Landsat TM Data Research Articles

Influence of Clique Potential Parameters on Classification Using Bayesian MRF Model for Remote Sensing Image in Dali Erhai Basin

Image classification of remote sensing data is an important topic and long-term tasks in applications [1]. Markov random field (MRF) has more advantages in processing contextual information [2]. Bayesian approach enables the incorporation of prior model and likelihood distribution, this paper has formulated a Bayesian-MRF classification model based on MAP-ICM framework. It uses Potts model in label field and assume Gaussian distribution in observation field. According to maximum a posteriori (MAP) criterion, each new classified label can be obtained by the minimum of energy using Iterated Conditional Modes (ICM) algorithm. Finally, classification tasks are carried out by Bayesian-MRF classification model. Experimental results show that: (1) Clique potential parameters affect classification greatly. When it is 0.5, the classification accuracy reaches maximum with the best classification result for study area of Dali Erhai Lake basin using landsat TM data. (2) Bayesian MRF model have obvious advantages in classification for neighbourhood pixels so that it can separate Shadow class from Water class because the Shadow in mountain areas is very similar to Water in spectrum. In this case study, the best classification accuracy reaches 95.8%. The approaches and results will have important reference value for applications such as land use/cover classification, environment/ecological monitoring etc.

Assimilating Remotely Sensed Information with the WheatGrow Model Based on the Ensemble Square Root Filter forImproving Regional Wheat Yield Forecasts

In this study, a deterministic algorithm named Ensemble Square Root Filter (EnSRF), an algorithm significantly improved from the Ensemble Kalman Filter (EnKF), was used to integrate remotely sensed information (ASD spectral data, HJ-1 A/B CCD and Landsat-5 TM data) with a wheat (Triticum aestivum L.) growth model (WheatGrow). The analyzed values of model variables, leaf area index (LAI) and leaf nitrogen accumulation (LNA), were calculated based on EnSRF without perturbed measurements. Independent datasets were used to test EnSRF and the root mean square error (RMSE) values were 0.81 and 0.82 g m-2, with relative error (RE) values of 0.15 and 0.13, for LAI and LNA, respectively. RMSE values for LAI and LNA were 1.39 and 1.70 g m-2, respectively (RE, 0.28 and 0.34) based on EnKF, 1.17 and 1.80 g m-2 (RE, 0.24 and 0.35), respectively, based on the WheatGrow model alone, and 0.97 and 1.25 g m-2 (RE, 0.21 and 0.24), respectively, based on the remote sensing models. These results indicated that the LAI and LNA values based on EnSRF matched the measured values well compared with the EnKF, WheatGrow and remote sensing models. In addition, the predicted results are consistent with the temporal and spatial distribution of winter wheat growth status and grain yields in the study area, with RE values of less than 0.2 and 0.1 for LAI and LNA, respectively. These results provide an important approach for simulating winter wheat growth status based on combining remote sensing and crop growth models.

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

The expansion of irrigated agriculture during the Soviet Union (SU) era made Central Asia a leading cotton production region in the world. However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the sustainability of agricultural production. With Landsat MSS and TM data from 1972/73, 1977, 1987, 1998, and 2000 the expansion and densification of the irrigated cropland could be reconstructed in the Kashkadarya Province of Uzbekistan, Central Asia. Classification trees were generated by interpreting multitemporal normalized difference vegetation index data and crop phenological knowledge. Assessments based on image-derived validation samples showed good accuracy. Official statistics were found to be of limited use for analyzing the plausibility of the results, because they hardly represent the area that is cropped in the very dry study region. The cropping area increased from 134,800 ha in 1972/73 to 470,000 ha in 2009. Overlaying a historical soil map illustrated that initially sierozems were preferred for irrigated agriculture, but later the less favorable solonchaks and solonetzs were also explored, illustrating the strategy of agricultural expansion in the Aral Sea Basin. Winter wheat cultivation doubled between 1987 and 1998 to approximately 211,000 ha demonstrating its growing relevance for modern Uzbekistan. The spatial-temporal approach used enhances the understanding of natural conditions before irrigation is employed and supports decision-making for investments in irrigation infrastructure and land cultivation throughout the Landsat era.

