Researchers Database

MURAMATSU Kanako

FacultyFaculty Division of Natural Sciences Research Group of Environmental Sciences
PositionProfessor
Last Updated :2022/10/06

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Profile and Settings

  • Name (Japanese)

    Muramatsu
  • Name (Kana)

    Kanako

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

Association Memberships

  • Remote sensing society of Japan
  • 日本リモートセンシング学会

Ⅱ.研究活動実績

Published Papers

  • Refereed, JOURNAL OF AGRICULTURAL METEOROLOGY, SOC AGRICULTURAL METEOROLOGY JAPAN, 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., Jul. 2017, 73, 3, 119, 132, Scientific journal
  • Refereed, リモートセンシング学会誌, 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, 36, 3, 236-246
  • Refereed, Journal of Remote Sensing Society of Japan, The Remote Sensing Society of Japan, 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, From 1940 to 1953, red pine and weed-tree forests were converted to bamboo for the production of edible bamboo shoots and mature culms for craft use. However, bamboo shoots are now imported, and mature culms are generally no longer used for crafts. Bamboo forests have not been maintained and have expanded into semi-natural areas (satoyama) near populated zones. Thus, it is important to determine their distribution.
    We mapped the bamboo distribution using seasonal ALOS/AVNIR-2 data for Nara and southern Kyoto Prefectures. To study seasonal variation in spectral reflectance, bamboo leaves were measured monthly using a spectral radiometer. Based on these data, we investigated the seasonal changes in the bamboo leaf reflectance factor corresponding to the wavelengths of the AVNIR-2 sensor, in three coefficients, and in a modified vegetation index (MVIUPD) based on the Universal Pattern Decomposition Method (UPDM). Our results showed that the MVIUPD was lowest in May due to seasonal variation in the bamboo leaf characteristics. We used spring (May) data for mapping bamboo forests using AVNIR-2 data, and winter (Jan) data for classifying deciduous vegetation. We displayed training data for bamboo forests in a scatter plot between Cv, the UPDM vegetation coefficient, and MVIUPD, resulting in a cluster with a gentle curve. We fit the relationship between Cv and MVIUPD using a natural logarithm function and labeled bamboo pixels according to this relationship. The results were verified using field survey data, and the Kappa coefficient was 0.75. Narrow or sparse bamboo forest distributions caused misidentification. The mapping results were compared with the forest stand database of Nara Prefecture and the vegetation survey dataset provided by the Ministry of the Environment of Japan. We investigated land cover in the main areas where the results differed from these datasets. About 20% of the areas were misclassified, and they included an evergreen broadleaf growing on an ancient tomb, a mixed deciduous broadleaf and red pine forest, and a red pine forest. The results show that bamboo had not been detected during the 6th vegetation survey (2001) in northern Nara Prefecture.
    Thus, we conclude that AVNIR-2 data are useful for mapping bamboo forests on large spatial scales., Apr. 2015, 35, 2, 77,88, 88
  • Refereed, Journal of the remote sensing society of Japan, The Remote Sensing Society of Japan, 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, Tree height is an important parameter in forestry for evaluating stem volume, selling trees, and scheduling tree felling. Current wood prices are low, and some forests are not managed, even in the traditional "Yoshino Forestry" area in Nara Prefecture. Recently, a new approach has been introduced to reduce cutting costs. To achieve cost savings, it is important to determine the distribution of tree heights. The Advanced Land Observing Satellite (ALOS) with the onboard Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) sensor was launched on January 24, 2006. PRISM observes land surfaces from three different directions, at a spatial resolution of 2.5 m and with a spectral range from 0.55 to 0.77μm. Digital surface models (DSMs) can be produced from PRISM data. For mountainous areas, the difference in height between the DSM and the ground height can be considered to be the crown height. This study estimated height differences using a DSM and a digital terrain model (DTM) for the Nara Prefecture area of Japan. Abnormal differences in height were recorded, including values lower than -10 m and higher than 70 m. The areas with negative height values corresponded to a lake formed by a dam, a river, and mountain slopes. Areas where heights were considered too high were affected by cloud. In Yoshino District, crown height was validated using two tree-height datasets, one consisting of tree heights measured in a field survey and the other composed of light-detection and ranging (LiDAR) data. Both datasets were compared with the forest stand database for Nara Prefecture. The crown height estimates were separated into two categories : areas with successful and failed estimates. Areas with successful estimates were often on sout-facing slopes. We hypothesize that brightness or texture differences between nadir and backward images resulted in mismatching when the DSM was calculated. For areas with successful estimates, there were differences of several meters between estimated crown heights and average tree heights in the smallest management areas. These results indicate that the estimated crown heights were not sufficiently accurate for estimating average tree heights in the smallest forest management areas. However, the crown height estimates can be used to extract areas of large-diameter trees within the cutting cycle and to note inconsistencies in tree heights compared with the forest management database. The pre-processing method needs to be improved to allow its use in areas where estimates failed., Sep. 2013, 33, 4, 308-318, 318
  • Refereed, REMOTE SENSING, MDPI AG, 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., Dec. 2012, 4, 12, 3689, 3720, Scientific journal
  • Refereed, Journal of Plant Research, 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, 125, 339-349
  • Refereed, 人間文化研究科年報, 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, 26, 261-274
  • Refereed, INTERNATIONAL JOURNAL OF REMOTE SENSING, TAYLOR & FRANCIS LTD, 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., 2010, 31, 11, 2941, 2957, Scientific journal
