Evaluation of optical remote sensing-based shallow water bathymetry for recursive mapping

Water depth estimation using optical remote sensing offers a reliable and efficient means of mapping coastal zones. Here, we aim to find a suitable model for fast and practical bathymetry of an estuary using Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor (LISS-3) images. The study examines three different models; (1) least square regression model, (2) spectral band-ratio method and (3) multi-tidal bathymetry model. The findings are supported with in situ observed depth values and statistical estimates. Although the least square regression model has provided best results with root mean square error (RMSE) of 0.4 m, it requires a large number of observed data points for absolute depth estimation. Spectral band-ratio and multi-tidal model provides results with RMSEs 2.1 and 0.9 m, respectively. The present investigation demonstrates that multi-date imagery exploitation at disparate tide levels is the best estimation technique for recursive shallow water bathymetry where in situ observation is not possible.     

Bathymetric mapping over coastal Andhra Pradesh using landsat Mss data

Satellite imagery can be used to map shallow water depth using techniques which utilize only a few selected depth sounding points as input. For the present work three different models were considered for water depth estimation, which relate different parameters with the water depth. The Landsat-4 MSS data of the sounding points were used for calculation of model parameters. Goodness of fit of the above models was tested using statistical tests, which showed that the exponential relation between water body radiance and water depth gives best fit for shallow water bathymetry. Contour map of water depths over the coastal region of Andhra Pradesh near Machlipattnam have been drawn with the help of the above models using remotely sensed data of the area.     

Remote determination of chromophoric dissolved organic matter in lakes,China

Chromophoric dissolved organic matter (CDOM) strongly influences the water-leaving radiance from aquatic ecosystems. In most inland waters, the remote determination of CDOM absorption presents a central challenge due to their complex optical conditions. However, identifying the temporal and spatial variability of CDOM is fundamental to the understanding of aquatic biogeochemical dynamics. In the present study, semi-analytical and empirical modeling approaches were used to examine CDOM absorption in four, shallow, inland water bodies using the spectral bands and sensitivities of major satellite observational systems. Of the models examined, an empirical multiband model was found to provide the highest correlation with measured CDOM absorption. The spectral characteristics of the MERIS sensors yielded the best results with respect to the other available satellite sensors. High detrital load was observed to be a major impediment to estimating CDOM absorption, while lakes with elevated phytoplankton biomass did not present similar problems.     

多光谱浅海水深提取方法研究

利用我国南海某岛礁的TM数据和实测水深资料,试验性地研究了一种在不同底质反射条件下多光谱定量提取水深信息的方法,计算了浅海岛礁水深,取得了较好的应用效果和较高的测深精度.     

高分辨率卫星立体双介质浅水水深测量方法

提出了一种基于高分辨率卫星多光谱立体像对的浅水水深测量方法。该方法利用多光谱近红波段消除太阳耀斑,以有理函数模型(RFM)构建测区原始DEM,通过水陆边界内插获得水面高程,并采用双介质近似折射改正模型消除目标点垂直坐标偏移。试验表明,本文的模型方法和处理流程在水面平静、底质纹理丰富的浅海岛礁水深反演中能取得优于20%的相对测深精度,可为浅水水深测量提供新手段。     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

近岸/内陆典型水环境要素定量遥感空间尺度问题研究

近岸/内陆水环境遥感的空间尺度问题研究包括空间变异尺度及遥感监测空间尺度需求,以及多源多尺度遥感数据及定量产品的空间尺度误差两个方面。利用长时序高分一号16 m遥感数据集高时空分辨率的综合优势,采用空间半变异函数分析方法获取了中国近岸/内陆典型水环境要素（以悬浮颗粒物为例）的空间变异尺度。基于水环境要素空间变异的连续性和泰勒级数展开理论,定量化地描述了空间尺度误差解析函数。结果表明,近岸/内陆水体等高动态水体的空间变异尺度平均在150 m以下,而外海等相对稳定水体空间变异尺度在300 m以上。随着空间分辨率的降低,受到空间变异和水环境要素非线性定量反演模型的共同影响,悬浮颗粒物的空间尺度误差显著增大,亟需重点研究区域化的尺度误差校正方法。     

Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models

ABSTRACT

Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R² of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.     

Bathymetry retrieval from optical images with spatially distributed support vector machines

This paper presents a spatially distributed support vector machine (SVM) system for estimating shallow water bathymetry from optical satellite images. Unlike the traditional global models that make predictions from a unified global model for the entire study area, our system uses locally trained SVMs and spatially weighted votes to make predictions. By using IKONOS-2 multi-spectral image and airborne bathymetric LiDAR water depth samples, we developed a spatially distributed SVM system for bathymetry estimates. The distributed model outperformed the global SVM model in predicting bathymetry from optical satellite images, and it worked well at the scenarios with a low number of training data samples. The experiments showed the localized model reduced the bathymetry estimation error by 60% from RMSE of 1.23 m to 0.48 m. Different from the traditional global model that underestimates water depth near shore and overestimates water depth offshore, the spatially distributed SVM system did not produce regional prediction bias and its prediction residual exhibited a random pattern. Our model worked well even if the sample density was much lower: The model trained with 10% of the samples was still able to obtain similar prediction accuracy as the global SVM model with the full training set.     

