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.     

Blue Earth River quality monitoring using ARCHER hyperspectral data and field measurements

In past decades, remote sensing studies on water quality mapping mainly focused on lakes using medium resolution imagery. Little research utilizes hyperspectral images to assess river water quality. This study aims to assess the capability of using Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) for river water quality mapping and to characterize spatial patterns of turbidity and chlorophyll in the Blue Earth River (BER) system of Minnesota. The BER was characterized by both hyperspectral imagery (HSI) and laboratory analysis of synchronously collected water quality data. The optimal bands for water quality mapping were determined using a method based on hyperspectral profiles and derivative analysis. Finally, based on the regression analysis and modelling, we mapped continuous surface water turbidity. The results revealed that the ratio of HSI band 17 to band 9 effectively determined turbidity and chlorophyll concentrations. The study also found that turbidity and chlorophyll in the river generally increases downstream.     

Glacier surface characteristics derivation and monitoring using Hyperspectral datasets: a case study of Gepang Gath glacier,Western Himalaya

Mountain Glaciers are natural resources of fresh water and these affect the stream flow of the rivers, regional climate and further global climate. Observed trends and projected future evolutions of climate and Cryospheric variables clearly suggest a need to monitor these changes. Accordingly, the article presents the glacier features mapping using Hyperspectral remote sensing imagery. A freely available Hyperion satellite imagery acquired over Gepang Gath glacier in Himachal Pradesh, India is used for the study. Each class is identified based on their surface characteristics of spectral reflectance properties. Identification is simplified by demarcating the study glacier into accumulation and ablation areas through snowline. Accumulation area is characterized with high reflectance clean snow/ice and reduced moderate reflectance Snow/firn. The identification of classes in Hyperion imagery is validated using the spectral library from USGS and ASTER, and field spectra obtained from literature.     

A method to generalize stream flowlines in small-scale maps by a variable flow-based pruning threshold

The aim of this paper is to explore and describe a method of automated generalization designed to produce a map which strikes a balance between cartographic and hydrologic representations. Following a discussion of scholarly literature on generalization, we describe a novel method for automated generalization of hydrographic stream data, using the National Hydrography Data Set (NHDPlus) as an example.

Traditional hydrography shows a fairly uniform density of stream flowlines over space. While this is pleasing to the eye, traditional methods tend to under-represent rivers in humid areas and over-represent them in arid areas. We address this problem through a method in automated generalization to produce a high-quality presentation of hydrographic data, suitable for display as a wall map or in an atlas. Streams are pruned based on a variable flow threshold, derived from the local mean annual precipitation by a regression equation.

After running the model using different parameters, we produce a more satisfactory portrayal of stream networks in the United States that communicates the flow of water through rivers and reflects the regional climate. Specific advantages in generalizing with variable flow threshold include (1) the method allows for fine gradations in output scale; (2) the output maps tend to minimize density variations in the raw data; (3) the subjective criteria are easily derived; and (4) the method can be performed rapidly on large data sets, as long as the stream data has been enriched with reliable flow rates.     

A study of a matching pixel by pixel (MPP) algorithm to establish an empirical model of water quality mapping,as based on unmanned aerial vehicle (UAV) images

Linear regression models are a popular choice for the relationships between water quality parameters and bands (or band ratios) of remote sensing data. However, this research regards the phenomena of mixed pixels, specular reflection, and water fluidity as the challenges to establish a robust regression model. Based on the data of measurements in situ and remote sensing data, this study presents an enumeration-based algorithm, called matching pixel by pixel (MPP), and tests its performance in an empirical model of water quality mapping. Four small reservoirs, which cover a mere several hundred-thousand m², in Kinmen, Taiwan, are selected as the study sites. The multispectral sensors, carried on an unmanned aerial vehicle (UAV), are adopted to acquire remote sensing data regarding water quality parameters, including chlorophyll-a (Chl-a), Secchi disk depth (SDD), and turbidity in the reservoirs. The experimental results indicate that, while MPP can reduce the influence of specular reflection on regression model establishment, specular reflection does hamper the correction of thematic map production. Due to water fluidity, sampling in situ should be followed by UAV imaging as soon as possible. Excluding turbidity, the obtained estimation accuracy can satisfy the national standard.     

