Shape Complexity Dynamics of Bangladesh Delta

excogitate Complexity Dynamics of Bangladesh DeltaShape complexness kinetics of Bangladesh delta A fractal ratio approachSugata Hazra Anirban Mukhopadhyay, Sandip Mukherjee, Abhra Chanda and Tuhin GhoshAbstractThe dispirit deltaic app atomic number 18nt of Ganges Brahmaputra Delta in Bangladesh is a tidally dynamical flood plain with anastomosing nedeucerk of rivers and tidal brooks. The promptly changing sound structure of this delta is mainly referable to huge sediment discharge transported down the drainage basins, its redistribution by tides and currents , erosion, deposition and ocean take aim change. The bring to pass complexity of this delta mainly come on the estuaries has been a major concern for the Geomorphologists for a long clip. During the recent past, the withdraw of morphology and pull downscape evolution has gone through a revolutionary change imputable to the advent of outside(a) sensing techniques. The present explore attempts an compend the a scertain change kinetics of this deltaic island section of Sundarban for the last cardinal decades on the basis of fractal prop index coupled with modern remote sensing techniques. It is observed that the formula of the islands with respect to their margin irregularities be cosmos caused referable to the change in fractal geometry at the micro direct which in turn is a function of ocean take jump over this time stop.Key words Delta course complexity fractal dimension, Sundarban Sea train approach.1. IntroductionSundarban mangrove forest of Bangladesh comprises a huge nedeucerk of comminuted alluvial islands formed by the deposition of sediments, transported down the drainage basins of the Ganges, Brahmaputra and Meghna rivers arrangement (Gopal et al., 2006). Deltaic erosion and morphological change be continuously seen in the Sundarban realm (Ghosh et al., 2003). Innumerable tidal creeks and channels with diurnal flow reversal and rapidly changing land forms make this delta a really dynamic one. Islands be undergoing erosion and accumulation, t presentfore the morphology is continuously changing. The changes in the morphology are mainly driven by the inconsistent supply of sediments (Brammer, 1993) and sea aim change.Shape analysis is a process which identifies the pattern of landscape. The process describes differentiation betwixt regularity and impairment of consideration (). The prime objective of the shape analysis is to understand spatial pattern of a geographical phenomena and its realizable cause and predicts a probable future pattern (). Shape index, in terms of magnitude of roundness of the object or the measure of irregularity in terms of roundness, is a statistical rule to quantify shape of all unit of playing area.In a geographic context, shape is practically characterized through a compactness indicator, which describes the form of a given arena based on how far it deviates from a specified norm (e.g., circle, squ are, or triangle). The method for calculating this number utilizes one or more(prenominal) than of the nonrepresentational parameters of the country being measured, such as area or molding (Elizabeth Wentz). The surface of the earth and peculiarly landforms are always changing due(p) to ever dynamic exogenetic forces contributing over the shift of landforms in every moment of time. due(p) to this dynamism, the shapes of the landforms are not static outside a proper(postnominal) scale of time thus by creating the irregularity of shapes during the transformation process. Considering the transformation process as the media, we are required to examine the quantifiable characterization of the shape irregularities of deltaic islands over the progressive temporal halts. The goal of the writing is to improve the ability to compare the shape dynamics caused due to external portions thereof over two decadal periods. Also it is to suggest a method for improving the ability to com pare the shape of landforms in a GIS surround with statistical base that is less helpless on direct mankind intervention or intuition or visual interpretation.t1As the halfway geometry especially known as fractal dimension of the object is a fundamental component of the objects geometry to measure the irregularity. Fractal dimension is a fractionary rank that describes the irregular of an object and how much of the space it occupies. It is a measure of how break a fractal object is which may be understood as a characterization of its self-similarity (Backes and Bruno, 2008). We accept taken this element as independent variable on the micro analytical base and by extracting the same for sensing of overall shape change and the temporal dynamics of islands as the dependent variable on the macro analytical base over two decades. Sea level rise is found to be causative eventor behind this dynamics. Present study aims at shape complexity dynamics study of Bangladesh Sunderban fr om 1999 to 2010 in the framework of Fractal Dimension (FD) and Shape world power (SI) analysis.2. conduct area and informationsetsBangladesh, a low lying flood plain delta is the land of rivers and canals. Thist2 delta is formed at the confluence of Ganges-Brahmaputra-Meghna river system and their respective tributaries. Pramanik (1983) has divided the coastal regularise of Bangladesh into leash main regions namely eastern region, primaeval region and western region. Our present study is mainly on the islands of central and western coastal regions. Central coastal regularize extends from Feni river estuary to the eastern corners of the Sunderban. The zone receives a large volume of silt deposition from