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Speaker: Hoang Thanh Tung (Univ. Tsukuba)

*Title: Analysis of land cover change in Northern Vietnam using high resolution remote sensing data

*Abstract: Land cover data play an important role in environmental modelling and other Earth sciences applications. This study attempts to produce 15-meter resolution and high accuracy land cover maps in over Northern Vietnam in 2015 and 2007. The change between the two years was then analysed to see the land cover dynamics in the recent decade. Variety of satellite image data including ASTER, Landsat, PALSAR mosaic were employed to produce the land cover maps, using a probabilistic approach of Kernel Density Estimation classifier. Results showed that the overall accuracy of the land cover maps is 89% and 81% in 2015, 2007 respectively. Forest area showed increasing trend whereas barren area experienced decreasing trend between the two years in Northern Vietnam.

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*¥¿¥¤¥È¥ë¡§A statistical analysis of urban Big-Data toward real-time heatwave risk management

*³µÍס§Urban heat island is increasingly severe in the global scale. Toward a district level and real-time heatwave risk management, this study attempts to estimate ground temperature, which can be considered as a good measure of heatwave risk. In this estimation, global spatial variations in ground temperatures in Tokyo metropolitan area are modeled considering monitored air temperature data and remotely sensed ground temperatures, and other global attributes (e.g., elevation, latitude). Furthermore, to capture the local variations in ground temperatures, high-resolution ground temperatures, which are observed from an airborne, and other local attributes (e.g., location of buildings and green areas) are also associated with this model. The estimation result reveals that the model is useful to estimate district level ground temperatures.

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*Abstract: Global land cover data is fundamental for environmental change studies,land resource management, sustainable development, etc. Since late 1990s, several global land cover maps were produced by different scientific groups using different methods and satellite data. However, the accuracy and the classification scheme is not consistent, some specific land cover category accuracy was hard to meet the need of end users. In order to produce a more accurate global land cover map, the input features should be considered. In this presentation, we want to discuss about how to make high quality global validation data, especially from Degree Confluence Project (DCP). With the help of good training and validation data set globally, we can make more accurate map for further analysis of our planet.

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*¥¿¥¤¥È¥ë:Using approximate Bayesian computation to infer parameters of a big-leaf models

*³µÍס§Terrestrial vegetation plays an important role in regulating global climate. Observation stations have been established in forests all over the world to investigate forest carbon fluxes. However, scaling up point measurements to estimate the global forest flux needs models, such as support vector machine, artificial neural network, and big-leaf model. In contrast to the former two black box models, the latter connects plant growth with environmental variables based on process model functions for a single leaf, hence providing some inside into the ecosystem as a whole. As multiple parameters are involved in nonlinear relationships, it is difficult to estimate parameters inversely. This presentation introduces an approximate Bayesian computation method for parameter estimation. A test with Tomakomai data indicates that the method can produced results that agree well with observations.

!2016ǯ2·î9Æü 17:30-19:00

ȯɽ¼Ô¡§¾®ÅÄÃι¨¡¡¡ÊUniversities Space Research Association/NASA Goddard Space Flight Center¡Ë

*¥¿¥¤¥È¥ë:The use of satellite data for mapping man-made CO2 emissions

*³µÍס§Fossil fuel CO2 emissions (FFCO2) are the largest input to the global carbon cycle over decadal time scales.   FFCO2 is often treated as a known quantify in the analyses of the carbon budget when inferring the natural fluxes.  FFCO2 thus needs to be accurately quantified to support carbon budget studies.  Since 2009, we have explored the use of satellite-observed nightlight (NTL) to map FFCO2 emissions at a fine spatial resolution.  The combined use of power plant database and NTL data we proposed allows us to produce emission fields with improved spatial distributions in a timely manner.  The use of NTL data from new instrument Suomi-National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) instrument will be a great plus for the mapping method given the improved instrument spatial resolution and data collection frequency compared to the Defense Meteorological Satellite Program (DMSP) sensors. Recently, we further explored the use of satellite data to map emissions within urban domains.  Urban domains account for very small portion of the total land area.  They however host the major portion of the total FFCO2 emissions.  At the spatial scale of resolving urban domains, the use of NTL data often do not work well beyond depicting the major patterns of urban extent.  We examined the use of the 30m impervious surface development data derived from LandSat data to improve emission spatial distributions.  We then implemented a high-resolution atmospheric CO2 inversion analysis using the resulting emission data to quantify emissions from a US city. In my talk, I will also touch on other developments in my own project as well as some of NASA projects I¡Çm participating.  At the end of the talk, I would like to discuss with the audience about future applications of satellite data to carbon cycle study, given the existing and planned observation and modeling capabilities.


!2016ǯ2·î2Æü 17:30-19:00

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