environ Monit Assess 158:169–179CrossRefGoogle Scholar
  64. Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N et al (2002) An overview of MODIS land data processing and product status. Rem Sen Environ 83:3–15CrossRefGoogle Scholar
  65. Kalluri S, Desch A, Curry T, Altstatt A, Devers D, Townshend JRG, Tucker CJ (2001) Historical satellite data used to map Pan-Amazon forest cover. EOS Transact 82:201CrossRefGoogle Scholar
  66. Kiran Chand TR, Badarinath KVS, Krishna Prasad V, Murthy MSR, Elvidge CD, Tuttle BT (2006) Monitoring forest fires over the Indian region using defense meteorological satellite program-operational Linescan system night time satellite data. Rem Sen Environ 103:165–178CrossRefGoogle Scholar
  67. Kokaly RF, Rockwell BW, Haire SL, King TVV (2007) Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing. Rem Sen Environ 106:305–325CrossRefGoogle Scholar
  68. Lamb D, Erskine PD, Parrotta JA (2005) Restoration of degraded tropical forest landscapes. Science 310:1628–1632CrossRefGoogle Scholar
  69. Lambin EF (1999) Monitoring forest degradation in tropical regions by remote sensing: some methodological issues. Glob Ecol Biogeogr 8:191–198CrossRefGoogle Scholar
  70. Lambin EF, Geist HJ, Lepers E (2003) Dynamics of land-use and cover change. Ann Rev Environ Resour 28:205–241CrossRefGoogle Scholar
  71. Lewis SL, Phillips OL, Baker TR, Lloyd J, Malhi Y, Almeida S et al (2004) Concerted changes in tropical forest structure and dynamics: evidence from 50 South American long-term plots. Philosoph Transact: Biol Sci 359:421–436CrossRefGoogle Scholar
  72. Luyssaert S, Inglima I, Jung M, Richardson AD, Reichstein M, Papale D, Piao SL, Schulze ED, Wingate L, Matteucci G, Aragao L, Aubinet M, Beer C, Bernhofer C, Black KG, Bonal D, Bonnefond JM, Chambers J, Ciais P, Cook B, Davis KJ, Dolman AJ, Gielen B, Goulden M, Grace J, Granier A, Grelle A, Griffis T, Grunwald T, Guidolotti G, Hanson PJ, Harding R, Hollinger DY, Hutyra LR, Kolari P, Kruijt B, Kutsch W, Lagergren F, Laurila T, Law BE, Le Maire G, Lindroth A, Loustau D, Malhi Y, Mateus J, Migliavacca M, Misson L, Montagnani L, Moncrieff J, Moors E, Munger JW, Nikinmaa E, Ollinger SV, Pita G, Rebmann C, Roupsard O, Saigusa N, Sanz MJ, Seufert G, Sierra C, Smith ML, Tang J, Valentini R, Vesala T, Janssens IA (2007) CO2 balance of boreal, temperate, and tropical forests derived from a global database. Global Change Biol 13:2509–2537CrossRefGoogle Scholar
  73. Malhi Y, Román-Cuesta RM (2008) Analysis of lacunarity and scales of spatial homogeneity in IKONOS images of Amazonian tropical forest canopies. Rem Sen Environ 112:2074–2087CrossRefGoogle Scholar
  74. Miles L, Newton AC, DeFries RS (2006) A global overview of the conservation status of tropical dry forests. J Biogeogr 33:491–506CrossRefGoogle Scholar
  75. Moya I, Camenen L, Evain S, Goulas Y, Cerovic ZG, Latouche G, Flexas J, Ounis A (2004) A new instrument for passive remote sensing - 1. Measurements of sunlight-induced chlorophyll fluorescence. Rem Sen Environ 91(2):186–197Google Scholar
  76. Murthy MSR, Giriraj A, Dutt CBS (2003) Geoinformatics for biodiversity assessment. Biol Lett 40:75–100Google Scholar
  77. Murthy MSR, Pujar GS, Giriraj A (2006) Geoinformatics-based management of biodiversity from landscape to species scale—an Indian perspective. Curr Sci 91:1477–1485Google Scholar
  78. Mutlu M, Popescu SC, Stripling C, Spencer T (2008) Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Rem Sen Environ 112:274–285CrossRefGoogle Scholar
