Derivation of biomass information for semi-arid areas using remote-sensing data

International Journal of Remote Sensing

Volumn 33, Issue 9, 2012, Pages 2937-2984

  Eisfelder, Christina ^a   Kuenzer, Claudia ^a   Dech, Stefan ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARID REGIONS; BIOMASS; CLIMATE CHANGE; ECOSYSTEMS; FORESTRY; RADAR MEASUREMENT; RADAR SIGNAL PROCESSING; SPACE OPTICS; SPACE-BASED RADAR; VEGETATION;

ABOVE GROUND BIOMASS; BIOMASS ESTIMATION; EMPIRICAL RELATIONSHIPS; ENVIRONMENTAL PROCESS; HYDROLOGICAL CYCLES; REMOTE SENSING DATA; RESEARCH ACTIVITIES; VEGETATION BIOMASS;

REMOTE SENSING;

BIOMASS; CLIMATE CHANGE; ESTIMATION METHOD; REMOTE SENSING; SEMIARID REGION;

EID: 84857941233     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2011.620034     Document Type: Review

References (161)

1. Addink, E.A., de Jong, S.M. and Pebesma, E.J., 2007, Object definition for aboveground biomass and leaf area index estimation. In 5th EARSeL Workshop on Imaging Spectroscopy, 23-25 April 2007, Bruges, Belgium, 14 pp http://5thearselsigis.vgt.vito.be/CD/Fullpapers/Addink_final.pdf
2. Anderson, G.L., Hanson, J.D. and Haas, R.H. 1993. Evaluating Landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of Environment, 45: 165-175.
3. Aranha, J.T., Viana, H.F. and Rodriguez, R. 2008. "Vegetation classification and quantification by satellite image processing. A case study in north Portugal". In Bioenergy: Challenges and Opportunities, International Conference and Exhibition on Bioenergy, Guimaraes, Portugal, 7: Universidad de Minho. 6-9 April 2008
4. Baban, S.M.J. 2005. Accomplishing sustainable development in Southern Kurdistan using geoinformatics: an overview. Zanin - Kurdish Scientific and Medical eJournal, 1: 31-43.
5. Baccini, A., Laporte, N.T., Goetz, S.J., Sun, M. and Huang, D. 2008. A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environmental Research Letters, 3: 9
6. Baret, F. and Buis, S. 2008. "Estimating canopy characteristics from remote sensing observations: review of methods and associated problems". In Advances in Land Remote Sensing, Edited by: Liang, S. 173-201. Dordrecht: Springer.
7. Baumann, G. 2009. How to assess rangeland condition in semiarid ecosystems? The indicative value of vegetation in the High Atlas Mountains, 195 Morocco, Dissertation: Mathematisch-Naturwissen-schaftlichen Fakultät, Universität Köln. Cologne, Germany
8. Bazilevisch, N.I. and Ye Rodin, L. 1967. Maps of productivity and the biological cycle in the Earth's principal terrestrial vegetation types. Izvestiya Vsesoyuznogo Geograficheskogo Obschestva, 99: 190-194.
9. Beeri, O., Phillips, R., Hendrickson, J., Frank, A.B. and Kronberg, S. 2007. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110: 216-225.
10. Bolin, B., Degens, E.T., Kempe, S. and Ketner, P. 1979. The Global Carbon Cycle, SCOPE 13, New York: Wiley.
11. Boyd, D.S., Foody, G.M., Curran, P.J., Lucas, R.M. and Honzak, M. 1996. An assessment of radiance in Landsat TM middle and thermal infrared wavebands for the detection of tropical forest regeneration. International Journal of Remote Sensing, 17: 249-261.
12. Breiman, L. 2001. Random forests. Machine Learning, 45: 5-32.
13. Brown, S. 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116: 363-372.
14. Calvão, T. and Palmeirim, J.M. 2004. Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour. International Journal of Remote Sensing, 25: 3113-3126.
15. CESBIO, 2010, Biomass monitoring mission for carbon assessment (BIOMASS) http://www.cesbio.ups-tlse.fr/us/indexbiomass.html
16. Chen, W., Blain, D., Li, J., Koehler, K., Fraser, R., Zhang, Y., Leblanc, S., Olthof, I., Wang, J. and McGovern, M. 2009. Biomass measurements and relationships with Landsat-7/ETM+ and JERS-1/SAR data over Canada's western sub-arctic and low arctic. International Journal of Remote Sensing, 30: 2355-2376.
17. Cho, M.A. and Skidmore, A.K. 2009. Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. International Journal of Remote Sensing, 30: 499-515.
