Top-down and bottom-up inventory approach for above ground forest biomass and carbon monitoring in REDD framework using multi-resolution satellite data

详细信息    查看全文

• 作者：Laxmi Kant Sharma ; Mahendra Singh Nathawat…
• 关键词：Geospatial ; REDD/REDD+ ; Biomass inventory ; Carbon ; MODIS ; NDVI
• 刊名：Environmental Monitoring and Assessment
• 出版年：2013
• 出版时间：October 2013
• 年：2013
• 卷：185
• 期：10
• 页码：8621-8637
• 全文大小：1131KB
• 参考文献：1. Augusteijn, M. F., Clemens, L. E., & Shaw, K. A. (1995). Performance evaluation of texture measures for ground cover identification in satellite images of a neural network classifier. / IEEE Transactions on Geoscience and Remote Sensing, 33, 616-25. ss="external" href="http://dx.doi.org/10.1109/36.387577">CrossRef
  2. Baccini, A., Friedl, A. M. A., Woodcock, C. E., & Warbington, R. (2004). Forest biomass estimation over regional scales using multisource data. / Geophysical Research Letters, 31, L10501. ss="external" href="http://dx.doi.org/10.1029/2004GL019782">CrossRef
  3. Biomass ECV report (2009). Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables. 38-1.
  4. Boyd, D. S., Foody, G. M., & Curran, P. J. (1999). The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-.0?μm). / International Journal of Remote Sensing, 20, 1017-023. ss="external" href="http://dx.doi.org/10.1080/014311699213055">CrossRef
  5. Brown, S., Gillespie, A. J. R., & Lugo, A. E. (1989). Biomass estimation methods for tropical forests with applications to forest inventory data. / Forest Science Journal, 35, 881-02.
  6. Canadell, J. G., LeQué, C., Raupach, M. R., Field, C. B., Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillett, N. P., Houghton, R. A., & Marland, G. (2007). Contributions to accelerating atmospheric CO<sub class="a-plus-plus">2sub> growth from economic activity, carbon intensity, and efficiency of natural sinks. / Proceedings of the National Academy of Science of the United States of America, 104, 18866-8870. doi:<span class="a-plus-plus non-url-ref">10.1073/pnas.0702737104 . ss="external" href="http://dx.doi.org/10.1073/pnas.0702737104">CrossRef
  7. Chacko, V. J. (1965). / A manual on sampling techniques for forest surveys. Delhi: Manager of Publications.
  8. Champion, H. G., & Seth, S. K. (1968). / A revised survey of forest types of India. New Delhi: Government of India Publication.
  9. Chavez, P. S. (1996). Image-based atmospheric corrections—revisited and improved. / Photogrammetric Engineering and Remote Sensing, 62, 1025-036.
  10. Chen, S., & Rao, P. (2009). Regional land degradation mapping using MODIS data and decision tree (DT) classification in a transition zone between grassland and cropland of northeast China. In: Information Science and Engineering (ICISE), 2009 1st International Conference, Nanjing, China, 26-8th December 2009 (pp. 1395-398). doi:<span class="a-plus-plus non-url-ref">10.1109/ICISE.2009.878 .
  11. Chhabra, A., Palria, S., & Dadhwal, V. K. (2002). Growing stock-based forest biomass estimate for India. / Biomass and Bioenergy, 22, 187-94. ss="external" href="http://dx.doi.org/10.1016/S0961-9534(01)00068-X">CrossRef
  12. Denman, K. L., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P. M., Dickinson, R. E., Hauglustaine, C., Heinze, E., Holland, D., Jacob, U., Lohmann, S., Ramachandran, P. L., DaSilva Dias, D., Wofsy, S. C., & Zhang, X. (2007). Couplings between changes in the climate system and biogeochemistry. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignorand, & H. L. Miller (Eds.), / Climate Change 2007 (pp. 541-84). Cambridge: Cambridge University Press.