  • Refereed, J. of the Remote Sensing Soc. of Japan, The Remote Sensing Society of Japan, 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, The GLI was a 36-channel near UV-visible-IR imager on board the ADEOS-II satellite. In spite of its short lifetime of only ten months due to solar paddle accident of the satellite, the GLI project made several significant contributions to the satellite remote sensing and climate studies, because of their useful products and related studies making use of its advanced functions as a satellite-borne imager. We like to overview the GLI mission regarding what we learned and what we lost from the satellite accident and to present several highlights gotten from the data analysis., 2009, 29, 1, 11-28, 28
  • Refereed, 日本リモートセンシング学会誌, 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, 29, 1, 114-123
  • Refereed, 日本リモートセンシング学会誌, 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, 29, 1, 102-113
  • Refereed, 大阪産業大学人間環境論集, 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, 7, 83-102
  • Refereed, INTERNATIONAL JOURNAL OF REMOTE SENSING, TAYLOR & FRANCIS LTD, 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., Jan. 2007, 28, 1-2, 107, 124, Scientific journal
  • Refereed, JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY, I S & T - SOC IMAGING SCIENCE TECHNOLOGY, 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., Mar. 2007, 51, 2, 141, 147, Scientific journal
  • Refereed, Int. J. of Remote Sensing, 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, 8, 16, 3493-3511
  • Refereed, Grassland Science, 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, 53, 217-225
  • Refereed, INTERNATIONAL JOURNAL OF REMOTE SENSING, TAYLOR & FRANCIS LTD, 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., Nov. 2006, 27, 21, 4899, 4910, Scientific journal
  • Refereed, J. of Remote Sensing Soc. of Japan,, 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, 25, 2, 179-190
  • Refereed, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing, 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., 2016, 41, 1207, 1211, International conference proceedings
  • Refereed, XXIII ISPRS CONGRESS, COMMISSION VIII, COPERNICUS GESELLSCHAFT MBH, 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., 2016, 41, B8, 1207, 1211, International conference proceedings
  • Refereed, PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS, IEEE COMPUTER SOC, 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)., 2007, 548, +, International conference proceedings
  • Refereed, INTERNATIONAL JOURNAL OF REMOTE SENSING, TAYLOR & FRANCIS LTD, 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., Jan. 2007, 28, 1-2, 125, 142, Scientific journal
  • Refereed, International Journal of Remote Sensing, 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, 28, 3493, 3511
  • Refereed, REMOTE SENSING OF OCEANOGRAPHIC PROCESSES AND LAND SURFACES; SPACE SCIENCE EDUCATION AND OUTREACH, ELSEVIER SCIENCE LTD, 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., 2006, 38, 10, 2191, +, International conference proceedings
  • Refereed, ADVANCES IN SPACE RESEARCH, ELSEVIER SCI LTD, 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., 2006, 38, 10, 2191, 2195, Scientific journal
  • Refereed, EARTH'S ATMOSPHERE, OCEAN AND SURFACE STUDIES, PERGAMON-ELSEVIER SCIENCE LTD, 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., 2002, 30, 11, 2517, 2522, Scientific journal
  • Refereed, HYPERSPECTRAL REMOTE SENSING OF THE LAND AND ATMOSPHERE, SPIE-INT SOC OPTICAL ENGINEERING, 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., 2001, 4151, 205, 213, International conference proceedings
  • Refereed, HYPERSPECTRAL REMOTE SENSING OF THE LAND AND ATMOSPHERE, SPIE-INT SOC OPTICAL ENGINEERING, 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., 2001, 4151, 164, 177, International conference proceedings
  • Refereed, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, SPIE, Use of chlorophyll index-green and the red-edge chlorophyll index to derive an algorithm for estimating gross primary production capacity, Kanako Muramatsu, 21 Oct. 2019, 11149, 1114906-1, 1114906-8, International conference proceedings
  • Refereed, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, The Reproducibility of Gross Primary Production Estimation From GPP Capacity and Canopy Conductance Index in Dry Area, Kanako Muramatsu, Jul. 2019, International conference proceedings
  • Refereed, Land Surface and Cryosphere Remote Sensing IV, SPIE, Canopy conductance index for GPP estimation from it's capacity, Kanako Muramatsu, 24 Oct. 2018, International conference proceedings
  • Earth Resources and Environmental Remote Sensing/GIS Applications VI, SPIE, 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, 20 Oct. 2015, International conference proceedings
  • Land Surface Remote Sensing, SPIE, Estimation of gross primary production capacity from global satellite observations, Kanako Muramatsu; Juthasinee Thanyapraneedkul; Shinobu Furumi; Noriko Soyama; Motomasa Daigo, 21 Nov. 2012, International conference proceedings
  • Physics Letters B, Elsevier BV, Measurement of the inclusive cross section of jets in γγ interactions at TRISTAN, H. Hayashii; A. Miyamoto; M. Iwasaki; S. Noguchi; N. Fujiwara; T. Abe; K. Abe; I. Adachi; 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; R. Kajikawa; S. Kato; S. Kawabata; H. Kichimi; M. Kobayashi; D. Koltick; I. Levine; K. Miyabayashi; K. Muramatsu; 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; Y. Watanabe; A. Yamaguchi; A. Yamamoto; M. Yamauchi, Sep. 1993, 314, 1, 149, 158, Scientific journal
  • Refereed, Geo-Spatial Information Science, Verification of net primary production estimation method in the mongolian plateau using landsat ETM+data, Xiong Yan; Kanako Muramatsu; Masahiro Hirata; Kazato Oishi; Ichirow Kaihotsu; Tamio Takamura; Shinobu Furumi; Noboru Fujiwara, We plan to estimate global net primary production (NPP) of vegetation using the Advanced Earth Observing Satellite-II (ADEOS-II) Global Imager (GLI) multi-spectral data. We derive an NPP estimation algorithm from ground measurement data on temperate plants in Japan. By the algorithm, we estimate NPP using a vegetation index based on pattern decomposition (VIPD) for the Mongolian Plateau. The VIPD is derived from Landsat ETM+multi-spectral data, and the resulting NPP estimation is compared with ground data measured in a semi-arid area of Mongolia. The NPP estimation derived from satellite remote sensing data agrees with the ground measurement data within the error range of 15% when all above-ground vegetation NPP is calculated for different vegetation classifications. © 2004 Taylor & Francis Group, LLC., 2004, 7, 2, 117, 122, Scientific journal

