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КУЛЬТУРНЫЙ ЛАНДШАФТ. РАЗВИТИЕ ТУРИЗМА НА СЕВЕРЕ
УДК 912.64
Romain Rollot1, Sébastien Gadal2, Yuri Danilov3
remote sensing and gis's multi-criteria analysis of urban flooding debacle exposure of Yakutsk (lena river, russia)*
* This paper is presented at Scientific conference "Landscape in narrative memory. Reality. Image. Modeling"supported by Russian Foundation for Humanities -Fondation Maison de Sciences de l'homme (France). Project №15-22-08501.
This research is supported by RSFproject №15-18-20047 "Landscape ontology: semantics, semiotics, and remote sensing».
This paper proposes a vulnerability assessment of the Yakutsk areas exposed to the spring breakup flood. The approach is based on the use of remote sensing and a GIS's multi-criteria analysis. Before assessing the vulnerability, potential flood-prone areas have been model with data from Global Digital Elevation Model (GDEM) ASTER v.2 (USGS/METI) and, next, correlated with the categorisation of the urban areas at risk of flooding by image processing with EO-1 ALI satellite images. The methodology developed permitted to build a typology of vuln caca Merable areas and consequently to supply information and data to assess with criteria the vulnerability in several dimensions. In this study, the multi-criteria analysis evaluates the vulnerability in three component and as a whole. Results of this work are focused on the geographic understanding of the vulnerability to the spring breakup flood risk than on the consequences.
Key words: Flood exposure, Debacle, Remote Sensing, GIS, DEM, Multi-criteria Analysis, Yakutsk, Lena River.
1 PhD Student in Aix-Marseille Université. E-mail: [email protected]
2 PhD, Professor in Aix-Marseille Université (France), M.K. Ammosov North-Eastern Federal University (Russia). E-mail: [email protected]
3 D.Sc., Professor. Head of Department of Geography, Institute of Natural Sciences, M.K. Ammosov NorthEastern Federal University (Russia). E-mail: [email protected]
1. Introduction
The Lena River, 7th biggest watershed of the World, ice break-up is an expected, a feared and an unavoidable annual moment. The spring break-up is an important natural phenomenon in hydrological regime of northern river (Beltaos, 1997). It is characterised by rupture of the ice of a river, driven by the current, causing a fast increase in flow and generating floods. River ice break-up takes place in high latitudes where winter is cold and long enough to freeze the water on substantial thicknesses. On the Lena in Yakutsk, the ice break-up happens every year in May. Due to the meridian outline of the river, breakup process begins in April in the upstream part of the river, gains Yakutsk in mid-May, to reach the mouth in June. Between the beginning and the end of the spring break-up, it may take about fifty days (Costard and Gautier, 2008). It is difficult to predict and to manage, ice breakup floods are a real risk for the populations living within the Lena valley, as is the case of Yakutsk's inhabitants. Indeed, the capital of Sakha Republic is partly build in the flooding plain. If the city center is on the alluvial terrace and protected by a dyke, a main part of the suburbs are under the flood plain. The level of the flooding risk is important and recurrent, particularly in the suburban areas of Yakutsk.
The geographic context accentuates the exposure of the urban flooding generated by the debacle of the Lena River: metropolisation with the transformation of the urban lifestyle, important urban growth, opacity of land use policies, permafrost, continental Arctic climate, limited access to social and economic data, or misreading of the research for this territory. The question of the vulnerability of Yakutsk is strong and the exposure of the different urban districts heterogonous. The recognition and the evaluation of the risk of flooding of the different urban areas of Yakutsk is made by remote sensing, geographic information system (GIS) and sampling techniques.
2. An approach by remote sensing and geographic information system 2.1. Spatial approach of the urban flood risk exposure
The methodology developed can be separated in 2 phases: (1) determine flood-prone areas with data from Global Digital Elevation Model (GDEM) ASTER v2 (USGS/METI). (2) Make a typology of the urban areas at ice breakup flood risk in Yakutsk by the use of the Earth Observing Advanced Land Imager (EO-1 ALI) satellite images of 10x10 meters spatial resolution in panchromatic mode and sampling techniques. Remote sensing imagery and local knowledge from sampling techniques established the typology of urban areas under flooding risk.