18. Chopping, M., Su, L., Rango, A., Martonchik, J.V., Peters, D.P.C. and Laliberte, A. 2008. Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model. Remote Sensing of Environment, 112: 19-34.
19. Cissé, M.I. 1980. "The browse production of some trees in the Sahel: relationships between maximum foliage biomass and various physical parameters". In Browse in Africa, the Current State of Knowledge, Edited by: Le Houérou, H.N. 205-210. Addis Ababa: International Livestock Centre for Africa.
20. Collins, J.N., Hutley, L.B., Williams, R.J., Boggs, G., Bell, D. and Bartolo, R. 2009. Estimating landscape-scale vegetation carbon stocks using airborne multi-frequency polarimetric synthetic aperture radar (SAR) in the savannahs of north Australia. International Journal of Remote Sensing, 30: 1141-1159.
21. Costanza, R., d'Arge, R, de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Caeem, S., O'Neill, R.V., Paruelo, J., Raskin, R.G., Sutton, P. and van den Belt, M. 1997. The value of the world's ecosystem services and natural capital. Nature, 387: 253-260.
22. Cronin, N.L.R. 2004. The potential of airborne polarimetric synthetic aperture radar data for quantifying and mapping the biomass and structural diversity of woodlands in semi-arid Australia, 517 Sydney, NSW, Australia: PhD thesis, School of Biological, Earth and Environmental Sciences, University of New South Wales.
23. Dancy, K.J., Webster, R. and Abel, N.O.J. 1986. Estimating and mapping grass cover and biomass from low-level photographic sampling. International Journal of Remote Sensing, 7: 1679-1704.
24. Dech, S., Bachmann, M. and Müller, A. November 2003 2003. "New remote sensing approaches in support of sustainable land use management in semi-arid environments". In 30th International Symposium on Remote Sensing of Environment November 2003, 10-14. Honolulu, HI, USA
25. Dech, S., Günther, K. and Strunz, G. 2001. "Möglichkeiten der Fernerkundung zur Bilanzierung des terrestrischen Kohlenstoffbudgets". In Wissenschaftlich-Technische Jahrestagung Der DGPF, Band 10, Edited by: Seyfert, E. 23-32. Berlin: Deutsche Gesellschaft für Photogrammetrie und Fernerkundung.
26. Diallo, O., Diouf, A., Hanan, N.P., Ndiaye, A. and Prévost, Y. 1991. AVHRR monitoring of savanna primary production in Senegal, West Africa: 1987-88. International Journal of Remote Sensing, 12: 1259-1279.
27. DIERCKE. 1992. Diercke Weltatlas, 3rd, 220-221. Braunschweig: Westermann Schulbuchverlag GmbH.
28. Diouf, A. and Lambin, E. 2001. Monitoring land-cover changes in semi-arid regions: remote sensing data and field observations in the Ferlo, Senegal. Journal of Arid Environments, 48: 129-148.
29. Dong, J., Kaufmann, R.K., Myneni, R.B., Tucker, C.J., Kauppi, P.E., Liski, J., Buermann, W., Alexeyev, V. and Hughes, M.K. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment, 84: 393-410.
30. du Plessis, W.P. 1999. Linear regression relationship between NDVI, vegetation and rainfall in Etosha National Park, Namibia. Journal of Arid Environments, 42: 235-260.
31. Duncan, J., Stow, D., Franklin, J. and Hope, A. 1993. Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico. International Journal of Remote Sensing, 14: 3395-3416.
32. Elvidge, C.D. and Lyon, R.J.P. 1985. Influence of rock-soil spectral variation on the assessment of green biomass. Remote Sensing of Environment, 17: 265-279.
33. Eswaran, H., Lal, R. and Reich, P.F. 2001. "Land degradation: an overview. In Responses to Land Degradation". In Proceedings of the 2nd International Conference on Land Degradation and Desertification, Edited by: Bridges, E.M., Hannam, I.D., Oldeman, L.R., Pening, F.W.T., de Vries, S.J., Scherr, S.J. and Sompatpanit. 20-35. Khon Kaen New Delhi, Thailand: Oxford Press. 2001
34. Etienne, M. 1989. Non destructive methods for evaluating shrub biomass: a review. Acta Oecologica - International Journal of Ecology, 10: 115-128.
35. Fabricius, C., Burger, M. and Hockey, P.A.R. 2003. Comparing biodiversity between protected areas and adjacent rangeland in xeric succulent thicket, South Africa: arthropods and reptiles. Journal of Applied Ecology, 40: 392-403.
