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21.
John Prince Soundranayagam P. Sivasubramanian N. Chandrasekar K.S.P. Durairaj 《地理学报》2011,21(1):79-88
The spatial differentiation of land use changes of Tuticorin is studied using high resolution LISS III satellite imagery and Maximum Likelihood algorithms. The classification accuracy of 95.2% was obtained. In this study, the land use of Tuticorin is classified as settlement, salt pan, agricultural land, wasteland, water bodies and shrubs. The settlement area is increased to 4.6 km2 during the year 2001 and 2006. The settlement area change is mainly driven by growth of industries and migration of people from peripheral villages. Shrub is increased to 3.63 km2 in the six year period. Water logging due to growth of shrubs in Tuticorin leads to several environmental and health hazard. This study warrants proper urban planning for Tuticorin for sustainable use of resource and environment. 相似文献
22.
Yasala Srinivas Stanley A. Raj Oliver D. Hudson Durairaj Muthuraj Nainarpandian Chandrasekar 《Studia Geophysica et Geodaetica》2013,57(3):520-534
Soft computing tools play a vital role in fixing certain non-linear problems related to the earth. More specifically, digging out the mysteries of subsurface of the earth, the nonlinearity can be converging to assemble an approximate solution which resembles the real characteristics of the earth. Adaptive Neuro Fuzzy Inference System (ANFIS) tool is one of the best soft computing tools to estimate the complex data analysis. ANFIS was applied to estimate the subsurface parameters of earth using the Vertical Electrical Sounding (VES) data. Classifying the lithology based on the resistivity values by ANFIS is employed here in this paper. As the resistivity of each formation varies in range of values, ANFIS tool thus approximates the subsurface features based on effective training. In this study, ANFIS performance was checked with training data, and successively it has been tested with the field data. Optimized ANFIS algorithm provides the necessary tool for predicting the non-linear subsurface features. The best training performance of this soft computing tool efficiently predicts the subsurface lithology. Also the interpreted results show the true resistivity and thickness of the subsurface layers of the earth. The proposed technique was represented in Graphical User Interface (GUI), and the lithological variables are predicted in texture format and linguistic variables. 相似文献
23.
The present study investigates the impact of wave energy and littoral current on shorelines along the south-west coast of Kanyakumari, Tamil Nadu, India. The multi-temporal Landsat TM, ETM+ images acquired from 1999 to 2011 were used to demarcate the rate of shoreline shift using GIS-based Digital Shoreline Analysis System. The statistical analysis such as net shoreline movement and end point rate were determined from the multi-temporal shoreline layers. Moreover, the wave energy and seasonal littoral current velocity were calculated for each coastal zone using mathematical equations. The results reveal that the coastal zones, which include Kanyakumari, Kovalam, Manavalakurichi and Thengapattinam coasts, consisting of maximum wave energy along with high velocity of littoral current, have faced continuous erosion processes. The estimated wave energy along these zones ranges from 6.5 to 8.5 kJ/km2 and the observed current velocity varies from 0.22 to 0.32 m/s during south-west and north-east monsoons. The cumulative effect of these coastal processes in the study area leads to severe erosion that is estimated as 300.63, 69.92, 54.12 and 66.11 m, respectively. However, the coastal zones, namely Rajakkamangalam, Ganapathipuram, Muttam and Colachel, have experienced sediment deposits due to current movement during the north-east monsoon. However, the trend changes during the south-west monsoon as a result of sediment drift through backwash. The spatial variation of shoreline and its impact on wave energy and the littoral current have been mapped using the geo-spatial technology. This study envisages the impact of coastal processes on site-specific shorelines. Hence, the study will be effective for sustainable coastal zone management. 相似文献
24.
GIS model-based morphometric evaluation of Tamiraparani subbasin, Tirunelveli district, Tamil Nadu, India 总被引：1，自引：0，他引：1
A morphometric evaluation of Tamiraparani subbasin was carried out to determine the drainage characteristics using GIS model technique. Extraction of the subbasin and stream network model has been developed to quantify the drainage parameters in the study area. The input parameters required to run this model are: a pour point, a minimum upstream area in hectares, and a digital elevation model. After execution, the model provides a drainage basin with Strahler’s classified stream network supported by thematic layers like aspect, slope, relief, and drainage density. The developed model reveals that the drainage area of this subbasin is 2,055 km2 and shows subdendritic to dendritic drainage pattern. The basin includes seventh order stream and mostly dominated by lower stream order. The slope of the study area varies from 0° in the east to 61° towards west. The presence of Western Ghats is the chief controlling factor for slope variation. Moreover, the slope variation is controlled by the local lithology and erosion cycles. The bifurcation ratio indicates that the geological structures have little influence on the drainage networks and the drainage density reveals that the nature of subsurface strata is permeable. 相似文献
25.
Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS 总被引：2，自引：0，他引：2
Shoreline is one of the rapidly changing landform in coastal area. So, accurate detection and frequent monitoring of shorelines are very essential to understand the coastal processes and dynamics of various coastal features. The present study is to investigate the shoreline changes along the coast between Kanyakumari and Tuticorin of south India, where hydrodynamic and morphologic changes occur continuously after the December 2004 tsunami. Multi-date satellite data of Indian Remote Sensing (IRS) satellites (1999, 2000, 2003, 2005, and 2006) are used to extract the shorelines. The satellite data is processed by using the ERDAS IMAGINE 9.1 software and analyzed by ArcGIS 9.2 workstation. The different shoreline change maps are developed and the changes are analyzed with the shoreline obtained from the Survey of India Toposheets (1969). The present study indicates that accretion was predominant along the study area during the period 1969–1999. But recently (from 1999 onwards), most of the coastal areas have experienced erosion. The study also indicates the reversal of shoreline modifications in some coastal zones. The coastal areas along the headlands have experienced both erosion and accretion. Though the coastal erosion is due to both natural and anthropogenic activities, the coastal zones where sand is mined have more impacts and relatively more rate of erosion than that of other zones. Improper and in-sustainable sand mining leads to severe erosion problem along this area. So the concept of sustainable management should be interpreted in the management of the near-shore coastal sand mining industry. 相似文献
26.
