XXII ISPRS Congress 2012: Technical Commission VIII

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

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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

2. METHODS AND MATERIALS 2.1 Study area and Data sets The research was conducted in the Huazhong Agriculture University in Wuhan, China (latitude 30°28'41"N, longitude 114?21'48"E). Part of the data was colleted in the tea garden of the university, while another part of the data was colleted from a greenhouse experiment (Figure 1). Figure 1. Location of the Huazhong Agriculture University, Wuhan, China (left part of the figure). The right part shows the pictures of the tea garden (top) and the greenhouse setup before fertilization (bottom) in the university. Six different varieties of tea including Fuding dabai (FD), Fu yun 6 (FY), E cha 1 (EC), Tai cha 12 (TC), Huang dan (HD) and Mei zhan (MZ) in the tea garden were selected as study objects, to detect whether the modelling methods can be extended to various tea varieties. The tea bushes are so dense that soil background is barely seen from the canopy above. For each tea variety, eight samples were randomly collected. Thus, in a total, 48 (8x6) samples were obtained. For greenhouse experiment, young plants of Fuding Dabai tea were planted in the greenhouse under controlled conditions. To stretch the chemical variation in the sample, eight soil treatments with different levels of available soil nutrient were designed (Table 1) . Each soil treatment had eight repetitions, and a total of 64 samples (8*8) were collected for the greenhouse experiment. 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 Nutrient level Nitrogen Phosphorous Potassium Low level (L) 0.75 0.3 0.3 High level (H) 7.5 3 3 Table 1. Levels of available soil nutrient used for the 8 treatments (unit: g/pot) 2.2 Canopy spectral measurement On a cloud-free sunny day, canopy reflectance was measured using ASD FieldSpec Pro FR spectrometer (Analytical Spectral Devices). The spectrometer covers a range from 350-2500 nm with sampling intervals of 1.4nm between 350nm and 1000 nm, and 2 nm between 1000 nm and 2500 nm. The fiber optic was handheld approximately 10-20 cm above the top of the canopy. To avoid bidirectional reflectance distribution function (BRDF), the pots were rotated 60° after every ninth measurement of the canopy. Before taking a canopy measurement, the radiance of a white spectralon panel was measured for normalization of the target reflectance. After the canopy measurements were finished, one bud with three or four leaves of tea bushes in the field were clipped. For tea plants growing in the greenhouse, four or five pots together were regarded as one observation and the tea leaves were plucked, to make sure enough tea leaves for chemical analysis in the laboratory. The weight of the fresh leaves for each sample unit has to been at least 40 grams to satisfy the need for wet chemistry analysis. 2.3 Biochemical Assay Standard wet chemistry methods were used to determine the concentrations of total polyphenols. The leaves were steamed for three and a half minutes to destroy enzyme activity causing oxidation of the tea (Yamamoto et al. 1997) before drying in an oven at 80°C. Next, the dried leaves were ground using an electric mill. Total tea polyphenols were determined by the ferrous tartrate colorimetry method and spectrometry at 540nm (Iwasa and Torii 1962). 2.4 Spectral pre-processing The bands regions 350 nm-400 nm, 1350-1420 nm, 1800-1970 nm and 2300-2500 nm displayed high levels of noise due to atmospheric absorption, and were excluded from the data. Before data analysis, the reflected spectra of 64 observations were mean-centered by subtracting their means (Araujo et al, 2001; Cho et al., 2007). 2.5 Partial least square regression (linear regression approach) Partial least squares regression (PLSR) combines the features of principal component analysis and multiple regressions. It compresses a large number of variables to a few latent variables (PLS factors). It is particularly useful when the size of independent variables is much larger than that of dependent variables. PLSR reduces the problem of over fitting found with the multiple regression (Card et al., 1988; Curran, 1989). Partial least squares regression was performed to establish the relationship between reflectance and biochemical contents across different tea varieties. The 48 observations in the sample were randomly divided into training data (N=30) and test data (N=18). The training dataset were used to calibrate the partial least squares regression model, and the performance of the model was validated by comparing the model predictions of the test data to the observations. The performances of the PLSR models were assessed by the coefficient of determination (R2) and the root mean square error of prediction (RMSEP, Equation (1)) between predicted and measured concentrations on test data set. RMSEP= Equation (1) $410» n where n is the number of test data, Ji is the observed value of data point i and Yi is the estimated value based on the model calibrated by training data. Inter 2.6 Ahy For one | network a relationshi feed-forw: were ado frequently et al. 1997 layer, we different maximum model pai Marquard in which | (Lera and technique and Chen, Before rui selection applied to a forward to selec informati hyperspec The avail three grou the valida dataset (n ANN mo predictior concentra To speed input datz 0 and 1(W Table 2 polyphen reported « accords v For tea p combinat potassiun versa. Six varie! / | | Í | A Soil treat

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