Long-time shoreline monitoring of the vellar estuarine complex, southeast coast of India: Using multispectral satellite data

The present study was carried out to track the path of Vellar estuary over a period of 38 years (1970–2008) and also to detect the impact of tsunami on estuarine complex of the Vellar estuary. Visual interpretation techniques were employed by using multispectral data of Landsat TM (1991) and IRS-P6 LISS III (2004, 2006, and 2008) to delineate shoreline changes in the Vellar estuarine complex. Results clearly revealed the changes that occurred along the estuarine path over the period, and severe erosion was noticed in the seaward side of the MGR Thittu and accretion, along the estuarine mouth. It is also visible that there is clear river path shift in the river course. Although the sandbar formation was seen with only little morphologic modification up to 2004, the Indian Ocean tsunami that struck this coast (26 December, 2004) distorted the sandbar of the Vellar estuarine mouth; however, the 2006 satellite images confirmed that the sandbar was formed again to its original structure as that of before the tsunami.

Pattern dynamics of thermal-environment effect during urbanization: A case study in Shenzhen City, China

The thermal-environment effect exists in the field of rapid urbanization. It has adverse effects on the urban atmosphere, regional climate, energy consumption, and public health. Shenzhen, a representative of rapidly urbanizing cities in China, was selected as a case for pattern dynamics analysis of the thermal environment. The surface temperature was acquired from the thermal infrared data of Landsat TM and ETM+ images in 1986, 1995, and 2005 by Jimenez-Munoz and Sobrino’s generalized single-channel method, which was used in assessing the distribution and spatial patterns of the thermal environment. The relative thermal environment curve (RTC) was combined with Moran’s I analysis to assess the pattern dynamics of the thermal environment in different urbanization periods. Moran’s I index and the RTC represent a process of aggregation-fragmentation-aggregation, which shows the aggregation pattern of a decrease during the rapid urbanization period and then an increase during the steady urbanization period. High-temperature areas gradually expanded to a uniform and scattered distribution in the rapid urbanization period; while the high thermal-environment effect was gradually transformed into a steady spatial pattern in the stable urbanization period. To characterize the increasing development in this multiple-center city, we chose profiles along an urban-development axis. The results suggest that heat islands have expanded from internal urban to external urban areas. Four profiles were obtained showing differences in shape due to spatial differences in the process of development.

Spatial Discrimination of Salt–affected Soil Surfaces Using Landsat TM Data : A Preliminary Study in the Middle Reaches of the Heihe River, China

Spatial Discrimination of Salt–affected Soil Surfaces Using Landsat TM Data : A Preliminary Study in the Middle Reaches of the Heihe River, China

Modeling Stand Volume using Landsat TM data for Fir stands (Abies bornmuelleriana Matth.) Located in Buyukduz Planning Unit, TURKEY

Remotely sensed data in the form of satellite images have been used for decades to estimate forest parameters in support of forest management planning (Leyk et al., 2002). Since satellite data can be repeatedly acquired with reliable data quality, methods about modeling some stand attributes with dataoriginated satellite images is appropriate for obtaining information on land cover on forest areas (Wulderand and Seemann, 2003). Based on 97 sample plots, it is aiming to model relationships between stand volume and band values based on Landsat TM data for fir stands (Abies bornmuelleriana Matth.) located in Buyukduz Planning Unit, TURKEY. Multiple linear regression models were used to predict stand volumes with band values, including TM 1 - TM 5 and TM 7, originated from Landsat TM satellite image. The regression models, including different independent variables alternatives and band values, were compared with some information criteria, e.g. the adjusted coefficient of determination (R 2 ), with Reduced Akaike’s Information Criterion (AIC), Sawa’s Bayesian Information Criteria (BIC), Schwarz Bayesian Criteria (SBC), the root mean square error (RMSE) and Mallow’s Cp, which criteria are measures of goodness of fit for regression models. These statistical analyses were performed by PROC REG and PROC RSQUARE procedures of the SAS/ETS V9 software (SAS Institute Inc, 2004). The best results for predictive performance were obtained by multiple linear regression model including TM 2 and TM 4 as independent variables. This model, statistically significant at 95% level with model parameters, explained 54.09% of the observed stand volume variability with 634.29 of AIC, 637.02 of BIC, 640.36 of SBC, 28.69 of RMSE and -0.315 of Cp. The results showed that the Landsat TM data are beneficial to estimate forest stand volume. Thus, forest managers could use remote sensing data, e.g. Landsat TM data, for predicting stand volume and for generating maps necessary for developing forest management plans.

