Forest degradation, a methodological approach usingremote sensing techniques: literature review( Télécharger le fichier original )par Jean-fiston Mikwa Ghent University - Master 2011 |
4. ConclusionAs conclusion of this literature review, we can see that a lot f conclusion can be formulated from this literature review. Our literature review had three important parts, the first one helped us to understand the concept of remote sensing, the second one was a brief explanation of the concept of forest degradation and the last one explained different methodologies to evaluate the problem of degradation using remote sensing As a change in forest structure it is not easy to detect the problem through remote sensing, the choice of different approaches depends on a number of factors including the type of degradation process, available (historical) data, capacities and resources and the potentials and limitations of various measurement and monitoring approaches. Mapping forest degradation should not be only a problem of scientist but a social problem putting together interdisciplinary stakeholders, they must have information of the problem in order to quantify the extent of the threat. the preceding discussion the point 3 of this literature review has explored how advancements in remote sensing techniques would help to gather information and to answering most of these questions about all sides of the definition of forest degradation The big challenge is now how those methodologies especially in developing country because of the availability of the satellites imagery, increase in new processing technologies methods, and the cost of spatial information. 20 5. ReferencesAcharya, K., and Dangi, R. 2009. Forest Degradation in Nepal: Review of Data and Methods. FAO Forest Resources Assessment Programme, Rome, Italy Anderson, J.E., L.C. Plourde, M.E. Martin, B.H. Braswell, M.L. Smith, R.O. Dubayah, M.A. Hof ton, and J.B. Blair. 2008. Integrating waveform lidar with hyperspectral imagery for inve ntory of a northern temperate forest. Remote Sensing of the Environment 112(4): 1856- 1870. Andersson, K., T.P. Evans, and K.R. Richards. 2009. National forest carbon inventories: Policy needs and assessment capacity. Climatic Change 93(1-2): 69-101. Askne, J., M. Santoro, G. Smith, and J.E.S. Fransson. 2003. Multitemporal repeat-pass SAR inter ferometry of boreal forests. IEEE Transactions on Geoscience and Remote Sensing 41(7): 1540-1550. Asner, G. P., M. Keller, R. Pereira, and J. C. Zweede. 2002. Remote sensing of selective logging in Amazonia: Assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis. Remote Sensing of Environment 80:483-496. Asner, G. P., M. Keller, R. Pereira, J. C. Zweede, and J. N. M. Silva. 2004. Canopy damage and recovery after selective logging in Amazonia: Field and satellite studies. Ecological Applications 14:S280-S298. Asner, G. P., D. E. Knapp, E. N. Broadbent, P. J. C. Oliveira, M. Keller, and J. N. M. Silva. 2005. Selective logging in the Brazilian Amazon. Science 310:480-482. Asner, G., Rudel, T., Aide, M., Defries, R., Emerson, R. 2009. A contemporary assessment of change in humid tropical forests. Conservation Biology 23:1386-1395. Baccini, A., N. Laporte, S.J. Goetz, M. Sun, and H. Dong. 2008. A first map of tropical Africa's aboveground biomass derived from satellite imagery. Environmental Research Letters 3: 1-9 Baikal region. Forest Ecology and Management 257(3): 911-922. Bicheron, P., P. Defourny, C. Brockmann, L. Schouten, C. Vancutsem, M. Huc, S. Bontemps, Leroy, F. Achard, M. Herold, F. Ranera, and O. Arino. 