GEDI による樹冠高推定の空間誤差評価
堤田成政; 加藤顕
2025, [Reviewed], [Lead, Corresponding]
Scientific journal
Encapsulating Spatially Varying Relationships with a Generalized Additive Model Alexis Comber; Paul Harris; Daisuke Murakami; Tomoki Nakaya; Narumasa Tsutsumida; Takahiro Yoshida; Chris Brunsdon
ISPRS International Journal of Geo-Information, Dec. 2024,
[Reviewed]Scientific journal
DOI：https://doi.org/10.3390/ijgi13120459DOI ID：10.3390/ijgi13120459,
ORCID：174122119 Investigating the use of deep learning models for land cover classification from street‐level imagery Narumasa Tsutsumida; Jing Zhao; Naho Shibuya; Kenlo Nasahara; Takeo Tadono
Ecological Research, Sep. 2024,
[Reviewed],
[Lead]English, Scientific journal
DOI：https://doi.org/10.1111/1440-1703.12470DOI ID：10.1111/1440-1703.12470,
ORCID：158309848 Retrieval of cherry flowering phenology on Flickr and YouTube: a case study along the Tarumi railway, Gifu, JapanNagai Shin; Taku M. Saitoh; Narumasa Tsutsumida
Frontiers in Sustainable Tourism,
Volume：3, Feb. 2024,
[Reviewed]To further develop the accuracy of monitoring cherry flowering dates, we require phenological records from multiple points in multiple years at the catchment scale, as well as conventional in situ phenological observations, phenological data published on the Internet, and analysis using statistics of Internet search engines. We tried to detect the dates of cherry flowering phenology along the Tarumi railway, Gifu Prefecture, Japan, by using Flickr, an image hosting service, and YouTube, an online video sharing and social media platform. We detected full bloom of Cerasus ×yedoensis and Cerasus jamasakura mainly at cherry blossom viewing spots (around some train stations) on Flickr and at both viewing spots and multiple points in the landscape along the railway on YouTube. Despite local climatological differences along the railway, the detected full blooming dates corresponded not only with each other, but also with the full bloom period of Neodani Usuzumi-zakura (Cerasus itosakura), a famous tree with long-term detailed records. We could not detect the date and location in many photographs on Flickr or in any videos on YouTube. However, the usefulness of both platforms is supported by the facts that we can evaluate the year-to-year variability of full bloom dates, especially at cherry blossom viewing spots, and detect flowering phenology even in a non-photogenic landscape. By applying our method to other railways, we expect to collect multi-year records of plant phenology dates at multiple points that cannot be collected by older methods.
Frontiers Media SA, English, Scientific journal
DOI：https://doi.org/10.3389/frsut.2024.1280685DOI ID：10.3389/frsut.2024.1280685,
eISSN：2813-2815 Mapping cherry blossom phenology using a semi-automatic observation system with street level photos Narumasa Tsutsumida; Shuya Funada
Ecological Informatics,
Volume：78,
First page：102314,
Last page：102314, Dec. 2023,
[Reviewed],
[Lead, Corresponding]Elsevier BV, Scientific journal
DOI：https://doi.org/10.1016/j.ecoinf.2023.102314DOI ID：10.1016/j.ecoinf.2023.102314,
ISSN：1574-9541 Spatiotemporal Mapping of Cherry Blossom Blooming by Semi-Automatic Observation System with Street-Level PhotosNarumasa Tsutsumida; Shuya Funada
Apr. 2023
Abstract
The importance of floral phenology as a critical indicator of regional climate change and ecosystem services is widely recognized. The annual blooming of cherry blossoms is a nationally celebrated event in Japan, and historical phenological records have been used to document regional climate change. The cultural ecosystem services provided by this phenomenon are important as they not only signal the arrival of spring but also offer a picturesque spring landscape. Despite its importance, constructing a spatiotemporal record of cherry blossom blooming is challenging due to the limited coverage of traditional stationary observations. To address this issue, citizen-based observation programs and remote sensing applications have been implemented; nevertheless, these strategies are still limited by infrequent and insufficient observations throughout space and time. To compensate, we developed a flower detection model for geographically and temporally dispersed street-level photos that may be used as the core component of a semi-automatic observation system. Specifically, we developed a customized YOLOv4 model for cherry blossom detection from street-level photos obtained through Mapillary, one of the social sensing data repositories. The detection model achieved an overall accuracy, recall, and precision of 86.7%, 70.3%, and 90.1%, respectively. By using observation coordinates and dates attached to Mapillary photos, we mapped the probability of cherry trees blooming in a spatial grid of dimensions 10 m x 10 m on a daily basis. With sufficient observations, start, peak, and end of blooming were estimated through time series analysis. A case study conducted at Saitama University’s main campus in 2022 confirmed the possibility of mapping the presence of cherry blossoms and their blooming timing automatically. Since our approach relies solely on geotagged street-level photos that can be taken by anyone with no prior knowledge of cherry tree species identification, we anticipate that it will be easier to build blooming records over space and time than conventional stationary observations or citizen-based observation programs. This novel approach also has potential applications for detecting other species as well.
Cold Spring Harbor Laboratory
DOI：https://doi.org/10.1101/2023.04.13.536831DOI ID：10.1101/2023.04.13.536831 Prediction of the visit and occupy of the sika deer (Cervus nippon) during the summer season using a virtual ecological approachTakeshi Osawa; Narumasa Tsutsumida; Hayato Iijima; Kimiko Okabe
Scientific Reports,
Volume：13,
Number：1, Mar. 2023,
[Reviewed]Abstract
Prediction of the spaces used by animals is an important component of wildlife management, but requires detailed information such as animal visit and occupy in a short span of the target species. Computational simulation is often employed as an effective and economical approach. In this study, the visit and occupy of sika deer (Cervus nippon) during the plant growing season were predicted using a virtual ecological approach. A virtual ecological model was established to predict the visit and occupy of sika deer based on the indices of their food resources. The simulation results were validated against data collected from a camera trapping system. The study was conducted from May to November in 2018 in the northern Kanto region of Japan. The predictive performance of the model using the kernel normalized difference vegetation index (kNDVI) was relatively high in the earlier season, whereas that of the model using landscape structure was relatively low. The predictive performance of the model using combination of the kNDVI and landscape structure was relatively high in the later season. Unfortunately, visit and occupy of sika deer could not predict in November. The use of both models, depending on the month, achieved the best performance to predict the movements of sika deer.
Springer Science and Business Media LLC, Scientific journal
DOI：https://doi.org/10.1038/s41598-023-31269-5DOI ID：10.1038/s41598-023-31269-5,
eISSN：2045-2322 The Impact of Changes in Anthropogenic Activity Caused by COVID-19 Lockdown on Reducing Nitrogen Dioxide Levels in Thailand Using Nighttime Light Intensity Nutnaree Thongrueang; Narumasa Tsutsumida; Tomoki Nakaya
Sustainability (Switzerland),
Volume：15,
Number：5, Mar. 2023,
[Reviewed]Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China and has since become a pandemic. Thailand’s first lockdown started in the middle of March 2020, restricting anthropogenic activities and inter-provincial traffic. There are few studies on the association between nitrogen dioxide (NO2) levels and human activity, primarily because of the difficulty in identifying the changes in anthropogenic activities at a high geographical resolution. Here, we have highlighted satellite-based nighttime light (NTL) as an indicator of anthropogenic activities and investigated the relationship between NTL and reductions in NO2 levels during Thailand’s first lockdown in 2020. We applied geographically weighted regression (GWR) to analyze the regional relationship between NTL and changes in NO2 levels during the first lockdown. Sentinel-5 Precursor satellite observation indicated that the NO2 levels decreased by 10.36% compared with those of the same period in 2019. The level of NTL decreased in most urban and built-up (31.66%) categories. According to GWR results, NTL and NO2 levels represent a positive local correlation around the country’s central, western, and northern parts and negative correlations in the peripheral regions. These findings imply that NTL observations can be used to monitor changes in NO2 levels caused by urban anthropogenic activities.
Scientific journal
DOI：https://doi.org/10.3390/su15054296Scopus：https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149692744&origin=inwardScopus Citedby：https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85149692744&origin=inwardDOI ID：10.3390/su15054296,
eISSN：2071-1050,
SCOPUS ID：85149692744 Perspective: Improving the accuracy of plant phenology observations and land-cover and land-use detection by optical satellite remote-sensing in the Asian tropicsNagai Shin; Chifuyu Katsumata; Tomoaki Miura; Narumasa Tsutsumida; Tomoaki Ichie; Ayumi Kotani; Michiko Nakagawa; Kho Lip Khoon; Hideki Kobayashi; Tomo’omi Kumagai; Shunsuke Tei; Runi anak Sylvester Pungga; Taizo Yamada; Akihiro Kameda; Masayuki Yanagisawa; Kenlo Nishida Nasahara; Hiroyuki Muraoka; Kazuhito Ichii; Yuji Tokumoto
Frontiers in Forests and Global Change,
Volume：6, Feb. 2023,
[Reviewed]Recent advances in satellite-borne optical sensors led to important developments in the monitoring of tropical ecosystems in Asia, which have been strongly affected by recent anthropogenic activities and climate change. Based on our feasibility analyses conducted in Indonesia in Sumatra and Sarawak, Malaysia in Borneo, we discuss the current situation, problems, recent improvements, and future tasks regarding plant phenology observations and land-cover and land-use detection. We found that the Multispectral Instrument (MSI) on board the Sentinel-2A/2B satellites with a 10-m spatial resolution and 5-day observational intervals could be used to monitor phenology among tree species. For the Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary satellite with a 1,000-m spatial resolution and 10-min observational intervals, we found that the time-series in vegetation indices without gaps due to cloud contamination may be used to accurately detect the timing and patterns of phenology among tree species, although the spatial resolution of the sensor requires further improvement. We also found and validated that text and pictures with geolocation information published on the Internet, and historical field notes could be used for ground-truthing land cover and land use in the past and present time. The future development of both high frequency (≤ 10 min) and high spatial resolution (≤ 10 m) optical sensors aboard satellites is expected to dramatically improve our understanding of ecosystems in the tropical Asia.
