Coastal geomorphology and sediment distribution mapping using Landsat—TM digital data —A study in Coastal area of parts of Orissa,India

An area of about 1100 sq.km forming a part of east coast of India was taken for digital analysis using the VAX 11/780 image analysis computer system. The data used for the analysis is Thematic mapper data of Landsat 5. By using the maximum likelihood algorithm and the existing ground truth, the area was classified geomorphologicall.. In addition a qualitative mapping of suspended sediments of coastal waters in Bay of Bengal around the estuary of Dhamra river in Orissa has been carried out. In seventeen classes, eleven correspond to geomorphic units and six belongs to different categories of sediments concentration in Coastal waters.     

曹妃甸近海表层悬浮泥沙遥感定量监测研究

在曹妃甸近海水体实测光谱数据分析的基础上,与同步采集的水体悬浮泥沙浓度进行相关分析,选用合适的TM数据的遥感变量参数,根据2008年实测的四个剖面29个站点的海洋水色数据建立了水体悬浮泥沙浓度与TM数据对应的遥感定量反演模型,并用18个独立样本点进行了精度评价.研究结果表明:曹妃甸近海水体光谱曲线基本呈现单峰现象,敏感...     

Chlorophyll — A Concentration Along West Coast of India Using IRS-P3 MOS-B Data

Remote sensing of ocean colour yields information on the constituents of sea water, such as the concentration of phytoplankton pigments, suspended sediments and yellow substances. It is well understood that the study of ocean colour is significantly related with the primary production and zonation of potential fishing sites in coastal and oceanic waters. The major pigment constituent is predominated by chlorophyll-a (ocean colour pigment of phytoplankton). The chlorophyll mapping on regular basis plays a major role in assessing water quality and classifying different water types. IRS P-3 MOS-B satellite data for three consecutive passes of path 94, during the period of January-February 1997 have been used to derive chlorophyll-a concentration. The present study emphasizes on the chlorophyll mapping using IRS-P3 MOS-B data for the coastal and offshore water of Maharashtra coast, India.     

Mapping of suspended sediments using site specific seasonal algorithms

This article reports a preliminary work in which two site specific seasonal algorithms have been proposed for estimating the suspended sediments concentration (SSC) from the digital numbers recorded on Indian Remote sensing Satellite, IRS-P4 Ocean Colour Monitor (OCM) sensor. For estimation of SSC, the proposed algorithms utilize dark pixel deduction atmospheric correction technique. The computations are performed with respect to north east monsoon phase situations of Palk Strait coastal stretch. The algorithms performance was satisfactory during the north east monsoon period. Although the results obtained cannot be generalized, we suggest that the authority of proposed algorithms can be extended to other seasons with the addition of more temporal experimental validation data sets and with numeric constants adjusted to present existing conditions. (As this area was severely affected by Tsunami, it may have dissimilar conditions at present).     

基于遥感反演结果的悬浮泥沙时空动态规律研究--以珠江河口及邻近海域为例

选择了珠江口及其邻近海域1995年~2000年152个时相的NOAA/AVHRR影像反演的悬浮泥沙浓度结果,通过对悬浮泥沙反演结果影像的叠加,求算叠加像素点像素平均值,计算了研究海域在1995~2000年间月、季及丰枯水期几个时间尺度上的悬浮泥沙平均浓度及其平均变化。结果大致表明了陆地径流对海域的影响范围和悬浮泥沙在海域的运动方向。     

黄河口水体光谱特性及悬沙浓度遥感估测

通过黄河口含沙水体野外遥感光谱反射率的观测实验，探讨了黄河口水体表观光谱特性，分析了悬浮体中有机颗粒含量和悬沙粒度对光谱特性的影响。针对Landsat TM／ETM^＋影像波段特性，对黄河口含沙水体在其可见光至近红外4个波段的光谱特性进行了模拟分析，并结合表观光谱观测数据建立了经验回归函数，以估测不同时相黄河口水体表层悬沙的浓度。     

Dynamics of coastal landform features along the southern Tamil Nadu of India by using remote sensing and Geographic Information System

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.     

Validations of suspended sediment concentration (ssc) derived using ocean colour monitor (ocm) data off Chennai coast, India

Indian Remote Sensing Satellite (IRS) — P4 Ocean Colour Monitor (OCM) data were used to estimate Suspended Sediment Concentration (SSC) in the coastal waters off Chennai and to study the distribution along the coast. Surface water samples were collected during May and October 2000 synchronized with satellite overpass, and quantitative estimates of Suspended Sediment Concentration (SSC) were done. OCM — Data Analysis System (DAS) software developed by the Space Applications Centre, Ahmedabad was used for OCM data processing and analysis. The field data and OCM derived SSC showed a correlation of r = 0.85 and r = 0.95 for the months of May and October respectively.     