36. Feng, X., Liu, Y. and Zhao, Y. 2005. "Remote sensing linked modelling of the aboveground biomass of semiarid grasslands in Inner Mongolia". In Proceedings of International Geoscience and Remote Sensing Symposium 2005, Vol. 5, 3047-3050. Seoul, Korea: IGARSS'. 05, 29 July 2005
37. Field, C.B., Campbell, J.E. and Lobell, D.B. 2008. Biomass energy: the scale of the potential resource. Trends in Ecology and Evolution, 23: 65-72.
38. Filella, I. and Peñuelas, J. 1994. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15: 1459-1470.
39. 2 uptake of a Mediterranean shrubland submitted to experimental warming and drought. Remote Sensing of Environment, 90: 308-318.
40. Food and Agriculture Organization (FAO), 1989, Arid zone forestry: a guide for field technicians http://www.fao.org/docrep/T0122E/t0122e03.htm (Accessed: 16 March 2010).
41. FAO, 2006, Global Forest Resources Assessment 2005: progress towards sustainable forest management (Rome: Food and Agriculture Organization of the United Nations) http://ftp://ftp.fao.org/docrep/fao/008/A0400E/A0400E00.pdf (Accessed: 16 March 2010).
42. Franklin, J. and Hiernaux, P.H.Y. 1991. Estimating foliage and woody biomass in Sahelian and Sudanian woodlands using a remote sensing model. International Journal of Remote Sensing, 12: 1387-1404.
43. Frison, P.L. and Mougin, E. 1996. Monitoring global vegetation dynamics with ERS-1 wind scatterometer data. International Journal of Remote Sensing, 17: 3201-3218.
44. Frison, P.L., Mougin, E. and Hiernaux, P. 1998. Observations and interpretation of seasonal ERS-1 wind scatterometer data over Northern Sahel (Mali). Remote Sensing of Environment, 63: 233-242.
45. Frison, P.L., Mougin, E., Jarlan, L., Karam, M.A. and Hiernaux, P. 2000. Comparison of ERS wind-scatterometer and SSM/I data for Sahelian vegetation monitoring. IEEE Transactions on Geoscience and Remote Sensing, 38: 1794-1803.
46. Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H. and Hurtt, G.C. 2009. Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research-Biogeosciences, 114: G00E02 doi:10.1029/2008JG000911
47. García, M., Riaño, D., Chivieco, E. and Danson, F.M. 2010. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 114: 816-830.
48. García-Haro, F.J., Gilabert, M.A. and Meliá, J. 1996. Linear spectral mixture modelling to estimate vegetation amount from optical spectral data. International Journal of Remote Sensing, 17: 3373-3400.
49. Gibbs, H.K., Brown, S., Niles, J.O. and Foley, J.A. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters, 2: 045023 doi:10.1088/1748 9326/2/4/045023
50. Global Climate Observing System (GCOS), 2003, The second report on adequacy of global observation systems for climate in support of the UNFCCC. Global climate observing system, world meteorological organisation, report GCOS-82 http://www.fao.org/gtos/doc/ECVs/T12/ECV-T12-biomass-ref04-GCOS-2nd-Adequacy-Report.pdf
51. Global Climate Observing System (GCOS), 2005, GCOS regional action plan for eastern and central Europe, December 2005 http://www.wmo.int/pages/prog/gcos/documents/GCOS_ECE_RAP_Dec05.pdf (Accessed: 16 March 2010).
52. Global Climate Observing System (GCOS), 2006, Systematic observation requirements for satellite-based products for climate. Supplemental details to the satellite-based component of the 'Implementation plan for the global observing system for climate in support of the UNFCCC'. GCOS-107, WMO/TD No. 1338 http://www.wmo.int/pages/prog/gcos/Publications/gcos-107.pdf (Accessed: 16 March 2010).
53. Global Terrestrial Observing System (GTOS), 2009, Biomass. Assessment of the status of the development of the standards for the terrestrial essential climate variables. GTOS 67 http://www.fao.org/gtos/doc/ECVs/T12/T12.pdf (Accessed: 16 March 2010).
54. Goetz, S., Baccini, A., Laporte, N.T., Johns, T., Walker, W., Kellndorfer, J., Houghton, R. and Sun, M., 2009, Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance and Management, 4, 7 http://www.cbmjournal.com/content/4/1/2
55. Graetz, R.D. and Gentle, M.R. 1982. The relationships between reflectance in the Landsat wavebands and the composition of an Australian semi-arid shrub rangeland. Photogrammetric Engineering and Remote Sensing, 48: 1721-1730.
56. Graetz, R.D., Pech, R.P. and Davis, A.W. 1988. The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data. International Journal of Remote Sensing, 9: 1201-1222.
57. Grassi, G., Monni, S., Federici, S., Achard, F. and Mollicone, D. 2008. Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates. Environmental Research Letters, 4: 035005 doi:10.1088/17489326/4/3/035005
58. Grippa, M. and Woodhouse, I.H. 2002. Retrieval of bare soil and vegetation parameters from wind scatterometer measurements over three different climatic regions. Remote Sensing of Environment, 84: 16-24.
59. Harris, R.W. 1992. Root-shoot ratios. Journal of Arboriculture, 18: 39-42.
60. Henry, M., Tittonell, P., Manlay, R.J., Bernoux, M., Albrecht, A. and Vanlauwe, B. 2009. Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder farming systems of western Kenya. Agriculture, Ecosystems and Environment, 129: 238-252.
61. Hirata, M., Kogab, N., Shinjo, H., Fujita, H., Gintzburger, G. and Miyazaki, A. 2001. Vegetation classification by satellite image processing in a dry area of northeastern Syria. International Journal of Remote Sensing, 23: 507-516.
62. Hobbs, T.J. 1995. The use of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia. International Journal of Remote Sensing, 16: 1289-1302.
63. Holdridge, L.R. 1967. Life Zone Ecology, San Jose: Tropical Science Center.
64. Holm, A.M., Cridland, S.W. and Roderick, M.L. 2003. The use of time-integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of Western Australia. Remote Sensing of Environment, 85: 145-158.
65. Huete, A.R., Jackson, R.D. and Post, D.F. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17: 37-53.
66. Imeson, A.C and Lavee, H. 1998. Soil erosion and climate change: the transect approach and the influence of scale. Geomorphology, 23: 219-227.
67. Integrated Global Carbon Observing System (IGCOS), 2004, Integrated global carbon observation theme. A strategy to realize a coordinated system of integrated global carbon cycle observations http://www.fao.org/gtos/doc/ECVs/T12/ECV-T12-biomass-ref19-IGCO-carbon-theme.pdf (Accessed: 16 March 2010).
68. Intergovernmental Panel on Climate Change (IPCC), 2003, Good Practice Guidance for land use, land-use change and forestry. Institute for Global Environmental Strategies, Kanagawa, Japan http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.html (Accessed: 16 March 2010).
69. Intergovernmental Panel on Climate Change (IPCC), 2006, Guidelines for national greenhouse gas inventories, Vol. 4, Agriculture, forestry and other land use. Kanagawa, Japan http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html (Accessed: 16 March 2010).
70. Jakubauskas, M., Kindscher, K. and Debinski, D. 2001. Spectral and biophysical relationships of montane sagebrush communities in multi-temporal SPOT XS data. International Journal of Remote Sensing, 22: 1767-1778.
71. Jarlan, L., Mangiarotti, S., Mougin, E., Mazzega, P., Hiernaux, P. and Le Dantec, V. 2008. Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation growth model. Remote Sensing of Environment, 112: 1381-1394.
72. Jarlan, L., Mazzega, P. and Mougin, E. 2002, Retrieval of land surface parameters in the Sahel from ERS wind scatterometer data: a. "brute force" method. IEEE Transactions on Geoscience and Remote Sensing, 40: 2056-2062.
73. Jarlan, L., Mazzega, P., Mougin, E., Lavenu, F., Marty, G., Frison, P.L. and Hiernaux, P. 2003. Mapping of sahelian vegetation parameters from ERS scatterometer data with an evolution strategies algorithm. Remote Sensing of Environment, 87: 72-84.
74. Justice, C.O. and Townshend, J.R.G. 1981. "Integrating ground data with remote sensing". In Terrain Analysis and Remote Sensing, Edited by: Townshend, J.R.G. 38-58. London: George Allen & Unwin.
75. Kasischke, E.S., Melack, J.M. and Dobson, M.C. 1997. The use of imaging radars for ecological applications - a review. Remote Sensing of Environment, 59: 141-156.
76. Kawamura, K., Akiyama, T., Yokota, H., Tsutsumi, M., Yasuda, T., Watanabe, O. and Wang, S.P. 2005. Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China. Agriculture, Ecosystems and Environment, 107: 83-93.
77. Kennedy, P.J. 1989. Monitoring the phenology of Tunisian grazing lands. International Journal of Remote Sensing, 10: 835-845.