机载激光浅海测深技术的现状和发展

水深测量是海洋测绘的重要内容。传统的船载多波束回声测深方法虽然具有较高的测量精度,但无法进入沿岸浅水区域和海礁密集区域。近年来,机载LiDAR的出现和发展填补了沿海浅水区域水深测量技术的空白,已成为一种快速高效的水深及海底地形探测方法。本文介绍了现有的几种有代表性的机载激光测深系统,给出了机载激光测深数据处理流程,分析了机载激光测深的关键技术,归纳并总结了该技术目前所存在的技术难点和未来的发展趋势。     

Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai: An Effect of Organic Matter

Geospatial approaches to monitoring and mapping water quality over a wide range of temporal and spatial scales have the potential to save field and laboratory efforts. The present study depicts the estimation of water quality parameters, namely turbidity and phosphate, through regression analysis using the reflectance derived from remote sensing data on the west coast of Mumbai, India. The predetermined coastal water samples were collected using the global positioning system (GPS) and were measured concurrently with satellite imagery acquisition. To study the influence of wastewater, the linear correlations were established between water quality parameters and reflectance of visible bands for either set of imagery for the study area, which was divided into three zones: creek water, shore‐line water and coastal water. Turbidity and phosphate have the correlation coefficients in the range 0.75–0.94 and 0.78–0.98, respectively, for the study area. Negative correlation was observed for creek water owing to high organic content caused by the discharges of domestic wastewater from treatment facilities and non‐point sources. Based on the least square method, equations are formulated to estimate turbidity and phosphate, to map the spatial variation on the GIS platform from simulated points. The applicability of satellite imagery for water quality pattern on the coast is verified for efficient planning and management.     

机载激光测深技术及其研究进展

水下地形测量是进行海洋科学研究的基础,也是海洋测绘的重要工作内容。近年来,机载激光测深技术的提出与应用有效地弥补了以舰船为载体的传统声学测深方法在近海浅水区作业存在的技术缺陷,也为相关工程问题的解决提供了新的技术手段。详细介绍了机载激光测深技术的基本原理与误差来源,概括与总结了国内外研究机构在系统研制及其有关算法研究方面的进展情况,并在此基础上分析了该技术在近海浅水区域的作业优势与所存在的关键性问题,以供相关研究参考。最后,结合机载激光测深技术目前的研究现状对未来该技术可能的发展方向进行了展望。     

Deriving Bathymetric Maps of the Shallow Coastal Water of the Gaza Strip Coastal Zone Using Passive Remotely Sensed Imagery

Bathymetry, the mapping of the shape of the surface of the seafloor, is a necessary science for coastal populations that rely heavily on the marine ecosystem for their survival and prosperity. Bathymetric maps are typically derived through a sound or light remote sensing system that is mounted on a boat or airplane that sweeps the designated study area. However, in regions with political constraints or logistical difficulties, reaching the study area and conducting in situ measurements can be difficult or impossible. Thus, using passive satellite imaging can be an alternative approach to traditional hydrographic surveying methods. As bathymetric maps are influenced by the local sea water conditions, the bottom type, and the water constituents, choosing a derivation algorithm for the satellite images which results in a robust and accurate map is very important. In this study, QuickBird imagery was selected to investigate the functionality and the accuracy of two different bathymetric derivation algorithms to obtain robust and reliable maps of the Gaza Strip coastal zone. Despite the high pollution level in the seawater of the investigated area, the findings confirmed the feasibility of obtaining reasonable accurate bathymetric maps.     

基于遥感影像的煤矿沉陷区水深反演研究

目前,用遥感研究水深多集中于沿海及内陆水域,煤矿塌陷地水深反演研究很少。本文以徐州九里塌陷湖为例,通过分析水深值与水体反射率的关系,得到各单因子的线性、非线性反演模型,经分析其误差相对较大;选取各水深分段内误差较小的因子,最终建立多元回归模型,其绝对误差、相对误差均优于单因子模型。     

无人船近海水深测量与抗风浪能力评估北大核心CSCD

传统载人测量船吃水深,无法进行浅海水深测量,因此吃水极浅的无人船为近海水深测量提供了新途径。本文利用无人船无验潮测深与GNSS网络RTK技术,在不同的风浪条件下进行了近海水深测量试验;并从轨迹跟踪与水深测量精度方面,定量评估了无人船野外作业的抗风浪能力。结果表明:0~0.3 m浪、0~2级风是无人船近海测量作业的理想风浪阈值;风浪过大会导致无人船路径跟踪精度较低,难以保证测量成果质量。在理想的风浪阈值内,无人船无验潮测深技术能实现近海水深的高精度测量,满足相关测深规范要求,在近海水深与海底地形测量中具有广阔的应用前景。     