  13. Dong, J., Kaufmann, R. K., Myneni, R. B., Tucker, C. J., Kauppi, P. E., Liski, J., Buermann, W., Alexeyev, V., & Hughes, M. K. (2003). Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. / Remote Sensing of Environment, 84, 393-10. ss="external" href="http://dx.doi.org/10.1016/S0034-4257(02)00130-X">CrossRef
  14. Fearnside, P. M., & Laurance, W. F. (2003). Comment on ‘Determination of deforestation rates of the world’s humid tropical forests- / Science, 299, 1015. ss="external" href="http://dx.doi.org/10.1126/science.1078714">CrossRef
  15. Fearnside, P. M., & Laurance, W. F. (2004). Tropical deforestation and greenhouse gas emissions. / Ecological Applications, 14, 982-86. ss="external" href="http://dx.doi.org/10.1890/03-5225">CrossRef
  16. Food and Agricultural Organization of the United Nations (FAO). (2005). FAO Statistical database 2005. Source: stat.fao.org/" class="a-plus-plus">http://faostat.fao.org.
  17. Foody, G. M., Cutler, M. E., McMorrow, J., Pelz, D., Tangki, H., Boyd, D. S., & Douglas, I. (2001). Mapping the biomass of Bornean tropical rain forest from remotely sensed data. / Global Ecology and Biogeography, 10, 379-87. ss="external" href="http://dx.doi.org/10.1046/j.1466-822X.2001.00248.x">CrossRef
  18. Foody, G. M., Boyd, D. S., & Cutler, M. E. J. (2003). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. / Remote Sensing of Environment, 85, 463-74. ss="external" href="http://dx.doi.org/10.1016/S0034-4257(03)00039-7">CrossRef
  19. Forest Survey of India (FSI). (2009). / State of forest report (pp. 90-3). Dehradun: Ministry of Environment and Forests.
  20. Franklin, S. E., & Peddle, D. R. (1989). Spectral texture for improved class discrimination in complex terrain. / International Journal of Remote Sensing, 10, 1437-443. ss="external" href="http://dx.doi.org/10.1080/01431168908903979">CrossRef
  21. Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007). Monitoring and estimating tropical forest carbon stocks: making REDD a reality. / Environmental Research Letters. doi:<span class="a-plus-plus non-url-ref">10.1088/1748-9326/2/4/045023 . online access: ss="a-plus-plus">erl.iop.org.
  22. Hakkila, P. (1989). / Utilization of residual forest biomass. Berlin: Springer. ss="external" href="http://dx.doi.org/10.1007/978-3-642-74072-5">CrossRef
  23. Hernandez, R.P., Koohafkan, P., & Antoine, J. (2004). Assessing carbon stocks and modelling win-win scenarios of carbon sequestration through land-use changes. FAO report. pp. 10-7.
  24. Houghton, R. A. (2005). Tropical deforestation as a source of greenhouse gas emissions. In P. Mutinho & S. Schwartzman (Eds.), / Tropical deforestation and climate change (pp. 13-2). Belem: IPAM.
  25. Indian Council for Forestry Research and Education (ICFRE). (2007). / Views from ICFRE, Dehradun, India (an observer organization) to UNFCCC on REDD (p. 5). Dehradun: ICFRE, Government of India.
  26. Intergovernmental Panel for Climate Change (IPCC). (2003). / Good practice guidance for land use land use change and forestry. Hayama: IGES.
  27. Intergovernmental Panel on Climate Change (IPCC). (2006). Agriculture, forestry and other land use. In: H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, K. Tanabe (Eds.). IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme. Japan: IGES. (s.or.jp/public/2006gl/vol4.html" class="a-plus-plus">http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html).
  28. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2007). / Remote sensing and image interpretation (5th ed., pp. 491-24). Singapore: Wiley.