MISC

  • Not Refereed, 『経済学論叢』(同志社大), 奈良県スギ?ヒノキ林における生長量調査, MURAMATSU Kanako, 2019, 70, 4, 277-288
  • Not Refereed, LAND SURFACE REMOTE SENSING II, SPIE-INT SOC OPTICAL ENGINEERING, 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., 2014, 9260, 92603R-1
  • Not Refereed, LAND SURFACE REMOTE SENSING, SPIE-INT SOC OPTICAL ENGINEERING, 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., 2012, 8524
  • Not Refereed, NETWORKING THE WORLD WITH REMOTE SENSING, COPERNICUS GESELLSCHAFT MBH, 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., 2010, 38, 814, 819
  • Not Refereed, NETWORKING THE WORLD WITH REMOTE SENSING, COPERNICUS GESELLSCHAFT MBH, 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., 2010, 38, 916, 919
  • Not Refereed, NETWORKING THE WORLD WITH REMOTE SENSING, COPERNICUS GESELLSCHAFT MBH, 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., 2010, 38, 920, 924
  • Not Refereed, NETWORKING THE WORLD WITH REMOTE SENSING, COPERNICUS GESELLSCHAFT MBH, 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., 2010, 38, 902, 907
  • Not Refereed, NETWORKING THE WORLD WITH REMOTE SENSING, COPERNICUS GESELLSCHAFT MBH, 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., 2010, 38, 908, 911
  • Not Refereed, NETWORKING THE WORLD WITH REMOTE SENSING, COPERNICUS GESELLSCHAFT MBH, 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., 2010, 38, 937, 940
  • Not Refereed, NETWORKING THE WORLD WITH REMOTE SENSING, COPERNICUS GESELLSCHAFT MBH, 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)., 2010, 38, 769, 774
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 10, 2, 7-13
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 10, 2, 35-53
  • Not Refereed, Doshisha University world wide business review, Doshisha University, Tree height measurement in Mt. Yatsugatake, Yamanashi, Japan, MURAMATSU Kanako; J. Thanyapraneedkul; K.Muramatsu; K. Ikegami; M. Daigo, 2009, 10, 2, 54-60, 60
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, ALOS/AVNIR-2を用いた皆伐地の検出に関する考察, MURAMATSU Kanako, 2009, 10, 2, 111-115
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 10, 2, 69-85
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 10, 2, 86-100
  • Not Refereed, Proceedings of the 30th Asian Conference on Remote Sensing, Land Cover Classification of Uganda using ADEOS-II/GLI Mosaic data., MURAMATSU Kanako; Noriko Soyama; Kanako Muramatsu; Shinobu Furumi; Motomasa Daigo; Noboru \nFujiwara, 2009
  • Not Refereed, 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
  • Not Refereed, Proceedings of the 30th Asian Conference of 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, TS12-04-2
  • Not Refereed, 同志社大学ワールドビジネスレビュー, 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, 9, 2
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 9, 2
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 9, 2
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 9, 2
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 10, 1, 161-167
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 10, 1, 180-187
  • Not Refereed, Doshisha University world wide business review, Doshisha University, 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, Within the framework of Kyoto protocol, carbon sink concept is a major topic for the global climate change. There is an urgent need to collect vital data on forest plantations. Therefore, there is an obvious need to develop methods for estimating the biomass at diverse sites with non-destructive methods. Modeling is one of the effective approaches and Process Based Models (PBMs) that can provide a better understanding of stand growth and dynamics. A simplified model of PBMs is 3PG (Physiological Principles Predicting Growth) [1] and modified version is 3PGS model (Physiological Principles Predicting Growth with Satellite data) [2]. The model enables to use parameters derived from satellite data (NDVI : Normalized Difference Vegetation Index and FPAR : The Fraction of Photosynthetically Active Radiation) as well as meteorological data. The objective of this study is to estimate aboveground biomass (W_) by using 3PGS model. W_ were validated with field-measured values. The Field measurements were conducted in Eucalyptus camaldulensis and Acacia mangium plantation that is the first time to apply 3PGS model for these 2 species. Sensitivity analysis was done and found 4 sensitive parameters that need to be accurately determined are NDVI_FPAR_Constant, NDVI_ FPAR_Intercept, Canopy Quantum Efficiency (Alpha) and Specific Leaf Area (SLA). The sensitive parameters were simulated to calculate W_ thus accurately sensitive parameters are required. Sensitive parameters are Alpha and SLA. We found suitable Alpha are 0.07 and 0.13 molC/mol PAR and SLA are 22.5 and 35 (m^2/kg)for E. camaldulensis and A. mangium respectively. Relationship between NDVI and FPAR values, Logarithm is the best for achieving NDVI_FPAR_Intercept, NDVI_FPAR_Constant values. NDVI were derived from satellite data (LANDSAT ETM+ and MODIS) and FPAR were obtained from MODIS FPAR product (Moderate Resolution Imaging Spectroradiometer ; 8-day composite) and field data. Result showed 3PGS model provided better W_ of E. camadulensis than A. mangium. Moreover, in my study area LANDSAT ETM+ and FPAR from field data are suitable to derived NDVI and FPAR respectively. In future for E. camaldulensis and A. mangium's aboveground biomass (W_) estimation by using 3PGS model, Alpha and SLA values from this research can be apply in other plantation as well., 2008, 10, 1, 144-160, 160
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 9, 1, 90-102
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュ-, 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, 9, 1, 153-166
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, ユニバーサルパターン展開法のLandsat/MSSへの適用, MURAMATSU Kanako, 2007, 9, 1, 137-152
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 9, 1, 123-136
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 9, 1, 103-122
  • Not Refereed, Doshisha University world wide business review, Doshisha University, 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, 9, 1, 35-44, 44
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 8, 1
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 8, 1
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 8, 1
  • Not Refereed, J. of the Japan Soc. of photogrammetry and remote sensing, 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, 6, 45, 25-40
  • Not Refereed, Proceedings of SPIE - The International Society for Optical Engineering, 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, 6043, 604312-1-9
  • Not Refereed, Proceedings of SPIE - The International Society for Optical Engineering, 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, 6043, 604316-1-8