78. Köchy, M., Mathaj, M., Jeltsch, F. and Malkinson, D. 2008. Resilience of stocking capacity to changing climate in arid to Mediterranean landscapes. Regional Environmental Change, 8: 73-87.
79. Kogan, F., Stark, R., Gitelson, A., Jargalsaikhan, L., Dugrajav, C. and Tsooj, S. 2004. Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. International Journal of Remote Sensing, 25: 2889-2896.
80. Kraus, T. and Samimi, C. 2002. Biomass estimation for land use management and fire management using Landsat-TM and -ETM+. Erdkunde, 56: 130-143.
81. Le Maire, G., François, C., Soudani, K., Berveiller, D., Pontailler, J.-Y., Bréda, N., Genet, H., Davi, H. and Dufrêne, E. 2008. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112: 3846-3864.
82. Liu, A.X., Liu, Z.J., Wang, C.Y. and Niu, Z. 2003. "Monitoring of desertification in central Asia and western China using long term NOAA-AVHRR NDVI time-series data". In Proceedings, IEEE International Geoscience and Remote Sensing Symposium, 2003, Vol. 4, 2278-2280. IGARSS. 21-25 July 2003, Toulouse, France
83. Lo Seen, D., Mougin, E., Rambal, S., Gaston, A. and Hiernaux, P. 1995. A regional Sahelian grassland model to be coupled with multispectral satellite data. II: toward the control of its simulations by remotely sensed indices. Remote Sensing of Environment, 52: 194-206.
84. Lu, D. 2006. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27: 1297-1328.
85. 0 satellite data. Remote Sensing of Environment, 112: 1365-1380.
86. McVicar, T.R. and Jupp, D.L.B. 1998. The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agricultural Systems, 57: 399-468.
87. Means, J.E., Acker, S.A., Harding, D.J., Blair, J.B., Lefski, M.A., Cohen, W.B., Harmon, M.E. and McKee, W.A. 1999. Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western cascades of Oregon. Remote Sensing of Environment, 67: 298-308.
88. Millennium Ecosystem Assessment Board (MEAB), 2005, Living beyond our means. Natural assets and human well-being. Statement from the Millennium Ecosystem Assessment Board http://www.millenniumassessment.org/documents/document.429.aspx.pdf (Accessed: 16 March 2010).
89. Mills, A.J., Turpie, J.K., Cowling, R.M., Marais, C., Kerley, G.I.H., Lechmere-Oertel, R.G., Sigwela, A.M. and Powell, M. 2007. "Assessing costs, benefits, and feasibility of restoring natural capital in subtropical thicket in South Africa". In Restoring Natural Capital: Science, Business, and Practice, Edited by: Aronson, J., Milton, S.J. and Blignaut, J.N. 179-187. Washington, DC: Island Press.
90. Mitchard, E.T.A., Saatchi, S.S., Woodhouse, I.H., Nangendo, G., Ribeiro, N.S., Williams, M., Ryan, C.M., Lewis, S.L., Feldpausch, T.R. and Meir, P. 2009. Using satellite radar backscatter to predict above-ground woody biomass: a consistent relationship across four different African landscapes. Geophysical Research Letters, 36: L23401 doi:doi:10.1029/ 2009GL040692
91. Moleele, N., Ringrose, S., Arnberg, W., Lunden, B. and Vanderpost, C. 2001. Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands. International Journal of Remote Sensing, 22: 741-756.
92. Montandon, L.M. and Small, E.E. 2008. The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sensing of Environment, 112: 1835-1845.
93. Montès, N., Bertaudiere-Montes, V., Badri, W., Zaoui, E.H. and Gauquelin, T. 2002. Biomass and nutrient content of a semi-arid mountain ecosystem: the Juniperus thurifera L. woodland of Azzaden Valley (Morocco). Forest Ecology and Management, 166: 35-43.
94. Mougin, E., Lo Seen, D., Rambal, S., Gaston, A. and Hiernaux, P. 1995. A regional sahelian grassland model to be coupled with multispectral satellite data. I. Description and validation. Remote Sensing of Environment, 52: 181-193.
95. Mulligan, M. 2009. "Integrated environmental modelling to characterise processes of land degradation and desertification for policy support". In Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment, Edited by: öder, A. R and Hill, J. 45-72. London: Taylor & Francis Group.
96. Musick, H.B. 1984. Assessment of Landsat multispectral scanner spectral indices for monitoring arid rangeland. IEEE Transactions on Geoscience and Remote Sensing, 22: 512-519.
97. Mutanga, O. and Rugege, D. 2006. Integrating remote sensing and spatial statistics to model herbaceous biomass distribution in a tropical savanna. International Journal of Remote Sensing, 27: 3499-3514.
