Environmental Geography Seminar 5/29

Dear all,

Hello!

This is an announcement of the upcoming Environmental Geography Seminar.

Please see all the details carefully below.

【Date】 29th. May. Wed. （Time: 15:00~）

【Place】 D101

【Content】

1. Presenters
i. Shi Muqing
Title: Large scale hydrologic and hydrodynamic modeling using limited data and a GIS based approach
Author: R. Paiva, W. Collischonn, C. Tucci
Publication info: Journal of Hydrology, 2011
Link: https://doi.org/10.1016/j.jhydrol.2011.06.007
Abstract:
In this paper, we present a large-scale hydrologic model with a full one-dimensional hydrodynamic module to calculate flow propagation on a complex river network. The model uses the full Saint–Venant equations and a simple floodplain storage model, and therefore is capable of simulating a wide range of fluvial processes such as flood wave delay and attenuation, backwater effects, flood inundation and its effects on flood waves. We present the model basic equations and GIS algorithms to extract model parameters from relatively limited data, which is globally available, such as the SRTM DEM. GIS based algorithms include the estimation of river width and depth using geomorphological relations, river cross section bottom level and floodplain geometry extracted from DEM, etc. We also show a case study on one of the major tributaries of the Amazon, the Purus River basin. A model validation using discharge and water level data shows that the model is capable of reproducing the main hydrological features of the Purus River basin. Also, realistic floodplain inundation maps were derived from the results of the model. Our main conclusion is that it is possible to employ full hydrodynamic models within large-scale hydrological models even using limited data for river geometry and floodplain characterization.

ii. Sugita
Title : “Storm response of a mixed sand gravel beach ridge plain under falling relative sea levels: A stratigraphic investigation using ground penetrating radar”
Earth Surface Process and Landforms (2019) Vol.44
Sebastian J. Pitman1, Harry M. Jol2, James Shulmeister3 and Deirdre E. Hart1
1 Department of Geography, University of Canterbury, Christchurch, New Zealand 2 Department of Geography & Anthropology, University of Wisconsin-Eau Claire, Eau Claire, WI USA 3 School of Earth and Environmental Sciences, University of Queensland, St Lucia, QLD Australia
https://doi.org/10.1002/esp.4598

iii. Armstrong
Modeling of urban growth in tsunami-prone city using logistic regression: Analysis of Banda Aceh, Indonesia
Authors
Ashfa Achmada; Sirojuzilam Hasyimb; Badaruddin Dahlanc; Dwira N.Auliad
Journal: Applied Geography
Impact Factor: 3.117 https://www.journals.elsevier.com/applied-geography
Publisher: Elsevier
https://doi.org/10.1016/j.apgeog.2015.05.001
A b s t r a c t
The urban development of Banda Aceh, Indonesia was very rapid after the tsunami in 2004, posing critical challenges in planning for its future sustainable development. Scientifically-derived information about its land change patterns and the driving factors of its rapid urbanization might provide vital information. However, the spatioetemporal patterns of its urban land use/cover (LUC) changes have not been examined. Hence, this study aims to: (1) detect and analyze the spatioetemporal changes in the urban LUC of Banda Aceh between 2005 and 2009; and (2) examine the driving factors that influence urban growth. The 2005 and 2009 LUC maps were derived from remote sensing satellite images using a supervised classification method (maximum likelihood). Both LUC maps contained four categories, namely built-up area, vegetation, water body, and wet land. The 2005 LUC map had an overall accuracy of 77.8%, while the 2009 LUC map had 89.4%. The two LUC maps were re-classed into two categories (i.e. built-up area and non built-up area) to facilitate logistic regression analysis. A total of seven variables or potential driving factors of urban growth were identified and examined, including two socio-economic factors (population density and distance to central business district) and five biophysical factors (distances to green open space, historical area, river, highway, and coastal area). The results showed that the LUC of Banda Aceh has changed drastically between 2005 and 2009, particularly its built-up area, which increased by 90.8% (1016.0 ha) at the expense of the other LUC categories. The socio-economic factors showed positive influence to the growth of the city, whereas the biophysical factors showed negative effect, except the distance to coastal areas. The importance of the findings for future landscape and urban planning for Banda Aceh is discussed.
Title: Extraction of impervious features from spectral indices using artificial neural network
Authors:
Nilanchal Patel; Rohit Mukherjee
Arabian Journal of Geosciences
Impact Factor: 0.860 https://link.springer.com/journal/12517
Publisher: Springer
http://doi.org/10.1007/s12517-014-1492-x
Abstract
An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.
Keywords
Impervious feature Spectral indices Backpropagation Feature extraction
2. Note this:

Please send me the detailed information about your presentation at least 5 days before your turn.

For a paper review: title and author of the paper, journal name, which volume, pages, also the link (if possible) should be provided.

If a paper written in Japanese is going to be presented, please kindly send me the information both in Japanese and English.

【Notice】

※ In case you are absent from the seminar or late for the seminar, please contact Professors or me in advance. Any absence without permission is not allowed;

※ Please be punctual (very important);

※ Please do your full preparation for the seminar;

※ Your active participation is always appreciated;

※ Please feel free to get in touch with me if you have any questions or comments.

Best Regards,

Chang Liang