Frontiers Media SA, Scientific journal
DOI：https://doi.org/10.3389/ffgc.2023.1106723DOI ID：10.3389/ffgc.2023.1106723,
eISSN：2624-893X Large, concealed islands in the urban sea: Scattered surrounding green space enhances the quality of grassland habitats in urban parks, TokyoSeiichiro Ohata; Takeshi Osawa; Nozomu Sato; Narumasa Tsutsumida
Urban Ecosystems,
Volume：26,
Number：3,
First page：641,
Last page：649, Dec. 2022,
[Reviewed]Springer Science and Business Media LLC, Scientific journal
DOI：https://doi.org/10.1007/s11252-022-01311-xDOI ID：10.1007/s11252-022-01311-x,
ISSN：1083-8155,
eISSN：1573-1642 Assessing the potential repercussions of the COVID-19 pandemic on global SDG attainmentHideyuki Doi; Takeshi Osawa; Narumasa Tsutsumida
Discover Sustainability,
Volume：3,
Number：1, Dec. 2022,
[Reviewed]AbstractThe coronavirus disease (COVID-19) pandemic has led to a worldwide lockdown, and this restriction on human movements and activities has significantly affected society and the environment. Some effects might be quantitative, but some might be qualitative, and some effects could prolong immediately and/or persistently. This study examined the consequences of global lockdown for human movement and nitrogen dioxide (NO
2) emissions using an air pollution index and dataset and satellite image analyses. We also evaluated the immediate (during lockdown) and persistent (after lockdown) effects of lockdown on achieving the SDGs. Our analysis revealed a drastic reduction in human movement and NO
2 emissions and showed that many SDGs were influenced both immediately and persistently due to the global lockdown. We observed the immediate negative impacts on four goals and positive impacts on five goals, especially those concerning economic issues and ecosystem conservation, respectively. The persistent effects of lockdown were likely to be predominantly reversed from their immediate impacts due to economic recovery. The global lockdown has influenced the global community’s ability to meet the SDGs, and our analysis provides powerful insights into the status of the internationally agreed-upon SDGs both during and after the COVID-19-induced global lockdown.
Springer Science and Business Media LLC, English, Scientific journal
DOI：https://doi.org/10.1007/s43621-021-00067-2DOI ID：10.1007/s43621-021-00067-2,
eISSN：2662-9984 Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? Alexis, Comber; Paul, Harris; Daisuke, Murakami; Narumasa,Tsutsumida; Chris, Brunsdon
15th International Conference on Spatial Information Theory (COSIT 2022),
Volume：240,
First page：13:1,
Last page：13:10, Sep. 2022,
[Reviewed]English, International conference proceedings
DOI：https://doi.org/10.4230/LIPIcs.COSIT.2022.13DOI ID：10.4230/LIPIcs.COSIT.2022.13 Large-Scale Spatial Prediction by Scalable Geographically Weighted Regression: Comparative Study Daisuke, Murakami; Narumasa, Tsutsumida; Takahiro, Yoshida; Tomoki, Nakaya
15th International Conference on Spatial Information Theory (COSIT 2022),
Volume：240,
First page：12:1,
Last page：12:5, Sep. 2022,
[Reviewed]English, International conference proceedings
DOI：https://doi.org/10.4230/LIPIcs.COSIT.2022.12DOI ID：10.4230/LIPIcs.COSIT.2022.12,
DBLP ID：conf/cosit/MurakamiTYN22 A Comparison of Geographically Weighted Principal Components Analysis Methodologies Narumasa, Tsutsumida; Daisuke, Murakami; Takahiro, Yoshida; Tomoki, Nakaya; Binbin, Lu; Paul, Harris; Alexis, Comber
15th International Conference on Spatial Information Theory (COSIT 2022),
Volume：240,
First page：21:1,
Last page：21:6, Sep. 2022,
[Reviewed],
[Lead, Corresponding]English, International conference proceedings
DOI：https://doi.org/10.4230/LIPIcs.COSIT.2022.21DOI ID：10.4230/LIPIcs.COSIT.2022.21,
DBLP ID：conf/cosit/TsutsumidaMYNLH22 Flood vulnerability assessment at the local scale using remote sensing and GIS techniques: a case study in Da Nang City, VietnamTran Thi An; Saizen Izuru; Tsutsumida Narumasa; Venkatesh Raghavan; Le Ngoc Hanh; Nguyen Van An; Nguyen Vinh Long; Ngo Thi Thuy; Truong Phuoc Minh
Journal of Water and Climate Change,
Volume：13,
Number：9,
First page：3217,
Last page：3238, Aug. 2022,
[Reviewed]Abstract
This paper has developed a cost-efficient framework for flood vulnerability assessment at a local scale using a multi-parametric approach integrated with the Open Source Geographical Information System (GIS) and Open Remote Sensing data. The study focuses on generating a set of criteria considering three dimensions of flood vulnerability: exposure, sensitivity, and adaptive capacity (AC) on an index-based approach. These indicators were decided based on a robust analysis considering the physical and socio-economic conditions of the study area. The flood exposure was generated from the geomorphological and hydrological parameters integrated with the flood water depth, the distance to river channels, and the Modified Normalized Difference Water Index. The flood sensitivity was determined by the aggregation of local income, land use, poverty index, population density, and other parameters reflecting the socio-economic condition. The AC has been evaluated based on the Normalized Difference Vegetation Index, the density of the community service facilities, and other factors related to the coping capacity to flood. Finally, the flood vulnerability at the local scale was determined based on the integration of its contributing factors using the Analytical Hierarchical Process-based aggregated model. Results indicated that a total of 20 parameters impacted the flood vulnerability of the research area. The findings also confirmed that among the indicators of flood vulnerability of Da Nang City, the flood depth, land-use condition, and drainage system are the key factors affecting the vulnerability level. The empirical assessment showed that the study area is significantly affected by flood vulnerability with more than 60% of the area having the vulnerability level from moderate to very high. In addition, this paper points out that the vulnerability research should be localized and is not always based on the administrative units. This practice can make the decision-making process and adaptation plan more appropriate locally. Especially, this study attempted to evaluate the accuracy of the flood vulnerability map for the first time by using field survey data and the statistical report on flood damage that most of the previous studies have not conducted yet. This framework provides a valuable toolkit for flood management in data-scarce regions all over the world.
IWA Publishing, Scientific journal
DOI：https://doi.org/10.2166/wcc.2022.029DOI ID：10.2166/wcc.2022.029,
ISSN：2040-2244,
eISSN：2408-9354 Monitoring of cherry flowering phenology with Google TrendsNagai Shin; Ayumi Kotani; Shunsuke Tei; Narumasa Tsutsumida
PLOS ONE,
Volume：17,
Number：7,
First page：e0271648,
Last page：e0271648, Jul. 2022,
[Reviewed]Google Trends (GT) is an online tool designed for searching for changes over time. We assessed its use for evaluating changes in the timing of cherry flowering phenology, which is of intense interest to Japanese people. We examined the relationship between time-series of relative search volume (RSV: relative change in search requests over time obtained from the GT access engine) and cherry flowering information published on websites (as ground truth) in relation to three famous ancient cherry trees. The time-series of RSV showed an annual bell-shaped seasonal variability, and the dates of the maximum RSV tended to correspond to the dates of full bloom. Our results suggest that GT allows monitoring of multiple famous cherry flowering sites where we cannot obtain long-term flowering data to evaluate the spatiotemporal variability of cherry flowering phenology.
Public Library of Science (PLoS), Scientific journal
DOI：https://doi.org/10.1371/journal.pone.0271648DOI ID：10.1371/journal.pone.0271648,
eISSN：1932-6203 Land Cover Classification from Street-Level Photos Narumasa Tsutsumida; Jing Zhao; Kenlo Nasahara; Takeo Tadono
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium,
First page：5524,
Last page：5526, Jul. 2022,
[Reviewed],
[Lead, Corresponding]Land cover classification mapping is a process to depict terrestrial surfaces by pre-defined thematic classes across space and is often implemented by a supervised classification approach with satellite and/or aero images. However, due to labor-intensive and time-consuming tasks to identify land cover class from those images manually, it is challenging to build rich-quantity and high-quality reference data sets for the mapping. To capture land covers on the ground, we developed a deep learning model to estimate land covers from street-level photos. A transfer learning from the pre-trained DenseNet was applied, and this model yields an overall accuracy of 0.91. This model contributes to the subsequent study of building a semiautomatic reference database from geo-tagged street-level photos.