基于MODIS 数据的悬浮泥沙定量遥感方法

以黄海及东海海域为对象,研究用MOD IS数据提取我国海域悬浮泥沙时空分布的定量遥感方法,建立了基于MOD IS数据的悬浮泥沙定量遥感实用模式。研究表明,用250 m和1 000 m分辨率的MOD IS数据进行悬浮泥沙浓度的定量遥感,可以达到实际应用的精度要求。这说明,MOD IS数据是研究近岸水体中悬浮物输运变化规律的一种经济实用数据源。     

Study of morphology and suspended sediment of Bhopal Upper Lake using spatial simulation technique and remote sensing data

The scope of this research was to study lake morphology using spatial simulation technique, to develop revised elevation-capacity curve, to develop elevation-water spread area curve, to study the relation between suspended sediment and remote sensing satellite data, and to estimate suspended sediment load in the lake using a Geographic Information System coupled with ground truth. The study area was the Bhopal Upper Lake, which has been classified as one of the major wetlands in India by the Ministry of Water Resources, India. A precise digital elevation model was created using 0.5 meter interval contour information collected from bathometric surveys. Water-spread areas at different water levels were simulated spatially in a Geographic Information System (GIS) through the neighbourhood connectivity operator. Revised elevation-capacity curve and elevation-area curve of the lake were prepared using the simulated results. Simulated water spread area at full tank level (FTL) was compared with the actual water spread area delineated using remote sensing data. Water samples at different locations of the lake were collected and located using the Global Positioning System (GPS) instrument. These samples were analysed in the laboratory for suspended sediment concentration. Different image processing techniques were applied to LANDSAT 5 TM satellite digital data (except thermal band). Correlation between radiance values of band 2 and suspended sediments was established and a positive linear equation was found to fit the data best. Spatial distribution of suspended sediment load was estimated using the developed regression equation and band 2 radiance image of the complete lake. Total suspended sediment load and loss of capacity at full tank level were computed.     

Geomatics Based Analysis of Predicted Sea Level Rise and its Impacts in Parts of Tamil Nadu Coast,India

The coastal zones around the world are very densely populated and hence heavily packed with related infrastructures. So, the territorial nations have obvious apprehensions against the IPCC SRES (Intergovernmental Panel on Climate Change, Special Report on Emission Scenario) predicted sea level rise, as it would cause flooding of the low lying coasts and also other related chains of environmental endangers. This has driven these nations to initiate research studies in multiple directions for scientifically evaluating the phenomenon and impacts of sea level rise using all possible technologies including the Geomatics which possesses unique credentials in geosystem mapping. But certain advanced virtues available with Geomatics technology are yet to be capitalized deservingly in this. In addition, almost all the earlier studies have focused only on the impacts of sea level rise (SLR) and not on the predicted shift of high tide line (HTL) and the related inter tidal activities, which would cause a series of environmental disaster. Hence, the present research study was undertaken in a test site of 750 km² in central Tamil Nadu coast to visualize the areas prone to submergence due to predicted SLR and areas prone to environmental disasters/degradation viz. erosion, deposition, salination of agricultural lands, pollution of aquifers, etc. due to predicted shift of HTL, using digital elevation models derived from SRTM data (Shuttle Radar Topographic Mission), geomorphology and land use/cover maps interpreted using IRS P6 LISS IV satellite data. The paper narrates the certain newer concepts and methodologies adopted in the study and the results.     

Total Suspended Sediments Mapping by Using ALOS Imagery Over the Coastal Waters of Langkawi Island, Malaysia

Remote sensing has been extensively used for water delineation and has played an important role in water quality evaluation and environmental management strategies. Suspended sediments are important determinants of water quality in coastal zones. Remote sensing enables the effective monitoring of total suspended sediments (TSS) and the detection of areas with critical water quality issues. This study aims to develop and implement regression models for estimating and mapping TSS concentrations from Advanced Land Observation Satellite (ALOS) images over the coastal waters of Langkawi Island, Malaysia. The algorithm was developed based on the water reflectance model, which is a function of the inherent optical properties of water. Such properties can then be linked to TSS concentration. In this study, an ALOS Advanced Visible and Near Infrared Radiometer type 2 device was used as the imaging sensor system. Concurrent complementary in-situ water samples were collected within the area coverage of the sensor, and digital numbers (DN) for each band corresponding to the sea-truth locations were determined. The extracted DN values were converted into reflectance values and then regressed with their respective sea-truth data. An algorithm was proposed to obtain the regression coefficient. This algorithm can estimate TSS concentrations with a high correlation coefficient (R²?=?0.96) and low root-mean-square error (RMSE?=?1.98 mg/l). Finally, a map of the TSS concentration was generated by using the proposed algorithm. This study found that TSS mapping can be conducted by using ALOS data over the coastal waters of Langkawi Island, Malaysia.     

