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:: The application of a vegetation index in correcting the infrared reflectance for soil background. J. G. P. W. Clevers

Document type:: Multivolume work

Structure type:: Chapter

During the 1982 growing season, reflectance mea surements of bare soil were obtained with a field spectroradiometer (described by Clevers, 1986c). Results are illustrated in figure 2 for a wet and for a dry soil. Estimated reflectances for the (si mulated) spectral bands used with multispectral aerial photography (MSP) are listed in table 1. These results confirm the validity of the assump tion that the ratio between the reflectance in any pair of the green, red and infrared bands is con stant for the soil type investigated. These ratios also tend to the value one. REFLECTANCE GREEN RED INFRARED Figure 2: Reflectance of bare soil as measured by means of the field spectroradiometer on two dates during the 1982 growing season. : wet soil (28 April 1982) : dry soil (10 June 1982). Table 1: Estimated reflectances of a wet soil and a dry soil for the MSP bands, ascertained by means of the field spectroradiometer. reflectance (%) green red infrared green/red infrared/ red 28 April 1982 12.4 13.6 15.0 0.91 1.10 10 June 1982 20.5 23.7 26.3 0.86 1.11 4.5 Estimating leaf area index The dates of the flights and of the field sampling did not coincide and therefore the smoothed LAI da ta provided the possibility of interpolating the LAI values for the dates of flight missions. These data were used for studying the relationship with reflec tance measurements. Method 0 for estimating LAI follows the line that the reflectance of bare soil and the reflectance of vegetation in the different spectral bands is known. For instance, after estimating soil cover by means of equation (2), the corrected infrared reflectance can be calculated by means of equation (3). Finally, LAI can be estimated by means of equation (5). The reflectance of vegetation may vary slightly because of differences in leaf colour, but the mean value (averaged over different dates and treatments) was used for the green and red bands: r = 5.0 and r = 2.0 (see Clevers, 1986c). During the 1982 seAson the soil was dry during missions up to mid- June (complete soil cover with barley), because there was little rainfall on days prior to the mis sions. Hence the reflectance value for bare soil in each band could be considered as being approximately constant. The following reflectance values for bare soil were used (see Clevers, 1986c): r = 12.4, r = 13.5 and r s ^ = 14-7. Mltflod 1 uses equation (4) for S 6aiculating the cor rected infrared reflectance. For Cj a value of 0.9 and for C 2 a value of 1.1 was applied. The reflec tances of the soil are not needed explicitly. Method 2 concerns the crude approximation of the corrected infrared reflectance as described by equa tion (8). This may give a good approximation of the corrected infrared reflectance, since the ratio of green reflectance to red reflectance and of infra red reflectance to red reflectance of the soil at the experimental farm were nearly equal to one. The reflectance of bare soil and of vegetation in the different spectral bands are not needed explicitly. Theoretically, method 1 is considered to be a more accurate method of correcting the infrared reflec tance for background than method 2 since the actual ratios of bare soil reflectance in the various spec tral bands are used. A disadvantage of method 1 com pared with method 2 is the fact that measurements in three spectral bands are required (of which the green and red are mostly strongly correlated) for calcu lating the corrected infrared reflectance, whereas with method 2 only the red and infrared reflectances are required. These latter two methods (1 and 2) may be superior to method 0 if soil reflectance cannot be regarded to remain constant between the recording dates, because neither of these methods explicitly require the reflectance values of soil. Results for all three methods are presented in table 2 and fi gures 3, 4 and 5. Table 2 shows that both method 0 and 2 gave sim ilar results. Method 1 gave slightly worse results (larger coefficients of variation). This indicates that method 2 may be very useful if soil reflec tance is not similar on different dates. The con clusion that the simplifications induced by method 2 did not yield worse results than method 0 or 1 was caused by the fact that the estimation of LAI is empirical. For instance, f M . is the asymptotic value of the corrected infrafea reflectance, and it is estimated from the measurements (e.g. from a training set). For most data sets, r . is an ex trapolation of the data, and this resiífís in consid erable variation in estimated r . values, but with oo xj- an optimal fit to the data. ' An important conclusion derived from the regres sion of LAI on corrected infrared reflectance for the vegetative stage of barley was that measure ments of all dates may be combined, resulting in one curve. So, leaf angle distribution did not vary to such an extent as to disturb the fit of the curve. Similar results were found for other cereals and in other seasons (Clevers, 1986c). A very important analysis of field measurements from agricultural field trials is an analysis of variance in order to investigate whether treatment effects are significant and whether interactions occur. Analogously with field measurements (LAI), an analysis of variance can be carried out on re flectance measurements in the various spectral bands for investigating whether the latter vari ables can be ascertained with relatively smaller variance and whether treatment effects can be as certained with larger power than by means of con ventional field measurements. An analysis of vari ance can also be carried out on the estimated LAI from the reflectance measurements. The results of this latter analysis are more comparable with the analysis of variance applied to the original LAI measurements, since they involve the same variable. Table 2: Results of different methods for correcting the infrared reflectance and subsequently estimating LAI. Field trial 116 in 1982. (CV = coefficient of variation). method equation number â r »,ir CV of residuals figure 0 2 and 3 0.255 71.21 0.217 3 1 4 0.228 75.70 0.230 4 2 8 0.252 68.57 0.214 5

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