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:: Spectral signatures of soils and terrain conditions using lasers and spectrometers. H. Schreier

Document type:: Multivolume work

Structure type:: Chapter

312 rate allows the laser to produce many individual measurements per single tree and it is thus possible to get a representative average height and reflection measurement per tree or plant species. Figure 1. Laser height and reflection profile of a forested transect. The separation of vegetation and terrain surfaces based on reflection alone is thus very restricted and this can be seen in Figure 2 where mean reflec tion values for a number of uniform vegetation sur faces are portrayed. It is obvious that the poten tial of laser remote sensing can greatly be enhanced if the analysis can be extended to include multi- spectral capabilities. Figure 2. Vegetation differentiation with laser reflection measurements at 904 nm wavelength. 2.3 Developing a multispectral airborne laser Having shown the potential of single wavelength air borne laser observation it is a logical extension to suggest that the use of multispectral lasers be con sidered. Unfortunately no tunable airborne lasers have yet been developed, but the logical alternative is to use several lasers simultaneously, where each type operates at a different wavelength. This raises the question as to the choice of wavelength to be used. This is not a simple question in view of the different requirements needed for the separation of vegetation type, for differentiations amongst rock types, and for identifying different soil conditions. Each user is likely to require reflection measure ments at different wavelength ranges. For the separ ation of vegetation types, vegetation stress, and vegetation vigour the chlorophyll sensitive red/near infrared reflection bands are likely to be most appropriate (Tucker 1979, Curran and Milton 1983, Horler et al 1983, Elridge and Lyon 1984). For geological applications bands at 600, 1200, 1600 and 2200 nm wavelengths appear to be most desirable (Siegrist and Schnetzler 1980, Buckingham and Sommer 1983, Gladwell et al 1983, and Elvidge and Lyon 1984). For soil assessments the most useful spectral bands are also in the visible and near infrared wave length range (Stoner and Baumgardner 1981) but these might differ from the geological band depending on soil conditions. At the current state of technology the testing of dual or triple lasers for specific applications is likely to be most profitable and the potential of such an approach is illustrated in the following example which deals with the quantification of soils to facilitate fertilizer assessment in agricultural fields. 3 SPECTRAL REFLECTION MEASUREMENTS TO DIFFERENTIATE SOIL CONDITIONS Most agricultural fields have great soil variability not only because soils have a naturally high chemical variability but most fields do not follow soil type boundaries and it is very common at least on the West Coast of Canada that two or three soil types are present in most agricultural fields. If the soils are contrasting management is considerably more difficult. The use of fertilizers in intensive agriculture is becoming a major component in the economics of farming and there is an increasing need to apply fertilizers in a more efficient manner so as to minimize over and under fertilization of any part of the field and to overcome production deficiencies in some parts of the field. It is pro posed that remote sensing using a multispectral laser system can aid in this process. To show how this potential can be realized a number of soil reflection measurements were made using a multi channel spectro-radiometer to demonstrate what soil properties can best be predicted from spectral reflection measurements, what wavelengths are most useful in separating soil types, and how this infor mation will aid in the development of a multispectral laser. It is well known that organic matter and soil provenance has a significant influence on spectral reflection, and soil mapping with multispectral data has been attempted by numerous researchers (e.g. Kristoff et al 1973, Westin and Frazee 1976, Cipra et al 1980) . The main problem is that vegetation cover, crop residue, disk pattern, and soil moisture (Gausman et al 1975 and 1977, Huete et al 1985) seriously affect the soil reflection spectra. It is anticipated that the use of a multispectral laser system will facilitate such analysis since it can provide an assessment of surface roughness at the same time the reflection data is obtained. 3.1 Method of analysis Three experiments were carried out at three different sites. The first site represented a bare field which had been plowed and which had a mixture of organic and marine clay rich soils. Thirty surface soil samples were collected for laboratory analysis and the samples were all fine textured and highly variabl WclS &n Canada trine d that si content tremely samples ings in with vi variabl The f samples method) (citrat (HF-tef er metb ble K a particl color ( cribed The s samples channel coverir measure under a and the The c correle mine tl propert differe 3.2 Res The ther a plot range s istic c differe also be spectre betweer partic] carbon, variab: enance fluence When ref led picture found 1 contenl spectre In eacl within data se lationj analyse and the carbon the 55< sample: had on 2.0% ar the to- sample: the eqi extendi threshi fluenci fairly such a tion s betwee: at 630 which ’ conten great

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