This article reveals an application of multi-spectral satellite data for analysing the dynamics of different coastal landform features along the southern coastal Tamil Nadu of India. An integrated approach comprising visual image interpretation and maximum-likelihood supervised classification has been employed to classify the coastal landforms by using IRS data (during the period 1999–2006). The quality of image classification has been assessed by performing the accuracy assessments with the existing thematic maps and finally the coastal landforms have been mapped. The study reveals that the dynamics of coastal landforms such as sandy beaches, mud-flats, sand dunes and salt marshes along the study area are mostly influenced by the coastal processes, sediment transport, geomorphology and anthropogenic activities. Major anthropogenic sources for the perturbation of beach sediment budgets and a cause of beach erosion along the study area are excessive sand mining, removal of sand dunes, coastal urbanization, tourism and developmental activities. 相似文献
27.
海岸带是动态地区,由于众多因素,包括海平面的上升、波浪与洋流的类型、飓风乃至人类的影响,海岸地区经常受到变动。2004年12月26日巨大海啸席卷了堪雅库玛里至奥瓦利之间的海岸。当海浪冲上海滩时波高达30英尺。许多人因为巨浪的冲击和退浪的强力推拉而淹死在海里,许多村庄被毁。死亡总数超过300人,而且财产损失严重。海啸的规模与洋底移动的面积、移动距离是有关系的。该区显示为海成阶地、沙丘、滩脊、河口、洪积平原、海滩、红树林、准平原、高地、海蚀崖等等。我们试图通过现场在线勘查、政府记录以及采用遥感技术所做的海岸地貌研究等资料,通过海滩剖面测量和海岸环境的变化,进行海岸线动力学研究。文中也对主要的破坏予以认定。 相似文献
28.
R. Jayangondaperumal M.K. Murari P. Sivasubramanian N. Chandrasekar A.K. Singhvi 《Quaternary Research》2012,77(3):468-481
The Holocene and late Pleistocene environmental history of the teri (‘sandy waste’ in local parlance) red sands in the southeast coastal Tamil Nadu was examined using remote sensing, stratigraphy, and optically stimulated luminescence (OSL) dating. Geomorphological surveys enabled the classification of the teri red sands as, 1) inland fluvial teri, 2) coastal teri and, 3) near-coastal teri dunes. The inland teri sediments have higher clay and silty-sand component than the coastal and near-coastal teri, suggesting that these sediments were deposited by the fluvial process during a stronger winter monsoon around > 15 ka. The coastal teri dunes were deposited prior to 11.4 ± 0.9 ka, and the near-coastal dunes aggraded at around 5.6 ± 0.4 ka. We interpret that the coastal dunes were formed during a period of lower relative sea level and the near-coastal dunes formed during a period of higher sea level. Dune reddening is post deposition occurred after 11.4 ± 0.9 ka for the coastal teri dunes and after 5.6 ± 0.4 ka for the near-coastal teri dunes. Presence of microlithic sites associated with the coastal dunes suggest that the cultures existed in the region during 11.4 ± 0.9 ka and 5.6 ± 0.4 ka. 相似文献
29.
The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters.In particular,the behavior of earth resembles the non-linearity applications.An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth.Artificial Neural Networks(ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used.The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network.The single-layer feed-forward neural network with the back propagation algorithm is chosen as one of the well-suited networks after comparing the results.Initially,certain synthetic data sets of all three-layer curves have been taken for training the network,and the network is validated by the Held datasets collected from Tuticorin Coastal Region(78°7′30″E and 8°48′45″N),Tamil Nadu.India.The interpretation has been done successfully using the corresponding learning algorithm in the present study.With proper training of back propagation networks,it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network.The network is trained with more Vertical Electrical Sounding(VES) data,and this trained network is demonstrated by the field data.Groundwater table depth also has been modeled. 相似文献
30.
Groundwater recharge is an important process for the management of both surface and subsurface water resources. The present study utilizes the application of analytical hierarchical process (AHP) on geospatial analysis for the exploration of potential zones for artificial groundwater recharge along Vaigai upper basin in the Theni district, Tamil Nadu, India. The morphology of earth surface features such as geology, geomorphology, soil types, land use and land cover, drainage, lineament, and aquifers influence the groundwater recharge in either direct or indirect way. These thematic layers are extracted from Landsat ETM+ image, topographical map, and other collateral data sources. In this study, the multilayers were weighed accordingly to the magnitude of groundwater recharge potential. The AHP technique is a pair-wise matrix analytical method was used to calculate the geometric mean and normalized weight of individual parameters. Further, the normalized weighted layers are mathematically overlaid for preparation of groundwater recharge potential zone map. The results revealed that 21.8 km2 of the total area are identified as high potential for groundwater recharge. The gentle slope areas in middle-east and central part have been moderately potential for groundwater recharge. Hilly terrains in south are considered as unsuitable zone for groundwater recharge processes. 相似文献