IEEE, English, International conference proceedings
DOI：https://doi.org/10.1109/igarss46834.2022.9883194DOI ID：10.1109/igarss46834.2022.9883194,
ISSN：2153-6996,
Web of Science ID：WOS:000920916605151 Exploratory Spatial Data Analysis with gwpcorMapper: an Interactive Mapping Tool for Geographically Weighted Correlation and Partial CorrelationJ. E. H. Percival; N. Tsutsumida; D. Murakami; T. Yoshida; T. Nakaya
Journal of Geovisualization and Spatial Analysis,
Volume：6,
Number：1, Jun. 2022,
[Reviewed],
[Corresponding]Springer Science and Business Media LLC, English, Scientific journal
DOI：https://doi.org/10.1007/s41651-022-00111-3DOI ID：10.1007/s41651-022-00111-3,
ISSN：2509-8810,
eISSN：2509-8829,
ORCID：113826430 Evaluation of Land Surface Phenology for Autumn Leaf Color Change Based on Citizen Reports across Japan Narumasa Tsutsumida; Nagai Shin; Tomoaki Miura
Remote Sensing,
Volume：14,
Number：2017, Apr. 2022,
[Reviewed],
[Lead, Corresponding]Autumn foliage color is an important phenological characteristic associated with climate and appeals to populations as a cultural ecosystem service (CES). Land surface phenology (LSP) analyzed using time-series remotely sensed imagery can facilitate the monitoring of autumn leaf color change (ALCC); however, the monitoring of autumn foliage by LSP approaches is still challenging because of complex spatio-temporal ALCC patterns and observational uncertainty associated with remote sensing sensors. Here, we evaluated the performance of several LSP analysis approaches in estimation of LSP-based ALCCs against the ground-level autumn foliage information obtained from 758 sightseeing (high CES) sites across Japan. The ground information uniquely collected by citizens represented ALCC stages of greening, early, peak, late, and defoliation collected on a daily basis. The ALCC was estimated using a second derivative approach, in which normalized difference vegetation index (NDVI), kernel normalized difference vegetation index (kNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and green red vegetation index (GRVI) were applied based on MODerate resolution Imaging Spectroradiometer (MODIS) MOD09A1 with four (Beck, Elmore, Gu, and Zhang) double logistic smoothing methods in 2020. The results revealed inconsistency in the estimates obtained using different analytical methods; those obtained using EVI with the Beck model estimated the peak stage of the ALCC relatively well, while the estimates obtained using other indices and models had high discrepancies along with uncertainty. Our study provided insights on how the LSP approach can be improved toward mapping the CESs offered by autumn foliage.
MDPI, English, Scientific journal
DOI：https://doi.org/10.3390/rs14092017DOI ID：10.3390/rs14092017,
eISSN：2072-4292,
Web of Science ID：WOS:000795327000001 Usefulness of Social Sensing Using Text Mining of Tweets for Detection of Autumn Phenology Nagai Shin; Yasuyuki Maruya; Taku M. Saitoh; Narumasa Tsutsumida
Frontiers in Forests and Global Change,
Volume：4, Oct. 2021,
[Reviewed]Can social sensing detect the spatio-temporal variability of autumn phenology? We analyzed data published on the Twitter social media website through the text mining of non-geotagged tweets regarding a forested, mountainous region in Japan. We were able to map the spatial characteristic of tweets regarding peak leaf coloring along an altitudinal gradient and found that text mining of tweets is a useful approach to the in situ collection of autumn phenology information at multiple locations over a broad spatial scale. Potential uncertainties in this approach were examined and compared to other online research sources and methods, including Google Trends and information on widely available websites and live camera images. Finally, we suggest ways to reduce the uncertainties identified within our approach and to create better integration between text mining of tweets and other online research data sources and methods.
Frontiers Media SA, English, Scientific journal
DOI：https://doi.org/10.3389/ffgc.2021.659910DOI ID：10.3389/ffgc.2021.659910,
eISSN：2624-893X,
Web of Science ID：WOS:000710797200001 Stable geographically weighted Poisson regression for count data
Murakami D; Tsutsumida N; Yoshida T; Nakaya T; Lu B; Harris P
GIScience, 2021, [Reviewed]
English, International conference proceedings
Geographically weighted regression for compositional data: An application to the U.S. household income compositions Yoshida T; Murakami D; Seya H; Tsutsumida N; Nakaya T
GIScience, 2021,
[Reviewed]English, International conference proceedings
DOI：https://doi.org/10.25436/E2G599DOI ID：10.25436/E2G599 A Review on Geographically Weighted Methods and their Future Directions
Narumasa Tsutsumida; Takahiro Yoshida; Daisuke Murakami; Tomoki Nakaya
Theory and Applications of GIS, Volume：29, Number：1, First page：11, Last page：21, 2021, [Reviewed], [Lead, Corresponding]
地理情報システム学会 ; [1993]-, Japanese, Scientific journal
ISSN：1340-5381, CiNii Articles ID：40022818340, CiNii Books ID：AN10457305
A GIS-based Approach for Flood Vulnerability Assessment in Hoa Vang District, Danang City, Vietnam
Tran T.A; Venkatesh R; Nguyen V. L; Saizen I; Tsutsumida N
IOP Conference Series: Earth and Environmental Science, Volume：652, Number：1, First page：012003, 2021, [Reviewed]
English, International conference proceedings
Carbon Stock Estimation of Selected Watersheds in Laguna, Philippines Using InVEST
Dida J; Tiburan C; Tsutsumida N; Saizen I
Philippine Journal of Science, Volume：150, Number：2, First page：501, Last page：513, 2021, [Reviewed]
English, Scientific journal
Impact of continuous Jakarta megacity urban expansion on the formation of the Jakarta-Bandung conurbation over the rice farm regions Ernan Rustiadi; Andrea Emma Pravitasari; Yudi Setiawan; Setyardi Pratika Mulya; Didit Okta Pribadi; Narumasa Tsutsumida
Cities,
First page：103000,
Last page：103000, Oct. 2020,
[Reviewed]Elsevier BV, English, Scientific journal
DOI：https://doi.org/10.1016/j.cities.2020.103000DOI ID：10.1016/j.cities.2020.103000,
ISSN：0264-2751 A Geographically Weighted Total Composite Analysis for Soft Classification
Tsutsumida N.; Yoshida T.; Murakami D.; Nakaya T.
IGARSS 2020, Jul. 2020, [Reviewed], [Lead]
English, International conference proceedings
Mapping Fragmented Impervious Surface Areas Overlooked by Global Land-Cover Products in the Liping County, Guizhou Province, China Jing Zhao; Narumasa Tsutsumida
Remote Sensing,
Volume：12,
Number：9,
First page：1527, May 2020,
[Reviewed],
[Last, Corresponding]English, Scientific journal
DOI：https://doi.org/10.3390/rs12091527DOI ID：10.3390/rs12091527,
ORCID：73773024 Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernelsMurakami D.; Tsutsumida N.; Yoshida T.; Nakaya T.; Lu B
Annals of the American Association of Geographers,
Volume：111,
Number：2,
First page：1,
Last page：22, 2020,
[Reviewed]Informa UK Limited, English, Scientific journal
DOI：https://doi.org/10.1080/24694452.2020.1774350DOI ID：10.1080/24694452.2020.1774350,
ISSN：2469-4452,
eISSN：2469-4460 Geographically Weighted Non-negative Principal Components Analysis for Exploring Spatial Variation in Multidimensional Composite Index
Tsutsumida N; Murakami D; Yoshida T; Nakaya T; Lu B; P. Harris
Geocomputation 2019, Sep. 2019, [Reviewed], [Lead, Corresponding]
English, International conference proceedings
Scalable geographically weighted regression for big data
Murakami D; Tsutsumida N; Yoshida T; Nakaya T; Lu B
Geocomputation 2019, Sep. 2019, [Reviewed]
English, International conference proceedings
Interactive mapping for geographically weighted correlation in big census dataTsutsumida N; Percival J; Murakami D; Yoshida T; Nakaya T
Abstracts of the International Cartographic Association,
Volume：1,
First page：1,
Last page：2, Jul. 2019,
[Reviewed]<p><strong>Abstract.</strong> Census data are widely available in many countries and are useful in describing socio-economic structures in a map at an administrative unit level. Multiple variables in census data can be investigated, however, selecting a large number of variables often leads to confusion and makes it difficult to discern which should be considered. Correlation analysis is often applied to measure the degree of association of a pair of variables, while a correlation matrix is used to summarize relationships amongst multiple variables at the same time. Although both provide important metrics, the spatial configuration of the data is not taken into account. For the purpose of mapping, it is often of interest to highlight the correlative relationship between a pair of variables across space in order to deal with any spatial heterogeneity hidden in the data. In this sense, geographically weighted correlation and partial correlation analyses have been proposed to map spatial variations of correlations in a spatial data set. The geographically weighted approach uses a moving-window kernel running across geographical space and calculates a statistical model or summary statistic with distance-decayed weighted data. The critical issue of mapping such correlation relationships amongst multiple variables is the large number of resultant maps produced. If we are interested in correlations amongst 100 variables, the correlation matrix has the dimension of 100 by 100, while 4,950 correlation maps are produced when we investigate local correlation relationships. Furthermore, the degree of scale (localness) which is an important parameter to understand the local correlative relationship should be explored. To this end, the purpose of this study is to build an interactive mapping system for visualizing spatial variations of correlative relationships amongst multivariate variables in big census data. This system is built on Shiny in R and an R package for calculating the geographically weighted correlation and partial correlation coefficients across multiple variables (https://github.com/naru-T/GWpcor) is implemented behind this system for analyses. We use the national census data set for 2005 with 204 variables regarding socio-economic structures in 23 wards in Tokyo with 3,134 administrative units as a case study. The system implements geographically weighted correlation and partial correlation analyses with varying scale parameters and produces an interactive spatial surface of the correlation coefficient. We demonstrate how our interactive mapping system enables users to achieve quick visualization of correlative relationships amongst multivariate census data which can be selected and changed easily from pull-down lists (Figure 1). Such a user-friendly interactive mapping system proposed in this study will help those who need to understand the spatial relationships of the data that is being mapped. This study is supported by ROIS-DS-JOINT (006RP2018).</p>
Copernicus GmbH, English, International conference proceedings
DOI：https://doi.org/10.5194/ica-abs-1-372-2019DOI ID：10.5194/ica-abs-1-372-2019,
eISSN：2570-2106 Impact of Soil Reflectance Variation Correction on Woody Cover Estimation in Kruger National Park Using MODIS Data Sa’ad Ibrahim; Heiko Balzter; Kevin Tansey; Renaud Mathieu; Narumasa Tsutsumida
Remote Sensing,
Volume：11,
Number：8,
First page：898, Apr. 2019,
[Reviewed]English, Scientific journal
DOI：https://doi.org/10.3390/rs11080898DOI ID：10.3390/rs11080898,
ORCID：161733049 Mapping spatial accuracy of forest type classification in jaxa's high-resolution land use and land cover map N. Tsutsumida; S. Nagai; P. Rodríguez-Veiga; J. Katagi; K. Nasahara; T. Tadono
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
Volume：4,
Number：3,
First page：57,
Last page：63, Mar. 2019
Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA's land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user's and producer's accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user's accuracy measures for DBF, DNF, and ENF are better than forest producer's accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures.
