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:: Multitemporal analysis of LANDSAT Multispectral Scanner (MSS) and Thematic Mapper (TM) data to map crops in the Po valley (Italy) and in Mendoza (Argentina). M. Menenti & S. Azzali, D. A. Collado & S. Leguizamon

Document type:: Multivolume work

Structure type:: Chapter

297 included are parent dis- ples (Si, S2) Lbution a Lfference be- Dtained as (2) amber of an estimate but ion given (3) of S-| , S2 . (2) , has The second is an accurate procedure, by testing the normality of sample distribution, equality of variances and finally significance of differences in mean values. The samples Sj do not necessarily belong to a normal parent distribution and, if so, the variances of their parent distribution are not neces sarily equal. The goodness of fit of Si, S2 to a normal distribution can be assessed (Steel and Torrie 1960) by calculating the value of the criterion: (4) where o^, e^ are the observed respectively expected occurrences in class i. To calculate e^, the normally distributed variable: (X - Xj) (5) is applied, with xj, Oj being the mean respectively the variance of sample Sj. Then the expected number of sample elements e^ in the i-th class, i.e. between zi and z^ + i are calculated on the basis of a normal distribution and of the total number of occurrences l i0i . Finally, the thus obtained x 2 ~value is compared with threshold x 2- values, corresponding with pre-set probabilities, to assess whether the observed dis tribution significantly deviates from a normal dis tribution. Assuming that the observed distributions belong to normal parent distributions, we have now to test the equality of variances. The following criterion can be applied (Kenney and Keeping 1959): F = (6) 2 . 2 where U is the larger and V the smaller estimated variance between: N 1 - 1 and 2 2 s 2 = nT^-T (7) The F-value thus obtained is compared with the select ed threshold F, e.g.^with a probability P = 0.01 of F being larger than F. After having established that both samples belong to the same normal distribution, the Student's t-test can be applied according to the first procedure. If not, the following procedure has to be applied (Steel and Torrie 1960) : t = and (0) 2 _ V? + V2 N 1 + N 2 (8) (9) The threshold t-value is obtained as: t* = Vi + V 2 (10) with Wl = J- and w 2 = Л threshold t-values at the while t^ , t^ are the same level of probability Table 4. Goodness of fit of observed distributions against the normal distribution; observed values of TVI in field plots in the Grande Bonifica Ferrarese and East Sesia irrigation districts (Po valley, Italy); reflectance measurements obtained with the LANDSAT TM, band 4 and 3; x 2- values having a probability P = 0.05 respectively 0.01 to be exceeded are in dicated (s = significant, ns = not significant) Variable Plots ВАЗ CA4 CA2 IA2 0B2 Total frequency 4 25 49 25 77 Skewness -0.1 7 -0.05 -2.4 -0.05 1.41 Kurtosis 317.1 664 2289.9 44.7 3.81 X 2 0.24 7.4 32 6.9 18.4 X 2 (P = 0.05) 3.84 11.1 7.8 6.0 7.8 X 2 (P = 0.01) Deviation from 6.63 15.1 11.1 9.2 1 1.3 normal distr. ns ns s ns s Table 5. Significance of differences in TVI-values, as obtained with LANDSAT TM 4 and TM 3 measurements (2 May 1985), Grande Bonifica Ferrarese, Po-valley, Italy; significance assessed by applying the accurate procedure respectively the simplified procedure (within brackets); ns = not significant, s = signif icant at probability P = 0.05, hs = highly signif icant at P = 0.01; for plot coding see text; largest observed differences in seeding dates: BB1, 30 April; BB2, 16 May; 0B1, 5 April; 0B2, 12 May; IB1, 10 April; IB2, 7 May Plots BB1 BB2 0B1 0B2 IB1 IB2 BB1 hs (hs) hs(hs) hs (hs) hs(hs) hs(hs) BB2 - hs(ns) s (ns) hs (hs) s( s) 0B1 - s( s) hs(hs) hs( s) 0B2 - hs (hs) hs( s) IB1 - hs(hs) IB2 - Table 6. Significance of differences in TVI-values, as obtained with LANDSAT TM 4 and TM 3 measurements (30 April 1985); East Sesia, Po-valley, Italy; for explanation of plot coding see text: ns = not sig nificant, s - significant at P - 0.05, hs = highly significant at P = 0.01; samples include between 20 and 77 pixels; largest observed phenological dif ferences: CA1, full cover = 20 March; CA2, full cover = 30 April; BA1, seeding date = 15 April; BA2, seed ing date - 24 April; LA1, seeding date = 1 April; IA2, seeding date = 5 May Plots CA1 CA2 BA1 BA2 IA1 IA2 CA1 - s hs hs hs hs CA2 - hs hs hs hs BA1 - ns hs hs BA2 - hs hs IA1 - hs IA2 -

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