Ganga, Brahmaputra and Meghna river system. The sediment load comprises more than 70% of the silt with additional 10% sand (Sarwar, 2005). The morphology of this zone is very much dynamic due to huge river discharge and rugged current leading to high rate of erosion and accre tion. Numerous islands are located in this region. Many islands postulate formed by the accretion and many have disappeared in last few years due to erosion.Western region is mainly covered by Sunderban mangrove forest. Due to presence of mangrove forest this zone is comparatively stable in terms of erosion. The main characteristics of this zone are mangrove swamps, tidal creeks and grime flats. This region lies at 0.9 to 2.1 m above mean sea level (Iftekhar and Islam, 2004). Soil is of mainly silt loam or alluvial type. This region is very important for tourism due to Sunderbant3.Landsat TM-5 images of the year 1999 and 2010, 30 m spatial resolution, of Bangladesh Sunderban have been taken for this study. The path/ row no of this selective informationsets ist4 . Satellite altimeter data of TOPEX (NASA) is taken for measurement of regional mean sea level employ Nadir Pointing Radar Altimeter. The sea level rise is computed from the tide calibre measurement of various observatory of Bangladesh such as Hiron Point, Khepupara and Charchanga. normal 13. MethodologyThe step by step procedures have been followed to examine the fact and to establish the concept. The raster and vector data bear on and statistical analysis have been implemented in the remote sensing and GIS environment, the detail of which is furnished in the flow chart auspicate 23.1 Satellite data processingTwo satellite imagery of different time (1999 and 2010) is taken into circumstance in this study. Landsat TM-5 datasets were downloaded from the http//glovis.usgs.gov website. All the datasets are projected in UTM projection with zone no 45 and WGS 84 datum.3.2 Measurement sea surface heyday variationThe measurement of regional mean sea level and sea level anomaly is computed from satellite altimeter data of TOPEX (NASA-built Nadir Pointing Radar Altimeter use C band, 5.3 GHz, and Ku band, 13.6 GHz, and POSEIDON (CNES-built solid State Nadir pointing Radar Altimeter using Ku band, 13.65 GHz ). The datasets are analyzed for measuring sea surface height from the year 1992 to 2012. change barometer correction was applied to improve thedata quality ().3.3 Delta morphology analysisThis raster data format is changed to vector format by three successive stages. premier is the digitization of the raw images in extraction layers. Once digitization is successfully completed, topology was built followed by the polygonal shape building. After polygon building, creek and landmass layers are separated for two years. The landmass layers of polygons have reborn to raster format again in order to use as the stimulant for fractal dimension and shape index calculation in Fragstats (version 4.1) software. Fractal dimension and shape index are presaged using equation 1 and 2 (Jorge and Garcia, 1997).Shape mightiness = (1)Where, P is the perimeter of the polygon and A is the polygon area. If the polygon jimmy is 1.0 it expresses maximum compaction, where the shape is circular. As t he shape becomes more complex the SI add-ons.FractalDimension indicant (D) = (2)The self similarity ratio and N is the number of step size here. Thent5 the curve is defined as self-similar with fractal dimension D. FD of a curve may be any value D ranges from 1.0 to less than 2.0 for lines, and from 2.0 to less than 3.0 for surfaces. The higher the spatial complexity of a line or surface, the higher its fractal dimension (Nayak, 2008). top executive Number Analysist6 is carried out to calculate the gradual changes of both the factors having the base year as 1999. The Simple mass Index of FD (Eq. 3) and Simple Aggregative Index of SI (Eq. 4) are calculate to identify the change in FD and SI. The Fishers saint Index (I0n) is to a fault computed to see the relative change of SI and FD during the period 1999 and 2010 (Eq. 5). It is a compound index calculated from Laspeyress Index and Paasches Index (). The semblance between FD and SI is analysed in terms of regression and coeffici ent of correlation to identify the relation between island shape and fractal geometryt7.Simple Aggregative Index of FD (I0n) = (pn / p0) x c(3)Simple Aggregative Index of SI (I0n) = (qn / q0) (4)Fishers Ideal Index(5)Laspeyress Index = qn p0/ q0 p0Paasches Index = qn pn/ q0 pnFishers Ideal Index (I0n) = (Laspeyress Index/ Paasches Index) x 100Result and discussionStatistical analysis of change in delta morphology..t8The histograms of Fractal Dimension Index (Figure 3) and Shape Index (Figure 4) have been analyzed on an individual basis to examine the general statistical trends of the data. The summery of the histograms of FD and SI of the year 1999 is listed in the put overt9 1. It is observed that the modal frequency class has been defragmented into the higher FD values beyond the median range of 1.056 in 2010 and also the fractal diversity increases by 2 new classes in this year. The histograms of FD and SI of the year 2010 are summarized in the Table 2. It is perceived that d espite of being the modal class persistent, the frequency in the modal class is defragmented and distributed into higher SI classes beyond the median value of 1.475 and also 4 new SI classes are detected in the progressive period of 2010.Figure 3Table 1Figure 4Table 2The Simple Aggregative Index of FD and SI are shown (Box 1) which is 101.49% and 117.26% respectively. The Simple Aggregative Index shows there is only 1.49% increase in FDI whereas SI increases by 17.26% in between 1999 and 2010, revealing close 8.63% changing effect of FDI over Shape Index. The Simple Aggregative Index of FDI and SI have confirmed that both the FDI and SI increases in this period and there is a definite changing effect of fractal geometry over the shape of the islands between 1999 to 2010 whereas the magnitude of the changing effect is only 8.63%.The Fisherst10 Ideal Index (Box 1) shows that the SI has increased with respect to FD by 5.19% from 1999 to 2010. It is signifying the there is a positive i ncrease of shape diversity with respect to fractal diversity at heart the specified time period.Relationship between FD and SIThe scatter plots and bilinear regression of FD and SI for 1999 and 2010 depicts that there is a strong positive relation of FD and SI of the Islands. The magnitude of Pearsons correlation (r-value) increases with strong positive rejoinder in the 2010 is revealing that trend of changing shape diversity of Islands in terms of FD is increasing towards the gradual period. Both the r-values are positive and it is also unornamented that the relation of Island shapes with their fractal geometry becomes stronger in the progressive period of 2010 as the r-values have changed from 0.44 to 0.73.Figure 5The causal factor of Shape Dynamics-Sea Level ChangesTo specify the root cause of the shapet11 dynamics of delta region, two main exogenetic factors have been examined on spatio-temporal basis such as creek density and sea level change. Creeks density is calculated for the year 1999 to 2010 by dividing the length of creek with the area of the island, which shows there is also a trend of gradual increase especially in mangrove forest area on the sea rim margin. It is observed that values of creek density increases towards the sea shore region where the sea water along with wave action is more brisk rather than dynamic river water in the inland areas which is shown in the Figure 6. Except one region the creek density is higher on the sea margin. The increase creek density may be the effort for formation of several islands in the central coastal zone due to defragmentation.Figure 6The temporal data of sea level changes of three observation points i.e., Hiron Point, Khepupara and Charchanga (Figure 7) are analyzed to identify the sea level changes within 1979 to 2000, shown in Figure 8. The progressive graph of the data of this temporal period exhibits an average positive gradual trend of sea level rise in this region.Figure 7Figure 8To examin e the causal source of that diversity and we have definitely found that there rest t12a positive sea level anomaly of 2.80 mm in between 1992 to 2012 in the concerned region (Figure 9). The fact again signifies that the sea level rise in the study area which contributes the changes of delta morphology capture in fractal geometry ultimately resolvinging into dynamism of island shapes over the progressive temporal periodt13.Figuret14 9 closureThe objective of the present study is to analyse the shape complexity dynamics of Bangladesh Sunderban delta in between 1999 to 2010. The dynamism of the delta shapes is analysed using in terms of fractal dimension and shape index. The change in fractional geometry of island/delta within the specified time period is captured and the observations are strengthening with the help of other statistical indices. The analysis of FD and SI parameters of islands indicate that there is an exponential relation of Shape complexity with the changing FD with in 1999 to 2010.Thet15 shape complexity of the islands of Bangladesh is increasing which is clearly evident from this study. There may be several factors for this complexity. Of these, sea level rise and creek density are important factors because Bangladesh is highly vulnerable to sea level rise (Brammer et al., 1993). But simmer down there is no specific regional scenario for net sea level rise because the Ganga-Brahmaputra-Meghna delta is still active and having dynamic morphology and delivers approximately 1.6 billion tone sediment at the face of Bangladesh annually (Broadus, 1993), while there are some move where land is subsiding due to tectonic activities (Huq et al., 1996). So this sediment reclamation is considered to balance subsidence of delta (Agarwala et al., 2003). This sediment deposition along with strong tidal current is the reason for the formation of some new islands in the central coastal zone in last few years. But still it needs more detail scientific study to reveal the dynamics of this delta complex and a lot of time series data of sea level rise to comment on this.It is also notable that result FD computation is varies over the scales. Hence, the observation and conclusion is valid only on the existing scale over which the experiment is carried out. It is also pointed out that gain ground study may be undertaken to make more reasonable opinion over it.t1Need modificationt2Co-ordinate, geographical extentt3No. of islands taken into considerationt4Path / rowt5Model namet6What these indices indicates or signifyt7Why used in this study (indices)t8Write something heret9Analyse more about table 1 and 2t10Significancet11Is there any other cause like thermal expansion . at to the lowest degree mention itt12modifyt13overall comments write something about physical logical implication at to the lowest degree one or two paragraph. Things are statistically analysed physical significance and observation is necessary.t14Try to give a or two d elta figure of two time with FD and SI value ebbraded to show the change in shape and FD relation.t15Check the conclution once