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/8: Land]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: SPECTRAL UNMIXING OF BLENDED REFLECTANCE FOR DENSER TIME-SERIES MAPPING OF WETLANDS Ryo Michishita, Zhiben Jiang, Bing Xu

Document type:: Multivolume work

Structure type:: Chapter

X-B8, 2012 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 SPECTRAL UNMIXING OF BLENDED REFLECTANCE FOR DENSER TIME-SERIES MAPPING OF WETLANDS Ryo Michishita * ^, Zhiben Jiang*, Bing Xu *"** * College of Global Change and Earth System Science, Beijing Normal University Beijing, 100875, China - zhibenjiang@ gmail.com 2 Department of Geography, University of Utah 260 S. Central Campus Dr. Rm. 270, Salt Lake City, Utah, 84112-9155, United States - ryo.michishita@geog.utah.edu * School of Environment, Tsinghua University Beijing, 100084, China - bingxu@tsinghua.edu.cn Commission VIII, WG VIII/8 KEY WORDS: Classification, Environment, Generation, Land cover, Landsat, Multiresolution, Multispectral, Multitemporal ABSTRACT: The orbiting cycle and frequent cloud contamination have limited the applications of the moderate-resolution remotely sensed data for detecting rapid land cover changes that are critical to the monitoring of wetlands. It is necessary to use multiple remotely sensed data sources that have different spatial resolution and temporal frequency, because both spatial and temporal details are important in understanding the mechanisms in wetland cover changes. This study examined the applicability of linear spectral mixture analysis to the blended reflectance that was generated by incorporating the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Nine TM and MODIS images of the Poyang Lake area, China acquired in 2004 and 2005 were used to blend the reflectance. In order to account for the spectral variations in materials, we incorporated the multiple endmember spectral mixture analysis (MESMA) in unmixing the blended reflectance. The average absolute differences between the land cover fractions derived from the blended image and those from the observed image were calculated as well as correlation coefficients. Our results demonstrated that MESMA could unmix the blended reflectance generated by ESTARFM. However, due to the existence of the blended pixels with large difference in reflectance from the observed reflectance, the land cover fractions derived from the blended reflectance did not match with those derived from the observed reflectance as well as expected. It is also suggested that the comprehensiveness of the endmember spectral libraries was another factor influencing the agreement. 1. INTRODUCTION Taking advantage of regular orbiting intervals and extensive coverage, satellite remote sensing has been utilized as a practical and economical means to monitor and inventory different types of wetlands (Ozesmi and Bauer, 2002). Although a wide variety of time-series remotely sensed data observed with differing sensor designs have been used for the mapping of wetland cover changes, previous studies have shown that wetland mapping using optical remotely sensed data is not as easy as the mapping of other ecosystems (Silva et al., 2008). This is because the spectra of wetland vegetation species show a high level of variability due to the species’ structural, biochemical, and biophysical diversity, as well as the spectral confusion among individual wetland components described above (Adam et al. 2010). In addition, due to the tradeoff between spatial resolution and temporal frequency, wetland cover changes has not been monitored with spatial and temporal details simultaneously using the imagery observed by single remotely sensor. For a better understanding of the spatio- temporal dynamics in all land cover components of wetland ecosystems, it is necessary to overcome these difficulties. In the goal of improving the accuracy of wetland mapping using remotely sensed data, spectral mixture analysis (SMA) have received more attention, due to their relative simplicity of use in * Corresponding author deriving physically interpretable information at subpixel level (Roberts et al., 1993). SMA models mixed spectra in pixels of a remotely sensed image as a combination of endmembers (EMs) — pure spectra representing distinct land cover types (Adams et al., 1993). In linear SMA, a spectrum within the instant field of view of a sensor is determined by the sum of each EM spectrum multiplied by its aerial coverage fraction and the residual error. Although many studies have incorporated SMA in the mapping of wetland vegetation and floodplain mapping, only a few studies have also been conducted on SMA using multitemporal remotely sensed data for the mapping of wetland land cover changes. (He et al., 2010; Melendez-Pastor et al., 2010). In order to increase temporal frequency of moderate-resolution remotely sensed data, several blending techniques have been developed and applied in some studies. Among them, The spatial and temporal adaptive reflectance fusion model (STARFM) (Gao et al., 2006) has been widely used. Recently, Zhu et al. (2010) modified the original STARFM to overcome the poor accuracy of STARFM in heterogeneous landscapes. A few application studies of ESTARFM has proved that the blended reflectance data is comparative to observed reflectance data in chlorophyll index derivation and supervised classification (Singh, 2011; Watts et al., 2011). However, no studies have investigated on the applicability of SMA to the blended reflectance data. In addition, Previous study has not applied these blending techniques in wetland environment.

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