  • Not Refereed, 同志社大学 ワールドワイドビジネスレビュー, The ratio of photosynthetically active radiation to global solar radiation, MURAMATSU; Kanako, K; Muramatsu; S. Furumi; Y. Xiong; M. Daigo; N.Fujiwara, 2005, 6, 2, 58-63
  • Not Refereed, 同志社大学 ワールドワイドビジネスレビュー, 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, 6, 2, 64-71
  • Not Refereed, Proceedings of SPIE - The International Society for Optical Engineering, 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, 6043, 604311-1-8
  • Not Refereed, Doshisha University world wide business review, Doshisha University, A study of generalization rule on classification for vegetation coverage of a pixel, MURAMATSU Kanako, 2005, 6, 2, 16-22, 22
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 7, 1, 54-66
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 7, 1, 67-77
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 7, 1, 111-123
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 7, 1, 124-134
  • Not Refereed, 同志社大学ワールドワイドビジネスレビュー, 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, 7, 1, 239-247
  • Not Refereed, Proceedings of SPIE - The International Society for Optical Engineering, 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, 6043, 604313-1-12
  • Not Refereed, Doshisha University world wide business review, Doshisha University, 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, 5, 2, 86-93, 93
  • Not Refereed, EARTH'S ATMOSPHERE, OCEAN AND SURFACE STUDIES, PERGAMON-ELSEVIER SCIENCE LTD, 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., 2002, 30, 11, 2523, 2528
  • Not Refereed, J. of the remote sensing society of Japan, The diurnal time series relationship between surface/air\ntemperature and global solar irradiance, MURAMATSU Kanako, 2001, 21, 5
  • Not Refereed, INTERNATIONAL JOURNAL OF REMOTE SENSING, TAYLOR & FRANCIS LTD, 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., Jan. 2000, 21, 1, 99, 119
  • Not Refereed, REMOTE SENSING FOR LAND SURFACE CHARACTERISATION, PERGAMON PRESS LTD, 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., 2000, 26, 7, 1137, 1140
  • Not Refereed, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, 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., Feb. 1999, E82D, 2, 453, 460
  • Not Refereed, 水処理技術, 「パターン展開法による大阪湾の水質解析」, MURAMATSU Kanako, 1999, 40, 315-320
  • Not Refereed, J. of remote sensing soc. of Japan, Relation between vegetation vigor and a new vegetation index based on pattern decomposition method, MURAMATSU Kanako, 1998, 18, 3, 17-34
  • Not Refereed, J. of remote sensing soc. of Japan, An algorithm and a new vegetation index for ADEOS-II/GLI data analysis, MURAMATSU Kanako, 1998, 18, 12, 28-50
  • Not Refereed, 日本リモートセンシング学会誌, 「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, 17, 14, 34-39
  • Not Refereed, 日本リモートセンシング学会誌, 「パターン展開法による水田解析」, MURAMATSU Kanako, 1997, 17, 2, 5-18
  • Not Refereed, 日本リモートセンシング学会誌, 「衛星データ解析のためのパターン展開法」, MURAMATSU Kanako, 1996, 16, 3, 17-34
  • Not Refereed, PHYSICS LETTERS B, ELSEVIER SCIENCE BV, 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., Jul. 1994, 332, 3-4, 477, 487
  • Not Refereed, ADEOS-II JRAモンゴル地上検証実験報告書, 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, 71, 82
  • Not Refereed, ナント経済月報2月号, 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, Introduction other
  • 日本リモートセンシング学会 第69回学術講演会論文集, 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, 15, 16
  • Not Refereed, 日本リモートセンシング学会 第69回学術講演会論文集, 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, 27, 28, Summary national conference
  • Not Refereed, 日本リモートセンシング学会 第69回学術講演会論文集, 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, 29, 30, Summary national conference
  • Not Refereed, 日本リモートセンシング学会 第69回学術講演会論文集, 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, 37, 38, Summary national conference
  • Not Refereed, 日本リモートセンシング学会 第69回学術講演会論文集, 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, 39, 40, Summary national conference
  • Not Refereed, 日本リモートセンシング学会 第69回学術講演会論文集, 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, 49, 50, Summary national conference
  • Not Refereed, 日本リモートセンシング学会 第69回学術講演会論文集, 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, 51, 52, Summary national conference
  • 日本リモートセンシング学会学術講演会論文集(CD-ROM), 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, 曽山典子; 佐々井崇博; 村松加奈子; 醍醐元正; 落合史生; 奈佐原顕郎, 2015, 59th
  • 日本リモートセンシング学会第71回学術講演会論文集, GCOM-C/SGLI データを用いた地表面温度及び短波長赤外域に関する指標の特徴解析 -乾燥農地に着目して-, 澤 美和子; 村松加奈子; 曽山典子, Nov. 2021, 41, 44
  • 日本リモートセンシング学会第71回学術講演会論文集, Sentinel-2 データを用いたインド・パンジャーブ州における野焼き箇所の抽出, 于琨; 村松加奈子, Nov. 2021, 67, 68
  • 日本リモートセンシング学会第71回学術講演会論文集, 植生指標CIgreen, CIred-edge を用いた総生産キャパシティー推定アルゴリズム, 宮本紗季; 村松加奈子, Nov. 2021, 39, 40
  • 日本リモートセンシング学会第70回学術講演会論文集, GCOM-C/SGLI データを用いた土壌指数と植被率の関係 -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, May 2021, 73, 74
  • 日本リモートセンシング学会第70回学術講演会論文集, Sentinel-2 データを用いたインド・パンジャーブ州における野焼き箇所の抽出条件, 于琨; 村松加奈子, May 2021, 13, 14
  • 日本リモートセンシング学会第70回学術講演会論文集, 植生指標CIgreen, CIred-edge を用いた総生産キャパシティー推定アルゴリズム, 宮本紗季; 村松加奈子, May 2021, 47, 48
  • 日本リモートセンシング学会第70回学術講演会論文集, Use of geostationary meteorological satellite data for estimating midday depression of photosynthesis in dry area, 村松加奈子; 森山雅雄, May 2021, 43, 46
  • 日本気象学会大会講演予稿集, GCOM-C観測プロダクトの検証に向けた地上観測機材の校正・性能評価の取り組み, 堀雅裕; 村上浩; 今岡啓治; 小野祐作; 谷川朋範; 原田昌朋; 佐久間史洋; 片山晴善; 中島康裕; 中島幸徳; 本多嘉明; 梶原康司; 青木輝夫; 朽木勝幸; 山崎明宏; 奈佐原顕郎; 秋津朋子; 鈴木力英; 村松加奈子, 2014, 106
  • 日本リモートセンシング学会学術講演会論文集, For GCOM-C/SGLI project, Estimating and Validating the Net Primary Production of Vegetation using around Yatsugatake Mountain area, Japan, 池上季美果; 村松加奈子; 本多嘉明; 梶原康司, 2009, 46th
  • 日本リモートセンシング学会学術講演会論文集, For GCOM-C/SGLI project, Estimating and Validating the Net Primary Production of Vegetation using around Yatsugatake Mountain area, Japan (II), 池上季美果; 村松加奈子; 醍醐元正; 古海忍; 曽山典子; 本多嘉明; 梶原康司, 2009, 47th
  • 日本リモートセンシング学会学術講演会論文集, Reflectance measurement of fertilized and nonfertilized paddy fields in Nara basin by unmanned helicopter, 古海忍; 古海忍; 梅垣佳代子; 陳路; 陳路; 村松加奈子; 小野朗子; 小野朗子; 本多嘉明; 本多嘉明, 2007, 42nd
  • 日本リモートセンシング学会学術講演会論文集, BRDF effect evaluation on the global net primary production estimation using the data of ADEOS-II GLI, CHEN L.; CHEN L.; 古海忍; 古海忍; 村松加奈子; 本多嘉明; 本多嘉明; 梶原康司; 梶原康司; 近田朝子; 近田朝子, 2007, 42nd
  • 生研フォーラム 宇宙からの地球環境モニタリング論文集, 針葉樹林NPPの推定におけるBRDF影響, 陳路; 古海忍; 熊彦; 村松加奈子; 本多嘉明; 梶原康司; 藤原昇, 2006, 15th
  • 日本リモートセンシング学会学術講演会論文集, BRDF effect evaluation of broadleaf forest and grassland with the reflectance data observed by radio-controlled helicopter, CHEN L.; CHEN L.; 古海忍; 古海忍; 村松加奈子; 本多嘉明; 本多嘉明; 梶原康司; 梶原康司, 2006, 41st
  • 日本リモートセンシング学会学術講演会論文集, Sensitivity analysis of Net Primary Production estimation with BRDF model using reflectance by RC helicopter, CHEN L.; 古海忍; XIONG Y.; 村松加奈子; 本多嘉明; 藤原昇, 2005, 39th
  • 日本リモートセンシング学会学術講演会論文集, Estimation of Net Primary Production using reflectance observed by unmanned helicopter on cedar forest, CHEN L; 古海忍; 村松加奈子; 本多嘉明; 藤原昇, 2003, 35th