2008. GLOBCOVER Products Description and Validation Report. MEDIAS-France, ftp://uranus.esrin.esa.int/pub/globc over v2/global/. DeFries, R. 2008. Terrestrial vegetation in the coupled human-earth system: Contributions of re mote sensing. Annual Review of Environment and Resources 33: 369-390. 21 Blaschke, T., Lang, S., and G. Hay (Eds.), 2008, Object-Based Image Analysis, Berlin, Germany: Springer-Verlag, 817 p Brockhaus, J A and S Khorram. 1992. A comparison of Spot andLandsat-TM data for use in conducting inventories of forest resources. IJRS 13, 16, pp 3035-3043. Campbell, J., 2007, Introduction to Remote Sensing, 4th ed., New York, NY: Guilford Press, 626p.Change Biology 11(6): 945-958. Chave, J., C. Andalo, S. Brown, M.A. Cairns, J.Q. Chambers, D. Eamus, H. Folster, et al. 2005. Comparison of various remote sensing data sources in the retrieval of forest stand Congalton, R. and K. Green, 2009, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed., Boca Raton, FL: CRC/Taylor & Francis, 183 p. Congalton, R., 2010, «How to Assess the Accuracy of Maps Generated from Remotely Sensed Data,» in Manual of Geospatial Science and Technology, 2nd ed., Bossler, J. (Ed.), Boca Raton, FL: Taylor & Francis, 403-421. Cook E A, L R Iverson and R L Graham. 1989. Estimating forest productivity with Thematic Mapper and biogeographical data.Remote Sens of Environ 28, pp 131-141. Cramer, W., A. Bondeau, S. Schaphoff, W. Lucht, B Smith and S. Sitch 2004. Tropical forests and the global carbon cycle: impacts of atmospheric carbon dioxide, climate change and rate of deforestation. Phil. Trans. Roy. Soc. Lond. B 359: 331-343. Danson, F M and P J Curran. 1993. Factors affecting the remotely sensed response of coniferous forest plantations. Remote Sensing of Environ 43, pp 55-65. De Wulf, R R, R E Goosens, B P De Roover and F C Borry. 1990.Extraction of forest stand parameters from panchromatic and multi-spectral Spot-1 data. IJRS 11, 9, pp 1571-1588. DeFries, R., F. Achard, S. Brown, M. Herold, D. Murdiyarso, B. Schlamadinger, and C. de Souz a. 2007. Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environmental Science and Policy 10(4): 385-394. GOFC-GOLD. 2009. A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals caused by deforestation, gains and 22 Donnellan, A., P. Rosen, J. Graf, A. Loverro, A. Freeman, R. Treuhaft, R. Oberto, et al. 2008. Deformation, ecosystem structure, and dynamics of ice (DESDynI). Paper presented at th e ESRI International User Conference. April 2008, Washington, DC. FAO. 2002. Food and Agriculture Organization. Proceedings: Second Expert Meeting on Harmonizing Forest-related Definitions for Use by Various Stakeholders. Rome, 11-13 September 2002. Rome. http://www.fao.org/docrep/005/y4171e/y4171e00.htm FAO. 2006a. Food and Agriculture Organization .Global Forest Resources Assessment FAO.2006b.Summaries of FAO's work in forestry. Rome, Italy. http://www.fao.org/forestry/foris/webview/forestry2 FAO. 2007. Food and Agriculture Organization. State of the World's Forests. United Nations, Rome. Available: http://www.fao.org/docrep/009/a0773e/a0773e00.htm. Fiorella, M and W J Ripple. 1993. Determining successional stage of temperate coniferous forests with Landsat satellite data. PE&RS59, 2, pp 239-246. Foody, G.M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment 80(1): 185-201 Franklin J F, F W Davis and P Lefebvre. 1991. Thematic Mapper analaysis of tree cover in semiarid woodlands using a model of canopy shadowing. Remote Sens of Environ 36, pp 189-202. Foody,G.M.,Boyad,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 84(4):463-474. Gao, X., A.R. Huete, W.G. Ni, and T. Miura. 2000. Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment 7 4(3): 609-620 Gibbs, H.K., S. Brown, J.O. Niles, and J.A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters 2(4): 045023. Jensen, J., 2005, Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed., Upper Saddle River, NJ: Pearson Prentice Hall, 526 p. 23 losses of carbon stocks in forest remaining forests, and forestation. GOFC-GOLD Report version COP15-1.Available at www.gofc-gold.uni-jena.de/redd/. Hame, T, E Tomppo and E Parmes. 1988. Stand based forest inventory from Spot Image. Symp proc: Spot-1, Image Utilisation,Assessment, Results. CNES, Cepadues Editions, Toulouse, France,pp 971-976. Herold, M., Yasumasa H., Patrick V.,Asner G., Victoria Heymell5, Rosa María Román-Cuesta6 Houghton, R.A. 2005. Aboveground forest biomass and the global carbon balance. Global Change Biology 11(6): 945-958 Huete, A. R., Miura, T., & Gao, X. (2003). Land cover conversion and degradation analyses through coupled soil-plant biophysical parameters derived from hyperspectral EO-1 Hyperion. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1268-1276. Hyyppa, J., H. Hyyppa, M. Inkinen, M. Engdahl, S. Linko, and Y.H. Zhu. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes . Forest Ecology and Management 128(1-2): 109-120. Hyyppa, J., H. Hyyppa, D. Leckie, F. Gougeon, X. Yu, and M. Maltamo. 2008. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in b oreal forests. International Journal of Remote Sensing 29(5): 1339-1366 IPCC. Intergovernmental Panel on Climate Change. 2003. Good Practice Guidance on Land Use, Land-Use Change and Forestry. Eggleston, H.S.,Buendia, L., Miwa, K., Ngara, T. and Tanabe, K. (eds.). National Greenhouse Gas Inventories Programme. Institute for Global Environmental Strategies (IGES). Japan.
http://www.ipcc- ITTO. 2005. International Tropical Timber Organization. 2005. Status of tropical forest management 2005. Available: Jensen, J. R., Im, J., Jensen, R., and P. Hardin, 2009, «Image Classification,» in Handbook of Remote Sensing, Nellis, D. and T. Warner (Eds.), Boca Raton, FL: CRC Press, 82-102 (Chapter 19). 24 Jensen, J., 2007, Remote Sensing of Environment: An Earth Resource Perspective, 2nd ed., Upper Saddle River, NJ: Pearson Prentice Hall, 592 p. Kauppi, P.E., J.H. Ausubel, J.Y. Fang, A.S. Mather, R.A. Sedjo, and P.E. Waggoner. 2006. Returning forests analyzed with the forest identity. Proceedings of the National Academy of Sciences of the United States 103(46): 17574-17579. Kayitakire, F., C. Hamel, and P. Defourny. 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment 102(3- 4): 390-401. Kellndorfer, J., W. Walker, D. Nepstad, C. Stickler, P. Brando, P. Lefebvre, A. Rosenqvist, and M. Shimada. 2008. Implementing REDD: The potential of ALOS/PALSAR for forest mapping and monitoring. Paper presented at the Second GEOSS Asia-Pacific Symposium. April 2008, Tokyo, Japan. Kimes, D.S., K.J. Ranson, G. Sun, and J.B. Blair. 2006. Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sensing of Environment 100(4): 503-511. Leprieur,P.E. ;Kerr ?Y.H.,Mastorchio,S.,Meunier,J .C.,2000 .Monitoring vegetation cover across semi-arid regions:comparision of remote observations from various scales.International journal of remote sensing21:281-300. Lu, D.S. 2006. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing 27(7): 1,297-1,328 Lund, H. 2009. What is a degraded forest. Forest Information Services. Gainesville, VA. USA. http://home.comcast.net/~gyde/2009forestdegrade.doc Luus, K.A., and R.E.J. Kelly. 2008. Assessing productivity of vegetation in the Amazon using M. Shimada. 2008. Implementing REDD: The potential of ALOS/PALSAR for forest Malhi, Y., J.T. Roberts, R.A. Betts, T.J. Killeen, W.H. Li and C.A. Nobre. 2008. Climate change, deforestation, and the fate of the Amazon. Science 319: 169-172. Maltamo, M., K. Eerikainen, P. Packalen, and J. Hyyppa. 2006. Estimation of stem volume mapping and monitoring. Paper presented at the Second GEOSS Asia-Pacific Marrakech Accords. Bonn, Germany. 25 Means, J.E., S.A. Acker, D.J. Harding, J.B. Blair, M.A. Lefsky,