Quantification of suspended solids in Dal lake,Srinagar using remote sensing technology

Indian Remote Sensing satellite (IRS)-1B, Linear Imaging Self Scanner (LISS)-II spectral digital data was analysed to determine the feasibility of quantifying the concentration of suspended solids in the surface water of inland water body, Dal lake, in Srinagar, India. The water samples collected in concurrent with IRS-1B overpass, were analysed to determine the concentration of suspended solids. The results indicate that a positive functional relationship exist between the concentration of suspended solids and the visible wave length bands 1 and 3 and near infrared band 4. It has been observed that as the concentration of suspended solids increase, the spectral response also increases. It is concluded that IRS LISS-H data can be effectively used to quantify suspended sediment concentration in the Dal lake surface water.     

随机森林回归模型的悬浮泥沙浓度遥感估算

三峡工程蓄水以来,清水下泄,坝下游河段发生了长时间、长距离的沿程冲刷,河流悬浮泥沙浓度发生改变,给沿岸生态系统带来了不利影响。随机森林算法灵活、稳健,已被广泛应用于各类生态环境变量的回归预测分析,但其在水体悬浮泥沙浓度估算方面的能力尚未得到充分认识。基于泥沙站点监测数据和MODIS卫星遥感反射率数据,通过构建随机森林非参数回归预测模型,对三峡工程坝下游宜昌至城陵矶河段在建坝前后14年间(2002年—2015年)各月的悬浮泥沙浓度进行遥感估算。研究表明:(1)基于随机森林的悬浮泥沙浓度估算模型表现较好,模型预测值与实测值间相关性好、预测精度高,优于其他模型(线性回归、支持向量机、人工神经网络模型)。(2)在参与模型构建的MODIS波段变量中,红波段被认为是最重要的预测变量,但不能单独使用它进行预测,悬浮泥沙遥感预测需要多变量共同参与。(3)将悬浮泥沙数据按季节分类所构建的随机森林模型,其平均误差为0.46 mg/L,平均相对均方根误差为12.33%,估算效果最优,能够满足较高精度下悬浮泥沙浓度估算的需求。综上,可以考虑以季节为划分依据,用随机森林回归模型估算悬浮泥沙浓度,并用于后期坝下游河道悬浮泥沙浓度时空反演。     

A three-band semi-analytical model for deriving total suspended sediment concentration from HJ-1A/CCD data in turbid coastal waters

The accurate assessment of total suspended sediment (TSM) concentration in coastal waters by means of remote sensing is quite challenging, due to the optical complexity and significant variability of these waters. In this study, three-band semi-analytical TSM retrieval (TSTM) model with HJ-1A/CCD spectral bands was developed for the retrieval of TSM concentration from turbid coastal waters. This model was calibrated and validated by means of one calibration dataset and three independent validation datasets obtained from three different turbid waters. It was found that the TSTM model may be used to retrieve accurate TSM concentration data from highly turbid waters without the spectral slope of the model requiring further optimization. Finally, the TSM concentration data were quantified from the HJ-1A/CCD images after atmospheric correction using the dark-object subtraction technique. Upon comparing the model-derived and field-measured TSM concentration data, it was observed that the TSTM model produced <29% uncertainty in deriving TSM concentration from the HJ-1A/CCD data. These findings imply that the TSTM model may be used for the quantitative monitoring of TSM concentration in coastal waters, provided that the atmospheric correction scheme for the HJ-1A/CCD imagery is available.     

Delineation of soil associations for a part of tamil nadu by digital processing of landsat multispectral data

The utility of Landsat multispectral data for small scale soil mapping has been demonstrated in this study. The scene used for the study has path-row number 153-054 of Landsat-1 dated 26th February, 1973 covering parts of Ramnad, Tirunelveli and Kanyakumari districts of Tamil Nadu. Associations of sub groups have been delineated on 1 : 250,000 scale using computer-aided multispectrcl data analysis system (M-DAS). Soil map prepared using the computer has been found to be camparable with the soil map prepared by conventional methods at the same scale. Apart from the soil associations, other land use/land cover classes like water bodies forest/scrub/hiliy areas, crcps etc. were also categorised in the colour coded soil map.     

Can suspended sediment concentrations be estimated from multispectral imagery using only image-derived information?

Sampling for suspended sediment concentrations (SSC) in inland waters is traditionally based on collecting samples at sparse locations and in limited intervals. A number of investigators explored the utility of earth-observing satellites and air-borne sensors for monitoring of SSC over vast areas. Two approaches are commonly deployed: (1) empirical relationships between a chosen remotely sensed quantity and the actual in-situ SSC; and (2) bio-optical models founded on radiative transfer modeling. Unfortunately, in-situ measurements are often unavailable for direct image calibration, and inherent optical properties of optically active constituents (specific scattering and absorption coefficients) are usually unknown. This paper examines the possibility to retrieve SSC from multispectral satellite imagery without any in-situ data, i.e. using only image-derived information. The fundamental principle of image selfcalibration relies on the fact that in the visual domain of wavelengths (∼400–700 nm) the at-sensor reflectance becomes “saturated“ at high SSC, whereas the near-infrared domain (∼700–900 nm) remains almost perfectly linearly related to sediment concentrations. The core idea of the self-calibrating procedure is rather simple and is based on fitting an exponential function between reflectance and SSC, with SSC replaced by a linear relationship between SSC and reflectance in the near-infrared domain. As a first approximation of the non-linearity between reflectance and SSC levels in the 400–700 nm range, we used the equation proposed by Schiebe et al. (1992), although other equations, especially those arising from optical theory could be used as well. The technique is illustrated on a moderately sediment-laden reservoir and two scenes acquired from Landsat ETM+. The standard error of the estimated SSC was below 15 mg/L (i.e. ∼25 % relative error for the observed range of SSC). Although the proposed algorithm does not yield better results than other models mentioned in the literature, the primary advantage of the outlined methodology is that no in-situ measurements (water sampling nor spectral profiling) are needed — i.e. only image-derived information is used.     

Characterization of spatial and temporal variation of suspended sediments in the Yellow and East China Seas using satellite ocean color data

Total suspended sediment (TSS) data concentrations are retrieved from two sets of satellite ocean color data (the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Korean Geostationary Ocean Color Imager (GOCI)) using an existing regional model to characterize spatial and temporal variation of TSS in the Yellow and East China Seas. MODIS-derived TSS maps show that TSS concentrations are, in general, high along the Korean and Chinese coasts including the Bohai Sea and the Yangtz River estuary, and lower in the middle of the Yellow Sea and the southeastern area of the East China Sea. The monthly average of 10-year MODIS data reveals that TSS values are highest during winter (January to February) and lowest in summer (July to August). Short-term TSS concentrations retrieved from GOCI data showed the dominant influence of semi-diurnal tidal changes on sediment dynamics through temporal (hourly) and spatial distribution in coastal zones of the Yellow sea. The results presented here demonstrate that the satellite-derived TSS products can be utilized as an application tool for future studies on long- and short-term sediment dynamics of turbid coastal waters. In particular, GOCI observations provide unique important capabilities to characterize and quantify the water properties at high temporal (hourly) and spatial (0.5 km) resolutions in the turbid coastal waters of the Yellow Sea and its vicinities.     

厦门同安湾滩槽演变趋势的遥感分析

利用多时相遥感图像判释厦门同安湾入海河流分布、海岸类型及浅海滩槽地貌,并根据海水表层流场特征和浑水带(悬浮泥沙)分布扩散特征及不同时期的海图,对同安湾泥沙供应现状和运移规律以及浅海滩槽冲淤变化、演变趋势进行了分析研究。结果表明,厦门同安湾海岸类型已由类型较齐全的天然海岸演变成单一的人工海岸,泥沙运移走向在平面上展示了逆时针泥沙水平环流特征,潮汐通道和局部海域出现了冲淤变化,部分潮滩处于缓慢淤积状态中。演变的动力因素主要是潮流,其次是径流,人工海岸形成后,陆源泥沙对海域基本不产生影响。     

基于Landsat影像的杭州湾最大浑浊带年际变化研究

杭州湾最大浑浊带（turbidity maximum zone,TMZ）受自然和人类活动的双重影响,年际变化显著。为探究杭州湾水域TMZ和表层悬浮泥沙浓度的年际变化特征,优选1984-2015年间30幅Landsat卫星影像,建立杭州湾水域表层悬沙浓度反演模型,模型经实测数据验证,平均相对误差为23.3%。对每张卫星影像进行悬沙浓度反演,进而提取TMZ面积数据。结果表明,杭州湾悬沙浓度面积分布类型均为正偏分布,且偏态系数由0.63增长至2.03,高悬沙浓度区域占比不断缩小。杭州湾各区域悬沙浓度均呈下降趋势,北岸芦潮港站下降趋势最为显著,减幅达73%。杭州湾TMZ面积年化下降率为4.57%,大于长江和钱塘江年输沙量的年化下降率3.74%。河流来沙减少和潮滩围垦导致的当地泥沙来源减少及水流携沙能力降低是影响TMZ面积降低的重要因素。