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

X-B8, 2012 s. Rural economic s. Rural à / ent and .pub. Baidam, ake of ts of t yield to d Coast, nsville. ‚The Sediment > and Sea edings of Patterns the Great ille, 1990, —209. )vernment 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 ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (CAMELLIA SINENSIS (L.)) USING HYPERSPECTRAL TECHNIQUES Meng Bian ab” Andrew K. Skidmore ^ Martin Schlerf ^, Yanfang Liu *, Tiejun Wang b ? School of Remote Sensing and Information Engineering, Wuhan University, 129 LuoYuRoad, Wuhan, 430079, P.R. China - bian@whu.edu.cn ? Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands - (skidmore, schlerf, tiejun)@itc.nl * School of Resource and Environmental Science, Wuhan University, 129 LuoYuRoad, Wuhan, 430079, P.R. China - yfliu610(2163.com Working Group, Theme or Special Session: VIII/6: Agriculture, Ecosystems and Bio-Diversity KEY WORDS: Agriculture, Quality, Hyper spectral, Estimation, Statistics ABSTRACT: Tea (Camellia Sinensis (L.)) is an important economic crop and the market price of tea depends largely on its quality. This research aims to explore the potential of hyperspectral remote sensing on predicting the concentration of biochemical components, namely total tea polyphenols, as indicators of tea quality at canopy scale. Experiments were carried out for tea plants growing in the field and greenhouse. Partial least squares regression (PLSR), which has proven to be the one of the most successful empirical approach, was performed to establish the relationship between reflectance and biochemical concentration across six tea varieties in the field. Moreover, a novel integrated approach involving successive projections algorithms as band selection method and neural networks was developed and applied to detect the concentration of total tea polyphenols for one tea variety, in order to explore and model complex nonlinearity relationships between independent (wavebands) and dependent (biochemicals) variables. The good prediction accuracies (r2 > 0.8 and relative RMSEP < 10 %) achieved for tea plants using both linear (partial lease squares regress) and nonlinear (artificial neural networks) modelling approaches in this study demonstrates the feasibility of using airborne and space- borne sensors to cover wide areas of tea plantation for in situ monitoring of tea quality cheaply and rapidly. 1. INTRODUCTION Tea consumption is rising in recent years, for the special flavour and the possible beneficial effects on human body. Consequently, it has become increasingly important to be able to give reliable estimates of the tea quality (Yan, 2007). Traditional methods to determine tea quality is mainly handled by tea experts, which may bring inconsistent and subjective results, or based on wet chemical analysis, which is time and labour consuming. Being effective and quantitative, the development of new techniques using hyperspectral remote sensing data has offered possibilities to estimate and monitor vegetation quality in space and time (Knox et al, 2011; Mutanga and Kumar, 2007). Hyperspectral remote sensing techniques have been developed from a laboratory-based near infrared spectroscopy (NIRS) technique (Curran et al., 2001). The narrow sensitive band range (10 nm or less) makes it possible to detect subtle variations in the reflectance spectra, which are caused by differences in biochemical composition and physiology of vegetation (Davey et al., 2009; Schlerf et al., 2010). In recent years, researchers have extended the technique of reflectance spectroscopy to measure biochemical parameters of vegetation by field Spectrometer or airborne or spaceborne sensors, trying to explore the chemical variation of vegetation in a spatial context (Curran, 1989; Schlerf et al., 2010; Skidmore et al., 2010). Tea polyphenols compose of four main substances as catechins, flavonoids, anthocyanins and phenolic acids, accounting for 20- 35% of the total dry matter. It contributes greatly to tea taste and quality. In practice, people only pluck the young tender buds and leaves for producing tea product with high-quality. Compared with older leaves, this part of tea plant contains the optimal ratio of polyphenols and amino acids, which forms the special taste of tea beverage (Mitscher and Dolby 1997). This research aims to estimate the concentrations of main tea quality-related compounds (total tea polyphenols) using reflectance spectroscopy for tea plants at canopy level. Both linear (partial least regression) and nonlinear (artificial neural network) regression methods have been attempted. To detect whether the spectral-chemical relationships exist for the whole tea species, partial least squares regression was performed to establish the relationship between reflectance and biochemical contents across different tea varieties. Furthermore, a hybrid approach of neural network and successive projection algorithms (variable selection) has been applied for the estimation of total tea polyphenols for one tea variety planting in a greenhouse.

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