Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

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:: [WP-3 FOREST INVENTORY APPLICATIONS]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: APPLICATION OF THE WEIGHTED DIFFERENCE VEGETATION INDEX TO SATELLITE DATA. Dr. J. G. P. W. Clevers

Document type:: Multivolume work

Structure type:: Chapter

shown that a similar approach can be applied to yellowing cereals, resulting into the same vegetation index. When satellite data are the information source, atmospheric correction is very important (Clevers, 1986, 1988b). Attention will be paid also to this aspect of a multitemporal analysis. 2. SIMPLIFIED REFLECTANCE MODEL FOR ESTIMATING LAI (CLAIR) 2.1 Introduction Recently, Clevers (1988a, 1989) has described a simplified, semi-empirical, reflectance model for estimating LAI of a green canopy (vegetative stage). In this model it is assumed that in the multitemporal analysis the soil type is given and soil moisture content is the only varying property of the soil. For estimating LAI a "corrected" (adjusted) infrared reflectance factor was calculated by subtracting the contribution of the soil in line of sight from the measured reflectance of the composite canopy-soil scene. This corrected infrared reflectance factor was ascertained as a weighted difference between the measured infrared and red reflectance factors (called WDVI = weighted difference vegetation index), assuming that the ratio between infrared and red reflectances of bare soil is constant, independent of soil moisture content (which assumption is valid for many soil types). Subsequently this WDVI was used for estimating LAI according to the inverse of a special case of the Mitscherlich function. This function contains two parameters that have to be estimated empirically from a training set. 2.2 CLAIR model The simplified reflectance model derived by Clevers (1988a, 1989) consists out of two steps. Firstly, the WDVI is calculated as: WDVI = r ir - C.r r (1) with r^ r = total measured near-infrared reflectance factor r r = total measured red reflectance factor and C = (2) r s, ir r s,r = near-infrared reflectance factor of the soil = red reflectance factor of the soil. empirically from a training set, but they have a physical interpretation (Clevers, 1988a). Equation (3) is the inverse of a special case of the Mitscherlich function (Mitscherlich, 1923). The combination of Eqs. (1) and (3) is the simplified, semi- empirical, reflectance model: CLAIR model ("Clevers Leaf Area Index by Reflectance" model). The main assumption was that C is a constant, meaning that the ratio of the infrared and red reflectance of the soil is independent of the soil moisture content. The validity of this assumption for many soil types is confirmed by results obtained by e.g. Condit (1970) and Stoner et al. (1980). For many soil types, there is only a slight monotonie increase in reflectance with increasing wavelength (e.g. Condit, 1970). For application of Eq. (1) in estimating LAI, a weighted difference between the infrared and red reflectance must be ascertained and then Eq. (3) must be used. In this regard r^ in Eq. (3) will be the asymptotically limiting value of the weighted difference between infrared and red reflectance at very high LAI. The vegetation index derived in Eq. (1) is similar to the Greenness of Kauth and Thomas (1976) for the two-dimensional case (see below), on the restriction that they used digital numbers (not reflectance factors). The assumption given in Eq. (2) describes a soil line, which can be defined by the vector (1,C) in a scatter plot of near-infrared against red data. A two-dimensional "greenness index" (in terms of the Greenness of Kauth and Thomas) could be defined orthogonal to this soil line: greenness index = r^ r - C.r r . This equals the WDVI in Eq. (1). Moreover, the vegetation index derived in Eq. (1) is also similar to the perpendicular vegetation index of Richardson and Wiegand (1977). By introducing the assumption r s r = C in Eq. (2) , this means that thé slope of the soil background line of Richardson and Wiegand equals 1/C (red against near-infrared plot). If it is assumed that the intercept does not differ statistically from zero, it can be shown that PVI = (1/(C 2 +1)) 1//2 . (r ir -C.r r ), with C being soil-specific. By working with reflectance factors the ratio in Eq. (2) appears to be constant, and so the intercept is indeed zero. This PVI also equals the above two-dimensional greenness index after normalization, assuming there is just one C valid. Secondly, the relation between WDVI and LAI is given by: LAI = -1/a . ln(l - WDVI/r^ ^) (3) The combination of Eqs. (1) and (3) is called the semi-empirical reflectance model. Parameters a and have to be estimated 3. APPLICATION OF CLAIR MODEL TO SATELLITE DATA 3.1 Background The described CLAIR model in section 2 is based on reflectance factors. Thus, this model is not directly applicable to satellite 438

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