Books etc

  • 基礎からわかるリモートセンシング, 理工図書, MURAMATSU Kanako, 分担, 2011, 224-230, Not Refereed

Presentations

  • 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, 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, 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, False
  • MURAMATSU Kanako; Wakai, Aika; Muramatsu, Kanako, the 39th Asia Conference on Remote Sensing, 2018, Determination of Tropical Forests Parameters in Gross Primary Production Capacity Estimation Algorithm in Brazil, Oct. 2018, Kuala Lumpur, Malaysia
  • MURAMATSU Kanako; Muramatsu, K, SPIE Asia-Pacific remote sensing, Canopy conductance index for GPP estimation from its\ncapacity, Sep. 2018, Honolulu, Hawai, True
  • MURAMATSU Kanako; Kanako Muramatsu, COSPAR,2018, An algorithm of gross primary production capacity estimation from global observing satellite and the difference between GPP capacity and GPP, Jul. 2018, Pasadena, California, USA, True
  • 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, 総生産量推定のための樹冠コンダクタンス指標 II, 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, False
  • 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, 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, 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, False
  • 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, 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, 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, False
  • 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, Google Earth Engineを用いた京阪奈地区の竹林の抽出-1, 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, False
  • 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, 総生産量推定のための樹冠コンダクタンス指標, 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), False
  • 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, リモートセンシングによるナラ枯れのモニタリング-1, 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), False
  • 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, 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, 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), False
  • 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, DIFFERENCES BETWEEN NEEDLE-LEAVES FOREST AND BROAD-LEAVES FOREST FROM PSEUDO MULTIDIRECTIONAL OBSERVATION DATA, May 2017, The remote sensing society of Japan, 名古屋大学, True
  • 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, STAND-OFF MEASUREMENT OF SOLAR INDUCED FLUORESCENCE FROM VEGETATION CANOPIES: APPLICATION TO FIELD AND FOREST, May 2017, The remote sensing society of Japan, 名古屋大学, True
  • 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, リモートセンシングによるナラ枯れのモニタリング, Dec. 2016, 奈良女子大学G棟2階G201教室, False
  • 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, 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, 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
  • 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, 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, 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
  • 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, 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, 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
  • 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, 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, Nov. 2015, 長崎,長崎大学
  • MURAMATSU Kanako, 日本リモートセンシング学会第59回学術講演会, 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, Nov. 2015, 長崎, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第59回学術講演会, 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, Nov. 2015, 長崎, False
  • MURAMATSU Kanako; Soyama, N; Muramatsu, K; Ohashi, I; Daigo, M; Ochiai, F; Tadono, T; Nasahara, K, SPIE remote sensing 2015, A scale-up method for reference data for validation of global land cover maps using ALOS/AVNIR-2 satellite data, Sep. 2015, Toulouse, France, True
  • MURAMATSU Kanako; Muramatsu, K; Furumi; Daigo, M, SPIE remote sensing 2015, Algorithm developing of gross primary production from it's capacity and a canopy conductance index using flux and global observing satellite data, Sep. 2015, Toulouse, France, True
  • MURAMATSU Kanako; Soyama, N; Muramatsu, K; Ochiai; F. Daigo, M; Sasai, T; Nasahara, K, 30th International symposium on space technology and science, Validation method of global land cover map using reference data with quality level, Jul. 2015, Kobe, Japan, True
  • MURAMATSU Kanako, 日本リモートセンシング学会第58回学術講演会, 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?, Jun. 2015, 千葉
  • MURAMATSU Kanako, 日本リモートセンシング学会第58回学術講演会, 多次期のLandsat-8データを用いた京阪奈地区の竹林抽出-III, Jun. 2015, 千葉, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第58回学術講演会, 最大光利用効率の季節変化推定アルゴリズム, Jun. 2015, 千葉, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第58回学術講演会, 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, Jun. 2015, 千葉, False
  • MURAMATSU Kanako, 生研フォーラム, 最大光利用効率の季節変化の推定, Mar. 2015, 東京,日本, False
  • 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, 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, Nov. 2014, 京都
  • MURAMATSU Kanako; Kanako Muramatsu; Shinobu Furumi; Noriko Soyama; Motomasa Daigo, SPIE Asis-Pacific remote sensing, 2014, Estimating the seasonal maximum light efficiency, Oct. 2014, Beijing, China
  • MURAMATSU Kanako; Noriko Soyama; Kanako Muramatsu; Motomasa Daigo; Fumio Ochiao, COSPAR,2014, A study of stratified class design for global land cover classification, Aug. 2014, Moscow, Russia, True
  • MURAMATSU Kanako; Kanako Muramatsu; Yukiko Mineshita; Noriko Soyama; Motomasa Daigo, COSPAR,2014, An algorithm of gross primary production capacity from GCOM-C1/SGLI, Aug. 2014, Moscow, Russia, True
  • MURAMATSU Kanako; Kanako MURAMATSU; Yukiko MINESHITA; Shinobu FURUMI; Daigo MOTOMASA, Asia Oceanin Geosciences Society, 2014, An Estimation Method of Capacity of Gross Primary Production from Global Observation Satellite, Jul. 2014, Sapporo, Japan
  • MURAMATSU Kanako; Noriko SOYAMA; Kanako MURAMATSU; Satomi MANABE; Daigo MOTOMASA; Fumio OCHIAI, Asia Oceanin Geosciences Society, 2014, A Method of Distinguishing Forest Types for Global Land Cover Classification Using Multi-angle Satellite Data, Jul. 2014, Sapporo, Japan