Shoreline and coral reef ecosystem changes in gulf of Mannar, Southeast coast of India

Changes in shoreline, coral reef and seafloor have been mapped using remote sensing satellite data of IRS LISS-III (1998), IRS LISS-II (1988), Survey of India Topographic sheet (1969), Naval Hydrographic Chart (NHO) 1975 and bathymetry data (1999) with ARC-INFO and ARC-VIEW GIS. The analysis of multi-date shoreline maps showed that 4.34 and 23.49 km2 of the mainland coast and 4.14 and 3.31 km² areas of island coast have been eroded and accreted, respectively, in the Gulf of Mannar. The analysis of multi-date coral reef maps showed that 25.52 km² of reef area and 2.16 km² of reef vegetation in Gulf of Mannar have been lost over a period of ten years. The analysis of multi-date bathymetry data indicates that the depth of seafloor has decreased along the coast and around the islands in the study area. The average reduction of depth in seafloor has been estimated as 0.51m over a period of twenty four years. The increased suspended sediment concentration due to coastal and island erosion, and raised reef due to emerging of coast by tectonic movement are responsible for coral reef degradation in the Gulf of Mannar. Validation by ground truth has confirmed these results.     

运用MODIS遥感数据评测南海北部区域机载激光雷达测深系统参数

为研究中国南海北部海域在CZMIL海道测量模式下的最大可测水深的空间分布情况,首先探讨了现有的南海北部海域漫衰减系数K_d（490）反演算法,运用南海北部海域水色实测数据建立了漫衰减系数K_d（490）和K_d（532）之间的数值关系,总结了漫衰减系数K_d（532）和CZMIL系统最大可测水深之间的关系。通过2014年Aqua-MODIS遥感光谱数据得到了南海北部海域1月、6月、10月的海水漫衰减系数K_d（532）参数,研究发现6月份时该区域平均漫衰减系数相对较小,于是进一步合成了该月份的CZMIL系统测深能力空间分布图。结果表明：CZMIL系统在南海北部海域的可测水深约为0~71.18 m;6月份比1月、10月更适合激光测深作业。该研究为南海北部海域开展激光测深作业的时间选择和飞行方案的制订提供了参考。     

江苏近海岸水深遥感研究

以江苏近海辐射沙脊群海域为典型研究区,通过实测水深数据和水体光谱测量与分析,发现对应TM3和TM4波段的水体光谱反射率对水深信息敏感,线性相关系数分别达到-0.561和-0.694。结合多光谱遥感信息传输方程所推导出的水深信息对数反演模式,针对本研究区TM4和TM3波段数据所建立的水深预测模式的复相关系数R2为0.4793,对0-15m水深,预测水深和实测水深之间拟合较好。利用TM5波段反射率、出露沙洲反射率以及海水反射率的差异,通过建立掩膜图像,可较有效地对TM遥感图像进行水陆分离,提取TM图像中海水部分,进一步可通过常用的图像处理软件绘制每隔5m的TM水深遥感制图、等深线图。随着高空间、高光谱、高辐射分辨率遥感技术的发展,对浅海水域的水深和水下地形进行遥感探测的技术方法和应用将会不断地深入开展。     

The Application of GF-1 Imagery to Detect Ships on the Yangtze River

Various satellite data are currently used to detect ships on the sea surface. However, no study on the use of Gaofen-1 (GF-1) data to monitor ships on the surface of inland rivers has been reported. Therefore, we proposed a method to extract inland river-surface ships from GF-1 imagery. The Normalized Differential Water Index was calculated to enhance the contrast between water and non-water areas after the preprocessing procedure. The multi-resolution segmentation method and object-oriented classification rule sets were used to detect the ships in the image. Results show that most of the ships, whose length-to-width ratio ranges from 3.0 to 7.2, could be identified correctly regardless of their size. The results also indicate that detecting ships on inland rivers using GF-1 imagery is feasible.     

高分辨率光学卫星遥感影像高精度无地面控制精确处理的理论与方法

卫星影像全球无地面控制高精度几何定位是卫星摄影测量技术发展追求的主要目标,也是实现困难地区和境外地区测图的关键支撑技术。本文围绕我国国产遥感卫星的技术发展,详细论述了高分辨率光学卫星遥感影像高精度无地面控制几何定位的理论与方法,在天星地一体化全链路误差建模分析的基础上,提出了在轨几何定标理论与方法、稳态重成像几何处理模型与方法及大规模无地面控制区域网平差理论与方法。将本文方法应用于资源三号卫星影像的数据处理,试验结果满足1∶50 000测图精度,证明了理论和方法的正确性。