  29. Lu, D. (2005). Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. / International Journal of Remote Sensing, 26, 2509-525. ss="external" href="http://dx.doi.org/10.1080/01431160500142145">CrossRef
  30. Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. / International Journal of Remote Sensing, 27, 1297-328. ss="external" href="http://dx.doi.org/10.1080/01431160500486732">CrossRef
  31. Lucas, R. M., Curran, P. J., Honzak, M., Foody, G. M., DoAmaral, I., & Amaral, S. (1998). The contribution of remotely sensed data in the assessment of the floristic composition, total biomass and structure of Amazonian tropical secondary forests. In C. Gascon & P. Moutinho (Eds.), / Regeneracao Florestal: Pesquisas na Amazonia (pp. 61-2). Manaus: INPA.
  32. Luckman, A., Baker, J. R., Kuplich, T. M., Yanasse, C. C. F., & Frery, A. C. (1997). A study of the relationship between radar backscatter and regenerating forest biomass for space borne SAR instrument. / Remote Sensing of Environment, 60, 1-3. ss="external" href="http://dx.doi.org/10.1016/S0034-4257(96)00121-6">CrossRef
  33. Luckman, A., Baker, J. R., Honzák, M., & Lucas, R. M. (1998). Tropical forest biomass density estimation using JERS-1 SAR: seasonal variation, confidence limits, and application to image mosaics. / Remote Sensing of Environment, 63, 126-39. ss="external" href="http://dx.doi.org/10.1016/S0034-4257(97)00133-8">CrossRef
  34. Malhi, Y., & Grace, J. (2000). Tropical forests and atmospheric carbon dioxide. / Trends in Ecology & Evolution, 15, 332-37. ss="external" href="http://dx.doi.org/10.1016/S0169-5347(00)01906-6">CrossRef
  35. Marceau, D. J., Howarth, P. J., Dubois, J. M., & Gratton, D. J. (1990). Evaluation of the grey-level co-occurrence matrix method for land-cover classification using SPOT imagery. / IEEE Transactions on Geoscience and Remote Sensing, 28, 513-19. ss="external" href="http://dx.doi.org/10.1109/TGRS.1990.572937">CrossRef
  36. Markham, B. L., & Barker, J. L. (1986). Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. / EOSAT Technical Notes, 1, 3-.
  37. Ministry of Environment and Forests (MoEF). (2009). / Climate change negotiations: India’s submissions to the United Nations Framework Convention on Climate Change (p. 58). New Delhi: Government of India.
  38. Mund, M., Kummetz, E., Hein, M., Bauer, G. A., & Schulze, E. D. (2002). Growth and carbon stocks of a spruce forest chronosequence in central Europe. / Forest Ecology and Management, 171, 275-96. ss="external" href="http://dx.doi.org/10.1016/S0378-1127(01)00788-5">CrossRef
  39. Nelson, R., Jimenez, J., Schnell, C. E., Hartshorn, G. S., Gregoire, T. G., & Oderwald, R. (2000). Canopy height models and airborne lasers to estimate forest biomass: two problems. / International Journal of Remote Sensing, 21, 2153-162. ss="external" href="http://dx.doi.org/10.1080/01431160050029486">CrossRef
  40. Pandya, M. R., Singh, R. P., Murali, K. R., Babu, P. N., Kirankumar, A. S., & Dadhwal, V. K. (2002). Bandpass solar exo-atmospheric irradiance and Rayleigh optical thickness of sensors onboard Indian Remote Sensing satellites-1B, 1C, 1D and P4. / IEEE Transactions on Geoscience and Remote Sensing, 40, 714-18. ss="external" href="http://dx.doi.org/10.1109/TGRS.2002.1000331">CrossRef
  41. Phua, M. H., & Saito, H. (2003). Estimation of biomass of a mountainous tropical forest using Landsat TM data. / Canadian Journal of Remote Sensing, 29, 429-40. ss="external" href="http://dx.doi.org/10.5589/m03-005">CrossRef
  42. Plugge, D., Baldauf, T., Ratsimba, H. R., Rajoelison, G., & K?hl, M. (2010). Combined biomass inventory in the scope of REDD (Reducing Emissions from Deforestation and Forest Degradation). / Madagascar Conservation and Development, 5, 23-4.