  • 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, 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, May 2014, つくば, False
  • 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, 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, May 2014, つくば, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第55回学術講演会, 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-, Nov. 2013, 福島, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第55回学術講演会, 針葉樹と落葉樹の分光?多方向反射率特性, Nov. 2013, 福島, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第55回学術講演会, 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, Nov. 2013, 福島, False
  • MURAMATSU Kanako; Yukiko Mineshita; Kanako Muramatsu; Motomasa Daigo; Noriko Soyama, International symposium on remote sensing, 2013, Estimation of global primary production capacity, May 2013, Chiba, Japan, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第53回学術講演会, 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, Nov. 2012, 日本リモートセンシング学会, 広島, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第53回学術講演会, 全球の総生産キャパシティ推定の適応性に関する研究, Nov. 2012, 広島, False
  • MURAMATSU Kanako; Noriko Soyama; Tenri Univ. (Ja; Kanako Muramatsu; Nara Women’s; Univ, SPIE, 2012, Asia-Pasific Remote Sensing, Global land cover classification using annual statistical values, Oct. 2012, KYOTO, JAPAN
  • MURAMATSU Kanako; Kanako Muramatsu; Juthasinee Thanyapraneedkul; Nara Women’s; Univ. (Japa; Shinobu Furumi; Narasaho College\n(Ja, SPIE, 2012, Asia-Pasific Remote Sensing, Estimating the gross primary production capacity from global observation satellite, Oct. 2012, KYOTO, JAPAN
  • MURAMATSU Kanako; K. Muramatsu; J. THanyapraneedkul; S. Furumi, 39th COSPAR Assembly, Estimating the Capacity of Gross Primary Production from Global Observation Satellite, Jul. 2012, Mysore, India, True
  • MURAMATSU Kanako; Soyama N; Muramatsu K; Daigo M, 39th COSPAR Assembly, A simple algorithm for global land cover classification using annual statistical values, Jul. 2012, Mysore, India, True
  • MURAMATSU Kanako, 日本リモートセンシング学会第52回学術講演会,, 熱赤外画像を用いた気孔開度推定へのアプローチ, May 2012, 日本リモートセンシング学会, 東京, False
  • MURAMATSU Kanako, 日本生態学会, 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?, Mar. 2012, 日本生態学会, 札幌, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第51回学術講演会, 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, Nov. 2011, 日本リモートセンシング学会, 弘前, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第51回学術講演会, ALOS/AVNIR-2データを用いた竹林分布図作成に関する考察II, Nov. 2011, 日本リモートセンシング学会, 弘前, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第51回学術講演会, 多方向観測による植生構造抽出インデックスの開発, Nov. 2011, 日本リモートセンシング学会, 弘前
  • MURAMATSU Kanako, 日本リモートセンシング学会第50回学術講演会, ALOS/AVNIR-2データを用いた竹林分布図作成に関する考察,, May 2011, 日本リモートセンシング学会, 東京 日本大学, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第50回学術講演会, 反射率ベースのパンシャープン処理アルゴリズムII, May 2011, 日本リモートセンシング学会, 東京 日本大学, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第50回学術講演会, 植生被覆度と植生被覆分布状態に関する一実験, May 2011, 日本リモートセンシング学会, 東京 日本大学
  • 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), Vegetation types mapping using ALOS/AVNIR-2 and PRISM data using universal pattern decomposition method, Aug. 2010, KYOTO, Japan
  • MURAMATSU Kanako; Tang, Y; Huang, X; Muramatsu, K; Zhang, L, ISPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8), Object-oriented change detection for high-resolution imagery using a generic algorithm, Aug. 2010
  • MURAMATSU Kanako; Soyama, N; Muramatsu, K; Furumi, S; Daigo, M, ISPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8), Development of validation data sets for global land cover classification using ALOS/AVNIL-2 data, Aug. 2010, KYOTO, JAPN, True
  • MURAMATSU Kanako; Nino, N; Muramatsu, K; Daigo, M; Soyama, N, SPRS Technical Commission VIII Symposium -Networking the World with Remote Sensing, XXXVIII (8),, A study on estimation of tree height in Japanese cedar and Japanese cypress in Nara Using ALOS/PRISM Satellite sensor,, Aug. 2010, KYOTO, Japan
  • 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), Estimating and validation the net primary production aournd Yatsugatake mountain area for GCOM-C/SGLI project, Aug. 2010, KYOTO, JAPAN
  • 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),, Improvement of terrestrial GPP estimation algorithm using satellite and FLUX data, Aug. 2010, KYOTO, JAPAN, True
  • 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, Integrated spatial information for automatic endmember extraction algorithm, Nov. 2009, 名古屋, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第47回学術講演会, 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), Nov. 2009, 名古屋, False
  • 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, Improvement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data, Nov. 2009, 名古屋, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第47回学術講演会, 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, Nov. 2009, 名古屋
  • MURAMATSU Kanako; K. Muramatsu; A. Tahara; N.Soyama; S. Furumi; M. Daigo; N. Fujiwara, 30th Asian Conference on Remote Sensing, Vegetation species classification using ALOS/AVNIR-2 data Vegetation species, Oct. 2009, True
  • MURAMATSU Kanako; Noriko Soyama; Kanako Muramatsu; Shinobu Furumi; Motomasa Daigo; Noboru Fujiwara, the 30th Asian Conference on Remote Sensing, Land Cover Classification of Uganda using ADEOS-II/GLI Mosaic data, Oct. 2009, True
  • MURAMATSU Kanako; J. Thanyapreneedkul; K. Muramatsu; M. Daigo; N. Soyama; S. Furumi; N. Fujiwara, the 30th Asian Conference of Remote sensing, Improvement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data, Oct. 2009, True
  • MURAMATSU Kanako, 日本リモートセンシング学会第46回学術講演会, 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, May 2009
  • MURAMATSU Kanako, 日本リモートセンシング学会第46回学術講演会, 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, May 2009, 東京
  • 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, mprovement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data (Part 1: Study on photosynthesis activity by using FluxNet tower sites data), May 2009, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第46回学術講演会, 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, May 2009, 東京, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第45回学術講演会, 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, Dec. 2008, 札幌, False