Salinity Assessment in Northeast Florida Bay Using Landsat TM Data

Las actividades humanas en el siglo pasado han causado una gran variedad de problemas ambientales en los Everglades del sur de la Florida. En el 2002, el Congreso autorizó el Plan Comprensivo de Restauración de los Everglades (CERP, por sus siglas en inglés), una misión de $10.5 millones de dólares para restaurar el ecosistema del sur de Florida. Muchos proyectos ambientales en los CERP requieren un seguimiento eficaz de la salinidad en la Bahía de Florida para proporcionar una medición del progreso y efectos de la restauración en el ecosistema de los Everglades. Modelos de salinidad son también importantes teniendo en consideración los impactos actuales y proyecciones futuras sobre el incremento del nivel del mar. Este estudio examinó el potencial del sensor Landsat Thematic Mapper (TM) para servir como un una herramienta de monitoreo regular de salinidad para la Bahía de Florida. Una combinación de datos de campo espaciales y temporales, e imágenes TM recolectadas durante “Water Years” 2004–2006 fueron empleados para establecer algoritmos que evaluaran cuantitativa y cualitativamente la salinidad en la zona noreste de la bahía. El Servicio Geológico de los EE.UU. (USGS) define “Water Year” como el periodo de 12 meses desde el 1 de octubre de un año hasta el 30 de septiembre del siguiente año, y se designa por el calendario anual en el que termina. Los algoritmos empíricos para la evaluación cuantitativa de salinidad generaron un nivel razonable de precisión, y una técnica neutral de redes bases para la evaluación cualitativa presentó resultados prometedores. Concluimos que la TM Landsar puede ser utilizada como herramienta de monitoreo para evaluar salinidad con resolución y precisión espacial y temporal deseable. La extensión de los algoritmos para toda la Bahía de Florida será probada en el futuro.

Land Classification for Optimal Use of Land by Remote Sensing and GIS Technology

In this paper, attempt has been made to prepare landuse map for the district of Brahmanbaria, situated in the east-central part of Bangladesh using remote sensing technique. The multi-spectral Landsat TM data for 3 November 2002, 15 December 2004, 01 February 2002, 14 March 2003 and some aerial photographs of December 2000 have been used for land-use mapping for major three crops namely aman rice (late July - early November), winter (rabi) crops and winter (boro) rice. The imagery covers the growing seasons of the above crops, where multi-spectral and multi-temporal signatures for the green vegetations have been shown in spatial domain. The interpretation of the aerial photographs have also been performed and prepared GIS layers containing the water bodies and settlements. All the signature files including the interpretations of aerial photographs have been combined to produce a composite file in GIS layers. These layers were then combined to prepare the landuse maps including the three major crops cultivated round the year. Moreover, the landuse map of Akhaura upazila was compared with the land-type map and a relation of the landuse with the land-type has also been derived. The extracted feature files corresponding to spectral signatures have been overlaid to estimate the distribution of three major crop types in the study area. The result implies that all the three major crops like, aman, rabi and boro were cultivated in the same land which was 9.2% of total land area. Similarly, areas under double and single crops were also estimated and the result revealed that all the three crop types cover 68.3% of the total land area of Brahmanbaria district. DOI: http://dx.doi.org/10.3329/jesnr.v5i1.11587 J. Environ. Sci. & Natural Resources, 5(1): 229-237, 2012

MONITORING OF GLACIAL CHANGE IN THE HEAD OF THE YANGTZE RIVER FROM 1997 TO 2007 USING INSAR TECHNIQUE

Abstract. Accurate monitoring of the glacier changes is essential to evaluate the environmental-ecological health in the scenario of global change. Conventional method for glacial monitoring is optical remote sensing. However, affected by cloud and snow cover, it is hard to monitor glacier by optical images. With the fast development of InSAR technique, interferometric coherence has been utilized for extracting glacial information. However, it is difficult to distinguish glacial area from non-glacial area when their coherence is similar, especially for short wavelength radar, such as X-band and C-band. In this case, interferometric phase can play an important role to identify glacier. In this paper, phase texture analysis method is proposed to extract glacier. 8 texture features were analyzed based on gray level co-occurrence matrix (GLCM), including mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. Among them, variance, contrast and dissimilarity can distinguish glacier from non-glacier clearly most, so they are chosen for RGB combination. Then the RGB combination image is classified into several land covers by maximum likelihood classification (MLC). After post-classification processing, glacial area can be extracted accurately. With this proposed method, two ERS-2 SAR single look complex (SLC) images acquired in 1997 and two ENVISAT ASAR SLC images acquired in 2007 are used to extract glacial area in 1997 and 2007 over Geladandong area, the head of the Yangtze River. The extracted areas are validated by Landsat TM data, which indicate that the proposed method can obtain accurate glacial area. The results also demonstrate during the 10 years, glacial area over Geladandong decreased fast, with a reduction of 22.97km2.

Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan

As a proxy measure of the human ecological footprint, impervious surface area (ISA) has recently become a key concept in the field of urban remote sensing, with a focus on estimation of the ISA at a city-scale by using Landsat-style satellite images. However, ISA estimation is also in demand in disciplines such as the environmental assessment and policy making at a national scale. This paper proposes a new method for estimating the ISA fraction in Japan based on a temporal mixture analysis (TMA) technique. The required inputs for the proposed method are rearranged MODIS NDVI time-series datasets at the temporal stable zone (i.e., the first to the sixth largest NDVI values in a year). Three ISA distribution maps obtained from Landsat-5 TM data were used as reference maps to evaluate the performance of the proposed method. The results showed that the proposed TMA-based method achieved a large reduction in the effects of endmember variability compared with the previous methods (e.g., SMA and NSMA), and thus the new method has promising accuracy for estimating ISA in Japan. The overall root mean square error (RMSE) of the proposed method was 8.7%, with a coefficient of determination of 0.86, and there was no obvious underestimation or overestimation for the whole ISA range.

Retrieved actual ET using SEBS model from Landsat-5 TM data for irrigation area of Australia

The idea of ground-based evapotranspiration (ET) is of the most interesting for land–atmosphere interactions, such as water-saving irrigation, the performance of irrigation systems, crop water deficit, drought mitigation strategies and accurate initialization of climate prediction models especially in arid and semiarid catchments where water shortage is a critical problem. The recent year's drought in Australia and concerns about climate change has prominent the need to manage water resources more sustainably especially in the Murrumbidgee catchment which utilizes bulk water for food security and production.This paper discusses the application of a Surface Energy Balance System (SEBS) model based on Landsat-5 TM data and field observations has been used and tested for deriving ET over Coleambally Irrigation Area (CIA), located in the southwest of NSW, Australia. 16 Landsat-5 TM scenes were selected covering the time period of 2009, 2010 and 2011 for estimating the actual ET in CIA. To do the validation the used methodology, the ground-measured ET was compared to the Landsat-5 TM retrieved actual ET results for CIA. The derived ET value over CIA is much closer to the field measurement. From the remote sensing results and observations, the root mean square error (RMSE) is 0.74 and the mean APD is 7.5%. The derived satellite remote sensing values belong to reasonable range.

Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions

Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r=0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r=0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+).

A Novel Remote Sensing Image Classification Scheme Based on Data Fusion, Multiple Features and Ensemble Learning

In this paper, we propose a novel scheme to improve the accuracy of remote sensing image classification by integrating data fusion, multiple feature combination and ensemble learning. Intensity-Hue-Saturation (IHS), Gram-Schmidt (GS), Brovey and wavelet fusion methods are first performed to obtain the optimal fusion images of high resolution and multispectral images. Support Vector Machine (SVM) classifier is then adopted to classify the fused image with different feature sets, and ensemble learning algorithm based on dynamic classifier selection (DCS) is finally used to integrate multiple classification maps. The proposed classification scheme is implemented with three remote sensing data sets, obtaining the highest overall accuracy and kappa coefficient in all cases (92.63% and 0.8917 for BJ-1 data set, 81.89% and 0.7513 for Landsat TM and SPOT4 data set, 92.21% and 0.8838 for ALOS data set respectively). The experimental results show that the integration of data fusion, feature combination and ensemble learning improves the classification performance obviously and has great potential in practical uses.

Assessment of Forest Diseases and Insects with Landsat TM Data

With the development of satellite remote sensing in the direction of high resolutionand multi-spectral,Landsat TM has become an advanced technique in the repeated acquir-ing of environmental resource information.Thought to be no-smoke forest fire damage,forest diseases and insects have caused tremendous loss to human being and the surround-ings.Landsat TM data has supplied as an practical technique in the monitoring and assess-ment of forest damage.Although it has been studied widely and many useful results havebeen achieved,it is still neccessary to probe into the mechanism and technical mothod inthe remote sensing monitoring of forest diseases and insects, so that it can be more practi-cal and easy to apply.Based on the experience of the research works in the eighth five-year key project,thispaper proved that TM data can be used in the mapping and assessment of forest diseasesand insects in county/city level, and it pointed out the neccessity to build different moni-toring parameters and models according to different forest distribution. The authors con-cluded that the probation of spectral contribution of multi-forest stand and photocommuni-ty green biomass should be the break-through to quantatively study the mechanism andmethod in the remote sensing monitoring and assessment of forest diseases and insects.

Study on the cooling effects of urban parks on surrounding environments using Landsat TM data: a case study in Guangzhou, southern China

The temperature cooling effects of ten urban parks on surrounding environments in Guangzhou, southern China, are analysed and quantified using Landsat Thematic Mapper data. The results show that there is a temperature rise (about 1.74°C) between green spaces of parks and bare-ground areas of the surroundings. For those parks whose green area percentage is more than 69% and length:width ratio is close to 1, the average temperature differences between boundaries and surrounding sites of parks have linear relationships with the green areas of parks (R 2 > 0.82). Moreover, the nonlinear relationship between the average cooling distance of parks and green areas can be simulated very well using a logarithmic curve (R 2 > 0.93). When the green areas of parks are smaller than 10 566 m2, parks will have no temperature cooling effects on their surrounding environments. When the green areas of parks reach 740 000 m2, the increase of temperature cooling distance is less than 1 m per 10 000 m2 increase of the green area. The most appropriate size of green areas of urban parks should fall between 10 566 and 740 000 m2. For those parks with water areas larger than 128 889 m2, the temperature cooling effects are usually more remarkable. When the length:width ratios of the green areas of urban parks are more than or equal to 2, their temperature cooling distances are always larger than those with length:width ratios equal to 1 given similar green area. Parks with larger green areas (37 163 m2) or larger water areas (>128 889 m2) will have more significant temperature cooling effects in June than in October.

Validation of a modified snow cover retrieval algorithm from historical 1-km AVHRR data over the European Alps

Seasonal snow cover is a valuable indicator of climatic variations due to its sensitivity to temperature and precipitation. Complementary to ground-based station data, satellite time series provide large-scale spatial capabilities. The primary disadvantage of this technique, however, is the relative brevity of records. Only AVHRR offers the opportunity to analyze more than 25years of medium-resolution satellite imagery on a daily basis. AVHRR thereby holds a great potential to detect, map and quantify long-term environmental changes. However, to serve this purpose though, adequate algorithms and careful validation are of major significance.Here, we describe and extensively validate snow cover retrieval from historical 1-km AVHRR data using a stable snow detection algorithm, which allows consistent snow sampling across all AVHRR sensors. As a new asset, a pixel-wise probability map based on logistic regression is provided for each snow mask. The spatial and seasonal validation includes a comparison to MOD10A1 and webcam imagery. In addition, the influence of acquisition geometry and the sensor-to-sensor consistency have been investigated using LANDSAT TM data and a snow climatology based on long-term station data.We conclude that the snow detection algorithm tested allows for a 1-km snow extent climatology to be generated from the 25-year full-resolution AVHRR data archived at the University of Bern with favorable accuracy and stability. Given the importance of mountainous regions for climate change studies, this satellite-based data set could become an important tool for assessing environmental changes in the European Alps.

