Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Sugar beet biomass estimation using spectral data derived from colour infrared slides. Robert R. De Wulf & Roland E. Goossens

Document type:: Multivolume work

Structure type:: Chapter

300 JULIAN DATE line) and x) and 1985 of GLAD in ï § 14000 16 000 ° 1 MASS KG/HA) line), PVIC 1984 (x) abd ÏENNESSC are but are not [high daily ghts) caused ), ND (NDC), ire computed, it sugar mass ng season is iteorological phenomena cause the less regular pattern for 1985. An attempt to predict sugar yield for 1985 from 1984 relationships by remote sensing methods only would be senseless. 5 CONCLUSIONS Remote sensing of sugar beet biomass with the purpose of sugar yield estimation implies essentially an indirect measurement of subterranean plant parts. Due to meteorological factors experimental results did not indicate simple relationships between crop canopy and root biomass. Cover percentage could be estimated rather precisely although further experiments using more data are necessary to confirm these results. Estimated GLAI is likely to be used in prediction models which should also include meteorological factors. Without doubt sugar beet yield prediction with remote sensing methods only are prone to produce results as inaccurate as most agromet models. The ultimate solution appears to be a procedure incorporating both meteo and remote sensing parameters. It was also concluded that spectral reflectance data derived from CIR film yield results comparable to those obtained by using more widely applied radiometers. AKNOVLEDGEMENTS This research is part of IWONL contract No. 4555. Field equipment and computer hardware of the Centre for Remote Sensing of Vegetation (CEVA) were used throughout the experiments. The authors gratefully aknowledge EUROSENSE NV for development of the CIR films, Luc Vandekerckove wrote customized software on the PDP 11/34. Peter Haelvoet and Dirk Tietens cheerfully assisted for densito métrie measurements, GLAI calculations and drawings. REFERENCES Allen, E.J. and SCOTT, R.K. 1980. An analysis of growth of the potato crop. J. Agr. Sci. Cambridge 94:583-606. Analogides, D.A. 1979. Simulating the effect of the starting date of harvest on sugar beet productivi ty. Proc. 42nd Winter Congress I.J.R.B. plll-127. Andrieu B. 1984. Utilisation de données photographi ques et radiométriques pour la mise en évidence de la rhizomanie de la betterave. Proc. 15th Int. Symp on Rem. Sens, of Environment, p.813-823. Badhwar, G.D., MacDonald, R.B. and Mehta, N.C. 1986. Satellite-derived leaf area index and vegetation maps as input to global carbon cycle models - a hierarchical approach. Int. J. Rem. Sens. 7<2): 265-281. Boehnel, H.J., Fischer, W. and Knoll, G. 1983. In si tu measurements for the determination of the spec tral characteristics of diseased and healthy sugar beets. In P. Reichert and G. Hildebrandt (eds.), Detection of sugar beet disease using remote sen sing techniques. Final Report. Publ. Comm. of the European Communities. Colwell, J.E., Rice D.P. and Nalepka R.F. 1977. Wheat yield forecasts using Landsat data. Proc. 12th Int. Symp. on Remote Sensing of Environment p.1245- 1254. De Wulf, R.R. and Goossens, R.E. 1986. Multispectral indicators of seasonal crop development derived from CIR slides. Rem. Sens, of Env. (under review) Evans, G.C. 1972. Quantitative analysis of plant growth. Oxford: Blackwell Scientific Publications. Gosse, G., Varlet-Grancher, C., Bonhomme, R., Chartier, M., Allirand, J.M., and Lemaire, G. 1986. Production maximale de matière sèche et rayonnement soliare intercepté par un couvert végétal. Agronom. 6(1):47-56. Hinzman, L.D. , Bauer, M.E. and Daughtry, C.S.T. 1986. Effects of nitrogen fertilization on growth and re flectance characteristics of winter wheat. Remote Sens, of Env. 19:47-61. Jackson, R.D. 1982. Spectral indices in n-space. Rem. Sens, of Env. 13:409-429. Leblon, B. 1983. Variations du rendement du blé d'hiver et de la betterave sucrière en Belgique. Effets du climat. Thesis. Fac. des sciences agrono miques. U.C.L. MacDonald, R.B., and Hall, F.G. 1976. Lacie: a look to the future. Proc. 11th Symp. on Remote Sensing of Environment p. 429-465. N.I.S. 1985. Landbouwstatistieken No. 1-2-3. Min. Ec. Zaken. Reichert, P. 1983. Evaluation of airborne multispec tral scanner data for the discrimination of healthy and diseased sugar beets. In Reichert, P. and Hil debrandt G. (eds.), Detection of sugar beet disease using remote sensing techniques. Final Report.Publ. Comm, of the European Communities. Richardson, A.J. and Wiegand C.L. 1977. Distingui shing vegetation from soil background information. Photogramm, Eng. and Rem. Sens. 43(12): 1541-1552. Steven, M.D., Biscoe, P.V. and Jaggard, K.W. 1981. Estimation of sugar beet productivity from reflec tion in the red and infrared spectral bands. In The Remote Sensing Society (publ.), Matching Remo te Sensing technologies and their application, p. 259-279. Thorne, G.N. 1971. Physiological factors limiting the yield of arable crops. In P.F. Wareing and J.P. Cooper(eds,),Potential Crop production p.131-157 , Heinemann Books. Tucker, C.J. 1977, Asymptotic nature of grass cano py spectral reflectance. Appl. Opt.16(5): 1151-1156. Tucker, C.J., Van Praet, C.L., Sharman, M.J. and Van Ittersum, G. 1985. Satellite Remote Sensing of to tal herbaceous biomass production in the Senegalese Sahel: 1980-1984. Remote Sensing of Environment 17: 233-249. Whyte, R.O. 1960. Crop production and environment. London: Faber and Faber.

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