  • 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, Improvement accuracy of terrestrial NPP estimation using ADEOS-II/GLI data, Dec. 2008, 札幌, False
  • 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, Remote sensing image retrieval based on semantic mining, Dec. 2008, 札幌, False
  • MURAMATSU Kanako, 日本リモートセンシング学会第45回学術講演会, 良県東吉野村におけるCO2濃度の動態解析III, Dec. 2008, 札幌, False
  • MURAMATSU Kanako, 日本リモートセンシング学会 第45回学術講演会, 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, Dec. 2008, 札幌
  • MURAMATSU Kanako, 日本リモートセンシング学会, 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--, May 2008
  • MURAMATSU Kanako, 日本リモートセンシング学会第43回学術講演会, 奈良県東吉野村におけるCO2濃度の動態解析II, May 2008, 横浜, False
  • 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, Estimation of Forest Plantation Productivity Using a Physiologically Based Model Driven with Meteorological Data and Satellite-derived Estimates of Canopy Photosynthetic Capacity., May 2008, 横浜, False
  • K. Muramatsu; M. Moriyama, COSPAR 2021, 43rd COSPAR Scientific Assembly, Gross primary production estimation algorithm including diurnal changes for satellite sensor data, Oral presentation, 02 Feb. 2021, 28 Jan. 2021, 04 Feb. 2021, rm:research_project_id
  • 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, 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, Oral presentation, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020
  • 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, 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-, Oral presentation, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020
  • 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, Landsat8 OLI/TIRS データを用いたインドの野焼き跡地抽出, Oral presentation, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id
  • 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, 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, Oral presentation, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id
  • 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, 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, Oral presentation, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id
  • 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, 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, Oral presentation, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020, rm:research_project_id
  • 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, 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-, Oral presentation, 21 Dec. 2020, 21 Dec. 2020, 22 Dec. 2020
  • 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, 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, 29 Nov. 2019, 28 Nov. 2019, 29 Nov. 2019
  • 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, Landsat8衛星データを用いたインドの野焼き抽出方法の検討, Poster presentation, 28 Nov. 2019, 28 Nov. 2019, 29 Nov. 2019
  • 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, 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, 28 Nov. 2019, 28 Nov. 2019, 29 Nov. 2019
  • Kanako Muramatsu, AsiaFlux2019-the 20th anniversary work shop-, GPP capacity estimation algorithm using light response curve in various vegetation types for global observing satellite data, 04 Oct. 2019, 01 Oct. 2019, 05 Oct. 2019
  • 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, 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, 05 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019
  • 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, 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-, 05 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019
  • 藤原由季; 村松加奈子; 酒井有紀; 松井 淳, 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-, 04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019
  • 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, 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, Poster presentation, 04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019
  • 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, Landsat8 衛星データを用いたインドの野焼き箇所抽出方法, 04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019
  • 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, 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, 04 Jun. 2019, 04 Jun. 2019, 05 Jun. 2019
  • Kanako Muramatsu, 42nd COSPAR Scientific Assembly 2018, AN ALGORITHM OF GROSS PRIMARY PRODUCTION CAPACITY ESTIMATION FROM GLOBAL OBSERVING SATELLITE AND THE DIFFERENCE BETWEEN GPP CAPACITY AND GPP., 20 Jul. 2018, 14 Jul. 2018, 22 Jul. 2018, rm:research_project_id
  • 于 琨; 村松 加奈子, (社)日本リモートセンシング学会 第71回(令和3年度秋季)学術講演会, Sentinel-2データを用いたインド・パンジャーブ州における野焼き箇所の抽出, Oral presentation, 16 Nov. 2021, 15 Nov. 2021, 16 Nov. 2021, rm:research_project_id
  • 宮本 紗季; 村松 加奈子, (社)日本リモートセンシング学会 第71回(令和3年度秋季)学術講演会, 植生指標CIgreen, CIred-edgeを用いた総生産キャパシティー推定アルゴリズム, Oral presentation, 16 Nov. 2021, 15 Nov. 2021, 16 Nov. 2021
  • 澤美和子; 村松加奈子; 曽山典子, (社)日本リモートセンシング学会 第71回(令和3年度秋季)学術講演会, GCOM-C/SGLIデータを用いた地表面温度及び短波長赤外域に関する指標の特徴解析 ー 乾燥農地に着目してー, Oral presentation, 16 Nov. 2021, 15 Nov. 2021, 16 Nov. 2021
  • M. Sawa; K. Muramatsu; N. Soyama, (社)日本リモートセンシング学会 第70回(令和3年度春季)学術講演会, Relationships between soil index and vegetation cover ratio using GCOM- C/SGLI data -Toward farm monitoring in drylands-, Oral presentation, 18 May 2021, 17 May 2021, 18 May 2021
  • S. Miyamoto; K. Muramatsu, (社)日本リモートセンシング学会 第70回(令和3年度春季)学術講演会, Algorithm for estimating GPP capacity using the green and red-edge band chlorophyll indices, Oral presentation, 18 May 2021, 17 May 2021, 18 May 2021, rm:research_project_id
  • K. Muramatsu; M.Moriyama, (社)日本リモートセンシング学会 第70回(令和3年度春季)学術講演会, Use of geostationary meteorological satellite data for estimating midday depression of photosynthesis in dry area, Oral presentation, 18 May 2021, 17 May 2021, 18 May 2021, rm:research_project_id
  • K. Yu; K. Muramatsu, 日本リモートセンシング学会 第70回(令和3年度春季)学術講演会, Extraction conditions of paddy stubble burning in Punjab, India with Sentinel-2, Oral presentation, 17 May 2021, 18 May 2021, rm:research_project_id

