XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

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

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/6: Agriculture, Ecosystems and Bio-Diversity]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (CAMELLIA SINENSIS (L.)) USING HYPERSPECTRAL TECHNIQUES Meng Bian, Andrew K. Skidmore, Martin Schlerf, Yanfang Liu, Tiejun Wang

Document type:: Multivolume work

Structure type:: Chapter

on panel was ce. one bud with re clipped. For > pots together i leaves were mical analysis or each sample > need for wet determine the re steamed for tivity causing e drying in an und using an mined by the etry at 540nm m, 1800-1970 ' noise due to om the data. I observations (Araujo et al., r regression the features of egressions. lt atent variables the size of of dependent ng found with 1989). ) establish the ical contents in the sample and test data ate the partial mance of the lictions of the of the PLSR mination (R2) SEP, Equation ns on test data (1) :rved value of on the model 2.6 A hybrid approach (nonlinear regression approach) For one tea variety growing in the greenhouse, the neural network approach were applied to build the spectral-chemical relationship using nonlinear regression way. A one hidden layer feed-forward, error-back propagation artificial neural network were adopted in this research, for this algorithm has been frequently and successfully used in previous studies (Skidmore et al. 1997). To find the optimal number of nodes in the hidden ! layer, we investigated the training and test accuracies using different number of neurons (1-20) in the network (the maximum number was designed no more than 20 to keep the model parsimony and save the calculation time). Levenberg- Marquardt optimization method was used to train the networks in which the parameters of networks were adjusted adaptively (Lera and Pinzolas, 2002; More, 1978) and an earlier stop technique was applied in this study to avoid overtraining (Lin and Chen, 2004). Before running the neural network model, an effective variable selection method named successive projections algorithm was applied to spectral data (350-2500 nm) after pre-processing. It is a forward selection approach. The purpose of this algorithm is to select wavebands containing minimally redundant information, so that collinearity problems caused by hyperspectral data can be minimized. The available data (64 samples) were randomly divided into three groups: the training dataset (n = 32, 50% of the sample), the validation dataset (n = 16, 25% of the sample) and the test dataset (n = 16, 25% of the sample). The performance of the ANN model was evaluated by the root mean square error of prediction (RMSEP) between the predicted and measured concentration based on the test dataset (Mutanga et al., 2004). To speed up the training process of neural networks models, the input data of chemical concentrations were normalized between 0 and 1(Mutanga et al., 2004). 3. RESULTS Table 2 shows the measured concentrations for total tea polyphenols by varieties and soil treatments. All values are reported on a dry-matter basis. The range of the chemical data accords with the values which have been previously reported. For tea polyphenols measured for greenhouse experiment , the combination of higher level of nitrogen, phosphorus and potassium resulted in the maximum concentration and vice Versa. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia No. Mean Minimum Maximum ; (mgg”)_ (mag) (mag ”) Six varieties A 8 176.20 167.46 181.81 B 8 180.67 472.21 195.35 C 8 186.31 173.48 199.36 D 8 208.72 203.90 213.77 E 8 270.92 260.63 288.99 E 8 218.87 201.16 235.94 All 48 206.95 167.47 288.99 Soil treatments à 8 126.28 118.40 134.62 b 8 132.36 126.57 138.42 8 132.87 125.77 137.27 d 8 133.83 129.83 141.35 e 8 132.59 127.32 138.98 f 9 143.88 137.02 149.53 g 8 133.24 12573 141.01 i h 8 145.95 141.89 149.99 All 64 135.13 129.07 141.40 Table 2. Descriptive statistics of the total tea polyphenols measured in the laboratory For different tea varieties, using partial least squares regression, observed versus predicted concentrations of tea polyphenols for both training (N=30) and test (N=18) data are shown in Figure 2. The satisfactory accuracy of prediction was obtained at canopy level : based on the independent data set, total tea polyphenols were estimated with high 12 values (> 0.8) and low RMSEP values (RMSEP = 13.68 mg g-1 , RMSEP/mean = 6.63%). 300 * training * test . + 47 x=y À 'O» 250 D E © fe o 9 A à te à “= 2 D - Tr. Sos x ; * Set s r? - 0.84 9 RMSEP = 13.68 150 150 200 250 300 Predicted (mg 9°!) Figure 2. Scatter plots describing the measured and predicted total tea polyphenols for training and test using canopy spectra (mean centred). r^ is coefficient of determination between model predictions and measured chemical concentrations on test data set, and RMSEP is the root mean square error of test data prediction. Figure 3 presents relationships between the predicted and measured biochemical concentrations using a hybrid of neural networks and SPA variable selections (SPA-ANN): on test data set, using the wavebands selected by the successive projections algorithm, the neural networks with optimal settings yielded coefficient of determination r2 of 0.82, for the prediction of total tea polyphenols in the greenhouse experiment, with a root mean square error of 4.30 mg g-1 (3.0% of the mean). Figure 4 shows the optimal choice of the number of wavelength selected by successive projections algorithm. According to the criterion of the root mean square error of validation, the best choice of 12 wavebands has been selected for the prediction of total tea polyphenols. In an order of importance (from most to least), wavelengths selected by SPA for the prediction of total tea polyphenols are 2001 nm, 2206 nm, 1424 nm, 1799 nm, 1439 nm, 1426 nm, 689 nm, 1971 nm, 1428 nm, 1435 nm, 1422 nm and 1502 nm.

Access restriction

Copyright

fullscreen: Technical Commission VIII (B8)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation