Researchers Database

MURAMATSU Kanako

    Faculty Division of Natural Sciences Research Group of Environmental Sciences Professor
Last Updated :2021/09/10

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Research Areas

  • Environmental science/Agricultural science, Environmental dynamics

Education

  • Nara Women's University, Graduate School of Humanities and Sciences, 生活環境学専攻, Japan

Committee Memberships

  • May 2016 Apr.2018Remote sensing society of Japan評議員会委員
  • 1999Remote sensing society of Japan学術委員会委員
  • 日本リモートセンシング学会学術委員会委員

Awards

  • Best paper award, the 30 ACRS2009, Oct. 2009
  • 大学婦人協会守田科学奨励賞, 2003
  • 日本リモートセンシング学会優秀論文発表賞, 2002
  • 日本リモートセンシング学会論文賞, 1997

Published Papers

  • Determination of rice paddy parameters in the global gross primary production capacity estimation algorithm using 6 years of JP-MSE flux observation data

    Kanako Muramatsu; Keisuke Ono; Noriko Soyama; Juthasinee Thanyapraneedkul; Akira Myata; Masayoshi Mano

    Gross primary production (GPP) capacity is defined as GPP under low stress, and the algorithm for its estimation was developed by Thanyapraneedkul etal. (2012) using a light-response curve. The idea behind this algorithm is that the light response curve under low stress is related to chlorophyll content. The parameter is estimated from a vegetation index derived from satellite observations of the green chlorophyll index (C/(green)) for seven vegetation types, including rice paddy. These previous studies included 1 year of data for the flux site and MODIS reflectance data. Recently, long-term data have become publicly available for flux data covering a period of 6 years, and MODIS reflectance data covering a period of more than 16 years. This study determined the parameters in the GPP capacity estimation algorithm for rice paddies using 6 years of Mase paddy flux site data and clear daytime reflectance data observed using MODIS. The fitted parameter-related initial slopes of the light -photosynthesis curves for each year were identical within the fitting error. Using the averaged parameter -related initial slope over 6 years, we were able to determine a linear relationship between agree, and the maximum photosynthesis rate at 2000 PAR (mu mol m-(2) s-(1) ), the slope of which was slightly higher than has been reported previously. Using the parameters for the period 2001-2006, we investigated how GPP capacity varied for irrigated rice paddy. The ratio of the average GPP capacity to the GPP after transplanting until harvesting was 0.91 for the period 2001 to 2006. This result shows that GPP capacity provides a useful first approximation of GPP for irrigated rice paddies as a framework of the global GPP estimation algorithm., SOC AGRICULTURAL METEOROLOGY JAPAN, Jul. 2017, JOURNAL OF AGRICULTURAL METEOROLOGY, 73 (3), 119 - 132, doi;web_of_science;rm:research_project_id

    Scientific journal

  • Determination of parameters for shrubs in the global gross primary production capacity estimation algorithm

    Yukiko Mineshita; Kanako Muramatu; Noriko Soyama; Juthasinee Thanyapraneedkul; Motomasa Daigo

    accepted, Dec. 2016, リモートセンシング学会誌, 36 (3), 236-246, rm:research_project_id

  • Determination of bamboo distribution in Nara and southern Kyoto prefectures using multi temporal ALOS/AVNIR-2 data

    MURAMATSU Kanako; Narumi Hanaki; Kanako Muramatsu; Fumio Ochiai; Noriko Soyama; Motomasa Daigo; Takeo Tadono

    Apr. 2015, Journal of Remote Sensing Society of Japan, 35 (2), 77,88

  • An examination of the possibility of using ALOS/PRISM data to estimate average tree heights in forests in Nara prefecture based on a validation study in Japan cedar and cypress forests in Yoshino district, Nara, Japan

    MURAMATSU Kanako; Nozomi Niino; Kanarako Muramatsu; Takeo Tadono; Shinobu Furumi; Noriko Soyama; Motomasa Daigo

    Sep. 2013, Journal of the remote sensing society of Japan, 33 (4), 308-318

  • A Vegetation Index to Estimate Terrestrial Gross Primary Production Capacity for the Global Change Observation Mission-Climate (GCOM-C)/Second-Generation Global Imager (SGLI) Satellite Sensor

    Juthasinee Thanyapraneedkul; Kanako Muramatsu; Motomasa Daigo; Shinobu Furumi; Noriko Soyama; Kenlo Nishida Nasahara; Hiroyuki Muraoka; Hibiki M. Noda; Shin Nagai; Takahisa Maeda; Masayoshi Mano; Yasuko Mizoguchi

    To estimate global gross primary production (GPP), which is an important parameter for studies of vegetation productivity and the carbon cycle, satellite data are useful. In 2014, the Japan Aerospace Exploration Agency (JAXA) plans to launch the Global Change Observation Mission-Climate (GCOM-C) satellite carrying the second-generation global imager (SGLI). The data obtained will be used to estimate global GPP. The rate of photosynthesis depends on photosynthesis reduction and photosynthetic capacity, which is the maximum photosynthetic velocity at light saturation under adequate environmental conditions. Photosynthesis reduction is influenced by weather conditions, and photosynthetic capacity is influenced by chlorophyll and RuBisCo content. To develop the GPP estimation algorithm, we focus on photosynthetic capacity because chlorophyll content can be detected by optical sensors. We hypothesized that the maximum rate of low-stress GPP (called "GPP capacity") is mainly dependent on the chlorophyll content that can be detected by a vegetation index (VI). The objective of this study was to select an appropriate VI with which to estimate global GPP capacity with the GCOM-C/SGLI. We analyzed reflectance data to select the VI that has the best linear correlation with chlorophyll content at the leaf scale and with GPP capacity at canopy and satellite scales. At the satellite scale, flux data of seven dominant plant functional types and reflectance data obtained by the Moderate-resolution Imaging Spectroradiometer (MODIS) were used because SGLI data were not available. The results indicated that the green chlorophyll index, CIgreen(rho(NIR)/rho(green)-1), had a strong linear correlation with chlorophyll content at the leaf scale (R-2 = 0.87, p < 0.001) and with GPP capacity at the canopy (R-2 = 0.78, p < 0.001) and satellite scales (R-2 = 0.72, p < 0.01). Therefore, CIgreen is a robust and suitable vegetation index for estimating global GPP capacity., MDPI AG, Dec. 2012, REMOTE SENSING, 4 (12), 3689 - 3720, doi;web_of_science

    Scientific journal

  • Simulations and observations of patchy stomatal behavior in leaves of Quercus crispula, a cool-temperate deciduous broad-leaved tree species.

    MURAMATSU Kanako; Kamakura M; Kosugi Y; Muramatsu K; Muraoka H

    2011, Journal of Plant Research, 125, 339-349

  • An improved tree-height measurement method for calculating net primary production in a larch forest on Mt. Yatsugatake, Japan

    MURAMATSU Kanako; J. Thanyapraneedkul; K. Ikegami; K.Muramatsu; N.Soyama; M.Daigo; K.Kajiwara; Y.Honda

    Mar. 2010, 人間文化研究科年報, (26), 261-274

  • Topographic effects on estimating net primary productivity of green coniferous forest in complex terrain using Landsat data: a case study of Yoshino Mountain, Japan

    Wei Huang; Liangpei Zhang; S. Furumi; K. Muramatsu; M. Daigo; Pingxiang Li

    In mountainous areas, irregular terrain significantly affects spatial variations of climatic variables and the reflectance of pixels in remote sensing imagery. Consequently, the variations may affect the estimation of net primary productivity (NPP). The light-use efficiency (LUE) model is used to analyse topographic influence on NPP by evaluating topographic effects on primary input data to the model, including both Normalized Difference Vegetation Index (NDVI) and climatic data. A typical green coniferous forest in Yoshino Mountain, Japan, was employed as the study area. The results show that the average NPP is significantly increased after removing topographic influences on NDVI; the average NPP has a relatively minimal change when only topographic effects on climatic data are considered. When both topographic effects on NDVI and climatic data are considered, the average NPP is 1.80kg m-2 yr-1, which is very similar to the ground measurement result of 1.74kg m-2 yr-1., TAYLOR & FRANCIS LTD, 2010, INTERNATIONAL JOURNAL OF REMOTE SENSING, 31 (11), 2941 - 2957, doi;web_of_science

    Scientific journal

  • Overview and Science Highlights of the ADEOS-II/GLI Project

    MURAMATSU Kanako; T. Nakajima; H. Murakami; M. Hori; T. Y. Nakajima; H. Yamamoto; J. Ishizaka; R. Tateishi; T. Aoki; T. Takamura; M. Kuji; N. D. Duong; A. Ono; S. Fukuda; K. Muramatsu

    2009, J. of the Remote Sensing Soc. of Japan, 29 (1), 11-28

  • Estimating and Validating the Net Primary Production of Vegetation using ADEOS-II/GLI Global Mosaic and 250-m Spatial Resolution Data

    MURAMATSU Kanako; K. Muramatsu; S. Furumi; L. Chen; Y. Xiong; M. Daigo

    2009, 日本リモートセンシング学会誌, 29 (1), 114-123

  • Global Land Cover Classification using ADEOS-II/GLI Global Mosaic Data

    MURAMATSU Kanako; N. Soyama; H. Tsujimoto; K. Muramatsu; S. Furumi; M. Daigo; F. Ochiai

    2009, 日本リモートセンシング学会誌, 29 (1), 102-113

  • 内モンゴル草原における生活様式の変遷と植生評価のためのALOS/AVNIR-2データの有効性

    MURAMATSU Kanako

    2008, 大阪産業大学人間環境論集, (7), 83-102

  • A new vegetation index based on the universal pattern decomposition method

    Lifu Zhang; S. Furumi; K. Muramatsu; N. Fujiwara; M. Daigo; Liangpei Zhang

    This study examined a new vegetation index, based on the universal pattern decomposition method (VIUPD). The universal pattern decomposition method (UPDM) allows for sensor-independent spectral analysis. Each pixel is expressed as the linear sum of standard spectral patterns for water, vegetation and soil, with supplementary patterns included when necessary. Pattern decomposition coefficients for each pixel contain almost all the sensor-derived information, while having the benefit of sensor independence. The VIUPD is expressed as a linear sum of the pattern decomposition coefficients; thus, the VIUPD is a sensor-independent index. Here, the VIUPD was used to examine vegetation amounts and degree of terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index (NDVI), an enhanced vegetation index (EVI) and a conventional vegetation index based on pattern decomposition (VIPD). The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI., TAYLOR & FRANCIS LTD, Jan. 2007, INTERNATIONAL JOURNAL OF REMOTE SENSING, 28 (1-2), 107 - 124, doi;web_of_science

    Scientific journal

  • Hyperspectral data transformation and vegetation index performance based on the universal pattern decomposition method

    Lifu Zhang; Liangpei Zhang; Lei Yan; Noboru Fujiwara; Motomasa Daigo

    Traditional vegetation indices are based on only a few spectral bands. However, hyperspectral spectrometers, such as the airborne visible infrared imaging spectrometer (AVIRIS), collect data with 224 contiguous spectral bands. Traditional vegetation index extraction methods lose much of the information contained in hyperspectral data. The universal pattern decomposition method (UPDM) is tailored for hyperspectral data analysis. In this article, we consider the UPDM as a type of multivariate analysis; standard patterns are interpreted as an oblique coordinate system and coefficients are thought of as the coordinates of a pixel's reflectance. This article describes UPDM hyperspectral data transformation of AVIRIS data, the performance of a vegetation index based on the universal pattern decomposition method (VIUPD), and the influences of a noise-to-vegetation index. The results demonstrate that the VIUPD is an effective vegetation information extraction approach for hyperspectral data. The VIUPD is more sensitive to vegetation conditions than the normalized difference vegetation index and enhanced vegetation index. Furthermore, noise influences can be neglected in VIUPD computations, with satisfactory accuracy. (c) 2007 Society for Imaging Science and Technology., I S & T - SOC IMAGING SCIENCE TECHNOLOGY, Mar. 2007, JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY, 51 (2), 141 - 147, doi;web_of_science

    Scientific journal

  • A new vegetation index derived from pattern decomposition method applied to Landsat-7/ETM+ image in Mongolia, Int. J. of Remote Sensing

    MURAMATSU Kanako; K. Muramatsu; Y. Xiong; S. Nakayama; F. Ochiai; M. Daigo; M. Hirata; K. Oishi; B. Bolortsetseg; D. Oyunbaatar; I. Kaihotsu

    2007, Int. J. of Remote Sensing, 8 (16), 3493-3511

  • Estimation of plant biomass and plant water content by dimensional measurement of plant volume in Dund-Govi Province of Mongolia

    MURAMATSU Kanako; Masahiro Hirata; Kazato Ohishi; Kanako Muramatu; Ichiro Kaihotu; Aya Nishiwaki; Jyoken Ishida; Hiroyuki Hirooka

    2007, Grassland Science, 53, 217-225

  • Sensor-independent analysis method for hyperspectral data based on the pattern decomposition method

    Lifu Zhang; S. Furumi; K. Muramatsu; N. Fujiwara; M. Daigo; Liangpei Zhang

    This paper describes a modified pattern decomposition method with a supplementary pattern. The proposed approach can be regarded either as a type of spectral mixing analysis or as a kind of multivariate analysis; the later explanation is more suitable considering the presence of the additional supplementary patterns. The sensor-independent method developed herein uses the same normalized spectral patterns for any sensor: fixed multi-band (1260 bands) spectra serve as the universal standard spectral patterns. The resulting pattern decomposition coefficients showed sensor independence. That is, regardless of sensor, the three coefficients had nearly the same values for the same samples. The estimation errors for pattern decomposition coefficients depended on the sensor used. The estimation errors for Landsat/MSS and ALOS/AVNIR-2 were larger than those of Landsat/TM (ETM+), Terra/MODIS and ADEOS-II/GLI. The latter three sensors had negligibly small errors., TAYLOR & FRANCIS LTD, Nov. 2006, INTERNATIONAL JOURNAL OF REMOTE SENSING, 27 (21), 4899 - 4910, doi;web_of_science

    Scientific journal

  • Approximation method for time-integral of photosynthesis for NPP estimation using remote sensing data: Case study in Mongolia

    MURAMATSU Kanako; Y. Xiong; K.Muramatsu; M.Hirata; K.Oishi; I.Kaihotsu; T.Takaumura; S. Furumi; N.Fujiwara

    2005, J. of Remote Sensing Soc. of Japan,, 25 (2), 179-190

  • A study on producing highly reliabile reference data sets for global land cover validation

    N. Soyama; K. Muramatsu; M. Daigo; F. Ochiai; N. Fujiwara

    Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers., International Society for Photogrammetry and Remote Sensing, 2016, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41, 1207 - 1211, doi

    International conference proceedings

  • A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

    N. Soyama; K. Muramatsu; M. Daigo; F. Ochiai; N. Fujiwara

    Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers., COPERNICUS GESELLSCHAFT MBH, 2016, XXIII ISPRS CONGRESS, COMMISSION VIII, 41 (B8), 1207 - 1211, doi;web_of_science

    International conference proceedings

  • Estimation of net primary production of the Kii peninsula using terra/MODIS data

    Yi Cen; Kanako Muramatsu; Liangpei Zhang

    Precise estimations of zonal net primary production (NPP) are vital for understanding global carbon circulation. To estimate NPP light use efficiency (LUE) model using normalized differential vegetation index (NDVI) has been used mainly. NDVI is obtained from red and near-infrared channels only, however recent sensors onboard satellites always have hyper-multi-spectral channels; In order to use these data effectively, which include more details about land cover a vegetation index based on pattern decomposition method (VIPD) has been set up to estimate NPP. The present study used 2001 Terra/MODIS (Moderate Resolution Imaging Spectroradiometer) data for the Kii Peninsula, which is mainly covered by temperate forest. To validate the proposed method, we compared the satellite data-based NPP values for the evergreen with values derived from land-survey data The proposed method estimated the annual NPP of a temperate forest on Yoshino Mountain to be 1.52 +/- 0.39 kg CO2/m(2)/year which agreed with the ground-survey result of 1.50 +/- 0.75 kg CO2/m(2)/year within the algorithm error An average zonal NPP of 1.55 +/- 0.40 kg CO2/m(2)/year was calculated for the whole land region from 32 degrees 30 ' to 36 degrees 24 ' N latitude, 134 degrees 30 ' to 137 degrees 06 ' E longitude with an area of 3.94 x 104 km(2)., IEEE COMPUTER SOC, 2007, PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS, 548 - +, doi;web_of_science

    International conference proceedings

  • Assessment of the universal pattern decomposition method using MODIS and ETM plus data

    Lifu Zhang; N. Fujiwara; S. Furumi; K. Muramatsu; M. Daigo; Liangpei Zhang

    The universal pattern decomposition method (UPDM) is a sensor-independent method in which each satellite pixel is expressed as the linear sum of fixed, standard spectral patterns for water, vegetation and soil. The same normalized spectral patterns can be used for different solar-reflected spectral satellite sensors. Supplementary patterns are included when necessary. The UPDM has been applied successfully to simulated data for Landsat/ETM +, Terra/MODIS, ADEOS-II/GLI and 92-band CONTINUE sensors using ground-measured data. This study validates the UPDM using MODIS and ETM + data acquired over the Three Gorges region of China. The reduced chi(2) values for selected area D, that with the smallest terrain influences, are 0.000409 (MODIS) and 0.000181 (ETM +), and the average linear regression factor between MODIS and ETM + is 1.0077, with root mean square (rms) value 0.0082. The linear regression factor for the vegetation index based on the UPDM (VIUPD) between MODIS and ETM + data for area D is 1.0089 with rms 0.0696. Both UPDM coefficients and VIUPD are sensor independent for the above sensors., TAYLOR & FRANCIS LTD, Jan. 2007, INTERNATIONAL JOURNAL OF REMOTE SENSING, 28 (1-2), 125 - 142, doi;web_of_science

    Scientific journal

  • A new vegetation index derived from the pattern decomposition method applied to Landsat-7/ETM+ images in Mongolia

    K. Muramatsu; Y. Xiong; S. Nakayama; F. Ochiai; M. Daigo; M. Hirata; K. Oishi; B. Bolortsetseg; D. Oyunbaatar; I. Kaihotsu

    2007, International Journal of Remote Sensing, 28, 3493 - 3511, doi

  • A case study of estimating thermal energy budget in Mongolian plateau using LANDSAT7/ETM+ data

    K. Muramatsu; S. Nakayama; I. Kaihotsu

    The thermal energy budget using satellite data was investigated in an experimental field of Advanced Earth Observing Satellite 11 (ADEOS-II) Mongolian Plateau EXperiment for ground truth (AMPEX) around Mandalgovi in Mongolia. Ground surface temperature changes periodically day by day. Therefore, we studied a method of estimating the radiant energy from the ground surface per day using satellite measurements of brightness temperature at satellite pass-over time. We applied this method to LANDSAT7/ ETM+ data measured on June 12. The ground surface albedo and ground surface temperature were estimated from the satellite data. Using the results and weather data, we estimated absorbed energy on the ground, radiative energy from the surface, and sensible heat flux in a day. Latent heat flux was calculated from the energy balance equation on the ground and estimated energy fluxes. To check this analysis, we calculated the evaporation efficiency. The average value of evaporation efficiency for the whole area except for pond was 0.11. (c) 2005 COSPAR. Published by Elsevier Ltd. All rights reserved., ELSEVIER SCIENCE LTD, 2006, REMOTE SENSING OF OCEANOGRAPHIC PROCESSES AND LAND SURFACES; SPACE SCIENCE EDUCATION AND OUTREACH, 38 (10), 2191 - +, doi;web_of_science

    International conference proceedings

  • A case study of estimating thermal energy budget in Mongolian plateau using LANDSAT7/ETM+ data

    K. Muramatsu; S. Nakayama; I. Kaihotsu

    The thermal energy budget using satellite data was investigated in an experimental field of Advanced Earth Observing Satellite 11 (ADEOS-II) Mongolian Plateau EXperiment for ground truth (AMPEX) around Mandalgovi in Mongolia. Ground surface temperature changes periodically day by day. Therefore, we studied a method of estimating the radiant energy from the ground surface per day using satellite measurements of brightness temperature at satellite pass-over time. We applied this method to LANDSAT7/ ETM+ data measured on June 12. The ground surface albedo and ground surface temperature were estimated from the satellite data. Using the results and weather data, we estimated absorbed energy on the ground, radiative energy from the surface, and sensible heat flux in a day. Latent heat flux was calculated from the energy balance equation on the ground and estimated energy fluxes. To check this analysis, we calculated the evaporation efficiency. The average value of evaporation efficiency for the whole area except for pond was 0.11. (C) 2005 COSPAR. Published by Elsevier Ltd. All rights reserved., ELSEVIER SCI LTD, 2006, ADVANCES IN SPACE RESEARCH, 38 (10), 2191 - 2195, doi;web_of_science

    Scientific journal

  • Development of estimation model for net primary production by vegetation

    S Furumi; K Muramatsu; A Ono; N Fujiwara

    Many researchers have developed models for estimation of net primary production using satellite data. Especially, NDVI (normalized differential vegetation index) are mainly used for the model because NDVI can be obtained from NOAA/AVHRR data for global area. However, recent sensors have hyper-multispectral data and these data is expected. to be more effective for monitoring vegetation condition. So we developed an estimation model using a new vegetation index VIPD that reflects all information of hyper-multispectral data, and validated the model using Landsat/TM data. (C) 2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved., PERGAMON-ELSEVIER SCIENCE LTD, 2002, EARTH'S ATMOSPHERE, OCEAN AND SURFACE STUDIES, 30 (11), 2517 - 2522, web_of_science

    Scientific journal

  • Estimation model of net primary production by vegetation for ADEOS-II/GLI data

    S Furumi; A Ono; K Muramatsu; N Fujiwara

    Many researchers have developed models for estimation of net primary production using satellite data. Especially, NDVI (normalized differential vegetation index) are mainly used for the model because NDVI can be obtained from NOAA/AVHRR data for global area. However, recent sensors have hyper-multispectral data and these data is expected to be effective for the monitoring of detail vegetation condition. So we developed the estimation model using a new vegetation index RVIPD that reflects all information of hyper-multispectral data and validated the model using Landsat/TM data., SPIE-INT SOC OPTICAL ENGINEERING, 2001, HYPERSPECTRAL REMOTE SENSING OF THE LAND AND ATMOSPHERE, 4151, 205 - 213, web_of_science

    International conference proceedings

  • Development of a model of radiation balance near ground level and application to satellite data analysis. Focus on the estimation of radiative energy from surface/air based on measurement data.