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

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

Research Projects

  • 人工衛星データの画像処理による自然環境変動の研究, 0, 0, 0, Competitive research funding
  • 環境研究助成, Nov. 2019, Oct. 2020, Development of gross primary production estimation algorithm using daily changes of photosynthesis, 住友財団, rm:misc;rm:misc;rm:misc;rm:presentations;rm:presentations;rm:presentations;rm:presentations;rm:presentations
  • 実践プロジェクト フルリサーチ(FR), Coinvestigator, 大気浄化、公衆衛生および持続可能な農業を目指す学際研究:北インドの藁焼きの事例, 総合地球環境学研究所, 総合地球環境学研究所, rm:misc;rm:misc;rm:misc;rm:misc;rm:presentations;rm:presentations;rm:presentations;rm:presentations
  • Jul. 2019, Mar. 2020, Principal investigator, 光―光合成曲線と樹冠コンダクタンス指標を用いた,総生産量推定アルゴリズムの開発, 奈良女子大学, 研究推進プロジェクト経費, rm:published_papers
  • Grant-in-Aid for Scientific Research (C), Apr. 2016, Mar. 2019, 16K00514, Principal investigator, Algorithm of gross primary production estimation using light-response curve and canopy conductance index, MURAMATSU Kanako; Soyama Noriko; Mineshita Yukiko; Yoneda Emi; Thanyapraneedkul Juthasinee, Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Nara Women's University, 4940000, 3800000, 1140000, This study focused on an algorithm development of gross primary production from it’s capacity and canopy conductance index. In the part of gross primary production capacity part, it was applicable to vegetation types in several climates that the method to determine a parameter of light-response curve of photosynthesis using chlorophyll index of green band. The canopy conductance index was defined as the changing rate of canopy conductance, and it was estimated using land surface temperatures at 11 a.m. and 1 p.m. observed by global observing satellite. In dry area, estimation results were in good reproducibility when the depression of photosynthesis around 11 a.m. However, daily depression occurred earlier than 11 a.m., it was under estimation. I will examine availability to land area analysis of the weather satellite in future., url;rm:published_papers;rm:published_papers;rm:published_papers;rm:presentations
  • Grant-in-Aid for Scientific Research (C), Apr. 2013, Mar. 2017, 25340007, Algorithm of estimating GPP based on right response curve using satellite sensor data, MURAMATSU Kanako; FURUMI Shinobu; SOYAMA Noriko; KAMAKURA Mai, Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Nara Women's University, 5200000, 4000000, 1200000, The algorithm of estimating gross primary production of vegetation using satellite sensor data was developed. The main characteristics of the algorithm are using the light response curve of photosynthesis in the algorithm, and dividing two parts as photosynthesis capacity, which is in the condition plants photosynthesizes with less stress, and it’s suppression. The parameters of a light-response curve were determined using the estimation formula. The formulas were divided into three groups as grass and open shrubs, woody plants except for a tropical rain forest. The suppression part was studied focusing on stomata opening and closing using thermal imagers for leaf and canopy level. The estimation accuracy of the diurnal variation pattern of the leaf and canopy conductance was better than the absolute value., url;rm:published_papers

Ⅲ.社会連携活動実績

1.公的団体の委員等(審議会、国家試験委員、他大学評価委員,科研費審査委員等)

  • 01 Apr. 2021, 31 Mar. 2021
  • 2020, 2021
  • May 2016, 9999, Society
  • 日本リモートセンシング学会, 学術委員会委員, 1999, 9999, Society
  • Jul. 2019, 9999, Autonomy
  • 01 Apr. 2021, 31 Mar. 2021
  • 01 Apr. 2021, 31 Mar. 2021


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