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:: Spectral components analysis: Rationale and results. C. L. Wiegand & A. J. Richardson

Document type:: Multivolume work

Structure type:: Chapter

347 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Spectral components analysis: Rationale and results C.L.Wiegand & AJ.Richardson US Department of Agriculture, Agricultural Research Sevices, Weslaco, Tex., USA ABSTRACT: The spectral components analysis (SCA) identities, LAI/VI x APAR/LAI = APAR/VI [1] and LAI/VI x YIELD/LAI = YIELD/VI, [2] wherein VI denotes any one of several spectral veqetation indices available, LAI is leaf area index, APAR is absorbed photosynthetically active radiation, and YIELD is salable plant part (grain, fiber, or root), express the information conveyed by canopies about their development, response to stresses, and yield capability. The rationale of SCA is carefully presented as are the relations between numerator and denominator of each term in equation [1] for the crops wheat, cotton, and maize. Results show that APAR can be estimated almost as well from VI as from LAI, and that the relation is nearly linear. Equations [1] and [2] help to: quantify remote assessments of crop productivity; unify field-observed interrelations among LAI, APAR, and YIELD; and, validate remotely observable IAI and APAR inputs for plant process crop growth and yield models, or for growth analysis. 1 INTRODUCTION Spectral components analysis implies that the plant stands integrate the growing conditions experienced, express the growth and yield responses through the canopies achieved, and that stresses severe enough to affect YIELD will be detectable through their effects on the development and persistence of photosynthetically active tissue in the canopies. Vegetation indices (Kauth and Thomas, 1976; Richardson and Wiegand, 1977; Tucker, 1979) are used to indicate the amount of photosynthetically active tissue in the canopies. The approach is expressed by equations [1] and [2] wherein each term represents the functional dependence of the numerator over the range of the seasonal values of the denominator variable. The equations are intended to convey the property of an identity; that is, if the individual terms on the left hold, then the right hand side of the equation follows. Additionally since the right hand side denominators are common the numerators APAR and YIELD must be related. We termed the approach "spectral components analysis" (SCA) by analogy with yield components analysis (Women et al., 1979; Black and Aase, 1982). The first application of SCA (Wiegand and Richardson, 1984) was to data for South Texas grain sorghum (Sorghum bicolor L., Moench) fields that had been sampled periodically to determine LAI and at maturity for grain yield. The spectral data were obtained by the Landsat multi spectral scanner (MSS) during grain filling whereas the APAR versus LAI relation was taken from Maas and Arkin (1978). Subsequently, snail plot experiments have been conducted for three crops: hard red spring wheat (Triticum aestivum L.), cotton (Gossypium hirsutum L.) (Wiegand et al., 1986) and corn (Zea mays, L.) (Maas et al., 1985). The purposes of this paper are to present the rationale for the approach and the relations term-by-term for equation [1] for these latter studies. 2 RATIONALE The rationale for the approach embodies the following principles: a. leaf area index is a fundamental attribute of plant canopies (Jordan, 1983; Pearson, 1984) because the leaves are the dominant photosynthetically active tissue in the canopies. Assimilates of photosynthesis support further development and the increase in dry weight of all plant parts, roots, leaves, stans, and reproductive organs. b. Foliar characteristics dominate the interaction of electromagnetic radiation with plants so that interpretation from remote observations is based primarily on characteristics of the foliage (Wiegand et al., 1972). Spectral vegetation indices relate to many plant canopy characteristics (LAI, green biomass and percent cover) and are now recognized as a good measure of the amount of photosynthetically active tissue in the canopy any time during the season (Wiegand et al., 1986a). Thus the vegetation indices can reliably estimate LAI and intercepted or absorbed PAR. c. The crop canopies attained integrate the soil and aerial environments experienced including stresses (soil water availability, nematodes, herbicide residues, soil salinity, diseases, atmospheric pollutants), past and present management and cultural practices (fertility, irrigations, tillage, residue management, growth regulators, ...) and natural soil variation (water holding capacity, rooting depth, texture, soil depth, slope ...). The reflectance factor observations sense the effects (without diagnosing the cause) and "quantify" the response through the vegetation indices. d. Comercial agriculture is tuned by experience and experimentation (seeding rates and planting configurations, fertility levels, adapted cultivars, irrigations ...) to achieve canopy closure by the plant reproductive stage because high yields can not be achieved unless the available PAR is effectively intercepted during the reproductive phase. Throughout the plant's life cycle, but especially during reproduction, pathologists and entomologists protect the foliage, fruit, and main stans from insect, arthropod, fungal, viral and other plant predators. The converse is not true. Effective light interception does not insure high yields; the reproductive organs must be set and be protected from predators until harvest. Thus the VI observed relate to actual YIELD unless conditions were so stressful as to inhibit fruit set or retention, or the development of the reproductive organs was

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