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:: An evaluation of different green vegetation indices for wheat yield forecasting. A. Giovacchini

Document type:: Multivolume work

Structure type:: Chapter

I en vegeta- rsus diffe MILKY MATURITY RHYS IOl0 GICAl HA TURITY " v I 250 ated vege- vegetation g to the ;es. From lase, the on indices i harvest, all the the same point out vegetation , if the iloited to e sensing observing L profiles tion cover ;ages. As ion (R) is vegetation functions vident in 3ver. That ig phase. .ated with : for soil leading is Leld (if a lition is assumed until the harvest) it is essential to adopt indices yielding the maximum variance between the index values of the wheat fields. In Fig. 2 this fact is illustrated taking into account three spectral vegetation indices; the maximum percentage variance at heading between index values of the wheat yields is related to the ratio index (R). The minimum variance between the field's index values, corresponds to the maximum correlation with the final grain yield. Considering the ideal spectral profile plotted in Fig. 3 from tillering to physiological maturity, it is possible to identify three spectral profile regions where the vegetation index families work to the maximum efficiency. From tillering to the end of the elongation stage, the biophysical parameters closer to the final^grain yield of the wheat, is the n° of plants per m . In addition, during this time interval, % of soil vegetation cover ranges from 10% to 45%. Thus the reflectance of the soil is substantial portion of the total radiance captured by the radiometre. The perpendicular index yield values are less influenced by the soil component. From booting to heading, the soil vegetation cover increases to 85%, the spectral component of the soil is not as influential as in the previous stages and the perpendicular indices are not as efficient as in the ratio family. During these stages the biophysical parameter most correlated to the final grain yield can be considered the leaf area index, that is closely tied to the soil vegetation cover. It is not easy to explain why the ratio family is more efficient than the vegetation indices belonging to the other ones. One explanation is that the ratio indices are less influenced by collateral effects due to the plant architetture. From flowering to the soft-dough stage the leaf yellow component increases. In this time interval it seems that the vegetation indices based on the difference concept yield the maximum correlation between the index values and the final grain yield of the wheat field. No satisfactory explanation exists in this case either, although it is possible to relate the grain-filling-process to the duration of the green leaf area index. This parameter can be detected more efficiently by using indices based on the difference concept rather than on the other index families. At the physiological maturity all the plant leaves are yellowed and collapsed so that all the vegetation indices proposed in this study have ^a significant correlation with the n° of plant per m . 4 CONCLUSIONS Wheat yield forecasting directly supported by means of vegetation indices is still in a preliminary phase since a significant correlation between spectral vegetation index values and final grain yield exists only at the heading phase. Moreover this study has pointed out that the most appropriate vegetation index can be selected only by considering the phenological stage of the wheat crop. Although it is not indicated in this report, the integrated values of the vegetation indices have been calculated and related to the final grain yield. The use of the vegetation index duration values does not significantly improved the relationship between the remote sensing data and the biophysical parameters of the wheat crop. On the other hand due to cloudness in the area the quantity of space remote sensing data is limited. Thus the possibility of carrying out multitemporal observations in Central and Northern Italy is rather low. 5 REFERENCES Aase, J.K., and Siddoway, F.H. (1981): Assessing winter wheat Dry-Matter production via Spectral Reflectance Measurements. Remote Sens. Environ. (11): 267-277. Badhwar, G.D., and Shen, S.S. (1984): Techniques for the estimation of leaf area index using spectral data, Proc. Symp. Machine Proc. Remote Sensing Data, Pordue Univ., West Lafayette, Indiana: 333- 338. Daughtry, C.S.T., Gallo, K.P., Bichl, L.L., Kanema- su, E.T., and Asrar, G. (1984): Spectral estimates of agronomic characteristic of crops, Proc. Symp. Machine Proc. Remote Sensing Data, Pordue Univ., West Lafayette, Indiana: 348-355. Goel, N.S., Henderson, K.E., and Pitts, D.E. (1984): Estimation of leaf area index from bidirectional spectral reflectance data by investing a canopy reflectance model, Proc. Symp. Machine Proc. Remo te Sensing Data, Pordue Univ., West Lafayette, In diana: 339-347. Tucker, C.J., Holben, B.N., Elgin, J.H., and McMur- trey III, J.E. (1980): Relationships of spectral data to grain-yield-variation, Photogram. Eng. and Remote Sensing (46): 657-666. Tucker, C.J., Holben, B.N., Elgin, J.H., and McMur- trey III, J.E. (1981): Remote Sensing of total Dry-Matter accumulation in winter wheat, Remote Sens. Environ. (11): 171-189. 267

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