  43. Podest, E., & Saatchi, S. (2002). Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation. / International Journal of Remote Sensing, 23, 1487-506. ss="external" href="http://dx.doi.org/10.1080/01431160110093000">CrossRef
  44. Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2003). Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. / Canadian Journal of Remote Sensing, 29, 564-77. ss="external" href="http://dx.doi.org/10.5589/m03-027">CrossRef
  45. Ravindranath, N. H., & Ostwald, M. (2008). Carbon inventory methods: handbook for greenhouse gas inventory, carbon mitigation and roundwood production projects (pp. 41-4, 181-99). Heidelberg: Springer.
  46. Riedel, T. (2008). Evaluierung alternative Stichprobenkonzepte für die Bundeswaldinventur. Unpublished Ph.D. dissertation. Department Biologie der Fakult?t für Mathematik, Informatik und Naturwissenschaften. Universit?t Hamburg.
  47. Rignot, E. J., Zimmermann, R., & vanZyl, J. J. (1995). Spaceborne applications of P band imaging radars for measuring forest biomass. / IEEE Transactions on Geoscience and Remote Sensing, 33, 1162-169. ss="external" href="http://dx.doi.org/10.1109/36.469480">CrossRef
  48. Roy, P. S., & Ravan, S. A. (1996). Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest. / Journal of Biosciences, 21, 535-61. ss="external" href="http://dx.doi.org/10.1007/BF02703218">CrossRef
  49. Sader, S. A., Waide, R. B., Lawrence, W. T., & Joyce, A. T. (1989). Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data. / Remote Sensing of Environment, 28, 143-56. ss="external" href="http://dx.doi.org/10.1016/0034-4257(89)90112-0">CrossRef
  50. Santos, J. R., Lacruz, M. S. P., Araujo, L. S., & Keil, M. (2002). Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data. / International Journal of Remote Sensing, 23, 1217-229. ss="external" href="http://dx.doi.org/10.1080/01431160110092867">CrossRef
  51. Santos, J. R., Freitas, C. C., Araujo, L. S., Dutra, L. V., Mura, J. C., Gama, F. F., Soler, L. S., & Sant-Anna, S. J. S. (2003). Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest. / Remote Sensing of Environment, 87, 482-93. ss="external" href="http://dx.doi.org/10.1016/j.rse.2002.12.001">CrossRef
  52. Schulze, E. D. (2000). / Carbon and nitrogen cycling in European forest ecosystems. Heidelberg: Springer. ss="external" href="http://dx.doi.org/10.1007/978-3-642-57219-7">CrossRef
  53. Sharma, R. K., Lall, P., & Hofstad, O. (2008). Forest biomass density, utilization and production dynamics in a western Himalayan watershed. / Journal of Forestry Research, 19, 171-80. ss="external" href="http://dx.doi.org/10.1007/s11676-008-0032-5">CrossRef
  54. Sharma, L. K., Pandey, P. C., & Nathawat, M. S. (2012). Assessment of land consumption rate with urban dynamics change using geospatial techniques. / Journal of Land Use Science, 7, 135-48. ss="external" href="http://dx.doi.org/10.1080/1747423X.2010.537790">CrossRef
  55. Sinha, S., Sharma, L. K., & Nathawat, M. S. (2012). Tigers losing grounds: impact of anthropogenic occupancy on tiger habitat suitability using integrated geospatial-fuzzy techniques. / The Ecoscan, 1, 259-63.
  56. Sobrino, J. A., Jiménez-Mu?oz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. / Remote Sensing of Environment, 90, 434-40. ss="external" href="http://dx.doi.org/10.1016/j.rse.2004.02.003">CrossRef
  57. Steininger, M. K. (2000). Satellite estimation of tropical secondary forest aboveground biomass data from Brazil and Bolivia. / International Journal of Remote Sensing, 21, 1139-157. ss="external" href="http://dx.doi.org/10.1080/014311600210119">CrossRef
  58. Stephens, B. S., et al. (2007). Weak Northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO<sub class="a-plus-plus">2sub>. / Science, 316, 1732-735. ss="external" href="http://dx.doi.org/10.1126/science.1137004">CrossRef
  59. Trigg, S. N., Curran, L. M., & McDonald, A. K. (2006). Utility of Landsat 7 satellite data for continued monitoring of forest cover change in protected areas in southeast Asia. / Singapore Journal of Tropical Geography, 27, 49-6. ss="external" href="http://dx.doi.org/10.1111/j.1467-9493.2006.00239.x">CrossRef
  60. United Nations Framework Convention on Climate Change (UNFCCC) (1998, 2006). Kyoto Protocol to the United Nations Framework Convention on Climate Change. United Nations. Source: ss="a-plus-plus">http://unfccc.int.