98. Mutanga, O. and Skidmore, A.K. 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25: 3999-4014.
99. Næsset, E. and Gobakken, T. 2008. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment, 112: 3079-3090.
100. Nelson, R.F., Kimes, D.S., Salas, W.A. and Routhier, M. 2000. Secondary forest age and tropical forest biomass estimation using thematic mapper imagery. Bioscience, 50: 419-431.
101. Nijland, W., Addink, E.A., de Jong, S.M. and van der Meer, F.D. 2009. Optimizing spatial image support for quantitative mapping of natural vegetation. Remote Sensing of Environment, 113: 771-780.
102. Nouvellon, Y., Moran, M.S., Seen, D.L., Bryant, R., Rambal, S., Ni, W., Bégué, A., Chehbouni, A., Emmerich, W.E., Heilman, P. and Qi, J. 2001. Coupling a grassland ecosystem model with Landsat imagery for a 10-year simulation of carbon and water budgets. Remote Sensing of Environment, 78: 131-149.
103. Okin, G.S., Roberts, D.A., Murray, B. and Okin, W.J. 2001. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment, 77: 212-225.
104. Otterman, J. 1996. Desert-scrub as the cause of reduced reflectances in protected versus impacted sandy arid areas. International Journal of Remote Sensing, 17: 615-619.
105. Ozdemir, I. 2008. Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing, 19: 5643-5655.
106. Parresol, B.R. 1999. Assessing tree and stand biomass: a review with examples and critical comparisons. Forest Science, 45: 573-593.
107. Patenaude, G., Milne, R. and Dawson, T.P. 2005. Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol. Environmental Science and Policy, 8: 161-178.
108. Pereira, J.M.C., Oliveira, T.M. and Paul, J.C.P. 1995. Satellite-based estimation of Mediterranean shrubland structural parameters. EARSeL Advances in Remote Sensing, 4: 14-20.
109. Pfaff, A.S.P., Kerr, S., Hughes, R.F., Liu, S., Sanchez-Azofeifa, G.A., Schimel, D., Tosi, J. and Watson, V. 2000. The Kyoto protocol and payments for tropical forest: an interdisciplinary method for estimating carbon-offset supply and increasing the feasibility of a carbon market under the CDM. Ecological Economics, 35: 203-221.
110. Philip's. 2003. In Philip's School Atlas, 74-75. London: Climatic Regions. Chancellor Press. George Philip Limited)
111. Pickup, G. 1996. Estimating the effects of land degradation and rainfall variation on productivity in rangelands: an approach using remote sensing and models of grazing and herbage dynamics. Journal of Applied Ecology, 33: 819-832.
112. Pieper, R.D. 1988. "Rangeland vegetation-productivity and biomass". In Vegetation Science Applications for Rangeland Analysis and Management, Handbook of Vegetation Science, Edited by: Tueller, P.T. 449-469. Dordrecht: Kluwer.
113. Prince, S.D. 1991. Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981-1988. International Journal of Remote Sensing, 12: 1301-1311.
114. Prince, S.D. and Astle, W.L. 1986. Satellite remote sensing of rangelands in Botswana. I: Landsat MSS and herbaceous vegetation. International Journal of Remote Sensing, 7: 1533-1553.
115. Prince, S.D. and Tucker, C.J. 1986. Satellite remote sensing of rangelands in Botswana II: NOAA AVHRR and herbaceous vegetation. International Journal of Remote Sensing, 7: 1555-1570.
116. Propastin, P. 2006. Remote sensing based study on vegetation dynamics in drylands of Kazakhstan. Dissertation, Mathematisch-Naturwissenschaftliche Fakultät, Georg-August-Universität Göttingen, Göttingen, Germany
117. Qi, J., Marsett, R.C. and Heilman, P. 2000. "Rangeland vegetation cover estimation from remotely sensed data". In 2nd International Conference on Geospatial Information in Agriculture and Forestry Lake Buena Vista, FL, pp. II243-II252 10-12 January 2000
118. Qi, J. and Wallace, O. 2002. "Biophysical attributes estimation from satellite images in arid regions". In Proceedings of the International Geoscience and Remote Sensing Symposium 2002, Vol. 4, 24-28. Toronto, Canada: IGARSS. June 2002
119. Radloff, F.G.T. and Mucina, L. 2007. A quick and robust method for biomass estimation in structurally diverse vegetation. Journal of Vegetation Science, 18: 719-724.