Copernicus GmbH, English, International conference proceedings
DOI：https://doi.org/10.5194/isprs-annals-IV-3-W1-57-2019DOI ID：10.5194/isprs-annals-IV-3-W1-57-2019,
ISSN：2194-9050,
SCOPUS ID：85065576788 Investigating spatial error structures in continuous raster data Narumasa Tsutsumida; Pedro Rodríguez-Veiga; Paul Harris; Heiko Balzter; Alexis Comber
International Journal of Applied Earth Observation and Geoinformation,
Volume：74,
First page：259,
Last page：268, Feb. 2019,
[Reviewed]Elsevier {BV}, English, Scientific journal
DOI：https://doi.org/10.1016/j.jag.2018.09.020DOI ID：10.1016/j.jag.2018.09.020,
ORCID：48926301 Mapping spatial accuracy of the forest type classification in JAXA’s high-resolution land use and land cover map
Tsutsumida N; Nagai S; Rodríguez-Veiga P; Katagi J; Nasahara K; Tadono T
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume：74, First page：259, Last page：268, 2019, [Reviewed]
English, Scientific journal
Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data Sa’ad Ibrahim; Heiko Balzter; Kevin Tansey; Narumasa Tsutsumida; Renaud Mathieu
International Journal of Remote Sensing,
Volume：39,
Number：9,
First page：2718,
Last page：2745, May 2018,
[Reviewed]Informa {UK} Limited, English, Scientific journal
DOI：https://doi.org/10.1080/01431161.2018.1430914DOI ID：10.1080/01431161.2018.1430914,
ORCID：41045422 The Application of a Geographically Weighted Principal Component Analysis for Exploring Twenty-three Years of Goat Population Change across Mongolia Narumasa Tsutsumida; Paul Harris; Alexis Comber
ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,
Volume：107,
Number：5,
First page：1060,
Last page：1074, Sep. 2017,
[Reviewed]The dzud are extreme weather events in Mongolia of deep snow, severe cold, or other conditions that render forage unavailable or inaccessible, which in turn results in extensive livestock deaths. Mongolia is economically vulnerable to extreme events due to an increase in nonprofessional herders and the livestock population, brought about by a deregularized industry. Thus, it is hugely informative to try to understand the spatial and temporal trends of livestock population change. To this end, annual livestock census data are exploited and a geographically weighted principal component analysis (GWPCA) is applied to goat data recorded from 1990 to 2012 in 341 regions. This application of GWPCA to temporal data is novel and is able to account for both temporal and spatial patterns in goat population change. Furthermore, the GWPCA methodology is extended to simultaneously optimize the number of components to retain and the kernel bandwidth. In doing so, this study not only advances the GWPCA method but provides a useful insight into the spatiotemporal variations of the Mongolian goat population. ?? ??????????????????????????, ?????????????????, ???????????????????????, ????????????????????????????????????????, ?????????????????, ????????????, ??????????? (GWPCA) ??? 1990 ?? 2012 ??, ?????????????????? GWPCA ?????????????, ?????????????????????????, ???? GWPCA ??, ?????????????????????, ????? GWPCA ??, ??????????????????????? En Mongolia, los dzud son eventos meteorologicos extremos caracterizados por alta precipitacion y acumulacion de nieve, frio severo u otras condiciones que comprometen la disponibilidad de forraje o lo hacen inaccesible, lo cual a su vez resulta en alta mortalidad de ganados. Mongolia es vulnerable economicamente a eventos extremos debido a un incremento de ganaderos no profesionales y de la poblacion ganadera, generados por una industria no regularizada. Entonces, es altamente informativo tratar de entender las tendencias espaciales y temporales del cambio en la poblacion ganadera. Para este fin, se explotan los datos del censo anual de la ganaderia y se aplica un analisis de componentes principales (GWPCA), geograficamente ponderado, a los datos sobre cabras registrados entre 1990 y 2012, en 341 regiones. Esta aplicacion del GWPCA a los datos temporales es novedosa y tiene la capacidad de tomar en cuenta los patrones temporales y espaciales en el cambio de la poblacion de cabras. Aun mas, la metodologia GWPCA se extiende para optimizar simultaneamente el numero de componentes a retener y la anchura de banda del kernel. Al hacerlo, este estudio no solo avanza el metodo GWPCA, sino que provee una perspicacia util en las variaciones espaciotemporales de la poblacion caprina mongola.
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, English, Scientific journal
DOI：https://doi.org/10.1080/24694452.2017.1309968DOI ID：10.1080/24694452.2017.1309968,
ISSN：2469-4452,
eISSN：2469-4460,
Web of Science ID：WOS:000405403500004 The late arrival at the Geocomputation party and the need for considered approaches to spatio-temporal analyses
Comber A; Harris P; Tsutsumida N
Geocomputation 2017, Sep. 2017, [Reviewed]
English, International conference proceedings
Specifying regression models for spatio-temporal data sets
Harris P; Comber A; Tsutsumida N
Geocomputation 2017, Sep. 2017, [Reviewed]
English, International conference proceedings
Geographically weighted partial correlation for spatial analysis
Percival J; Tsutsumida N
GI_forum Journal, Volume：1, First page：36, Last page：43, Jul. 2017, [Reviewed]
English, Scientific journal
Spatial accuracy measures of soft classification in land cover
Tsutsumida N; Comber A
Peer-reviewed short paper of the GIScience 2016, First page：340, Last page：343, Sep. 2016, [Reviewed]
English, International conference proceedings
Improving land cover classification using input variables derived from a geographically weighted principal components analysis Alexis J. Comber; Paul Harris; Narumasa Tsutsumida
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,
Volume：119,
First page：347,
Last page：360, Sep. 2016,
[Reviewed]This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested. (C) 2016 Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
ELSEVIER SCIENCE BV, English, Scientific journal
DOI：https://doi.org/10.1016/j.isprsjprs.2016.06.014DOI ID：10.1016/j.isprsjprs.2016.06.014,
ISSN：0924-2716,
eISSN：1872-8235,
Web of Science ID：WOS:000384777300025 Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas Narumasa Tsutsumida; Alexis Comber; Kirsten Barrett; Izuru Saizen; Ernan Rustiadi
REMOTE SENSING,
Volume：8,
Number：2, Feb. 2016,
[Reviewed]Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001-2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km(2)/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches.
MDPI AG, English, Scientific journal
DOI：https://doi.org/10.3390/rs8020143DOI ID：10.3390/rs8020143,
ISSN：2072-4292,
Web of Science ID：WOS:000371898800041 Spatial analysis of land cover configuration for sustainable water quality management in the Silang-santa Rosa river basin, Laguna, Philippines
Saizen I; Asano S; Tiburan C.L. Jr; Tokito M; Hara Y; Tsutsumida N
37th Asian Conference on Remote Sensing 2016, 2016
English, International conference proceedings
The detection of increasing vulneravility to flash flood in peri-urban area of Ulaanbaatar, Mongolia through spatial analysis of VHR satellite imageries Saizen I; Tsutsumida N; Matsuoka M; Ishii R
Asian Conference on Remote Sensing 2015, Oct. 2015,
[Reviewed]During the last 15 years, the growth of urbanization in Ulaanbaatar, the capital city in Mongolia, has been rapidly accelerated. In a peripheral area of Ulaanbaatar, residential areas called "ger areas" have developed rapidly in an unplanned manner owing to a land reform policy enacted in 2003, which allows Mongolians to have free private land for residential purposes and considerable migration caused by a natural hazard. Mongolia is a mostly arid country where it would be widely unknown that flood was one of the relevant disaster threats. The low precipitation and high evaporation of surface water are a result of the low absorptive capacity of the soil and, accordingly, higher than normal precipitation often results in runoffs, which have the potential to become flash floods. According to the land privatization law in the land reform policy, privatizing lands near water including flood prone areas is not allowed for Mongolian citizens. But in fact, ger areas have been encroaching on flash-flood prone areas. This study clarifies the both direct and indirect factors of ger areas expansion and the actual situation of their spatial distribution over flash-flood prone areas in the peripheral area of Ulaanbaatar using VHR satellite imageries. Those results showed disordered developments of house construction on the flash-flood prone areas spatially. Considering the factors of ger areas formulation based on previous studies and the detected results in this study, it was clarified that residents' first priority in making decision about their living places was not security from hazards but accessibility to urban areas. In addition, this study pointed out the fact that social infrastructure constructions were contributing to the formation of ger areas in flash-flood prone areas. The Mongolian government should not only develop legal systems for ger areas formation but also conduct initial inquiries immediately.
