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:: Assessment of soil properties from spectral data. G. Venkatachalam & V. K. R. Jeyasingh

Document type:: Multivolume work

Structure type:: Chapter

Table 1 : Reflectance values Description of Samples Reflectance in percentate (Average of 10 samples) Band 4 Band 5 Band 6 1 Band 7 River Sand 8.36 13.76 15.78 13.99 Marine Clay 8.04 13.46 16.33 17.11 Red Muram 8.19 16.35 20.32 18.54 Black cotton soil 8.20 10.44 14.02 19.18 Powai soil 13.38 20.50 26.76 33.54 to obtain the four Principal Components. The effectiveness of the transformation is evident from the fact that the maximum correlation of 0.34 with the original data has been improved to 0.66 with the third Principal Components. Taking advantage of the fact that trans formation improves the correlations, other forms of transformations and their combinations have also been tried. It has been observed that the correlation can be improved with systematic transformation of data. Further, to arrive at a most suitable transformation, optimization techniques have been employed and the best transformation that enables to obtain the maximum correlation has been arrived at. 3 MATHEMATICAL MODEL 3.1 Linear model Having established the existence of a high correlation between the grain sizes and the third Principal Compo nents, linear regression analysis of grain sizes on the third Principal components was done. A linear mathema tical model with a correlation coefficient of 0.66 has been dëtermined as r L r L CL O M 10 C 10 d 20 M 20 C 20 O. O M30 c 30 d 40 M 40 c 40 O »A ~o = M 50 [0.18B 7 +0.25B 6 -0.14B 5 -0.94B 4 ] + C 50 d 60 M 60 C 60 d 70 M 70 C 70 CL OO O M 80 C 80 d 90 M90 C 90 This model is applicable for diameters, d^, d^Q, d^, d 40’ d 50’ d 60’ d 70’ d 80’ d 90" In general, = ^x- 1 ^l^ + *2 B 6 + *3 B 5 + *4 B 4 - 1 + ^ wnere B_, EC, EC, B. - bare soil reflectances in bands 7,6,5 / , i D J 4 and 4 11 ’ 12’J3’ 14 “ ^irecton cosines M , C7 - regression constants for d size d - diameter of particles less than x percent in mm The model constants are given in Table2. 3.2 Bi-linear model The 1, II, III and IV Principal Components determined from the original data are termed as Primary Principal Components. By keeping some of the original axes as fixed and rotating the others in turn, Secondary and Tertiary Principal Components have been obtained (Venkatachalam and Jeyasing, 1986). On the whole there are one set of Primary Principal Components (PPC), four sets of Secondary Principal Ciomponents (SPC) and six sets of Tertiary Principal Components (TPC). The axes corresponding to the minimum variance in the original data have been used as fixed axes in the present analysis. It is understood that, in a properly designed regression analysis, inclusion of all the plausible predictor variables and the absence of multicollinearity among them, will increase predictive ability of the model. Hence, the four PPC, three SPC and the two TPC obtained, have been combined two at a time and a multiple linear regression analysis was done. It has been found that the multiple regression model obtained by fourth PPC (PPC^) and the third SPC (SPC^) as independent vari ables had a high correlation coefficient of 0.94 for all the grain sizes from djQ to d^Q. The bi-linear model suggested is as follows. [d ] = [A ] + [B ] [PPC ] + [C ] [SPC ] where A , B , C - Model constants v" x 7 X The model constants are given in Table 2. 3.3 Linear model based on optimization If n is the number of samples observed, then the four band data observed will be X-- ( i = 1, n; j = 1, 4 ) and the corresponding diameter of particles less than x percentage is given by d- (i = 1, n; x = 10, 20, 30, 40, 50, 60, 70, 80 and 9of. This four band data can be plotted in a four dimensional space and it is possible to obtain a linear transformation of the data on an axis in the four dimensional space with direction cosines 1., 12, L and 1^ which satisfy a stipulated requirement. ence i i (i = 1, n) are the transformed reflectance values of the n samples, this can be stated as n 2 ¡=1 4 j=1 X. . = T; J 'J ' The T. values for the model have been determined using standard non-linear optimization technique (Mitai 1977). Based on this, a simple linear model with a corre lation coefficient of 1.0 has been suggested, whose model constants are given in Table 3. 3.4 Model for large samples The models suggested so far are based on observations made on five types of soils with ten samples for each type. To study the behaviour of the model for large samples, a group of 14 soils from a large number of samples collected from different parts of India has been selected. These were falling in the Munsell colour range of hue 10 YR, value3 to 6 and chroma 1 to 6. Reflectance values of these samples have been observed in the laboratory under identical conditions described for the initial study. Particle sizes have also been deter mined in the laboratory. A simple linear model based on non-linear optimization technique has been evolved for the same diameters chosen earlier. The values of the model constants are given in table 4. To test the predictive ability of the model, six more naturally available surface soil samples falling within the above Munsell colour range were collected from different parts of India. The locations of all these sam ples are given in Figure 1. The reflectance values were determined in the laboratory and the grain sizes were predicted from the above model. The predicted values were compared with actual values determined in the laboratory. There is a close aggreement between the two values, as evident from Table 5.

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