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

Damen, M. C. J.

Bibliographic data

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:: LANDSAT temporal-spectral profiles of crops on the South African Highveld. B. Turner

Document type:: Multivolume work

Structure type:: Chapter

328 0 IO 20 30 40 50 60 MSS 5 MSS 5 BAND-TO-BAND DISPERSION Figure 4. Structure of MSS data for Grootvlei test site ponents contain very little information (0.03% and 0.005% of the variance). Thus, the use of combina tions of MSS 5 and MSS 7 as vegetation indices utiliz es much of the information content of the data. The vegetation ratio (VI) and the normalized vegetation index NVI defined as MSS 7 MSS 7 - MSS 5 VI = NVI = MSS 5 MSS 7 + MSS 5 The Gram-Schmidt theorem states: If A = {X}, X2 ... x s ) is any linearly indepen dent set whatever, there exists an orthonormal set X = (yi, y2 ••• y s ) such that k yk = I a ik x i* (Neiring, 1963) i=l The coefficients of the matrix T were calculated by means of the Gram-Schmidt process (Nering, 1963) using the averages of four data clusters representa tive of dark and light soil, green vegetation and senescent vegetation. 3.3.4. Extraction of field sample means from satel lite data The mean value for each field sample of standardized MSS 4, 5, 6, 7, VI, NVI, SBI, GVI was extracted for each satellite overpass. These mean values together with the ground reference data set were stored for statistical analysis. 4 RESULTS Two types of data were processed in the present study, the ground reference data and the MSS data. 4.1 The analysis of the ground reference data were selected (Jordan, 1969; Pearson, 1972; Colwell, 1974; Rouse et al., 1973; Maxwell, 1976). These mathematical combinations of the bands of MSS data partially compensate for the inherent error components in the LANDSAT data. The effects of external factors such as haze, changing illumination conditions, viewing aspect, surface slope and atmospheric effects over a LANDSAT scene are thought to be reduced by the use of vegetation indices. However, since some of the above variables are wavelength-dependent, the effects of these external factors cannot be eliminated by means of a vegetation index alone. Despite the high correlation between the signals, differences do exist. The range of MSS 4, which is centred on the cellulose reflectance peak around 0.55 ym, extends significantly into the chlorophyll absorption region which dominates the reflectance of the canopy in the range of MSS 5. Thus differences between signals in MSS 4 and MSS 5 are particularly significant for vegetative canopies at the yellowing stage. In view of the above, a vegetation index using data in all four spectral bands was investigated. Kauth and Thomas (1976) exploited the structure of LANDSAT MSS data and proposed the Tasseled cap transformation of the four-dimensional LANDSAT MSS data space into four indices which they called Brightness (BR), Green ness (GR), Yellowness (Y) and Non-such (NS). The Tasseled cap transformation is an orthogonal rotation of the original LANDSAT data space into four new axes. (i) Along the line of soils; called the Brightness axis (ii) perpendicular to the Brightness axis and passing through the peak of Greenness; called the Greenness axis (iii) perpendicular to Brightness and Greenness axis, called the Yellowness axis (iv) orthogonal to (i), (ii) and (iii) above; called the Non-such axis. Thus Brightness (SBI) MSS 4 Greenness (GVI) = T MSS 5 Yellowness (YVI) MSS 6 Non-such (NS) MSS 7 Planting dates for six crops within nine test sites were extracted (see Table 2). Figures 5a, b, c illustrate composite temporal plots of the growth stages of maize, sorghum and sun flower for the WRS 182-79 scene. The planting date varies considerably, with resulting variability in growth stage. Growth stage is also affected by local climatic conditions and cultural practices. 4.2 The processing of the MSS data 4.2.1 Standardization of digital counts of MSS data As noted above, it was necessary to compensate for station-to-station differences in digital count and for satellite-to-satellite calibration. 4.2.1.1 Compensation station-to-station difference Absolute radiances R can be calculated from EROS digital values y using a linear relationship: - Rn ) • Y + «ir (1) Similarly CCRS digital values y' can be converted to absolute radiances R by means of D I R I n r 11 max n min ^ , Dl (0 \ . R = l J * y' + R min < 2 > V where Rmax* Rmin> R'max» Rmin -*- n m W/cm Sr. are given in Table 4 (Anon, 1976) and Strome et al., 1975) and V and V' are the ranges of digital values of EROS and CCRS respectively. From (1) and (2) y' = A«y + B where A V (R max - R min) — and B V (R'max^'min) V(Rmin - R'min) (R 1 max-R'min)

Access restriction

Copyright

fullscreen: Remote sensing for resources development and environmental management (Volume 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation