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:: REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN Indira Wittamperuma, Mohsin Hafeez, Mojtaba Pakparvar and John Louis

Write comment:: Textverlust im Original.

Document type:: Multivolume work

Structure type:: Chapter

REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN Indira Wittamperuma!, Mohsin Hafeez" 2, Mojtaba Pakparvar’and John Louis* !School of Environmental Sciences, Charles Sturt University, Wagga Wagga NSW 2678, Australia. Email: iwittamperuma@csu.edu.au GHD Pty Ltd, 201 Charlotte Street, Brisbane QLD, 4000 3Faculty of Bioscience Engineering Gent University, 673 Cupour Links, Gent 9000, Belgium “School of Computing and Mathematics, Charles Sturt University, Wagga Wagga NSW 2678, Australia. KEY WORDS: GIS, LAL, LANDSAT TM, NDVI, Remote Sensing, ABSTRACT: Remote sensing is a rapid and reliable method for estimating crop growth data from individual plant to crops in irrigated agriculture ecosystem. The LAI is one of the important biophysical parameter for determining vegetation health, biomass, photosynthesis and evapotranspiration (ET) for the modelling of crop yield and water productivity. Ground measurement of this parameter is tedious and time-consuming due to heterogeneity across the landscape over time and space. This study deals with the development of remote-sensing based empirical relationships for the estimation of ground-based LAI (LAG) using NDVI, modelled with and without atmospheric correction models for three irrigated crops (corn, wheat and rice) grown in irrigated farms within Coleambally Irrigation Area (CIA) which is located in southern Murray Darling basin, NSW in Australia. Extensive ground truthing campaigns were carried out to measure crop growth and to collect field samples of LAI using LAI- 2000 Plant Canopy Analyser and reflectance using CROPSCAN Multi Spectral Radiometer at several farms within the CIA. A Set of 12 cloud free Landsat 5 TM satellite images for the period of 2010-11 were downloaded and regression analysis was carried out to analyse the co-relationships between satellite and ground measured reflectance and to check the reliability of data sets for the crops. Among all the developed regression relationships between LAI and NDVI, the atmospheric correction process has significantly improved the relationship between LAI and NDVI for Landsat 5 TM images. The regression analysis also shows strong correlations for corn and wheat but weak correlations for rice which is currently being investigated. 1. INTRODUCTION LAI is a dimensionless vegetation biophysical parameter which defines the status of the vegetation growth and it is a key input parameter in crop growth and yield models (Doraiswamy et al., 2005), plant photosynthesis (Duchemin et al, 2006), evapotranspiration (ET) and carbon flux (Chen et al, 2007). Therefore, direct or indirect estimation of LAI measurements are key input in many ecosystem models. The direct method of LAI calculation involves steps including leaf collection, area determination and measurement of dry weight of leaves to derive the ratios of leaf area and mass per unit ground area (Zheng et al., 2009). In many agricultural and forestry applications, LAI was directly estimated to assess crop growth and health of vegetation around the globe. Sarlikioti et al., (2011) measured LAI destructively to explore a way for the online estimation of LAI and PAR interception in two greenhouse grown crops (tomato and sweet paper). Casanova et al., (1998) monitored the rice crop status during the growing season using LAI at Ebro Delta in Spain. These researchers estimated LAI directly by measuring the leaf blade of the rice crop with a LI-300 Area Meter. Even though the direct measurements of LAI provide more accurate estimation, it is time consuming and work intensive to use over large agricultural areas which are rapidly changing over the time. In the indirect method, LAI is estimated in terms of canopy gap fraction or gap size distribution. The hand held optical instruments such as plant canopy analysers (LI-COR LAI-2000) are usually used to measure canopy gap fraction. Liu et al, (2010) has used indirect measurements of LAI-2000 plant canopy analyzer to compare the LAI estimates derived from vertical gap fraction measurements obtained from digital colour photography over the top of canopy of corn, soybean and wheat canopies in Eastern Canada. The performance of indirect methods using hand held instruments can only provide reasonable estimates if the basic assumptions such as data is collected at dawn or dusk conditions are strictly followed (Wilhelm et al., 2000) and this method is not practical to collect LAI data over large vegetation areas. Stroppiana et al, (2006) estimated LAI directly and indirectly to evaluate the adequacy and the range of reliability of LAI-2000 estimates for rice in Northern Italy. Similarly, Liang et al, (2003) took LAI measurements using non-destructive methods to create an algorithm for estimating LAI using Advanced Land Imager (ALI) multi-spectral satellite images and it was validated using multiple small plots within large crop fields in the CIA, Australia. However, the direct comparison was not possible due to geo-location and registration uncertainties of the images; therefore, the average LAI value for each field in the CIA was calculated for the validation of the algorithm.

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