120. Ringrose, S., Matheson, W., Matlala, C.J.S.S., O'Neill, T. and Werner, P.A. 1994. Vegetation spectral reflectance along a north-south vegetation gradient in northern Australia. Journal of Biogeography, 21: 33-47.
121. Rosema, A. 1993. Using METEOSAT for operational evapotranspiration and biomass monitoring in the Sahel region. Remote Sensing of Environment, 46: 27-44.
122. Rosenqvist, Å, Milne, A., Lucas, R., Imhoff, M. and Dobson, C. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science and Policy, 6: 441-455.
123. Running, S., Loveland, T.R., Pierce, L.L., Nemani, R.R. and Hunt, E.R. 1995. A remote sensing based vegetation classification logic for global land cover analysis. Remote Sensing of Environment, 51: 39-48.
124. Samimi, C. and Kraus, T. 2004. Biomass estimation using Landsat-TM and -ETM+. Towards a regional model for Southern Africa? GeoJournal, 59: 177-187.
125. Sannier, C.A.D., Taylor, J.C. and Plessis, W.D. 2002. Real-time monitoring of vegetation biomass with NOAA-AVHRR in Etosha National Park, Namibia, for fire risk assessment. International Journal of Remote Sensing, 23: 71-89.
126. Santos, J.R., Pardi Lacruz, M.S., Araujo, L.S. and Keil, M. 2002. Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data. International Journal of Remote Sensing, 23: 1217-1229.
127. Segoli, M., Ungar, E.D. and Shachak, M. 2008. Shrubs enhance resilience of a semi-arid ecosystem by engineering and regrowth. Ecohydrology, 1: 330-339.
128. Shoshany, M. 2000. Satellite remote sensing of natural Mediterranean vegetation: a review within an ecological context. Progress in Physical Geography, 24: 153-178.
129. Strahler, A.N. and Strahler, A.H. 1984. Elements of Physical Geography, 3rd, 538 New York: Wiley.
130. Suganuma, H., Abe, Y., Taniguchi, M., Tanouchi, H., Utsugi, H., Kojima, T. and Yamada, K. 2006. Stand biomass estimation method by canopy coverage for application to remote sensing in an arid area of Western Australia. Forest Ecology and Management, 222: 75-87.
131. Svoray, T. and Shoshany, M. 2002. SAR-based estimation of areal aboveground biomass (AAB) of herbaceous vegetation in the semi-arid zone: a modification of the water-cloud model. International Journal of Remote Sensing, 23: 4089-4100.
132. Svoray, T. and Shoshany, M. 2003. Herbaceous biomass retrieval in habitats of complex composition: a model merging SAR images with unmixed Landsat TM data. IEEE Transactions on Geoscience and Remote Sensing, 41: 1592-1601.
133. Svoray, T., Shoshany, M., Curran, P.J., Foody, G.M. and Perevolotsky, A. 2001. Relationship between green leaf biomass volumetric density and ERS-2 SAR backscatter of four vegetation formations in the semi-arid zone of Israel. International Journal of Remote Sensing, 22: 1601-1607.
134. Thenkabail, P.S., Stucky, N., Griscom, B.W., Ashton, M.S., Diels, J., van der Meer, B. and Enclona, E. 2004. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data. International Journal of Remote Sensing, 25: 5447-5472.
135. Tietema, T. 1993. Biomass determination of fuelwood trees and bushes of Botswana, Southern Africa. Forest Ecology and Management, 60: 257-269.
136. Todd, S.W., Hoffer, R.M. and Milchunas, D.G. 1998. Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing, 19: 427-438.
137. Tucker, C.J. 1980. A critical review of remote sensing and other methods for non-destructive estimation of standing crop biomass. Grass and Forage Science, 35: 177-182.
138. Tucker, C.J., Vanpraet, C.L., Sharman, M.J. and van Ittersum, G. 1985. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980-1984. Remote Sensing of Environment, 17: 233-249.
139. Tueller, P.T. 1987. Remote sensing science applications in arid environments. Remote Sensing of Environment, 23: 143-154.
140. United Nations (UN), 1994, Elaboration of an international convention to combat desertification in countries experiencing serious drought and/or desertification, particularly in Africa. UN General Assembly. A/AC.241/27 http://www.unccd.int/convention/text/pdf/conv-eng.pdf (Accessed: 17 March 2010).
141. United Nations (UN), 2004, A more secure world: our shared responsibility. Report of the secretary-general's high-level panel on threats, challenges and change. United Nations Department of Public Information, DPI/2367, December 2004 http://www.un.org/secureworld/report2.pdf (Accessed: 17 March 2010).
142. United Nations Environment Programme (UNEP), 1999, National action programme to combat desertification in Republic of Uzbekistan. Tashkent 1999 http://www.unccd.int/actionprogrammes/asia/national/1999/uzbekistan-eng.pdf
143. United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), 2008, Carbon in drylands: desertification, climate change and carbon finance. In A UNEP-UNDP-UNCCD Technical Note for Discussions at CRIC 7, Istanbul, Turkey, 3-14 November 2008 http://www.unep-wcmc.org/pdfs/Carbon%20in%20drylands.pdf (Accessed: 17 March 2010).
144. United Nations Sahelian Office/United Nations Development Programme (UNSO/UNDP). 1997. Aridity zones and dryland populations: an assessment of population levels in the World's drylands, New York: United Nations Office to Combat Desertification and Drought/United Nations Development Programme, 23.
145. van der Meer, F., Bakker, W., Scholte, K., Skidmore, A., de Jong, S., Clevers, J., Addink, E. and Epema, G. 2001. Spatial scale variations in vegetation indices and above-ground biomass estimates: implications for MERIS. International Journal of Remote Sensing, 22: 3381-3396.
146. Vanamburg, L.K., Trlica, M.J., Hoffer, R.M. and Weltz, M.A. 2006. Ground based digital imagery for grassland biomass estimation. International Journal of Remote Sensing, 27: 939-950.
147. Verbesselt, J., Somers, B., van Aardt, J., Jonckheere, I. and Coppin, P. 2006. Monitoring herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems. Remote Sensing of Environment, 101: 399-414.
148. Verón, S.R., Parualo, J.M. and Oesterheld, M. 2010. Grazing-induced losses of biodiversity affect the transpiration of an arid ecosystem. Oecologia, doi: 10.1007/s00442-010-1780-4, 10
149. Verstraete, M.M. 1986. Defining desertification: a review. Climatic Change, 9: 5-18.
150. Verstraete, M.M. and Pinty, B. 1991. The potential contribution of satellite remote sensing to the understanding of arid lands processes. Vegetation, 91: 59-72.
151. Wang, C. and QI, J. 2008. Biophysical estimation in tropical forests using JERS-1 SAR and VNIR imagery II. Aboveground woody biomass. International Journal of Remote Sensing, 29: 6827-6849.
152. 2 advanced very high resolution radiometer (AVHRR) NDVI in Kruger National Park, South Africa. International Journal of Remote Sensing, 27: 951-973.
153. Winrock. 1997. A guide to Monitoring Carbon Storage in Forestry and Agroforestry Projects. Forest Carbon Monitoring and Verification Services, Arlington, VA: Winrock International.
154. Wiskerke, W.T., Dornburg, V., Rubanza, C.D.K., Malimbwi, R.E. and Faaij, A.P.C. 2010. Cost/benefit analysis of biomass energy supply options for rural smallholders in the semi-arid eastern part of Shinyanga Region in Tanzania. Renewable and Sustainable Energy Reviews, 14: 148-165.
155. World Resources Institute (WRI), 2003, Drylands, people, and ecosystem goods and services: a Web-based geospatial analysis. World Resources Institute http://www.wri.org
156. World Resources Institute (WRI), 2008, Ecosystem services. A guide for decision makers http://pdf.wri.org/ecosystem_services_guide_for_decisionmakers.pdf
157. Wylie, B.K., Denda, I., Pieper, R.D., Harrington Jr, J.A., Reed, B.C. and Southward, G.M. 1995. Satellite-based herbaceous biomass estimates in the pastoral zone of Niger. Journal of Range Management, 48: 159-164.
158. Wylie, B.K., Harrington, J.A., Prince, S.D. and Denda, I. 1991. Satellite and ground-based pasture production assessment in Niger: 1986-1988. International Journal of Remote Sensing, 12: 1281-1300.
159. Wylie, B.K., Meyer, D.J., Tieszen, L.L. and Mannel, S. 2002. Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: a case study. Remote Sensing of Environment, 79: 266-278.
160. Xu, B., Yang, X.C., Tao, W.G., Qin, Z.H., Liu, H.Q., Miao, J.M. and Bi, Y.Y. 2008. MODIS-based remote sensing monitoring of grass production in China. International Journal of Remote Sensing, 29: 5313-5327.
161. Zine, S., Jarlan, L., Frison, P.L., Mougin, E., Hiernaux, P. and Rudant, J.P. 2005. Land surface parameter monitoring with ERS scatterometer data over the Sahel: a comparison between agro-pastoral and pastoral areas. Remote Sensing of Environment, 96: 438-452.