English, International conference proceedings
Scopus：https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964030910&origin=inwardScopus Citedby：https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84964030910&origin=inwardSCOPUS ID：84964030910 Measures of spatio-temporal accuracy for time series land cover data Narumasa Tsutsumida; Alexis J. Comber
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,
Volume：41,
First page：46,
Last page：55, Sep. 2015,
[Reviewed]Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatiotemporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001-2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multitemporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed. (C) 2015 Elsevier B.V. All rights reserved.
ELSEVIER SCIENCE BV, English, Scientific journal
DOI：https://doi.org/10.1016/j.jag.2015.04.018DOI ID：10.1016/j.jag.2015.04.018,
ISSN：0303-2434,
Web of Science ID：WOS:000356996500005 Addressing urban expansion using feature-oriented spatial data in a peripheral area of Ulaanbaatar, Mongolia Narumasa Tsutsumida; Izuru Saizen; Masayuki Matsuoka; Reiichiro Ishii
HABITAT INTERNATIONAL,
Volume：47,
First page：196,
Last page：204, Jun. 2015,
[Reviewed]Because of the lack of time-series spatial data on urban components, urban expansion in developing countries has usually been studied using a pixel-based approach, despite the coarse spatial resolution associated with this technique. To understand the residential-scale processes involved in urban expansion, we developed feature-oriented GIS data extracted from very high spatial resolution satellite images (IKONOS for 2000 and Quickbird for 2006 and 2008). We selected a fringe area of Ulaanbaatar, the capital municipality of Mongolia, as a case study. Residential plots in this area have developed in an unplanned manner owing to the poor execution of land reform policy. This study facilitated the residential-scale delineation of the significantly expanding area occupied by private land plots in time series. It also permitted the identification of geographical factors driving the expansion. Using a logistic regression model, we found that such expansion is related to social infrastructure rather than to natural landforms. In particular, new plots of private land tended to be built near pre-existing plots and in proximity to roads and water kiosks (which provide essential drinking water for residents). These findings and the probability map predicted by the model have implications for urban planners and decision makers. (C) 2015 Elsevier Ltd. All rights reserved.
PERGAMON-ELSEVIER SCIENCE LTD, English, Scientific journal
DOI：https://doi.org/10.1016/j.habitatint.2015.01.024DOI ID：10.1016/j.habitatint.2015.01.024,
ISSN：0197-3975,
eISSN：1873-5428,
Web of Science ID：WOS:000353179200020 モンゴル国ウランバートル特別行政区の人口集中現象に関する人口移動分析
堤田成政; 西前 出
ESTRELA, Volume：254, Number：254, First page：36, Last page：41, May 2015, [Invited]
Japanese, Research society
ISSN：1343-5647, CiNii Articles ID：40020461416, CiNii Books ID：AN10464515
Measures of spatio-temporal accuracy for time series land cover data Narumasa Tsutsumida; Alexis J. Comber
International Journal of Applied Earth Observation and Geoinformation,
Volume：41,
First page：46,
Last page：55, 2015,
[Reviewed]Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatiotemporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001-2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multitemporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.
Elsevier B.V., English, Scientific journal
DOI：https://doi.org/10.1016/j.jag.2015.04.018DOI ID：10.1016/j.jag.2015.04.018,
ISSN：1872-826X,
SCOPUS ID：84943598001 Detection of Spatial Clusters of Flood- and Landslide- Prone Areas using Local Moran Index in Jabodetabek Metropolitan Area, Indonesia
Pravitasari A. E; Saizen I; Tsutsumida N; Rustiadi E
International Journal of Ecology and Environmental Sciences, 2015, [Reviewed]
English, Scientific journal
Local spatially dependent driving forces of urban expansion in an emerging Asian megacity: The case of Greater Jakarta (Jabodetabek) Andrea Emma Pravitasari; Izuru Saizen; Narumasa Tsutsumida; Ernan Rustiadi; Didit Okta Pribadi
Journal of Sustainable Development,
Volume：8,
Number：1,
First page：108,
Last page：119, 2015,
[Reviewed]Urban expansion and urbanization have been continuing to grow rapidly, especially in Asian megacities. Greater Jakarta (Jabodetabek) has emerged as the world's second largest urban area, with a population of 28 million in 2010, where urban expansion has a significant impact on the local as well as the global environment. Efforts to control urban expansion must start from a clear understanding of its various driving forces at a local, regional, and global level. Studies of the interdependencies between these driving forces in the local spatial relationships in emerging Asian megacities remain limited. This study explores the driving forces of urban expansion in Jabodetabek by considering local spatial dependency and analyzes the spatial characteristics of this urbanized area as well as identifies spatial variations in the relationship between urban expansion and its driving forces by using Geographically Weighted Regression. The presented findings show that the driving forces affecting urban expansion in the Jabodetabek region vary spatially. Owing to the influence of the global and regional economies on Jabodetabek, we find that the demographic, infrastructural, and natural elements driving forces significantly affect urban expansion in this region according to location. Outside the core of this megacity, urban expansion in most areas is significantly affected by local demographic as well as infrastructural driving forces. Jakarta city, as the core of the Jabodetabek megacity, is becoming independent of these local driving forces, however, since it is now more characterized as a global city and thus tending to have more linkages with the world market.
Canadian Center of Science and Education, English, Scientific journal
DOI：https://doi.org/10.5539/jsd.v8n1p108DOI ID：10.5539/jsd.v8n1p108,
ISSN：1913-9063,
SCOPUS ID：84924434517 The Impact of Agricultural Expansion on Forest Cover in Ratanakiri Province, Cambodia Sanara Hor; Izuru Saizen; Narumasa Tsutsumida; Tsugihiro Watanabe; Shintaro Kobayashi
Journal of Agricultural Science,
Volume：6,
Number：9,
First page：46,
Last page：59, Aug. 2014,
[Reviewed]Canadian Center of Science and Education, English, Scientific journal
DOI：https://doi.org/10.5539/jas.v6n9p46DOI ID：10.5539/jas.v6n9p46,
ORCID：32256655 Internal Regional Migration Analysis and Modeling of Population Concentration for Ulaanbaatar, Mongolia Tsutsumida N; Saizen I
Papers on Environmental Information Science,
Volume：28,
First page：25,
Last page：30, 2014,
[Reviewed]Rapid urbanization of Ulaanbaatar (UB) has caused serious urban issues such as the expansion of its urban extents, owing to the increased migration of Mongolians from rural areas. This study analyzes the population concentration of UB from 2005-2011 using the migration preference index (MPI) and multiple regression models. The MPI results show constant and excessive outflows from rural to UB. The multiple regression models indicate that these flows are related to high wage, small numbers of unemployed people, and proximity to UB. Large livestock losses were also an important factor during the period in which the huge natural hazard, called the dzud, occurred.
Center for Environmental Information Science, English, Scientific journal
DOI：https://doi.org/10.11492/ceispapers.ceis28.0_25DOI ID：10.11492/ceispapers.ceis28.0_25,
ISSN：0389-6633,
CiNii Articles ID：130005162741 ＮＤＶＩを用いたウランバートルにおける植生変動の空間解析
堤田成政; 西前出; 小林愼太郎
First page：369, Last page：374, Nov. 2012, [Reviewed]
Japanese
J-Global ID：201302234728894380, CiNii Articles ID：40019554804
Determination of the Factors of Spatial Autocorrelation of Social Capital:: A Questionnaire Survey on every Household of 445 Rural Community Settlements in Northern Kyoto Prefecture FUKUSHIMA Shintaro; TSUTSUMIDA Narumasa; SAIZEN Izuru; KOBAYASHI Shintaro
Papers on Environmental Information Science,
Volume：26,
Number：26,
First page：171,
Last page：176, 2012,
[Reviewed]This research aimed 1) to determine the ambiguity of the borders of community trust (bonding type of trust) and generalized trust (bridging type of trust) among neighboring areas, and 2) to identify the factors of the ambiguity of the borders of those two different types of trust. The datasets were collected through a household-level questionnaire survey conducted in all agricultural areas of three cities in northern Kyoto prefecture in 2006. As a result, 1) the ambiguity of the borders of community trust was found, whereas no ambiguity of the borders of generalized trust was found. 2) Household density, residence year, and family number were correlated to the ambiguity of the borders of community trust. 3) The ambiguity of the borders of community trust and generalized trust were positively interrelated to each other.