  79. Myers N, Mittermeier RA, Mittermeier CG, de Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858CrossRefGoogle Scholar
  80. Nagendra H, Rocchini D (2008) High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail. Biodivers Conserv 17:3431–3442CrossRefGoogle Scholar
  81. Nagendra H, Rocchini D, Ghate R et al (2010) Assessing plant diversity in a dry tropical forest: comparing the utility of Landsat and Ikonos satellite images. Remote Sens 2:478–496CrossRefGoogle Scholar
  82. Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ et al (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300:1560–1563CrossRefGoogle Scholar
  83. Newton AC, Hill Ross A, EcheverrÃa C et al (2009) Remote sensing and the future of landscape ecology. Prog Phys Geogr 33:528–546CrossRefGoogle Scholar
  84. Ouma YO, Tetuko J, Tateishi R (2008) Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery. Int J Remote Sens 29:3417–3457CrossRefGoogle Scholar
  85. Penner JE, Dickinson RE, O’Neill CA (1992) Effects of aerosol from biomass burning on the global radiation budget. Science 256:1432–1434CrossRefGoogle Scholar
  86. Peres CA, Barlow J, Laurance WF (2006) Detecting anthropogenic disturbance in tropical forests. Trends Ecol Evol 21:227–229CrossRefGoogle Scholar
  87. Podest E, Saatchi S (2002) Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation. Int J Remote Sens 23:1487–1506CrossRefGoogle Scholar
  88. Puissant A, Hirsch J, Weber C (2005) The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery. Int J Remote Sens 26:733–745CrossRefGoogle Scholar
  89. Read JM, Marcelo PM, Eduardo MV, Marcelo PM (2003) Application of merged 1-m and 4-m resolution satellite data to research and management in tropical forests. J Appl Ecol 40:592–600CrossRefGoogle Scholar
  90. Robinson JM (1991) Fire from space: global evaluation using infrared remote sensing. Int J Remote Sens 12:3–24CrossRefGoogle Scholar
  91. Rudel TK (2006) Shrinking tropical forests, human agents of change, and conservation policy. Conserv Biol 20:1604–1609CrossRefGoogle Scholar
  92. Saatchi SS, Houghton RA, Dos Santos Alvala RC et al (2007) Distribution of aboveground live biomass in the Amazon basin. Global Change Biol 13:816–837CrossRefGoogle Scholar
  93. Sabine CL, Heimann M, Artaxo P, Bakker DCE, Chen CTA, Field CB, Gruber N, Quéré Cl, Prinn RG, Richey JE, Lankao PR, Sathaye JA, Valentini R (2004) Current status and past trends of the global carbon cycle. In: Field CB, Raupach MR (eds) The global carbon cycle: integrating humans, climate and the natural world. Island Press, Washington, DC, pp 17–44Google Scholar
  94. Schmerbeck J, Seeland K (2007) Fire supported forest utilisation of a degraded dry forest as a means of sustainable local forest management in Tamil Nadu/South India. Land Use Policy 24:62–71CrossRefGoogle Scholar
  95. Sgrenzaroli M, De Grandi GF, Eva H, Achard F (2002) Tropical forest cover monitoring: estimates from the GRFM JERS-1 radar mosaics using wavelet zooming techniques and validation. Int J Remote Sens 23:1329–1355CrossRefGoogle Scholar
  96. Siegert F, Boehm H-D (2001) Land use change and (Il)-legal logging in central Kalimantan, Indonesia. Int Peat J 11:51–57Google Scholar
  97. Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Rem Sen Environ 81(2–3):337–354CrossRefGoogle Scholar
  98. Stibig H-J, Achard F (2003) Assessment of tropical forest cover from satellite images at different geographical scales: case studies from Southeast Asia. In: Roy PS (ed) Geoinformatics for tropical ecosystems. Bishen Singh Mahendra Pal Singh, Dehradun, India, pp 33–48Google Scholar
  99. Thenkabail PS, Enclona EA, Ashton MS, Legg C, De Dieu MJ (2004) Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Rem Sen Environ 90:23–43CrossRefGoogle Scholar
  100. Townsend AR, Asner GP, Cleveland CC (2008) The biogeochemical heterogeneity of tropical forests. Trends Ecol Evol 23:424–431CrossRefGoogle Scholar
  101. Townshend JRG, Bell V, Desch A, Havlicek C, Justice C (1995) The NASA landsat pathfinder humid tropical deforestation project. In: ASPRS conference—proceedings of land satellite information in the next decade, Vienna, pp 76–87Google Scholar
  102. Trigg SN, Curran LM, McDonald AK (2006) Utility of landsat 7 satellite data for continued monitoring of forest cover change in protected areas in Southeast Asia. Singap J Trop Geogr 27:49–66CrossRefGoogle Scholar
  103. Turner DP, Ritts WD, Cohen WB, Maeirsperger TK, Gower ST, Kirschbaum AA, Running SW, Zhao M, Wofsy SC, Dunn AL, Law BE, Campbell JL, Oechel WC, Kwon HJ, Meyers TP, Small EE, Kurc SA, Gamon JA (2005) Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring. Global Change Biol 11(4):666–684Google Scholar
  104. Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54(6):523–534CrossRefGoogle Scholar
  105. van Wagtendonk JW, Root RR, Key CH (2004) Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Rem Sen Environ 92:397–408CrossRefGoogle Scholar
  106. Varghese AO, Murthy YVNK (2006) Application of geoinformatics for conservation and management of rare and threatened plant species. Curr Sci 91:762–769Google Scholar
  107. Vina A, Henebry GM (2005) Spatio-temporal change analysis to identify anomalous variation in the vegetated land surface: ENSO effects in tropical South America. Geophys Res Lett 32: L21402Google Scholar
  108. Wardle DA, Walker LR, Bardgett RD (2004) Ecosystem properties and forest decline in contrasting long-term chronosequences. Science 305:509–513CrossRefGoogle Scholar
  109. White MA, Thornton PE, Running SW, Nemani RR (2000) Parameterization and Sensitivity Analysis of the BIOME-BGC Terrestrial Ecosystem Model: net primary production controls. Earth Interactions 4(3):1–85Google Scholar
  110. Williams C, Hanan N, Neff J et al (2007) Africa and the global carbon cycle. Carbon Balance Manag 2:3CrossRefGoogle Scholar
  111. Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D (2006) Object-based detailed vegetation classification with airborne high spatial resolution remotesensing imagery. Photogramm Eng Remote Sens 72:799–811Google Scholar
  112. Zaremba MB, Gougeon FA (2006) Fusion of high-resolution satellite and lidar data for individual tree recognition. In: 2006 Canadian conference on electrical and computer engineering, pp 1112–1115Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Shijo Joseph
    • 1
    • 3
    • 4
  • M. S. R. Murthy
    • 2
  • A. P. Thomas
    • 3
  1. 1.Department of Natural ResourcesInternational Institute for Geo-Information Science and Earth Observation (ITC)EnschedeThe Netherlands
  2. 2.Forestry and Ecology DivisionNational Remote Sensing Centre, Indian Space Research OrganizationHyderabadIndia
  3. 3.School of Environmental SciencesMahatma Gandhi UniversityKottayamIndia
  4. 4.Ashoka Trust for Research in Ecology and the Environment (ATREE)BangaloreIndia

Personalised recommendations