    K Muramatsu; N Fujiwara

    Solar irradiance, surface and air temperatures change periodically day by day. Then we measured the surface temperature, air temperature, humidity and wind velocity on various types of ground object such as concrete, asphalt, soil and grass every hour for one cycle namely 24 hours. The relationship between the diurnal radiative energy from surface/air and diurnal solar irradiance was studied as a function of time. If the phases of them were adjusted to each other, a linear relationship was established between them. The relationship between diurnal radiative energy from surface and that from air were studied. The clear linear relationship was found to hold between them. The values of parameters of the relationship were determined using measured data and compared with the estimated values using the radiation balance model. Finally, we tried to estimate radiative energy from surface/air integrated over a day using Landsat/TM data., SPIE-INT SOC OPTICAL ENGINEERING, 2001, HYPERSPECTRAL REMOTE SENSING OF THE LAND AND ATMOSPHERE, 4151, 164 - 177, web_of_science

    International conference proceedings

  • Use of chlorophyll index-green and the red-edge chlorophyll index to derive an algorithm for estimating gross primary production capacity

    Kanako Muramatsu

    SPIE, 21 Oct. 2019, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 11149, 1114906-1 - 1114906-8, doi;rm:research_project_id

    International conference proceedings

  • The Reproducibility of Gross Primary Production Estimation From GPP Capacity and Canopy Conductance Index in Dry Area

    Kanako Muramatsu

    IEEE, Jul. 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, doi;url;rm:research_project_id

    International conference proceedings

  • Canopy conductance index for GPP estimation from it's capacity

    Kanako Muramatsu

    SPIE, 24 Oct. 2018, Land Surface and Cryosphere Remote Sensing IV, doi;rm:research_project_id

    International conference proceedings

  • A scale-up method for reference data for validation of global land cover maps using ALOS/AVNIR-2 satellite data

    Noriko Soyama; Kanako Muramatsu; Itsuko Ohashi; Motomasa Daigo; Fumio Ochiai; Takeo Tadono; Kenlo Nasahara

    SPIE, 20 Oct. 2015, Earth Resources and Environmental Remote Sensing/GIS Applications VI, doi

    International conference proceedings

  • Estimation of gross primary production capacity from global satellite observations

    Kanako Muramatsu; Juthasinee Thanyapraneedkul; Shinobu Furumi; Noriko Soyama; Motomasa Daigo

    SPIE, 21 Nov. 2012, Land Surface Remote Sensing, doi

    International conference proceedings

MISC

  • 奈良県スギ?ヒノキ林における生長量調査

    MURAMATSU Kanako

    2019, 『経済学論叢』(同志社大), 70 (4), 277-288

  • Estimating the seasonal maximum light use efficiency

    Kanako Muramatsu; Shinobu Furumi; Noriko Soyama; Motomasa Daigo

    Light use efficiency (LUE) is a key parameter in estimating gross primary production (GPP) based on global Earth-observation satellite data and model calculations. In current LUE-based GPP estimation models, the maximum LUE is treated as a constant for each biome type. However, the maximum LUE varies seasonally. In this study, seasonal maximum LUE values were estimated from the maximum incident LUE versus the incident photosynthetically active radiation (PAR) and the fraction of absorbed PAR. First, an algorithm to estimate maximum incident LUE was developed to estimate GPP capacity using a light response curve. One of the parameters required for the light response curve was estimated from the linear relationship of the chlorophyll index and the GPP capacity at a high PAR level of 2000 (mu molm(-2)s(-1)), and was referred to as the maximum GPP capacity at 2000". The relationship was determined for six plant functional types: needleleaf deciduous trees, broadleaf deciduous trees, needleleaf evergreen trees, broadleaf evergreen trees, C3 grass, and crops. The maximum LUE values estimated in this study displayed seasonal variation, especially those for deciduous broadleaf forest, but also those for evergreen needleleaf forest., SPIE-INT SOC OPTICAL ENGINEERING, 2014, LAND SURFACE REMOTE SENSING II, 9260, 92603R-1, doi;web_of_science

  • Global land cover classification using annual statistical values

    Noriko Soyama; Kanako Muramatsu; Motomasa Daigo

    Global land cover data sets are required for the study of global environmental changes such as global biogeochemical cycles and climate change, and for the estimation of gross primary production. To determine land cover classification condition, producers examine the phenological feature of each land cover class's sample area with vegetation indices or only reflectance. In this study, to detect the phenological feature of land surfaces, we use the universal pattern decomposition method (UPDM) three coefficients and two indices; the modified vegetation index based on the UPDM (MVIUPD) and the chlorophyll index (CIgreen). The UPDM three coefficients are corresponded to actual objects; water, vegetation and soil. To detect the phenological feature of each land cover class simply, we use annual statistical values of the UPDM coefficients and two indices. By visualizing three statistical values with combination of RGB, land areas with similar phenological feature are able to detect globally. We produced the global land cover products by applying this method with MODIS Aqua Surface Reflectance 8-Day L3 Global 500m data sets of 2007. The result was roughly similar to the MOD12Q1 of the same year., SPIE-INT SOC OPTICAL ENGINEERING, 2012, LAND SURFACE REMOTE SENSING, 8524, doi;web_of_science

  • IMPROVEMENT OF TERRESTRIAL GPP ESTIMATION ALGORITHMS USING SATELLITE AND FLUX DATA

    J. Thanyapraneedkul; K. Muramatsu; M. Daigo; S. Furumi; N. Soyama

    In our research approach, Gross Primary Production (GPP) is directly estimated from canopy reflected light. Photosynthesis is done only exposed area by solar light. We consider that reflected radiance has information of the exposed area, since photosynthesis can be directly estimated from reflected light(1)). Photosynthesis process in chlorophyll consists of 2 processes. The one is light reactions that can detect by vegetation index. The other is carbon reduction is controlled by stomata opening and closing which effected by weather conditions. We study the relationship between these variables and photosynthesis conditions. This research objective is to improve accuracy of terrestrial GPP estimation algorithm by using Vegetation Index (VI) and combine with Fluxes data that can reveal empirical photosynthesis rate in each site around the world. The first part of GPP estimation algorithm is to find maximum GPP (Pmax_best) of plant under most favourable conditions (No stresses) from light response curve. Next step, we will analyze with weather conditions to find Pmax for GPP estimation. Present research's results show Pmax_best highest in deciduous needle leaf forest, grassland and evergreen needle leaf forest, respectively. Our results indicated that Pmax_best and VI have a tendency. Linear relationship was found between Pmax_best and NDVI in grassland (r(2) = 0.92), deciduous needle leaf forest (r(2) = 0.71) and paddy filed (r(2) = 0.87). These relationships can be one of representative for improving global GPP estimation algorithms in GCOM-C/SGLI project., COPERNICUS GESELLSCHAFT MBH, 2010, NETWORKING THE WORLD WITH REMOTE SENSING, 38, 814 - 819, web_of_science

  • ALGORITHM DEVELOPMENT OF VEGETATION INDICES FOR MONITORING AND ESTIMATING VEGETATION QUANTITIES FOR GCOM-C PROJECT.-FOCUSING ON PADDY FIELDS

    S. Furumi; K. Muramatsu; M. Daigo; N. Soyama

    Monitoring the vegetation activity is important for understanding the carbon cycle and the vegetation response to environmental changes. GCOM-C (Global Change Observation Mission for Climate) satellite will be launched in 2014, and it has a SGLI (Second-generation global imager) sensor with 250 m spatial resolution. In generally, it is used for monitor vegetation activities with satellite observation that vegetation indices such as normalized vegetation index NDVI ((Kidwell, KB., 1990), enhanced vegetation index EVI ((Huete et al., 1999)) and so on. From the satellite observation, reflected light from the ground object has a mixture information such as vegetation coverage, depth, kinds of vegetation and vegetation activities. Vegetation indices characteristics are studied and reported in a lot of papers. We consider that a set of vegetation indices is needed to extract the quantities of vegetation. The set of vegetation indices is planed to use for estimating gross primary production of vegetation. To estimate gross primary production, chlorophyll content per leaf area is important parameter. We have studied the relationship between chlorophyll content per leaf area and spectral reflectance for several vegetation states for several kinds of broad leaf. In the previous study (S. Furumi et. al, 1998 (Furumi et al., 1998)), the reflectance is reducing against the chlorophyll content is increasing for blue, green and red wavelength. For near infrared wavelength, the reflectance is not changed against the chlorophyll contents is increasing. In this study, we focus on paddy field. Appropriate fertilizer causes higher chlorophyll content of a leaf. We measured the spectral reflectance, photosynthesis, height of rice plant, the number of stem at the ground, the spectral reflectance using RC helicopter for fertilization and no-fertilization area at Nara prefectural agriculture center in Kashihara, Nara. Using these data, we studied the characteristics of spectral reflectance difference between fertilization and no-fertilization area. For ground measurement spectral reflectance, we found that the spectral shape difference between them. For fertilization rice leaf, the normalized reflectance of visible wavelength is lower than that for no-fertilization rice leaf. And the normalized reflectance of near infrared wavelength is higher for fertilization rice leaf than that for no-fertilization rice leaf. These results' tendency was agree with the previous study of characteristics of reflectance against chlorophyll contents of a leaf. Using this characteristics, the index for chlorophyll content is studied., COPERNICUS GESELLSCHAFT MBH, 2010, NETWORKING THE WORLD WITH REMOTE SENSING, 38, 916 - 919, web_of_science

  • ESTIMATING AND VALIDATION THE NET PRIMARY PRODUCTION AROUND YATSUGATAKE MOUNTAIN AREA, JAPAN FOR GCOM-C/SGLI PROJECT

    K. Ikegami; K. Muramatsu; M. Daigo; S. Furumi; Y. Honda; K. Kajiwara

    GCOM-C satellite will be launched in 2014 and carry Second generation Global Imager (SGLI) that observes land area with 250m spatial resolution. SGLI sensor is a follow-on mission of GLI sensor on boarded ADEOS-II satellite. For SGLI project, it's needed to increase the accuracy of net primary production (NPP) estimation. NPP with 250m spatial resolution data of ADEOS-II/GLI is estimated, and compared with validation data, the validation data was taken by the field survey of the Larch forest data at Yatsugatake site in Yamanashi, Japan. NPP estimation value using GLI data was higher than value using field survey data. Two reasons are considered. One is that the model calculation meteorological data used for NPP estimation. Another is that light-photosynthesis curve on NPP estimation algorithm is not applicable for the Larch forest. Since the NPP was estimated another data sets such as the meteorological data observed on the ground. Annual NPP difference between them was 0.5 KgCO(2)/m(2)/year. It was the 40% of the difference of NPP estimation between satellite data and model calculation meteorological data sets, and field survey. The cause of remaining difference will be in the light-photosynthesis curve on NPP estimation algorithm. To increase accuracy of estimation, photosynthesis of a Larch tree was studied., COPERNICUS GESELLSCHAFT MBH, 2010, NETWORKING THE WORLD WITH REMOTE SENSING, 38, 920 - 924, web_of_science

  • VEGETATION TYPES MAPPING USING ALOS/AVNIR-2 AND PRISM DATA USING UNIVERSAL PATTERN DECOMPOSITION METHOD

    K. Muramatsu; K. Masugi; N. Soyama; S. Furumi; M. Daigo

    Vegetation species maps are useful for forestry managements and environmental ecological study. From the forestry management, broad and conifer leaf forest should be mapped. In addition to them, land-cover mapping data with high resolution is needed as validation data sets for low resolution's land-cover mapping results. SGLI sensor on board GCOM-C satellite, which will be launched in 2014, has 250m spatial resolution and it's data will be used for making global land-cover data set. ALOS satellite was launched in 2006. It has AVNIR-2 sensor and PRISM sensor. AVNIR-2 sensor has four spectral bands 460, 560, 650 and 830nm with 10-m spatial resolution. PRISMsensor has panchromatic band from 520nm to 770 nm with 2.5m spatial resolution. If use the both of image, pseudo high spatial multi-spectal image can be processed. Because of the spatial resolution and multi-spectral information, these sensor data are expected to useful for making high resolution land-cover data set. We have developed Universal Pattern Decomposition Method (UPDM)(Zhang, L.F. et. al, 2006 (Zhang et al., 2006)) and Modified Vegetation Index based on UPDM (MVIUPD)(Zhang, L. F. et. al, 2007 (Zhang et al., 2007) and Xiong, Y., 2005 (?)) for satellite sensor data analysis for land cover mapping and vegetation monitoring. In the UPDM method, three coefficients of water, vegetation and soil is calculated using three standard patterns of water, vegetation and soil. One of this method's characteristics is the UPDM coefficients from different sensors for the same object being same as each other. The capability of vegetation species mapping was studied with ALOS/AVNIR-2 data and UPDM method. Japanese cedar, Japanese cypress, deciduous forest, bamboo forest, orchard and grass land can be classified using AVNIR-2 summer and winter data. In this study, AVNIR-2 and PRISM data are used for vegetation types mapping using universal pattern decomposition method. Firstly, the pan-sharpen image was processed using AVNIR-2 and PRISM data. Each band's Digital Number (DN) value of AVNIR-2's band is calculated using DN of PRISM and AVNIR-2. The reflectance of AVNIR-2 is calculated from calculated DN values. UPDM method is applied to the set of re-calculated reflectance. Using the coefficients of UPDM and vegetation index, evergreen forest and deciduous forest were classified using two seasonal data. These results are compared with forest resource information. The tendency was agree with each other, although detailed validation is needed. From these results, the UPDM method can be applied to pan-sharpen image and the pan-sharpen image can be used for vegetation type classification. In the near future, calculation methods with retaining the original reflectance should be improved., COPERNICUS GESELLSCHAFT MBH, 2010, NETWORKING THE WORLD WITH REMOTE SENSING, 38, 902 - 907, web_of_science

  • A STUDY ON ESTIMATION OF TREES HEIGHT IN JAPANEASE CEDAR AND JAPANEASE CYPRESS IN NARA USING ALOS/PRISM SATELLITE SENSOR

    N. Nino; K. Muramatsu; M. Daigo; N. Soyama

    An estimation of wood's volume is very important issue for forest management, and estimating the carbon stock, and absorption of carbon from the atmosphere for global warning. To estimate wood's volume, tree height is important key parameter. ALOS satellite was launched on January 24, 2006 by Japan Aerospace Exploration Agency (JAXA). The PRISM instrument was mounted on ALOS satellite. Its spatial resolution is 2.5m, its spectral range from 0.55 to 0.77 mu m, and it provides of the different directional image, nadir, backward, forward. Using the sets of different directional image, Surface height can be estimated. The surface height will be different from ground level. Focusing on this difference, we consider that the difference is trees height. In this paper, trees height was estimated from PRISM and compare with real trees height., COPERNICUS GESELLSCHAFT MBH, 2010, NETWORKING THE WORLD WITH REMOTE SENSING, 38, 908 - 911, web_of_science

  • DEVELOPMENT OF VALIDATION DATA SETS FOR GLOBAL LAND COVER CLASSIFICATION USING ALOS/AVNIR-2 DATA

    N. Soyama; K. Muramatsu; S. Furumi; M. Daigo

    The GCOM Climate first generation satellite (GCOM-C1) project started in October 2009 and each research has proceeded to the aim of project. Our final aim in GCOM-C1 project is producing global land cover data sets that provide much useful information for the study of global environmental changes such as global biogeochemical cycles and climate change, and for the estimation of net primary production. In this study, we proposal a land cover class structure of global land cover, and apply to produce validation data sets for the land cover classification system with low resolution data using high resolution satellite data sets, ALOS/AVNIR-2 data sets. To determine the classification conditions in our classification system, we used the universal pattern decomposition method (UPDM) coefficients and the modified vegetation index based on the UPDM (MVIUPD). As validation data sets, we produced vegetation coverage degree rank data sets and dominant class data sets. Comparisons between the ground truth data sets and the results of our land cover classification system with ALOS/AVNIR-2 showed many areas where classes agreed. Using the classification results, validation data sets were produced., COPERNICUS GESELLSCHAFT MBH, 2010, NETWORKING THE WORLD WITH REMOTE SENSING, 38, 937 - 940, web_of_science

  • OBJECT-ORIENTED CHANGE DETECTION FOR HIGH-RESOLUTION IMAGERY USING A GENETIC ALGORITHM

    Yuqi Tang; Xin Huang; Kanako Muramatsu; Liangpei Zhang

    In this paper, we propose a novel method of change detection for high-resolution remote sensing imagery. The method has three primary processes: image segmentation, image difference and change detection. The pre-processed multi-temporal images are segmented into objects by a multi-resolution segmentation algorithm. Then a difference image is generated according to the pair of segmented maps with an interval. Each object is represented by the mean value of some spectral absolute-valued differences, which are calculated for each pixel in it. Finally, by defining the iterative degree, a genetic algorithm (GA) was employed to search the optimal binary change detection map. In the searching procedure, the crossover and mutation was applied to produce new individuals. According to our experiments using the QuickBird imagery, the Overall Errors of the proposed method decreased by more than 2500 pixels compared with the pixel-based change detection using a GA. Meanwhile, it decreased by more than 1000 pixels compared with the object-oriented change vector analysis (CVA)., COPERNICUS GESELLSCHAFT MBH, 2010, NETWORKING THE WORLD WITH REMOTE SENSING, 38, 769 - 774, web_of_science

  • 関西周辺領域における二酸化炭素排出量マップの作成

    MURAMATSU Kanako

    2009, 同志社大学ワールドワイドビジネスレビュー, 10 (2), 7-13

  • 奈良県東吉野村における二酸化炭素濃度の動態解析III

    MURAMATSU Kanako

    2009, 同志社大学ワールドワイドビジネスレビュー, 10 (2), 35-53

  • Tree height measurement in Mt. Yatsugatake, Yamanashi, Japan

    MURAMATSU Kanako; J. Thanyapraneedkul; K.Muramatsu; K. Ikegami; M. Daigo

    2009, 同志社大学ワールドワイドビジネスレビュー, 10 (2), 54-60

  • ALOS/AVNIR-2を用いた皆伐地の検出に関する考察

    MURAMATSU Kanako

    2009, 同志社大学ワールドワイドビジネスレビュー, 10 (2), 111-115

  • ADEOS-II/GLI空間分解能250mデータを用いた八ヶ岳周辺における植生純一次生産量の推定と検証

    MURAMATSU Kanako

    2009, 同志社大学ワールドワイドビジネスレビュー, 10 (2), 69-85

  • 奈良県東吉野森林における二酸化炭素濃度観測データの解析

    MURAMATSU Kanako

    2009, 同志社大学ワールドワイドビジネスレビュー, 10 (2), 86-100

  • Land Cover Classification of Uganda using ADEOS-II/GLI Mosaic data.

    MURAMATSU Kanako; Noriko Soyama; Kanako Muramatsu; Shinobu Furumi; Motomasa Daigo; Noboru \nFujiwara

    2009, Proceedings of the 30th Asian Conference on Remote Sensing

  • Vegetation species classification using ALOS/AVNIR-2 data

    MURAMATSU Kanako; K. Muramatsu; A. Tahara; N.Soyama; S. Furumi; M. Daigo; N. Fujiwara

    2009, Proceedings of The 30th Asian Conference on Remote Sensing

  • Improvement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data

    MURAMATSU Kanako; J. Thanyapreneedkul; K. Muramatsu; M. Daigo; N. Soyama; S. Furumi; N. Fujiwara

    2009, Proceedings of the 30th Asian Conference of Remote sensing, (TS12-04-2)

  • ADEOS-II/GLIデータを用いた全球土地被覆分類に関する考察(II)

    MURAMATSU Kanako

    2008, 同志社大学ワールドビジネスレビュー, 9 (2)

  • ユニバーサルパターン展開法(UPDM)を用いたLandsat/MSSデータでの雲除去に関する研究

    MURAMATSU Kanako

    2008, 同志社大学ワールドワイドビジネスレビュー, 9 (2)

  • 奈良県東吉野村における二酸化炭素濃度の動態解析

    MURAMATSU Kanako

    2008, 同志社大学ワールドワイドビジネスレビュー, 9 (2)

  • 衛星データの熱赤外バンドデータを用いた気温推定に関する研究(II)

    MURAMATSU Kanako

    2008, 同志社大学ワールドワイドビジネスレビュー, 9 (2)

  • 奈良県における自然環境データベース作成のための植生変動解析--ALOS/AVNIR-2データのユニバーサルパターン展開法の適用--

    MURAMATSU Kanako

    2008, 同志社大学ワールドワイドビジネスレビュー, 10 (1), 161-167

  • 奈良県東吉野村における二酸化炭素濃度の動態解析II

    MURAMATSU Kanako

    2008, 同志社大学ワールドワイドビジネスレビュー, 10 (1), 180-187

  • Parameterization of 3PGS model for aboveground biomass estimation in Eucalyptus camadulensis and Acacia mangium plantation

    MURAMATSU Kanako; Juthasinee Thanyapraneedkul; K. Muramatsu; J. Suzaki; M. Daigo

    2008, 同志社大学ワールドワイドビジネスレビュー, 10 (1), 144-160

  • ADEOS-II/GLIデータを用いた全球植生純一次生産量推定における二方向性反射率の影響評価

    MURAMATSU Kanako

    2007, 同志社大学ワールドワイドビジネスレビュー, 9 (1), 90-102

  • 奈良市街域と森林地帯でのCO2濃度測定タワーで観測した風向風速の特徴解析

    MURAMATSU Kanako

    2007, 同志社大学ワールドワイドビジネスレビュ-, 9 (1), 153-166

  • ユニバーサルパターン展開法のLandsat/MSSへの適用

    MURAMATSU Kanako

    2007, 同志社大学ワールドワイドビジネスレビュー, 9 (1), 137-152

  • ADEOS-II/GLIデータを用いた全球土地被覆分類図作成に関する考察

    MURAMATSU Kanako

    2007, 同志社大学ワールドワイドビジネスレビュー, 9 (1), 123-136

  • ADEOS-II/GLI250mデータを用いた紀伊半島周辺地域における植生純一次生産量の推定と検証

    MURAMATSU Kanako

    2007, 同志社大学ワールドワイドビジネスレビュー, 9 (1), 103-122

  • Topographical effects on estimating vegetation index in mountain areas from Landsat data

    MURAMATSU Kanako; Wei Hiang; Liangpei Zhang; M. Daigo; S. Furumi; K.Muramatsu

    2007, 同志社大学ワールドワイドビジネスレビュー, 9 (1), 35-44

  • 古海忍、大村友希、陳路、村松加奈子、醍醐元正,\n人工衛星データによる奈良県スギ・ヒノキ林における純一次生産量推定,

    MURAMATSU Kanako

    2006, 同志社大学ワールドワイドビジネスレビュー, 8 (1)

  • 緯度の異なる奈良・香港における紫外線・長波放射量の観測とその季節変動

    MURAMATSU Kanako

    2006, 同志社大学ワールドワイドビジネスレビュー, 8 (1)

  • GLIセンサを用いた森林域の地表面温度の推定と検証に関する考察

    MURAMATSU Kanako

    2006, 同志社大学ワールドワイドビジネスレビュー, 8 (1)

  • Sensitivity analysis of net primary production estimation using a semi-empirical BRDF model and reflectance observed by RC helicopter for Japanese cedar forest

    MURAMATSU Kanako; L. Chen; S. Furumi; Y.Xiong; K. Muramatsu; Y.Honda; K.Kajiwara; N.Fujiwara

    2006, J. of the Japan Soc. of photogrammetry and remote sensing, 6 (45), 25-40

  • Estimation of plant water content using ABEOS-II/GLI data

    Kanako Muramatsu; Ichirow Kaihotsu

    Algorithms to estimate soil moisture using Advanced Microwave Scanning Radiometer (AMSR) data and experiments to determine their validity have been developed. Since estimations of soil moisture using AMSR data are affected by vegetation moisture content, determination of the quantity and distribution of vegetation is necessary. A variety of information can be obtained simultaneously using optical sensors such as Global Imager (GLI). In this study, we attempted to estimate plant water content using the vegetation index obtained with GLI to determine its sensitivity to vegetation coverage as well as the relationships among vegetation coverage, biomass and plant water content based on field survey data., 2005, Proceedings of SPIE - The International Society for Optical Engineering, 6043, 604312-1-9, doi

  • Estimation of net primary production using the retrieved reflectance by unmanned helicopter with semi-empirical BRDF model

    L. Chen; S. Furumi; Y. Xiong; K. Muramatsu; Y. Honda; K. Kajiwara; N. Fujiwara

    A method of net primary production (NPP) estimation from pattern-decomposition-based vegetation index (VIPD) using ADEOS-II/GLI data has been developed. But since the global sensor (GLI) could not be directly above the objective when observing, it is necessary to consider the effect of bi-directional reflectance distribution function (BRDF). To validate the method of NPP estimation and for the algorithm for the retrieval of albedo from GLI, bi-directional reflectance factors (BRF) observations for the reflectance of a cedar forest on the Kii peninsula with a sensor onboard an unmanned helicopter were held in July, 2002. In this paper, a kernel-based BRDF model is used to remove the BRDF's effect on the reflectance. The semi-empirical Ross-Li (reciprocal RossThick-LiSparse) model and its performance under conditions of BRF observations are discussed, showing that the retrievals obtained are reliable. The retrieved reflectance is the nadir viewing and the overhead sun could be achieved by this model. And then VIPD could be calculated from the retrieved reflectance. With the data of VIPD and photosynthetically active radiation (PAR), NPP is estimated as 0.36 KgCO 2/m 2/month., 2005, Proceedings of SPIE - The International Society for Optical Engineering, 6043, 604316-1-8, doi

  • The ratio of photosynthetically active radiation to global solar radiation

    MURAMATSU; Kanako, K; Muramatsu; S. Furumi; Y. Xiong; M. Daigo; N.Fujiwara

    2005, 同志社大学 ワールドワイドビジネスレビュー, 6 (2), 58-63

  • 奈良県スギ・ヒノキ林における現地調査による植生純一次生産量の推定

    MURAMATSU Kanako

    2005, 同志社大学 ワールドワイドビジネスレビュー, 6 (2), 64-71

  • Classification of Kii peninsula area by vegetation coverage level

    Noriko Soyama; Shinobu Awa; Kanako Muramatu; Motomasa Daigo

    In order to study land cover classification and natural environment, we must analyze vegetation cover states of the local scale in which we can know the subject in detail, as well as and the global scale. Therefore, we need to analyze various satellite data sets which are measured with different wavelengths region and different number of bands. However, it is difficult to compare analysis results obtained using such data sets. By the universal pattern decomposition method (UPDM), which is sensor independent analysis method, we examined vegetation coverage of a pixel on data sets measured different wavelength range and different resolution which is acquired at the same place and time. In this study, in order to develop a generalization rule of vegetation coverage, we examine vegetation coverage of a pixel on 1-kilometer resolution data sets using results obtained by analyzing 250-m resolution data sets which are acquired at the same place and time as the pixel of 1-kilometers'. We defined the rule of classifying into five levels of vegetation coverage using results of high resolution data sets analyzed by the UPDM. Using the results of the analysis, we calculate vegetation coverage of Kii peninsula area., 2005, Proceedings of SPIE - The International Society for Optical Engineering, 6043, 604311-1-8, doi

  • A study of generalization rule on classification for vegetation coverage of a pixel

    MURAMATSU Kanako

    2005, 同志社大学ワールドワイドビジネスレビュー, 6 (2), 16-22

  • ADEOS-II/GLI全球モザイクデータを用いた土地被覆分類の研究

    MURAMATSU Kanako

    2005, 同志社大学ワールドワイドビジネスレビュー, 7 (1), 54-66

  • 人工衛星データによる全球陸域純一次生産量の推定

    MURAMATSU Kanako

    2005, 同志社大学ワールドワイドビジネスレビュー, 7 (1), 67-77

  • ADEOS-Ⅱモンゴル高原実験領域における被覆率の推定と分類図の作成

    MURAMATSU Kanako

    2005, 同志社大学ワールドワイドビジネスレビュー, 7 (1), 111-123

  • ADEOS-II/GLIデータを用いたモンゴル高原実験領域における植生の含水量分布図作成の試み

    MURAMATSU Kanako

    2005, 同志社大学ワールドワイドビジネスレビュー, 7 (1), 124-134

  • ADEOS-II/GLIデータを用いた山形県酒田市の水田における植生純一次生産量の推定

    MURAMATSU Kanako

    2005, 同志社大学ワールドワイドビジネスレビュー, 7 (1), 239-247

  • Estimation of global terrestrial net primary production using ADEOS-II/GLI data

    Yan Xiong; Lu Chen; Shinobu Furumi; Kanako Muramatsu; Motomasa Daigo; Noboru Fujiwara

    Satellite ADEOS-II was launched on 14 December 2002 by Japan Aerospace Exploration Agency (JAXA). The purpose of this study is to estimate global terrestrial net primary production (NPP) with a newly developed algorithm that estimates gross photosynthesis using ADEOS-II/GLI data as input. It is the first time that ADEOS-II/GLI data have been used as input to estimate NPP. The NPP estimation error is 26%. In this study, total ANPP estimates between 60°N and 60°S in the world were 65.2±17.0 [10 15gC/year]. The results were compared with NPP ground measurement data, and NPP values estimated by other studies, such as NPP derived from climatic model and NPP estimated using other satellite data (e.g., NOAA/AVHRR, Terra/MODIS). The pattern of this study's distribution of estimated NPP in the world was similar to that of other studies., 2005, Proceedings of SPIE - The International Society for Optical Engineering, 6043, 604313-1-12, doi

  • Energy consumption, solar energy and net primary production by vegetation in Kii peninsula, Japan

    MURAMATSU Kanako; K. Muramatsu; M.Otsuka; Y.Xiong; Y.Cen. S.Furumi; N.Fujiwara; M.Daigo

    2004, 同志社大学ワールドワイドビジネスレビュー, 5 (2), 86-93

  • The diurnal time series relationship between radiant energy from surface and from air for satellite data analysis

    K Muramatsu; N Fujiwara; AB Adamezyk

    Solar irradiance, surface and air temperatures change periodically day by day. We measured surface temperature, air temperature, humidity and wind velocity on concrete, asphalt, soil and grass every hour for 24 hours. The relationship between the diurnal radiant energy from surface and air, and diurnal solar irradiance were studied as a function of time. A linear relationship was established between them, when the phases were synchronized. The relationship between diurnal radiant energy from the surface and from air was studied. A clear linear relationship was found between them. The values of parameters of the relationship were determined using measured data and compared with the estimated values using the radiation balance model. Using a linear relationship, we can estimate the surface and air radiant integrated over one day using Landsat/TM or ADEOS-II/GLI data. (C) 2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved., PERGAMON-ELSEVIER SCIENCE LTD, 2002, EARTH'S ATMOSPHERE, OCEAN AND SURFACE STUDIES, 30 (11), 2523 - 2528, web_of_science

  • The diurnal time series relationship between surface/air\ntemperature and global solar irradiance

    MURAMATSU Kanako

    2001, J. of the remote sensing society of Japan, 21 (5)

  • Pattern decomposition method in the albedo space for Landsat TM and MSS data analysis

    K Muramatsu; S Furumi; N Fujiwara; A Hayashi; M Daigo; F Ochiai

    We have developed a 'pattern decomposition method' based on linear spectral mixing of ground objects for n-dimensional satellite data. In this method, spectral response patterns for each pixel of an image are decomposed into three components using three standard spectral shape patterns determined from the image data. Applying this method to Landsat Thematic Mapper data, six-dimensional data are successfully transformed into three-dimensional data. Nearly 94% of the information in the six-dimensional data is retained in the three components. This method is very useful for classifying and monitoring changes in land cover., TAYLOR & FRANCIS LTD, Jan. 2000, INTERNATIONAL JOURNAL OF REMOTE SENSING, 21 (1), 99 - 119, web_of_science

  • Pattern decomposition method and a new vegetation index for hypermultispectral satellite data analysis

    K Muramatsu; S Furumi; A Hayashi; Y Shiono; A Ono; N Fujiwara; M Daigo; F Ochiai

    We have developed the "pattern decomposition method" based on linear spectral mixing of ground objects for n-dimensional satellite data. In this method, spectral response patterns for each pixel in an image are decomposed into three components using three standard spectral shape patterns determined from the image data. Applying this method to AMSS (Airborne Multi-Spectral Scanner) data, eighteen-dimensional data are successfully transformed into three-dimensional data. Using the three components, we have developed a new vegetation index in which all the multispectral data are reflected. We consider that the index should be linear to the amount of vegetation and vegetation vigor. To validate the index, its relations to vegetation types, vegetation cover ratio, and chlorophyll contents of a leaf were studied using spectral reflectance data measured in the field with a spectrometer. The index was sensitive to vegetation types and vegetation vigor. This method and index are very useful for assessment of vegetation vigor, classifying land cover types and monitoring vegetation changes. (C) 2000 COSPAR. Published by Elsevier Science Ltd., PERGAMON PRESS LTD, 2000, REMOTE SENSING FOR LAND SURFACE CHARACTERISATION, 26 (7), 1137 - 1140, web_of_science

  • Automated detection and removal of clouds and their shadows from landsat TM images

    B Wang; A Ono; K Muramatsu; N Fujiwara

    In this paper, a scheme to remove clouds and their shadows from remotely sensed images of Landsat TM over land has been proposed. The scheme uses the image fusion technique to automatically recognize and remove contamination of clouds and their shadows, and integrate complementary information into the composite image from multitemporal images. The cloud regions can be detected on the basis of the reflectance differences with the other regions. Based on the fact that shadows smooth the brightness changes of the ground, the shadow regions can be detected successfully by means of wavelet transform. Further, an area-based detection rule is developed in this paper and the multispectral characteristics of Landsat TM images are used to alleviate the computational load. Because the wavelet transform is adopted for the image fusion, artifacts are invisible in the fused images. Finally, the performance of the proposed scheme is demonstrated experimentally., IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, Feb. 1999, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, E82D (2), 453 - 460, web_of_science

  • 「パターン展開法による大阪湾の水質解析」

    MURAMATSU Kanako

    1999, 水処理技術, 40, 315-320

  • Relation between vegetation vigor and a new vegetation index based on pattern decomposition method

    MURAMATSU Kanako

    1998, J. of remote sensing soc. of Japan, 18 (3), 17-34

  • An algorithm and a new vegetation index for ADEOS-II/GLI data analysis

    MURAMATSU Kanako

    1998, J. of remote sensing soc. of Japan, 18 (12), 28-50

  • 「Landsat/MSS,TMデータを使ったパターン展開法による関西地域の植生変動解析」

    MURAMATSU Kanako

    1997, 日本リモートセンシング学会誌, 17 (14), 34-39

  • 「パターン展開法による水田解析」

    MURAMATSU Kanako

    1997, 日本リモートセンシング学会誌, 17 (2), 5-18

  • 「衛星データ解析のためのパターン展開法」

    MURAMATSU Kanako

    1996, 日本リモートセンシング学会誌, 16 (3), 17-34

  • MEASUREMENT OF THE PHOTON STRUCTURE-FUNCTION F-2(GAMMA) AND JET PRODUCTION AT TRISTAN

    K MURAMATSU; H HAYASHII; S NOGUCHI; N FUJIWARA; K ABE; T ABE; ADACHI, I; M AOKI; M AOKI; S AWA; R BELUSEVIC; K EMI; R ENOMOTO; H FUJII; K FUJII; T FUJII; J FUJIMOTO; K FUJITA; B HOWELL; N IIDA; H IKEDA; R ITOH; H IWASAKI; M IWASAKI; R KAJIKAWA; K KANEYUKI; S KATO; S KAWABATA; H KICHIMI; M KOBAYASHI; D KOLTICK; LEVINE, I; S MINAMI; K MIYABAYASHI; A MIYAMOTO; K NAGAI; T NAGIRA; E NAKANO; K NAKABAYASHI; O NITOH; F OCHIAI; Y OHNISHI; H OKUNO; T OKUSAWA; K SHIMOZAWA; T SHINOHARA; A SUGIYAMA; N SUGIYAMA; S SUZUKI; K TAKAHASHI; T TAKAHASHI; M TAKEMOTO; T TANIMORI; T TAUCHI; F TERAMAE; Y TERAMOTO; N TOOMI; T TOYAMA; T TSUKAMOTO; S UNO; T WATANABE; Y WATANABE; A YAMAGUCHI; A YAMAMOTO; M YAMAUCHI

    We have measured the photon structure function F2gamma in the reaction e+e- --> e+e- hadrons for average Q2 values from 5.1 to 338 GeV2 by using data collected by the TOPAZ detector at TRISTAN. The data have been corrected for detector effects and are compared with theoretical expectations based on QCD. The structure function F2gamma increases as In Q2, as expected. A sample of events with one or two distinct jets has been identified in the final state. Although two-jet events can be explained solely by the point-like perturbative part, one-jet events require a significant hadron-like part in addition., ELSEVIER SCIENCE BV, Jul. 1994, PHYSICS LETTERS B, 332 (3-4), 477 - 487, web_of_science

  • Estimation of vegetation coverage and land cover mapping using the pattern decomposition method in ADEOS-II Mongolian plateau experiment (AMPEX) site

    K. Muramatsu; Y. Xiong; S. Nakayama; F. Ochiai; M. Hirata; K. Oishi; B. Bolortsetseg; D. Oyunbaatar; I. Kaihotsu

    2005, ADEOS-II JRAモンゴル地上検証実験報告書, 71 - 82

  • 人工衛星データを用いた環境モニタリング〜解析事例:竹林の分布状況調査〜

    村松加奈子

    Feb. 2021, ナント経済月報2月号

    Introduction other

  • GCOM-C/SGLI データを用いた植生・土壌指数と気象データの関係 -インドの乾燥地における農地モニタリングに向けて-

    澤美和子, 村松加奈子, 曽山典子

    Dec. 2020, 日本リモートセンシング学会 第69回学術講演会論文集, 15 - 16

  • Sentinel-2/MSIデータを用いたクロロフィルインデックスの季節変化の特徴解析

    宮本紗季; 村松加奈子

    Dec. 2020, 日本リモートセンシング学会 第69回学術講演会論文集, 27 - 28, rm:research_project_id

    Summary national conference

  • 地上における太陽励起のクロロフィル蛍光の猛暑日での日中変化の観測

    山本奈央; 村松加奈子; 栗山健二

    Dec. 2020, 日本リモートセンシング学会 第69回学術講演会論文集, 29 - 30, rm:research_project_id

    Summary national conference

  • GCOM-C/SGLI データを用いたインド・パンジャーブ州の野焼きの抽出

    于 琨; 村松加奈子; 曽山典子

    Dec. 2020, 日本リモートセンシング学会 第69回学術講演会論文集, 37 - 38, rm:research_project_id

    Summary national conference

  • Landsat8 OLI/TIRS データを用いたインドの野焼き跡地抽出

    大林真菜, 村松加奈子

    Dec. 2020, 日本リモートセンシング学会 第69回学術講演会論文集, 39 - 40, rm:research_project_id

    Summary national conference

  • 現地調査データとパンシャープン画像を用いたナラ枯れ枯死木の抽出-樹冠サイズに注目して-

    藤原由季; 村松加奈子; 酒井有紀; 松井 淳

    Dec. 2020, 日本リモートセンシング学会 第69回学術講演会論文集, 49 - 50

    Summary national conference

  • 多時期衛星データを用いた奈良県高円山周辺におけるナラ枯れのモニタリング

    前川穂乃香, 村松加奈子

    Dec. 2020, 日本リモートセンシング学会 第69回学術講演会論文集, 51 - 52

    Summary national conference

Books etc

  • 基礎からわかるリモートセンシング

    MURAMATSU Kanako (, Range: 分担)

    理工図書, 2011, 224-230

Presentations

  • 乾燥域における総生産キャパシティーと樹冠コンダクタンス指標を用い た総生産量推定の再現性

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第65回(平成30年度秋季)学術講演会, Nov. 2018, (社)日本リモートセンシング学会, サンポートホール高松

  • Determination of Tropical Forests Parameters in Gross Primary Production Capacity Estimation Algorithm in Brazil

    MURAMATSU Kanako; Wakai, Aika; Muramatsu, Kanako

    the 39th Asia Conference on Remote Sensing, 2018, Oct. 2018, Kuala Lumpur, Malaysia

  • Canopy conductance index for GPP estimation from its\ncapacity

    MURAMATSU Kanako; Muramatsu, K

    SPIE Asia-Pacific remote sensing, Sep. 2018, Honolulu, Hawai

  • An algorithm of gross primary production capacity estimation from global observing satellite and the difference between GPP capacity and GPP

    MURAMATSU Kanako; Kanako Muramatsu

    COSPAR,2018, Jul. 2018, Pasadena, California, USA

  • 総生産量推定のための樹冠コンダクタンス指標 II

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第64回(平成30年度春季)学術講演会, May 2018, (社)日本リモートセンシング学会, 東京大学柏キャンパス

  • ブラジルの熱帯地域における総生産量キャパシティ推定アルゴリズム の決定

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第64回(平成30年度春季)学術講演会, May 2018, (社)日本リモートセンシング学会, 東京大学柏キャンパス

  • 奈良県高円山周辺におけるリモートセンシングによるナラ枯れの解析

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第64回(平成30年度春季)学術講演会, May 2018, (社)日本リモートセンシング学会, 東京大学柏キャンパス

  • Google Earth Engineを用いた京阪奈地区の竹林の抽出-1

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第64回(平成30年度春季)学術講演会, May 2018, (社)日本リモートセンシング学会, 東京大学柏キャンパス

  • 総生産量推定のための樹冠コンダクタンス指標

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第63回(平成29年度秋季)学術講演会, Nov. 2017, (社)日本リモートセンシング学会, 酪農学園大学 (北海道江別市文京台緑町582番地)

  • リモートセンシングによるナラ枯れのモニタリング-1

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第63回(平成29年度秋季)学術講演会, Nov. 2017, (社)日本リモートセンシング学会, 酪農学園大学 (北海道江別市文京台緑町582番地)

  • 多時期データのSentinel-2データを用いた京阪奈地区の竹林の抽出?1

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第63回(平成29年度秋季)学術講演会, Nov. 2017, (社)日本リモートセンシング学会, 酪農学園大学 (北海道江別市文京台緑町582番地)

  • DIFFERENCES BETWEEN NEEDLE-LEAVES FOREST AND BROAD-LEAVES FOREST FROM PSEUDO MULTIDIRECTIONAL OBSERVATION DATA

    MURAMATSU Kanako; Noriko Soyama; Kanako Muramatsu; Motomasa Daigo; Koji Kajiwara; Yoshiaki Hond; Tenri University; Nara Women’s; University \n; Doshisha University; Chiba University

    International Symposium on Remote Sensing 2017, May 2017, The remote sensing society of Japan, 名古屋大学

  • STAND-OFF MEASUREMENT OF SOLAR INDUCED FLUORESCENCE FROM VEGETATION CANOPIES: APPLICATION TO FIELD AND FOREST

    MURAMATSU Kanako; Kenji Kuriyama; Naohiro Manago; Koki Homma; Kanako Muramatsu; n Kenichi Yoshimura; Yuji Kominami; Hiroaki Kuze; Faculty of Engineering; Shizuoka University, Japan; Center for Environmental Remote Sensing (CEReS; Chiba University, Japan; Graduate School of Agricultural Science; Tohoku University, Japan; Kyousei Sciences Center for Life; Nature; Nara Women’s; University, Japan\n; Forestry and Forest Products Research; Institute, Japan

    International Symposium on Remote Sensing 2017, May 2017, The remote sensing society of Japan, 名古屋大学

  • リモートセンシングによるナラ枯れのモニタリング

    MURAMATSU Kanako

    第20回紀伊半島研究会シンポジウム,第16回奈良女子大学共生科学研究センターシンポジウム, Dec. 2016, 奈良女子大学G棟2階G201教室

  • 光ー光合成曲線を用いた総生産量推定アルゴリズムの開発:気候モデルによる気象要素の 時間変化データ利用に関する考察

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第61回(平成28年度秋季)学術講演会, Nov. 2016, (社)日本リモートセンシング学会, 新潟県新潟市新潟テルサ

  • 全球の総生産量キャパシティ推定アルゴリズムの開発:植生指標CIgreenの異常値検出条件

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第61回(平成28年度秋季)学術講演会, Nov. 2016, (社)日本リモートセンシング学会, 新潟県新潟市新潟テルサ

  • 多時期のLandsat-8データを用いた京阪奈地区の竹林の抽出-4

    MURAMATSU Kanako

    (社)日本リモートセンシング学会第61回(平成28年度秋季)学術講演会, Nov. 2016, (社)日本リモートセンシング学会, 新潟県新潟市新潟テルサ

  • 酸素Aバンドを利用した植物蛍光の分光画像計測:森林計測への応用

    MURAMATSU Kanako

    日本リモートセンシング学会第59回(平成27年度秋季)学術講演会, Nov. 2015, 長崎,長崎大学

  • 全球土地被覆分類データのための精度検証データ作成 :ボランティアによる情報の利用

    MURAMATSU Kanako

    日本リモートセンシング学会第59回学術講演会, Nov. 2015, 長崎

  • 全球の総生産量キャパシティー推定アルゴリズムにおける低ストレス下の総生産量の抽出条件の考察

    MURAMATSU Kanako

    日本リモートセンシング学会第59回学術講演会, Nov. 2015, 長崎

  • A scale-up method for reference data for validation of global land cover maps using ALOS/AVNIR-2 satellite data

    MURAMATSU Kanako; Soyama, N; Muramatsu, K; Ohashi, I; Daigo, M; Ochiai, F; Tadono, T; Nasahara, K

    SPIE remote sensing 2015, Sep. 2015, Toulouse, France

  • Algorithm developing of gross primary production from it's capacity and a canopy conductance index using flux and global observing satellite data

    MURAMATSU Kanako; Muramatsu, K; Furumi; Daigo, M

    SPIE remote sensing 2015, Sep. 2015, Toulouse, France

  • Validation method of global land cover map using reference data with quality level

    MURAMATSU Kanako; Soyama, N; Muramatsu, K; Ochiai; F. Daigo, M; Sasai, T; Nasahara, K

    30th International symposium on space technology and science, Jul. 2015, Kobe, Japan

  • 全球の総生産キャパシティー推定アルゴリズム?ヨーロッパサイトに着目して?

    MURAMATSU Kanako

    日本リモートセンシング学会第58回学術講演会, Jun. 2015, 千葉

  • 多次期のLandsat-8データを用いた京阪奈地区の竹林抽出-III

    MURAMATSU Kanako

    日本リモートセンシング学会第58回学術講演会, Jun. 2015, 千葉

  • 最大光利用効率の季節変化推定アルゴリズム

    MURAMATSU Kanako

    日本リモートセンシング学会第58回学術講演会, Jun. 2015, 千葉

  • 総生産キャパシティーと気孔開度指標を用いた総生産量推定アルゴリズムの枠組み

    MURAMATSU Kanako

    日本リモートセンシング学会第58回学術講演会, Jun. 2015, 千葉

  • 最大光利用効率の季節変化の推定

    MURAMATSU Kanako

    生研フォーラム, Mar. 2015, 東京,日本

  • 多時期のLandsat-8データを用いた京阪奈地区の竹林の抽出-2

    MURAMATSU Kanako

    日本リモートセンシング学会\n第57回(平成26年度秋季)学術講演会, Nov. 2014, 京都

  • Estimating the seasonal maximum light efficiency

    MURAMATSU Kanako; Kanako Muramatsu; Shinobu Furumi; Noriko Soyama; Motomasa Daigo

    SPIE Asis-Pacific remote sensing, 2014, Oct. 2014, Beijing, China

  • A study of stratified class design for global land cover classification

    MURAMATSU Kanako; Noriko Soyama; Kanako Muramatsu; Motomasa Daigo; Fumio Ochiao

    COSPAR,2014, Aug. 2014, Moscow, Russia

  • An algorithm of gross primary production capacity from GCOM-C1/SGLI

    MURAMATSU Kanako; Kanako Muramatsu; Yukiko Mineshita; Noriko Soyama; Motomasa Daigo

    COSPAR,2014, Aug. 2014, Moscow, Russia

  • An Estimation Method of Capacity of Gross Primary Production from Global Observation Satellite

    MURAMATSU Kanako; Kanako MURAMATSU; Yukiko MINESHITA; Shinobu FURUMI; Daigo MOTOMASA

    Asia Oceanin Geosciences Society, 2014, Jul. 2014, Sapporo, Japan

  • A Method of Distinguishing Forest Types for Global Land Cover Classification Using Multi-angle Satellite Data

    MURAMATSU Kanako; Noriko SOYAMA; Kanako MURAMATSU; Satomi MANABE; Daigo MOTOMASA; Fumio OCHIAI

    Asia Oceanin Geosciences Society, 2014, Jul. 2014, Sapporo, Japan

  • 衛星観測による地表面温度データの常緑樹/落葉樹における特徴解析

    MURAMATSU Kanako

    日本リモートセンシング学会\n第56回(平成26年度春季)学術講演会, May 2014, つくば

  • 多時期のLandsat-8データを用いた京阪奈地区の竹林の抽出-1

    MURAMATSU Kanako

    日本リモートセンシング学会\n第56回(平成26年度春季)学術講演会, May 2014, つくば

  • 全球の総生産キャパシティ推定アルゴリズムの改良-Shrubに着目して-

    MURAMATSU Kanako

    日本リモートセンシング学会第55回学術講演会, Nov. 2013, 福島

  • 針葉樹と落葉樹の分光?多方向反射率特性

    MURAMATSU Kanako

    日本リモートセンシング学会第55回学術講演会, Nov. 2013, 福島

  • 衛星観測による植生/非植生における地表面温度の特徴解析

    MURAMATSU Kanako

    日本リモートセンシング学会第55回学術講演会, Nov. 2013, 福島

  • Estimation of global primary production capacity

    MURAMATSU Kanako; Yukiko Mineshita; Kanako Muramatsu; Motomasa Daigo; Noriko Soyama

    International symposium on remote sensing, 2013, May 2013, Chiba, Japan

  • 多方向観測とマルチバンドデータを用いた植生機能タイプの分類方法の考察

    MURAMATSU Kanako

    日本リモートセンシング学会第53回学術講演会, Nov. 2012, 日本リモートセンシング学会, 広島

  • 全球の総生産キャパシティ推定の適応性に関する研究

    MURAMATSU Kanako

    日本リモートセンシング学会第53回学術講演会, Nov. 2012, 広島

  • Global land cover classification using annual statistical values

    MURAMATSU Kanako; Noriko Soyama; Tenri Univ. (Ja; Kanako Muramatsu; Nara Women’s; Univ

    SPIE, 2012, Asia-Pasific Remote Sensing, Oct. 2012, KYOTO, JAPAN

  • Estimating the gross primary production capacity from global observation satellite

    MURAMATSU Kanako; Kanako Muramatsu; Juthasinee Thanyapraneedkul; Nara Women’s; Univ. (Japa; Shinobu Furumi; Narasaho College\n(Ja

    SPIE, 2012, Asia-Pasific Remote Sensing, Oct. 2012, KYOTO, JAPAN

  • Estimating the Capacity of Gross Primary Production from Global Observation Satellite

    MURAMATSU Kanako; K. Muramatsu; J. THanyapraneedkul; S. Furumi

    39th COSPAR Assembly, Jul. 2012, Mysore, India

  • A simple algorithm for global land cover classification using annual statistical values

    MURAMATSU Kanako; Soyama N; Muramatsu K; Daigo M

    39th COSPAR Assembly, Jul. 2012, Mysore, India

  • 熱赤外画像を用いた気孔開度推定へのアプローチ

    MURAMATSU Kanako

    日本リモートセンシング学会第52回学術講演会,, May 2012, 日本リモートセンシング学会, 東京

  • 熱赤外画像による樹木葉と樹冠の温度分布解析?リモートセンシング技術による気孔開度推定へのアプローチ?

    MURAMATSU Kanako

    日本生態学会, Mar. 2012, 日本生態学会, 札幌

  • 土地被覆分類項目定義の開発者・利用者間の共通認識の形成に向けて: Webデータベースの利用

    MURAMATSU Kanako

    日本リモートセンシング学会第51回学術講演会, Nov. 2011, 日本リモートセンシング学会, 弘前

  • ALOS/AVNIR-2データを用いた竹林分布図作成に関する考察II

    MURAMATSU Kanako

    日本リモートセンシング学会第51回学術講演会, Nov. 2011, 日本リモートセンシング学会, 弘前

  • 多方向観測による植生構造抽出インデックスの開発

    MURAMATSU Kanako

    日本リモートセンシング学会第51回学術講演会, Nov. 2011, 日本リモートセンシング学会, 弘前

  • ALOS/AVNIR-2データを用いた竹林分布図作成に関する考察,

    MURAMATSU Kanako

    日本リモートセンシング学会第50回学術講演会, May 2011, 日本リモートセンシング学会, 東京 日本大学

  • 反射率ベースのパンシャープン処理アルゴリズムII

    MURAMATSU Kanako

    日本リモートセンシング学会第50回学術講演会, May 2011, 日本リモートセンシング学会, 東京 日本大学

  • 植生被覆度と植生被覆分布状態に関する一実験

    MURAMATSU Kanako

    日本リモートセンシング学会第50回学術講演会, May 2011, 日本リモートセンシング学会, 東京 日本大学

  • Vegetation types mapping using ALOS/AVNIR-2 and PRISM data using universal pattern decomposition method

    MURAMATSU Kanako; Muramatsu, K; Masugi, K; Soyama, N; Furumi, S; Daigo, M

    ISPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8), Aug. 2010, KYOTO, Japan

  • Object-oriented change detection for high-resolution imagery using a generic algorithm

    MURAMATSU Kanako; Tang, Y; Huang, X; Muramatsu, K; Zhang, L

    ISPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8), Aug. 2010

  • Development of validation data sets for global land cover classification using ALOS/AVNIL-2 data

    MURAMATSU Kanako; Soyama, N; Muramatsu, K; Furumi, S; Daigo, M

    ISPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8), Aug. 2010, KYOTO, JAPN

  • A study on estimation of tree height in Japanese cedar and Japanese cypress in Nara Using ALOS/PRISM Satellite sensor,

    MURAMATSU Kanako; Nino, N; Muramatsu, K; Daigo, M; Soyama, N

    SPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8),, Aug. 2010, KYOTO, Japan

  • Estimating and validation the net primary production aournd Yatsugatake mountain area for GCOM-C/SGLI project

    MURAMATSU Kanako; K. Ikegami; K. Muramtsu; M. Daigo; F. Furumi; Y. Honda; K, Kajiwara

    ISPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8), Aug. 2010, KYOTO, JAPAN

  • Improvement of terrestrial GPP estimation algorithm using satellite and FLUX data

    MURAMATSU Kanako; J. Thanyapraneedkul, K,Muramatsu; M.Daigo; S, Furumi; N. Soyama

    ISPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8),, Aug. 2010, KYOTO, JAPAN

  • Integrated spatial information for automatic endmember extraction algorithm

    MURAMATSU Kanako; Li Huali; 村松 加奈子; 醍醐 元正; Zhang Liangpei; Li Pingxiang(Wuhan University

    日本リモートセンシング学会第47回学術講演会, Nov. 2009, 名古屋

  • GCOM-C/SGLIプロジェクトに向けた八ヶ岳周辺における植生純一次生産量の推定と検証(Ⅱ)

    MURAMATSU Kanako

    日本リモートセンシング学会第47回学術講演会, Nov. 2009, 名古屋

  • Improvement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data

    MURAMATSU Kanako; Juthasinee Thanyapraneedkul; 村松 加奈子; 醍醐 元正; 古海忍

    日本リモートセンシング学会第47回学術講演会, Nov. 2009, 名古屋

  • 奈良県のスギ、ヒノキにおけるALOS/PRISMデータを用いた樹高推定に関する考察2

    MURAMATSU Kanako

    日本リモートセンシング学会第47回学術講演会, Nov. 2009, 名古屋

  • Vegetation species classification using ALOS/AVNIR-2 data Vegetation species

    MURAMATSU Kanako; K. Muramatsu; A. Tahara; N.Soyama; S. Furumi; M. Daigo; N. Fujiwara

    30th Asian Conference on Remote Sensing, Oct. 2009

  • Land Cover Classification of Uganda using ADEOS-II/GLI Mosaic data

    MURAMATSU Kanako; Noriko Soyama; Kanako Muramatsu; Shinobu Furumi; Motomasa Daigo; Noboru Fujiwara

    the 30th Asian Conference on Remote Sensing, Oct. 2009

  • Improvement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data

    MURAMATSU Kanako; J. Thanyapreneedkul; K. Muramatsu; M. Daigo; N. Soyama; S. Furumi; N. Fujiwara

    the 30th Asian Conference of Remote sensing, Oct. 2009

  • 奈良県東吉野村の森林域および奈良市街域における二酸化炭素濃度の動態解析

    MURAMATSU Kanako

    日本リモートセンシング学会第46回学術講演会, May 2009

  • 奈良県のスギ、ヒノキにおけるALOS/PRISMデータを用いた樹高推定に関する考察

    MURAMATSU Kanako

    日本リモートセンシング学会第46回学術講演会, May 2009, 東京

  • mprovement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data (Part 1: Study on photosynthesis activity by using FluxNet tower sites data)

    MURAMATSU Kanako; mprovement accuracy of terrestrial NPP; estimation using; ADEOS-II/GLI data (Par; Study on photosynthesis activity; by using FluxNet tower sites dat

    日本リモートセンシング学会第46回学術講演会, May 2009

  • GCOM-C/SGLプロジェクトに向けた八ヶ岳周辺における植生純一次生産量の推定と検証

    MURAMATSU Kanako

    日本リモートセンシング学会第46回学術講演会, May 2009, 東京

  • 人工衛星データを用いた全球陸域純一次生産量推定の系統誤差に関する考察

    MURAMATSU Kanako

    日本リモートセンシング学会第45回学術講演会, Dec. 2008, 札幌

  • Improvement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data

    MURAMATSU Kanako; Thanyapraneedkul J; 村松加奈子; 醍醐元正

    日本リモートセンシング学会第45回学術講演会, Dec. 2008, 札幌

  • Remote sensing image retrieval based on semantic mining

    MURAMATSU Kanako; Liu T; 村松加奈子; 醍醐元正

    日本リモートセンシング学会第45回学術講演会, Dec. 2008, 札幌

  • 良県東吉野村におけるCO2濃度の動態解析III

    MURAMATSU Kanako

    日本リモートセンシング学会第45回学術講演会, Dec. 2008, 札幌

  • ADEOS-II/GLI250mモザイクデータを用いたウガンダの土地被覆分類

    MURAMATSU Kanako

    日本リモートセンシング学会 第45回学術講演会, Dec. 2008, 札幌

  • 奈良県における自然環境データベース作成のための植生変動解析--ALOS/AVNIR-2データへのユニバーサルパターン展開法の適用--

    MURAMATSU Kanako

    日本リモートセンシング学会, May 2008

  • 奈良県東吉野村におけるCO2濃度の動態解析II

    MURAMATSU Kanako

    日本リモートセンシング学会第43回学術講演会, May 2008, 横浜

  • Estimation of Forest Plantation Productivity Using a Physiologically Based Model Driven with Meteorological Data and Satellite-derived Estimates of Canopy Photosynthetic Capacity.

    MURAMATSU Kanako; JuthasineeThanyapraneedkul; 村松加奈子; 須崎純一

    日本リモートセンシング学会第43回学術講演会, May 2008, 横浜

  • Gross primary production estimation algorithm including diurnal changes for satellite sensor data

    K. Muramatsu; M. Moriyama

    COSPAR 2021, 43rd COSPAR Scientific Assembly, 02 Feb. 2021, 28 Jan. 2021, 04 Feb. 2021, rm:research_project_id

  • 多時期衛星データを用いた奈良県高円山周辺におけるナラ枯れのモニタリング

    前川穂乃香; 村松加奈子

    日本リモートセンシング学会第69回(令和2年度秋季)学術講演会, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020

  • 現地調査データとパンシャープン画像を用いたナラ枯れ枯死木の抽出-樹冠サイズに注目して-

    藤原由季; 村松加奈子; 酒井有紀; 松井 淳

    日本リモートセンシング学会第69回(令和2年度秋季)学術講演会, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020

  • Landsat8 OLI/TIRS データを用いたインドの野焼き跡地抽出

    大林真菜; 村松加奈子

    日本リモートセンシング学会第69回(令和2年度秋季)学術講演会, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id

  • GCOM-C/SGLI データを用いたインド・パンジャーブ州の野焼きの抽出

    于 琨; 村松加奈子; 曽山典子

    日本リモートセンシング学会第69回(令和2年度秋季)学術講演会, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id

  • 地上における太陽励起のクロロフィル蛍光の猛暑日での日中変化の観測

    山本奈央; 村松加奈子; 栗山健二

    日本リモートセンシング学会第69回(令和2年度秋季)学術講演会, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id

  • Sentinel-2/MSIデータを用いたクロロフィルインデックスの季節変化の特徴解析

    宮本紗季; 村松加奈子

    日本リモートセンシング学会第69回(令和2年度秋季)学術講演会, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id

  • GCOM-C/SGLI データを用いた植生・土壌指数と気象データの関係 -インドの乾燥地における農地モニタリングに向けて-

    澤美和子; 村松加奈子; 曽山典子

    日本リモートセンシング学会第69回(令和2年度秋季)学術講演会, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020

  • Sentinel-2/MSIデータを用いた総生産キャパシティー推定における クロロフィルインデックス の緑とレッドエッジ波長帯の比較

    村松加奈子

    )日本リモートセンシング学会 第67回(令和元年度秋季)学術講演会, 29 Nov. 2019, 28 Nov. 2019, 29 Nov. 2019

  • Landsat8衛星データを用いたインドの野焼き抽出方法の検討

    大林 真菜; 村松 加奈子

    日本リモートセンシング学会 第67回(令和元年度秋季)学術講演会, 28 Nov. 2019, 28 Nov. 2019, 29 Nov. 2019

  • 分光反射特性を保存したパンシャープン画像におけるナラ枯れ分類条件の決定

    藤原 由季; 村松 加奈子

    日本リモートセンシング学会 第67回(令和元年度秋季)学術講演会, 28 Nov. 2019, 28 Nov. 2019, 29 Nov. 2019

  • GPP capacity estimation algorithm using light response curve in various vegetation types for global observing satellite data

    Kanako Muramatsu

    AsiaFlux2019-the 20th anniversary work shop-, 04 Oct. 2019, 01 Oct. 2019, 05 Oct. 2019

  • Google Earth Engine を用いたニューデリーの大気汚染と近郊の野焼きとの関連の分析

    落合史生; 大林真菜; 村松加奈子

    日本リモートセンシング学会 第66回(令和元年度春季)学術講演会, 05 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019

  • アマゾンの常緑広葉樹林における総生産量キャパシティ推定アルゴリズムのパラメータの決定 -薄い雲の影響を受けた MODIS データの除去-

    若井愛香; 村松加奈子

    日本リモートセンシング学会 第66回(令和元年度春季)学術講演会, 05 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019

  • 高分解能衛星画像を用いたナラ枯れ分布の解析-パンシャープン処理の適用-

    藤原由季; 村松加奈子; 酒井有紀; 松井 淳

    04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019

  • バングラデシュのシュンドルボンのマングローブ林における JERS-1 データによる 環境変化モニタリングに関する考察

    森川志美; 村松加奈子

    日本リモートセンシング学会 第66回(令和元年度春季)学術講演会, 04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019

  • Landsat8 衛星データを用いたインドの野焼き箇所抽出方法

    大林真菜; 村松加奈子; 落合史生

    日本リモートセンシング学会 第66回(令和元年度春季)学術講演会, 04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019

  • 地上での太陽励起によるクロロフィル蛍光の日中変化の観測

    山本奈央; 村松加奈子; 栗山健二

    日本リモートセンシング学会 第66回(令和元年度春季)学術講演会, 04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019

  • AN ALGORITHM OF GROSS PRIMARY PRODUCTION CAPACITY ESTIMATION FROM GLOBAL OBSERVING SATELLITE AND THE DIFFERENCE BETWEEN GPP CAPACITY AND GPP.

    Kanako Muramatsu

    42nd COSPAR Scientific Assembly 2018, 20 Jul. 2018, 14 Jul. 2018, 22 Jul. 2018, rm:research_project_id

Association Memberships

  • Remote sensing society of Japan

  • 日本リモートセンシング学会

Works

  • 光合成の日変化パターンを導入した,総生産量推定アルゴリズムの開発

    Apr. 2015, Mar. - 2016

  • GCOM-C/SGLIセンサによる総生産量推定アルゴリズムの開発

    Apr. 2014, Mar. - 2015

  • 総生産量推定のアルゴリズム開発とその検証

    Apr. 2013, Mar. - 2015

  • GCOM-C/SGLIセンサによる総生産キャパシティー推定アルゴリズムの開発

    Apr. 2013, Mar. - 2014

  • 植生機能タイプの分類方法と被覆度レベル分け方法の開発

    Apr. 2012, Mar. - 2013

  • 多方向観測による植生の構造抽出インデックスの開発

    Apr. 2011, Mar. - 2012

  • 人工衛星データを用いた植生純一次生産量推定の精度向上のための基礎研究IV

    Apr. 2009, Apr. - 2010

  • Construction of database of land cover mapping in Kii peninsula

    2001, - 2010

  • Construction of database of land cover mapping in Kii peninsula

    2001, - 2010

  • 人工衛星データを用いた植生純一次生産量推定の精度向上のための基礎研究III

    Apr. 2008, Mar. - 2009

  • Study on improvement of accuracy of net primary production estimation using satellite sensor data III

    Apr. 2008, Mar. - 2009



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