  61. van der Meer, F. (1996). Spectral mixture modeling and spectral stratigraphy in carbonate lithofacies mapping. / ISPRS Journal of Photogrammetry and Remote Sensing, 51, 150-62. ss="external" href="http://dx.doi.org/10.1016/0924-2716(95)00009-7">CrossRef
  62. van der Werf, G. R., Morton, D. C., DeFries, R. S., Olivier, J. G. J., Kasibhatla, P. S., Jackson, R. B., Collatz, G. J., & Randerson, J. T. (2009). CO<sub class="a-plus-plus">2sub> emissions from forest loss. / Nature Geoscience, 2, 737-38. ss="external" href="http://dx.doi.org/10.1038/ngeo671">CrossRef
  63. Wirth, C., Czimczik, C. I., & Schulze, E. D. (2002). Beyond annual budgets: carbon flux at different temporal scales in fire-prone Siberian Scots pine forests. / Tellus, 54B, 611-30.
  64. Wirth, C., Schumacher, J., & Schulze, E. (2004). Generic biomass functions for Norway spruce in Central Europe—a meta-analysis approach toward prediction and uncertainty estimation. / Tree Physiology, 24, 121-39. ss="external" href="http://dx.doi.org/10.1093/treephys/24.2.121">CrossRef
  65. Woodcock, C. E., Collins, J. B., Jakabhazy, V. D., Li, X., Macomber, S., & Wu, Y. (1997). Inversion of the Li–Strahler canopy reflectance model for mapping forest structure. / IEEE Transactions on Geoscience and Remote Sensing, 35, 405-14. ss="external" href="http://dx.doi.org/10.1109/36.563279">CrossRef
  66. Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Moine, J. L., & Ryu, R. (2004). Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. / Remote Sensing of Environment, 93, 402-11. ss="external" href="http://dx.doi.org/10.1016/j.rse.2004.08.008">CrossRef
  
• 作者单位：Laxmi Kant Sharma (1)
  Mahendra Singh Nathawat (2)
  Suman Sinha (3)
  
  1. Centre for Land Resource Management, Central University of Jharkhand, Ranchi, Jharkhand, India
  2. School of Sciences, Indira Gandhi National Open University (IGNOU), New Delhi, India
  3. Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India
  
• ISSN：1573-2959

文摘

This study deals with the future scope of REDD (Reduced Emissions from Deforestation and forest Degradation) and REDD+ regimes for measuring and monitoring the current state and dynamics of carbon stocks over time with integrated geospatial and field-based biomass inventory approach. Multi-temporal and multi-resolution geospatial synergic approach incorporating satellite sensors from moderate to high resolution with stratified random sampling design is used. The inventory process involves a continuous forest inventory to facilitate the quantification of possible CO2sub> reductions over time using statistical up-scaling procedures on various levels. The combined approach was applied on a regional scale taking Himachal Pradesh (India), as a case study, with a hierarchy of forest strata representing the forest structure found in India. Biophysical modeling implemented revealed power regression model as the best fit (R 2--.82) to model the relationship between Normalized Difference Vegetation Index and biomass which was further implemented to calculate multi-temporal above ground biomass and carbon sequestration. The calculated value of net carbon sequestered by the forests totaled to 11.52?million tons (Mt) over the period of 20?years at the rate of 0.58?Mt per year since 1990 while CO2sub> equivalent reduced from the environment by the forests under study during 20?years comes to 42.26?Mt in the study area.