Two calculus are made to process geographic and spatial information in GIS to evaluate the vulnerability levels of flooding zones. A multi-criteria analysis has been created to assess the level of the vulnerability of the different urban areas recognised (urban typology made by remote sensing analysis). These geographic criteria are environment, built-ups and territory. These 3 criteria of vulnerability constitute a geographic base to evaluate the vulnerability of exposed areas on ice breakup flood risk in Yakutsk.
2.2. Modelling of the potential flood areas
The calculus and the modelling of the potential flood areas are processed with two Global Digital Elevation Model (GDEM) ASTER v2 (USGS/METI) assembled in GIS with a maximal horizontal resolution up to 30x30 meters for covering Yakutsk. A grid with 100x100 meters spatial resolution was built in GIS to "smooth" the DEM information, to reduce effects of grain caused by ASTER GDEM and to have a standardised model. From each square of the grid, we extracted the average altitudes of pixels from ASTER GDEM to model the potential flood areas. The maximum potential extension of the Lena River debacle flooding was calculi according of the flood of the century in 2001. The water river rises of 10 meters because of a huge ice jam downstream of the city (Cabanne and Tchistiakova, 2005); (Costard and Gauthier, 2008); (Dmitry et al., 2005). Lena River altitude is of 85 meter and was extracted grid pixels of 95 meters altitude maximum for the delimitation of floods expansion areas.
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Figure 1: Map of the potential flood areas in Yakutsk using ASTER GDEM v.2. Modelled in a grid with 100x100 meters spatial resolution
2.3. Categorisation of the vulnerable urban areas
EO-1 ALI satellite images of the 15th of August 2014 acquired between 1 h 14 a.m. and 1h19 a.m. was used for the categorisation of the different urban districts. August is considered as the best period to analyse and map the urban structures of Yakutsk: absence of snow coverage during the winter time, and flooding in May. The image processing methodology developed is based on the use of "high-pass" convolution filter as Laplacien enhancement in a 3*3 window on the panchromatic band (Gadal, 2003/ Gadal, 2005), and next on supervised classification (Distance minimum) which allows a mapping categorisation of urban areas based on "training" regions of interest. The results of the convolution filtering and supervised classification are merged by ensemblist image processing (Gadal, 2003) (Gadal, 2005), (Gadal, 2011).
Figure 2: Image processing pattern
2.4. Evaluation of the vulnerability by multi-criteria analysis
Multifactor dimension of vulnerability requires an evaluation taking various criteria that "multi-criteria analysis allows to weight and aggregate into a synthetic indicator" (Grivault and Barczak, 2007). Because of the absence of socio-economic data for the districts or any other at the intra-urban scale, the multi-criteria analysis is based into three dimensions of vulnerability: the environmental vulnerability, the built-up vulnerability and vulnerability of the territory. This approach is founded on the research initiated by the National Program of Coastal Environment in France: "the vulnerability of coastal territories: assessment, challenges and public policy" (Meur-Ferec et al. 2003), including the "Analyse et evaluation
de la vulnérabilité aux inondations du bassin de l'orge aval " [Analysis and evaluation of the flooding vulnerability of the Orge-Aval basin] (Lefort, 2004; Berroca, Potter, Lefort, 2005). A specific grid was developed for the ice breakup flood in Yakutsk.
The developed tool process vulnerability in all three dimensions described earlier: environmental vulnerability, building vulnerability and the vulnerability of the territory. Through the grid, analysis is done in four steps: (1) the selection of the criteria of vulnerability; (2) the assessment of the vulnerability criteria; (3) the calculation of synthetic indices; (4) the construction of an overall indicator of vulnerability.
vulnerabilities vulnerability criteria Evaluation of vulnerability criteria Synthetic index of vulnerability Global indicator of vulnerability
Environmental vulnerability Altitude 1 (low vulnerability), 2 (medium) or 3 (Important vulnerability) weighted mean of vulnerability criteria
Wetlands and lakes 1, 2 or 3
Permafrost 1, 2 or 3
Vegetation 1, 2 or 3 The weighted mean
Building type 1, 2 or 3 of the synthetic index of vulnerability
Vulnerability of the built-up area Building material 1, 2 or 3 Arithmetic mean of vulnerability criteria
Building quality 1, 2 or 3
Accessibility 1, 2 or 3
Territorial vulnerability Basic infrastructures 1, 2 or 3 Arithmetic mean of vulnerability criteria
Density of the built fabric 1, 2 or 3
Function of territory 1, 2 or 3
figure 3: Vulnerability assessment grid
3. Typology of vulnerable urban areas 3.1. urban clusters
Eight categories were chosen to represent the diversity of urban landscapes potentially exposed to ice breakup flood. These eight categories of urban spaces figured on the map have similar characteristics in terms of building morphology, location in space and urban environment.
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figure 4: Clustering of urban areas potentially exposed to the flood risk
3.2. assessment of the vulnerabilities
The environmental vulnerability dimension is evaluated by one hectare cell using the 100m2 spatial resolution grid covering the whole of potentially flood areas. «Built-ups vulnerability» and «the vulnerability of the territory» dimensions are estimated with the urban typology of the flood areas in Yakutsk. The synthetic index are calculated accordingly to these areas and reported in the grid. Following the same calculus, the global indicator of vulnerability is itself applied to the grid.
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(1) (2) (3)
Figure 5: Assessment of the vulnerabilities
(1) Synthetic index of the environmental dimension of vulnerability: the North East part of the Yakutsk is more vulnerable than the southern part. There are also significant disparities of one case to another with variations on a very local scale.
(2) Synthetic index of the built-up vulnerability: growth gradient of vulnerability from the center of Yakutsk to the suburban areas.
(3) Synthetic index of territorial vulnerability: Any districts is categorised as very vulnerable. The whole built territory has a territorial vulnerability rated as medium and homogeneous because despites differences that exist between the urban categories, most of these areas are located in the suburbs areas of the territory.
3.3. vulnerability assessment
Indicator of the global vulnerability is the weighted mean of the synthetic index of vulnerability permits the modelling of the overall vulnerability of flood areas in Yakutsk at risk of spring breakup flood. The average overall vulnerability of all floodplains (4205 squares in the grid) of Yakutsk is 1.98 on an index calibrated between 1 (not vulnerable) and 3 (very vulnerable). The standard deviation is 0.39 which highlights a degree of uniformity of the dispersion of the values. The highest overall vulnerability is 2.75 and the lowest is 1.05. It also notes that 12.1% of the values are greater than 2.5 that is to say an important vulnerability. Unlike, 10.8% of the values are less than 1.5, that being is a low vulnerability.
The calculation of the correlation coefficient between these three variables allows: First, there is only a very weak negative correlation between environmental vulnerability and the two other dimensions of vulnerability. The second thing that reveals the correlation matrix is that there is a very strong positive correlation (> 0.9) between the vulnerability of buildings and territorial vulnerability. This figure means that there is a tendency to find a strong built-up area vulnerability where the territorial vulnerability is strong too.
Data indicate that the standard deviation is well below half the average, could then be precisely that the values are just a little scattered. In practice, this means that there are no strong variations between average global vulnerabilities of different areas.
figure 6: Assessment of the Yakutsk global vulnerability to the flood risk
4. Conclusion
All Yakutsk flood areas are not equal. Analyses and spatial models present, a multitude of situations. The vulnerability is not limited to exposure to a hazard but can be environmental, built-ups and territorial. Some other dimensions could be taken implemented with the access of socio-economic and demographic databases. Other data with news vulnerability criteria (including socioeconomic), more accurate, with better estimated weights can improve the precision and the accuracy of the approach and the results. Overall, the average vulnerability of Yakutsk flood potential areas is estimated as medium. But at local level, there are significant differences, with little vulnerable areas and other highly vulnerable districts.
Indeed, areas with a same overall of vulnerability can be explained by the vulnerability criteria selected and implemented in the model. New criteria could provide the accuracy
and the spatial resolution to a better understanding and mapping of the vulnerability of areas exposed at spring breakup flood. Thus, the purpose was to study the patterns of vulnerability, to understand the risk and not the consequences. This approach of the urban vulnerability analysis constitutes a part of the risk management. Modelling and conclusions can be used for the land management and urban planning.
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