Topographic imaging simulation of optical remote sensing based on Landsat TM data

In this study, topographic imaging simulation was realized based on the Landsat TM image and DEM data. Firstly, the surface reflectance was retrieved with the scs+c correction method. Secondly, the slope surface irradiance look-up table was set up by combining the radiative transfer equation and bi-directional reflectance ratio with the plane surface irradiance at different slopes, aspects, image acquisition times and elevations with 6s radiative transfer software. Finally, the apparent radiance reaching at the sensor, covering the Liaoning mountain terrain, was simulated at 8am, 12pm and 16pm on 11 August, respectively.

IDENTIFYING DESERT THRESHOLDS BY MAPPING INVERSE ERODIBILITY AND RECOVERY POTENTIALS IN PATCH PATTERNS USING SPECTRAL AND MORPHOLOGICAL ALGORITHMS

ABSTRACTDesert thresholds are narrow zones of spatial instability between semi‐arid and arid regions. Identifying and mapping such thresholds is difficult because of the high diversity of vegetation, soil and bare rock patterns in these regions. Here, we suggest incorporating pattern information from remote sensing imagery in the assessment of eco‐geomorphic transitions. A new methodology was developed for this purpose based on the analysis of potential inverse trends of erosion and recovery embedded in these patterns. The methodology applies spectral mixing analysis to map surface conditions and uses mathematical morphology algorithms of dilation and erosion to identify potential erodibility and recovery trends. Boundary lines are then delineated by classifying combinations of these inverse trends and assessed with reference to their separability. This approach is implemented along the climatic gradient between the Judean Mountains and the Dead Sea in central Israel using mid‐resolution Landsat TM data. The identified threshold lines fit well with lithological, soil and phytogeographical boundaries as delineated in low‐scale maps, yet add significant explicitness to their spatial definition. Such explicitness is instrumental for assessing future desert shifts because of global warming and extended overgrazing. Copyright © 2012 John Wiley & Sons, Ltd.

Spit Dynamics along the Central West Coast of India: Implications for Coastal Zone Management

HEGDE, V.S.; NAYAK, S.R.; SHALINI, G.; KRISHNAPRASAD, P.A.; RAJAWAT, A.S.; GIRISH, K.H., and TEJASWINI, B., 2012. Spit dynamics along the central west coast of India: Implications for coastal zone management.Spit growth is observed across many rivers of the central west coast of India. Studies on the seasonal dynamics of the spits of the central west coast of India on the basis of the multidate satellite images LISS III, landsat™ data, panchromatic camera (PAN) data etc. in a geographic information system (GIS) environment using ERDAS imagine 9.0 version followed by field-check and wave data analysis have been discussed in this paper. All types of spits such as paired spits, winglike spits, single spits, etc. are observed along the coast of Karnataka, central west coast of India. Spit growth is responsible not only for shifting of the river mouths, but also for rapid changes in estuarine morphology and in turn on the sedimentation process. It is observed that river mouths with a spit across them have a submerged bar in front of them, whereas rivers devoid of spits are also devoid of submerged bars in front of them. This spatial association of spit and submerged bar is believed to have a genetic relationship. Along the coast many perpendicular faults and uplifts have been observed. Rate of spit growth, along with their direction and inland drainage pattern, suggest a role of sea-level changes and tectonic control in addition to alongshore currents. Rapid spit growth is observed near the inferred tectonic axis. Seasonal dynamics and modification in the spit configuration are related to alongshore drift as well as the wave refraction around the mouth of the rivers.