Center for Environmental Information Science, Japanese
DOI：https://doi.org/10.11492/ceispapers.ceis26.0_171DOI ID：10.11492/ceispapers.ceis26.0_171,
ISSN：0389-6633,
CiNii Articles ID：40019551192
地理的加重法による空間内挿
堤田 成政
Volume：34, First page：14, Last page：19, 2022, [Invited], [Lead]
Japanese, Introduction commerce magazine
再現可能性を重視したオンラインでのジオコンピュテーション教育
堤田成政
Volume：29, Number：2, First page：59, Last page：64, 2022, [Lead, Last, Corresponding]
Japanese, Introduction scientific journal
疎なカウントデータのための地理的加重ポアソン回帰の安定化・高速化
村上大輔; 堤田 成政; 吉田 崇紘; 中谷 友樹
2021
Japanese, Summary national conference
カーネルの重みをスパース推定する新たな地理加重回帰の提案
堤田 成政; 村上大輔; 吉田 崇紘; 中谷 友樹
2021, [Lead]
Japanese, Summary national conference
Impact of COVID-19 Outbreak on Air Quality in Thailand
Thongrueang N; Nakaya T; Tsutsumida N
2021
English, Summary national conference
定数和制約と誤差相関を考慮した組成データのための地理的加重回帰
吉田 崇紘; 村上大輔; 瀬谷創; 堤田 成政; 中谷 友樹
2021
Japanese, Summary national conference
Geographically weighted regression for compositional data: An application to land use analysis
Yoshida T.; Murakami D.; Tsutsumida N.; Nakaya T.; Tsutsumi M.
The XIV World Conference of the Spatial Econometrics Association 2020, Nov. 2020, [Reviewed]
English, Summary international conference
分類結果比較法による土地被覆・土地利用変化抽出の課題
堤田成政
Volume：39, Number：3, First page：286, Last page：289, 2020, [Invited], [Lead, Last, Corresponding]
Japanese, Introduction scientific journal
組成データのための地理的加重回帰モデル
吉田崇紘; 村上大輔; 瀬谷創; 堤田成政; 中谷友樹; 堤盛人
Volume：28, 2020, [Invited]
Japanese, Summary national conference
Past and Future Changes in Urban Areas and Paddy Fields in Bandung Metropolitan Area using Landsat Time Series
Stanny A.Y; Saizen I; Tsutsumida N; Barus B; Pravitasari A.E
The 3rd International Conference of AGLE – IGU 2019, Oct. 2019
English, Summary international conference
Spatial analysis of land cover fraction and land surface temperature in Jakarta, Indonesia
Utami N.P; Tsutsumida N
the International Association of Geo-informatics (IAG'i), Volume：28, Oct. 2019
English, Summary international conference
Exploring spatial scale by interactive map for geographically weighted correlation
Percival J; Tsutsumida N; Murakami D; Yoshida T; Nakaya T
the International Association of Geo-informatics (IAG'i), Volume：28, Oct. 2019
English, Summary international conference
Changes in built-up areas and population in a typical mountainous region—a case study of Liping county, southwest China
Zhao J; Tsutsumida N
the International Association of Geo-informatics (IAG'i), Volume：28, Oct. 2019
English, Summary international conference
Machine learning of spatiotemporal processes: investigating livestock changes in Mongolia with LSTM recurrent neural networks
Comber A; Heppenstall A; Takahashi T; Tsutsumida N; Harris P
Accepted Short Papers and Posters from the 22nd AGILE Conference on Geo-information Science, 2019, [Reviewed]
English, Summary international conference
Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels
Murakami D; Tsutsumida N; Yoshida T; Nakaya T; Lu B
arXiv preprint, 2019
English, Technical report
連続値土地被覆データの空間誤差
堤田 成政
First page：23, Last page：24, Nov. 2018
Japanese, Summary national conference
空間統計データによる総合指標作成のための地理的加重非負値主成分分析の検討
堤田成政; 村上大輔; 吉田崇紘; 中谷友樹
Volume：27, Oct. 2018
Japanese, Summary national conference
大規模データのための地理的加重回帰と住宅地価分析への応用
村上大輔; 堤田成政; 吉田崇紘; 中谷友樹
Volume：27, Oct. 2018
Japanese, Summary national conference
Spatially varying coefficient modeling for large data: Application for land price analysis in Tokyo
Murakami D; Nakaya T; Tsutsumida N; Yoshida T
International Conference on Spatial Analysis and Modeling, Sep. 2018, [Reviewed]
English, Summary international conference
Spatially explicit exploratory factor analysis on urban statistical data – Geographically weighted approach –
Tsutsumida N; Murakami D; Yoshida T; Pravitasari A.E; Nakaya T
International Conference on Spatial Analysis and Modeling, Sep. 2018, [Reviewed]
English, Summary international conference
格子データにおける精度評価指標の空間拡張
堤田 成政
Mar. 2018
Japanese, Report research institution
Geographically Weighted Principal Component Analysis for Spatio-temporal Statistical Dataset
Tsutsumida N; Harris P; Comber A
Abstract for IASC-ARS/NZSA 2017, Dec. 2017, [Reviewed]
English, Introduction international proceedings
Exploring spatially explicit relation amongst land surface phenology and geographic data at a global scale by geographically weighted approach
Tsutsumida N; Percival J
Abstract for Statistics in Ecology and Environmental Monitoring 2017, Dec. 2017, [Reviewed]
English, Summary international conference
Spatial estimation of accuracy for JAXA land-use land-cover map
堤田成政; 奈佐原顕郎; 田殿武雄
日本リモートセンシング学会学術講演会論文集(CD-ROM), Volume：63rd, First page：37, Last page：39, Nov. 2017
Japanese, Summary national conference
J-Global ID：201802265684825950
The importance of habitat connectivity and structure on fish biodiversity and assemblages within Samoan mangroves
Joseph Percival; Kiran Liversage; Narumasa Tsutsumida; Rebecca Stirnemann; Schannel van Dijken; Edouard Lavergne
The 10th Indo-Pacific fish conference, Oct. 2017, [Reviewed]
English, Introduction international proceedings
Spatial considerations of accuracy for satellite-based biomass mapping
Tsutsumida N; Rodríguez-Veiga P; Harris P; Comber A; Percival J
RSPsoc, Sep. 2017, [Reviewed]
English, Summary international conference
Mapping of mangrove ecosystems using a UAV with a simple RGB sensor
Percival J; Tsutsumida N
RSPsoc, Sep. 2017, [Reviewed]
English, Summary international conference
Spatial correlation for phenological responses to climate
Percival J; Tsutsumida N
JpGU-AGU Joint Meeting 2017, May 2017, [Reviewed]
English, Summary international conference
Long-term monitoring of land surface phenological changes
Tsutsumida N
JpGU-AGU Joint Meeting 2017, May 2017, [Reviewed]
English, Summary international conference
Impact of spatial scale for phenological indices derived from remotely sensed data
Tsutsumida N; Kaduk J
JpGU-AGU Joint Meeting 2017, May 2017, [Reviewed]
English, Summary international conference
SAfforestation monitoring using long-term remotely sensed data in Chinese semi-arid area
Hara Y; Tsutsumida N; Saizen I
JpGU-AGU Joint Meeting 2017, May 2017, [Reviewed]
English, Summary international conference
Impacts of changes in phenology on land-atmosphere interactions in temperate and boreal regions
Kaduk J.D; Barrett K; De Mendoza; Rosales A. H; Tsutsumida N
JpGU-AGU Joint Meeting 2017, May 2017, [Reviewed]
English, Summary international conference
Long-term changes in global land cover in 1982-2015 Tsutsumida Narumasa
Proceedings of the General Meeting of the Association of Japanese Geographers,
Volume：2017,
First page：100335,
Last page：100335, Mar. 2017
気候変動モデルや生物多様性の推定などに不可欠な土地被覆情報であるが，長期変動が起こりうるにもかかわらず，時間軸に着目した高精度の時空間データの構築は容易ではない。近年では単一クラス型の土地被覆図（森林図、水域図など）の開発が盛んである一方で、複合クラス型の土地被覆図は分類精度がおよそ60～80% (Overall accuracy)と高くなく，多くの誤差が含まれている場合が多い。そこで本研究では， マクロな植生変動や生態系サービス評価など様々な研究領域で応用されるGIMMS NDVI3gデータ（バージョン１）をもちい，過去34年間（1982−2015年）の全球土地被覆図を１年毎に作成し，その変遷を明らかにすることを目的とする。
The Association of Japanese Geographers, Japanese, Summary national conference
DOI：https://doi.org/10.14866/ajg.2017s.0_100335DOI ID：10.14866/ajg.2017s.0_100335,
CiNii Articles ID：130005635795 Monitoring Urban Land Use Change in the Southwestern Portion of Laguna Lake, Philippines
Tiburan C; Saizen I; Tsutsumida N
International Symposium on Global Environmental Studies Education and Research in Asia, Nov. 2016
English, Summary international conference
月平均気温・降水量の空間相関分布とその季節性
堤田 成政; Percival Joseph
First page：6, Nov. 2016
Japanese, Summary national conference
Fractional Land Cover Dynamics during 1983-2012 using GIMMS NDVI3g
Tsutsumida N; Balzter H; Barrett K; Harris P; ComberA; Padilla-Parellada M; Rodriguez-Veiga P; Tansey K; Tate N; Saizen I; Shinjo H
RSPSoc Annual Conference 2016, Sep. 2016, [Reviewed]
English, Summary international conference
Spatial accuracy assessment of soft classification land cover map
Tsutsumida N
Jpgu, May 2016, [Reviewed]
English, Summary international conference
The impacts of imperfect reference data on land cover classification accuracy
Tsutsumida N; Comber A
RSPSoc, NCEO and CEOI-ST Joint Annual Conference 2015, Sep. 2015, [Reviewed]
English, Summary international conference
A study on the sustainability of a ethnic minoritiy's livelihood from the viewpoints of the carrying capacity of land -a case of a hamlet in Nam Dong district, Vietnam-
Saizen I; Tokito M; Morikawa S; Imura M; Asano S; Tsutsumida N; Tran TD; Le VA
The 3rd International Symposium on Formulation of the cooperation hub for Global Environmental Studies in Indochina Region, Jul. 2015
English, Summary international conference
The use of geographically weighted PCA to classify land cover from multispectral image data.