W.B. Cohen, M.E. Harmon, and Mollicone D., Achard F., Federici S., Eva H.D., Grassi G., Belward A., Raes F., Seufert G., Stibig H.-J., Matteucci G., Schulze E.-D. 2007. An incentive mechanism for reducing emissions from conversion of intact and non-intact forests. Climatic Change 83: 477- 493. Nelson, R F, R S Latty and G Mott. 1984. Classifying northern forests using thematic mapper simulator data. PE&RS 50, 5, pp 607-617. Nemani, R, P S Running and L Band. 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed LeafArea Index estimates. IJRS 14, 13, pp 2519-2534. Oliveira, P. J. C., G. P. Asner, D. E. Knapp, A. Almeyda, R. Galvan-Gildemeister, S. Keene, R. Raybin, and R. C. Smith. 2007. Land-use allocation protects the Peruvian Amazon. Science 317:1233-1236. Page, S.E., F. Siegert, J.O. Rieley, H.D.V. Boehm, A. Jaya, and S. Limin. 2002. The amount of c arbon released from peat and forest fires in Indonesia during 1997. Nature 420(6911): 61 -65. Palace, M., Keller, M., Asner, G., Hagen, S., and B. Braswel. 2008. Amazon Forest Structure from IKONOS Satellite Data and the Automated Characterization of Forest Canopy Properties. Biotropica 40: 141-150 Patenaude, G., R. Milne, and T.P. Dawson. 2005. Synthesis of remote sensing approaches for forest carbon estimation: Reporting to the Kyoto Protocol. Environmental Science and Policy 8(2): 161-178. Peres, C., Barlow, J., and Laurance, W. 2006. Detecting anthropogenic disturbance in tropical forests. TRENDS in Ecology and Evolution 21, 227-229. Peterson, L.K., K.M. Bergen, D.G. Brown, L. Vashchuk, and Y. Blam. 2009. Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region. Forest Ecology and Management 257(3): 911-922 Popescu, S.C., R.H. Wynne, and R.F. Nelson. 2003. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing 29(5): 564-577. 26 Rosenqvist, A., A. Milne, R. Lucas, M. Imhoff, and C. Dobson. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science and Policy 6(5): 441-455 Simula, M. 2009. Towards defining forest degradation: comparative analysis of existing definitions. Forest Resources Assessment. Pp 57. Working Paper 154. FAO, Rome. ftp://ftp.fao.org/docrep/fao/012/k6217e/k6217e00.pdf Smith, J A, T L Lin et al. 1980. The Lambertian assumption andLandsat data. PE&RS 46, 9, pp 1183-1189. Song, C. 2007. Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery. International Journal of Remote Sensing 28(15): 3305- 3322 Song, C., T.A. Schroeder, and W.B. Cohen. 2007. Predicting temperate conifer forest successional stage distributions with multitemporal Landsat Thematic Mapper imagery. Remote Sensing of Environment 106(2): 228-237 Souza, C., L. Firestone, L. M. Silva, and D. Roberts. 2003. Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models. Remote Sensing of Environment 87:494-506. Souza, C., D. A. Roberts, and M. A. Cochrane. 2005. Combining spectral and spatial information to map canopy damages from selective logging and forest fires. Remote Sensing of Environment 98:329-343. Souza, C., Cochrane, M., Sales, M., Monteiro, A., Mollicone, D. 2009. Integrating forest transects and remote sensing data to quantify carbon loss due to forest degradation in the Brazilian Amazon, FRA Working Paper 161 Tucker, C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Rem Sens of Environ 8, pp 127-150. Tucker, C.J., J.R. Townshend, and T.E. Goff. 1985. African land-cover classification using satellite data. Science 227: 369-375. UNFCCC (United Nations Framework Convention on Climate Change). 2001. COP-7: The Marrakech Accords. Bonn, Germany. |
|