Harris P; Tsutsumida N; Comber A.J
The 36th International Symposium on Remote Sensing of Environment, May 2015, [Reviewed]
English, Summary international conference
Harmonic analysis of desertification processes measured by vegetation greenness data from GIMMS3g NDVI
Tsutsumida N; Balzter H
JpGU, May 2015, [Reviewed]
English, Summary international conference
Internal Regional Migration Analysis and Modeling of Population Concentration for Ulaanbaatar N. Tsutsumida; I. Saizen
28th conference of Environmental Information Science, Dec. 2014
English, Summary national conference
DOI：https://doi.org/10.11492/ceispapers.ceis28.0_25DOI ID：10.11492/ceispapers.ceis28.0_25 A time series analysis of land cover change: random forest models of annual changes in urban land cover
N. Tsutsumida; A. J. Comber; K. Barrett; I. Saizen
GIScience 2014, First page：446, Last page：449, Sep. 2014, [Reviewed]
English, Summary international conference
Urban development vs environmental degradation in JABODETABEK region, Indonesia: Investigating Spatial Distribution Pattern of Floods and Landslides using Local Moran Index
A.E. Pravitasari; I. Saizen; N. Tsutsumida; E. Rustiadi
10th World Congress of the RSAI, virtual conference, May 2014, [Reviewed]
English, Summary international conference
Land Use Changes in Northeastern Cambodia: Local Practice and Policy Response
H. Sanara; K. Mizuno; S. Kobayashi; T. Watanabe; I. Saizen; N. Tsutsumida
Japanese society of regional and agricultural development 2013 Autumn Conference, Nov. 2013
English, Summary national conference
Exploring the Driving Forces of Land-Cover Change Behind Urban Expansion in Jabodetabek, Indonesia.
Pravitasari A.E; Saizen I; Tsutsumida N; Watanabe T; Rustiadi E
4th Jabodetabek Study Forum Seminar-Resilient Megacities: Idea, Reality, and Movement-, Oct. 2013, [Reviewed]
English
Spatial Detection of Vegetation Biomass/Cover Changes using NDVI Time Series in Ulaanbaatar, Mongolia
Tsutsumida N; Saizen I
4th Jabodetabek Study Forum Seminar- Resilient Megacities: Idea, Reality, and Movement-,, Oct. 2013, [Reviewed]
English, Summary international conference
Time-spatial analysis of in-migration to Ulaanbaatar, Mongolia
N. Tsutsumida; I. Saizen; A. Amarbal; A. Otomo
The 15th Korea & Japan International Symposium on GIS, Oct. 2013
English, Summary international conference
Land Cover Assessment in Mongolia by Principal Component Analysis of Phonological Parameters using MODIS Imagery.
Tsutsumida N; Saizen I
American Society of Photogrammetry and Remote Sensing (ASPRS) 2013 Aunnual Conference, Mar. 2013, [Reviewed]
English, Summary international conference
モンゴル国における植生変動の空間分析
堤田成政; 西前 出; 小林愼太郎
Nov. 2012
Japanese, Summary national conference
Land use planning and remote sensing analysis.
Saizen I; Tsutsumida N
Japan Special Session: Bilateral Collaboration on Disaster Risk Reduction, Oct. 2012, [Invited]
English, Summary international conference
GIS-based Analysis of Private Lands Expansion using High-resolution Satellite Imagery in Ulaanbaatar, Mongolia
Tsutsumida N; Saizen I; Matsuoka M; Ishii R
International Conference on Future Environment and Energy (ICFEE), Mar. 2012, [Reviewed]
English, Summary international conference
高解像度衛星画像を用いたウランバートルのスプロール現象の空間的把握と地理的立地特性
堤田成政; 西前 出; 小林愼太郎
Nov. 2011
Japanese, Summary national conference
Future collaborative studies of urban sprawl and linkage to rural areas in Channai
Saizen I; Tsutsumida N
A National Level Training on Application of Geoinformatics for the Impact of Climate Change on Natural Resource Management, Aug. 2011, [Reviewed]
English
Adjustment of Satellite LiDAR Footprint Location Using Genetic Algorithm
Tsutsumida N.
ISRSE40, Mar. 2025
Poster presentation
Advancing Street-Level Image Management by landlensdb
Advancing Street-Level Image Managemen; by landlensdb
ISRSE40, Mar. 2025
Oral presentation
Reduction of Snow Contamination in Himawari-8/-9 AHI NDVI for Imoroved Phenology Monitoring
Miura T.; Yamamoto Y.; Shin N.; Tsutsumida N.; Ichii K.
Feb. 2025
Oral presentation
衛星Lidarフットプリントの位置補正手法の開発
堤田成政
Feb. 2025
Poster presentation
Spatial Analysis of Errors in Fractional Land Cover Classification
Tsutsumida N.; Yoshida T.; Murakami D.; Nakaya T.; Comber A.
AGU Annual Meeting 2024, Dec. 2024
Poster presentation
Utilizing Socially Sensed Images for Monitoring Local Landscapes
Tsutsumida N.
AGU Annual Meeting 2024, Dec. 2024
Poster presentation
Enhanced Flood Mapping Using Combined SAR and Multi-Spectral Sensor Data with a Transformer-Based Dual Encoders Model
Tanaka T.; Tsutsumida N.
AGU Annual Meeting 2024, Dec. 2024
Oral presentation
SAR・マルチスペクトル衛星観測データの融合モデルによる洪水浸水域推定手法の提案
田中智大; 堤田成政
Nov. 2024
Oral presentation
日本におけるウミネコの生息地分布の将来予測
張 園苡; 堤田 成政
Nov. 2024
Oral presentation
埼玉県さいたま市における建物ごとのCO2排出量の推定
村上 拓; 堤田 成政; 吉田 崇紘
Nov. 2024
Oral presentation
人工衛星観測データをもちいた洪水浸水域推定,
堤田成政
Nov. 2024, [Invited]
Public discourse
車載カメラからランドスケープの変化を検出する
堤田成政
Oct. 2024, [Invited]
Public discourse
空間予測のためのモラン固有ベクトルに基づく空間組成回帰モデル
吉田崇紘; 村上大輔; 堤田成政
Oct. 2024
Oral presentation
高速で柔軟な非ガウス時空間可変パラメータモデルとRへの実装
村上大輔; 吉田崇紘; 堤田成政; 中谷友樹
Oct. 2024
Oral presentation
土地被覆ソフト分類における空間誤差評価手法の提案
堤田成政; 吉田崇紘; 村上大輔; 中谷友樹
Oct. 2024
Oral presentation
Mapping of Forest Structural Patterns by GEDI, Sentinel-1 and 2
Tsutsumida N.; Kato A.; Osawa T.; Doi H.
ForestSAT2024, Sep. 2024
Oral presentation
マルチリモートセンシングによる土地被覆研究
堤田成政
Jul. 2024, [Invited]
Public discourse
A machine learning approach for reliable near-real-time prediction of solar irradiance from geostationary satellite imagery
Sultana Nima N.; Tsutsumida N.
JpGU 2024, May 2024
Oral presentation
A Self-Supervised Learning framework for Rapid Estimation of Flood Inundation Area Using Multi-Source Remote Sensing Data
Tanaka T.; Tsutsumida N.
JpGU 2024, May 2024
Poster presentation
Street-level imageを活用した地域環境モニタリング
堤田成政
May 2024, [Invited]
Public discourse
ドローンレーザーを用いた森林内空隙解析
加藤顕; 鈴木智; 早川裕弌; 中田 敏是; 堤田成政; 笠井美青
Mar. 2024
Mar. 2024 - Mar. 2024, Japanese, Oral presentation
ソーシャルセンシングによる桜開花フェノロジー
堤田成政
Mar. 2024
Mar. 2024 - Mar. 2024, Japanese, Oral presentation
Utilizing geostationary satellite data for ultra-short-term forecasting of solar irradiance by a multivariate Long-Short-Term Memory Recurrent Neural Network
Nifat Sultana; Narumasa Tsutsumida
The 26th CEReS Environmental Remote Sensing Symposium, Feb. 2024
Feb. 2024 - Feb. 2024, English, Poster presentation
Snow detection in Himawari-8 Advanced Himawari Imager NDVI for improved autumn phenology monitoring
Miura T; Yamamoto Y; Nagai S; Tsutsumda N; Ichii K
AGU Annual Meeting 2023, Dec. 2023
English, Poster presentation
Enhancing Land Cover Classification Mapping with Dynamic World and Decision Fusion
Tsutsumida N; Nasahara K; Tadono T
AGU Annual Meeting 2023, Dec. 2023
English, Poster presentation
Sub-model aggregationによる地理的加重回帰の安定化・高速化
村上大輔 , 堤田 成政, 吉田 崇紘, 中谷 友樹
Oct. 2023
Oral presentation
総合指標作成のためのSpatially Hierarchical Benefit of the Doubt (SH-BoD)の提案
堤田成政; 村上大輔; 吉田崇紘; 中谷友樹
Oct. 2023
Oral presentation
大学・大学院でのジオデータサイエンス教育の実践
堤田成政
Oct. 2023, [Invited]
Nominated symposium
パノプティックシーングラフによる土地被覆オブジェクト間の関係性抽出
渋谷南帆; 堤田成政
Oct. 2023
Japanese, Oral presentation
1kmメッシュ別滞在人口データを用いた時系列パターン分類手法の開発
黄晨安; 堤田 成政
Oct. 2023
Oral presentation
Issues in land cover classification from remotely sensed imagery
Narumasa Tsutsumida
Urban design and modeling for sustainable and smart urban communities , Tokyo: 2023 Summer International Smart City Workshop, school of City and Regional Planning and School of Architecture, College of Design, Georgia Institute of Technology Center for Spatial Information Science and Department of Urban Engineering, the University of Tokyo, Jun. 2023, [Invited]
Public discourse
1 kmメッシュ別滞在人口データを用いたコロナ禍における都市人口の時空間分析
黄 晨安; 堤田 成政
May 2023
Poster presentation
Detecting Snow Contaminations in Advanced Himawari Imager NDVI Time Series Data for Improved Autumn Phenology Characterization,
Tomoaki Miura; Nagai Shin; Yuhei Yamamoto; Narumasa Tsutsumida; Kazuhito Ichii
JpGU2023, May 2023
Oral presentation
探索的空間データ解析のためのインタラクティブ空間相関マッピング
堤田成政; Joseph Percival; 吉田崇紘; 村上大輔; 中谷友樹
Nov. 2022
Public symposium
Uncertain Vegetation Trends by Index Selections
Tsutsumida N; Mochizuki R
2022 Geo for Good Summit, Oct. 2022
Poster presentation
Aboveground Woody Biomass Dynamics in the African Continent
Rodriguez-Veiga, Pedro; Carreiras, Joao; Quegan, Shaun; Heiskanen, Janne; Pellikka, Petri; Adhikari, Hari; Araza, Arnan; Herold, Martin; Smallman, Thomas Luke; Williams, Mathew; Ryan, Casey; Brade, Thom; Nwobi, Chukwuebuka; Tsutusmida, Narumasa; Cartus, Oliver; Santoro, Maurizio; Balzter, Heiko
UK EO 2022, Sep. 2022
Sep. 2022 - Sep. 2022, English, Oral presentation
Analysing Aboveground Woody Biomass Dynamics in Africa
Rodriguez-Veiga, Pedro; Carreiras, Joao; Quegan, Shaun; Heiskanen, Janne; Pellikka, Petri; Adhikari, Hari; Araza, Arnan; Herold, Martin; Smallman, Thomas Luke; Williams, Mathew; Ryan, Casey; Brade, Thom; Nwobi, Chukwuebuka; Tsutusmida, Narumasa; Cartus, Oliver; Santoro, Maurizio; Balzter, Heiko
ForestSAT 2022, Sep. 2022
Aug. 2022 - Sep. 2022, English, Oral presentation
A Decade of Aboveground Woody Biomass Dynamics in the African Continent
Pedro Rodríguez-Veiga; Joao Carreiras; Shaun Quegan; Janne Heiskanen; Petri Pellikka; Hari Adhikari; Arnan Araza; Thomas Luke Smallman; Mathew Williams; Ebuka Nwobi; Narumasa Tsutsumida; Heiko Balzter
Living Planet Symposium 2022, May 2022
May 2022 - May 2022, English, Oral presentation
⾞載カメラ画像を⽤いた桜開花マッピング
舟田宗弥; 堤田 成政
Mar. 2022
Japanese, Poster presentation
Estimating Land Cover from Geo-tagged Street-level Photos
Tsutsumida N; Zhao J; Nasahara K; Tadono T
Dec. 2021
English, Poster presentation
土地被覆分類データの空間精度・空間誤差
堤田成政
Oct. 2021, [Invited]
Japanese, Public discourse
Himawari-8 AHI NDVI Temporal Signature Variability of Broadleaf Deciduous Forests Along Temperature, Elevation, and Latitudinal Gradients in Northern Japan
Miura T; Nagai S; Tsutsumida N; Yamamoto Y
May 2021
English, Oral presentation
地理的加重モデルの開発と空間データ解析
堤田成政
Feb. 2021, [Invited]
Japanese, Nominated symposium
1982 年以降の土地被覆変動解析
堤田成政
Feb. 2021
Japanese, Poster presentation
Geographically weighted regression for compositional data,
Yoshida T; Murakami D; Seya H; Tsutsumida N; Nakaya T
Jan. 2021, [Invited]
Public discourse
Are Mapillary data useful to identify land covers?
Tsutsumida N.
Mar. 2020
Mar. 2020 - Mar. 2020, Japanese, Poster presentation
土地被覆比率分類データにおける空間誤差評価
堤田成政
Feb. 2020
Feb. 2020 - Feb. 2020, Japanese, Poster presentation
Spatial heterogeneity of errors in land cover data
Tsutsumida N.
Model spatial heterogeneity in environmental and ecological processes, RIMS TBMA XVI, Jan. 2020, [Invited]
Public discourse
Exploring spatial scale by interactive map for geographically weighted correlation
Percival J; Tsutsumida N; Murakami D; Yoshida T; Nakaya T
the International Association of Geo-informatics (IAG'i), Oct. 2019, [International conference]
English, Oral presentation
Spatial analysis of land cover fraction and land surface temperature in Jakarta, Indonesia
Utami N.P; Tsutsumida N
the International Association of Geo-informatics (IAG'i), Oct. 2019, [International conference]
English, Poster presentation
Changes in built-up areas and population in a typical mountainous region—a case study of Liping county, southwest China
Zhao J; Tsutsumida N
the International Association of Geo-informatics (IAG'i), Oct. 2019, [International conference]
English, Poster presentation
Interactive mapping for geographically weighted correlation in big census data
Tsutsumida N; Percival J; Murakami D; Yoshida T; Nakaya T
FOSS4G 2019 KANSAI.KOBE, Oct. 2019, [Domestic conference]
English, Poster presentation
Development of a geocomputation tool using publicly open data
Narumasa Tsutsumida
“Kyoto & G0v” Meetup about Open Governance, Aug. 2019, [Invited], [International conference]
English, Public discourse
Interactive mapping for geographically weighted correlation in big census data
Tsutsumida N; Percival J; Murakami D; Yoshida T; Nakaya T
29th International Cartographic Association, May 2019, [International conference]
English, Poster presentation
Landsat-based Phenological Classification Mapping in Japan
Tsutsumida N; Nagai S; Rodriguez-Veiga P; Miura T
JPGU 2019, May 2019, [International conference]
English, Oral presentation
Detecting and quantifying forest dynamics using SAR time-series data in Indonesia
Rodriguez-Veiga P; Tsutsumida N
JPGU 2019, May 2019, [International conference]
English, Poster presentation
日本国内を対象とした土地被覆データの不確実性の検証
堤田 成政; 大澤 剛士; 土居 秀幸
Mar. 2019, [Domestic conference]
Japanese, Poster presentation
リモートセンシングによる広域観測データの空間誤差推定に関する研究
堤田 成政; Rodríguez-Veiga P
Feb. 2019, [Domestic conference]
Japanese, Poster presentation
NDVI-based threshold Approach to Estimate Potential Vegetation Cover Using Integrated Google earth engine (GEE) and GIS
Putri U.N; Tsutsumida N; Saizen I
IPB and KU International Symposium on Education and Research in Global Environmental Studies In Asia, Nov. 2018, [International conference]
English, Poster presentation
オープンデータで活躍する地理情報科学
堤田 成政
Jun. 2018, [Invited]
Japanese, Public discourse
Global Phenological Classification Mapping
Narumasa Tsutsumida
Google Earth Engine mini summit 2018 in Tokyo, Mar. 2018, [International conference]
English, Invited oral presentation
大規模リモートセンシングデータをもちいた土地被覆分類
堤田 成政
Mar. 2018, [Domestic conference]
Japanese, Oral presentation
Land Surface Phenologyと気候変動の空間相関
堤田 成政
Mar. 2018, [Domestic conference]
Japanese, Oral presentation
時系列衛星画像データ処理ツールの紹介
堤田 成政
Oct. 2017, [Domestic conference]
Japanese, Public discourse
全球レベルの時系列衛星画像データを処理してOSSに渡すまで
堤田成政
FOSS4G HOKKAIDO, Jul. 2017, [Domestic conference]
Japanese, Public discourse
Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas in Jakarta Metropolitan Areas
Sub-Pixel Classification of; MODIS EVI for Annual Mappings of; Impervious Surface Areas in Jakarta Metropolitan Areas
Padjadjaran University, Mar. 2017, [Invited]
English, Public discourse
Introduction of this meeting and Spatio-temporal analysis for land cover and climate data
Tsutsumida N
research meeting of Applied Geographic Information Science, Feb. 2017
English, Oral presentation
Land cover classification and accuracy assessment
堤田 成政
Feb. 2017, [Invited]
Public discourse
GRASS GIS7とRによる時系列ラスター処理
堤田 成政
Sep. 2016, [Domestic conference]
Japanese, Public discourse
Volunteer-based Mapping -Active learning tools for geographical educations-
Tsutsumida N
Teacher’s Forum in UK-Japan Science Workshop, Jul. 2016
English, Others
Introduction to spatial analysis in R
N.Tsutsumida
Special workshop on spatial analysis using R software, University of Philippines, Los Banos, Philippines, Mar. 2015, [Invited]
English, Public discourse
Cluster analysis using Open GeoDa
N.Tsutsumida
Special lecture at National Statistical Office, NSO office, Ulaanbaatar, Mongolia, Jun. 2014, [Invited]
Public discourse