青藏高原区域水储量变化的GRACE RL06和TRMM联合反演

近10年来,独立成分分析（ICA）逐渐被引入地球时变重力场的成分提取中。ICA因其提取成分的独立性,解决了运用主成分分析（PCA）时各提取成分仅互不相关的这一关键性问题。基于负熵的FastICA算法是常见的ICA方法,该算法的收敛较梯度算法快,且负熵的稳健性较峭度更好。本文采用上述算法,旨在探寻青藏高原区域水储量成分与降水成分之间的联系。研究表明,青藏高原的水储量变化较降水存在着7个月的延迟效应,显示出除季节性冻土与冰川的冻融和季风气候所带来降水贡献之外,还客观存在着因区域内水交换造成的延迟。结合特征信号来看,青海中部、西藏西部与西藏中东部之间的年均水储量变化刚好相反,在全球变暖所导致的冻土与冰川加速消融的大背景下,因气候与内外流区的不同造成区域水储量变化的差异性,此外上述区域的水储量变化也因青藏高原南北板块构造的不同而体现出差异性。研究证明了在大尺度范围内运用FastICA算法分析区域水储量成分及其相关因素变化的可行性。     

应用GA-SVM的渭河水质参数多光谱遥感反演

建立了基于支持向量机的遥感水质参数反演模型, 构建了基于浮点数编码的遗传算法优选模型参数。以渭河为研究对象, 基于高分辨率多光谱遥感SPOT-5数据和水质实地监测数据, 分别建立了一元和多元经验模型进行渭河水质参数的反演。在样本数目有限的情况下, 提出的GA-SVM方法的反演结果比神经网络和传统的统计回归方法好, 且各方法的多元回归结果均好于一元回归的结果。SVM具有强的非线性映射能力, 适合小样本情况, 由GA实现了模型参数的自动优选, 使GA-SVM用于解决回归问题表现出优势。将机器学习和全局优化智能     

RivWidth: A Software Tool for the Calculation of River Widths From Remotely Sensed Imagery

RivWidth is an implementation in ITT Visual Information Solutions IDL of a new algorithm that automates the calculation of river widths using raster-based classifications of inundation extent derived from remotely sensed imagery. The algorithm utilizes techniques of boundary definition to extract a river centerline, derives a line segment that is orthogonal to this line at each centerline pixel, and then computes the total river width along each orthogonal. The output of RivWidth is comparable in quality to measurements derived using manual techniques; yet, it continuously generates thousands of width values along an entire stream course, even in multichannel river systems. Uncertainty in RivWidth principally depends on the quality of the water classification used as an input, though pixel resolution and the values of input parameters play lesser roles. Source code for RivWidth can be obtained by visiting http://pavelsky.googlepages.com/rivwidth.     

Study on estimating the evapotranspiration cover coefficient for stream flow simulation through remote sensing techniques

This study focuses on using remote sensing techniques to estimate the evapotranspiration cover coefficient (CV) which is an important parameter for stream flow. The objective is to derive more accurate stream flow from the estimated CV. The study area is located in the Dan-Shuei watershed in northern Taiwan. The processes include the land-use classification using hybrid classification and four Landsat-5 TM images; the CV estimations based on remote sensing and traditional approaches; comparison of stream flow simulation according to the above two CV values. The result indicated that the study area was classified into seven land-use types with 88.3% classification accuracy. The simulated stream flow using remote sensing approach could represent more accurate hydrological characteristics than a traditional approach. Obviously integrating remote sensing technique and the SEBAL model is a useful approach to estimate the CV. The CV parameter estimated by remote sensing technique did improve the accuracy of the stream flow simulation. Therefore, the results can be extended to further studies such as forest water management.     

GIS技术在数字化地质填图中的实施方法

针对区域地质调查特点,介绍了数字化地质填图过程中基于地理信息系统(G IS)技术的地质图数字化的流程及进行该项工作所需要的软件。阐述了建立在G IS技术基础上的数字化地质填图的工作流程及实施过程中的关键技术。     

苏州运河水质的TM分析

本文利用苏州地区陆地卫星TM数据和同期的地面水质监测资料建立了TM图像遥感水质模型,并将该模型应用于TM可见光彩色合成图像的分割处理,得到苏州地区水质空间分布图像。结合苏州运河水网的水文及水污染特征对遥感水质空间分布图像作了分析。结果表明TM数据真实地反映了苏州地区水质的空间分布规律,并得到当地环境监测部门的验证。     

Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing

ABSTRACT

Monitoring of inland water quality is of significant importance due to the increase in water quality related issues, especially within the Midwestern United States. Traditional monitoring techniques, although highly accurate, are vastly insufficient in terms of spatial and temporal coverage. Using a virtual constellation by harmonizing Landsat-8 and Sentinel-2 data a high temporal frequency dataset can be created at a relatively fine spatial scale. In this study, we apply a novel deep learning method for the estimation of blue-green algae (BGA), chlorophyll-α (Chl), fluorescent dissolved organic matter (fDOM), dissolved oxygen (DO), specific conductance (SC), and turbidity. The developed model is evaluated against previously studied machine learning methods and found to outperform multiple linear regression (MLR), support vector machine regression (SVR), and extreme learning machine regression (ELR) generating R² of 0.91 for BGA, 0.88, 0.89, 0.93, 0.87, and 0.84 for Chl, DO, SC, and turbidity respectfully. This model is then applied to all available data ranging from 2013–2018 and time series for each variable were generated for four selected waterbodies. We then use the Empirical Data Analytics (EDA) anomaly detection method on the time series to identify abnormal data points. Upon further analysis, the EDA method successfully identifies abnormal events in water quality. Our results also demonstrate strong correlation between non-optically active variables such as SC with Chl and fDOM. The framework developed in this study represents an efficient and accurate empirical method for inland water quality monitoring at the regional scale.     

格网DEM上径流长度计算误差的定量分析

基于格网DEM的径流长度的计算误差主要来源于DEM数据和流径提取算法。通过数据独立的误差分析方法,定量分析流径提取算法模型产生的误差。首先选取5种数学曲面作为模拟DEM数据源,计算出径流长度,以由曲面公式推导的径流长度公式计算的径流长度作为理论真值,通过两者的对比,分析误差及其空间分布。结果表明,汇水区的径流长度计算精度明显高于分水区;分水区的径流长度含有一定的系统误差。     

高光谱遥感积雪制图算法及验证

雪盖面积是高山地区和季节雪盖区水文和气象模型的重要输入因子。机载和星载遥感数据提取的雪盖面积是融雪径流模型的重要组成部分。对应不同传感器件的光谱特征，多种分类方法被相继提出。但是，缺乏相对独立的验证手段来评价各种分类方法，其主要原因是缺乏地面真实状态。针对该现状，本研究利用高光谱图像的细分光谱特征，建立高光谱影像及其对应“地面真相”的像对数据库来发展和验证积雪制图算法，并展示MODIS积雪制图算法验证和ASTER混合像元分解雪盖制图算法研究的应用实例。     

Mapping Large Spatial Flow Data with Hierarchical Clustering

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high‐level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi‐resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.     

一种全场景水系流向自动检查算法

水系流向是基础地理信息数据库水系数据的重要特征之一,准确的流向是下一步数据分析应用的基础。而基于摄影测量先内后外的采集方式容易导致流向采集错误,若以人工方式检查费时费力且容易有遗漏。针对这一情况,本文设计了全场景水系线方向自动化检查算法,包括交汇冲突检查与落差异常检查两部分。算法全面可靠,无需叠加外部数据,大大减少了人工比对的工作量,提高了质检效率。     

合成孔径雷达图像填图方法与应用探讨

以ERS-2和RadarSat-1卫星雷达数据为信息源，通过新疆1∶25万大黑山幅遥感地质填图研究，从方法与应用角度分析论述了雷达图像的地质反映，总结出回波强度与地形单元和岩石之间变化规律、宏观粗糙度对回波强度的控制以及灰度-结构单元作为填图基本单位的方法技术和填图含义。     

局部曲面正切曲率与流线曲率的水流路径算法

在局部特殊地形如细窄的沟渠等地带,水流方向可能是惟一的,即当前点的水应全部流入下游惟一的格网点,此时若采用多流向算法不仅会导致水流方向产生较大误差,而且可能影响区域汇水面积的精确计算。为解决此问题,本文提出了一种水流路径算法,该算法通过以各格网点为中心建立3×3窗口范围内的局部趋势面,利用该点在局部趋势面中的正切曲率和流线曲率选取合适的单流向或多流向算法来确定并计算该点的水流流向及流量分配比例。最后通过实例证明了该方法在水流累积量计算中的良好效果,并在一定程度上为格网DEM沟谷段和分水线段的提取奠定了基础。     

Understanding the potential relationship between the socio-economic variables and contributions to OpenStreetMap

OpenStreetMap (OSM) has seen an exponential increase in the last few years and large volumes of geodata have been received from volunteered individuals. The collected geodata are heterogeneous in terms of different dimensions such as spatial patterns of contributions, quality, patterns of contributing individuals, and type of contributions. Because contributors’ personal information is anonymously stored by the OSM administrators, alternative methods are needed to investigate the role of contributors’ characteristics on their mapping behavior. This study is intended to explore the potential socio-economic characteristics of contributors in highly contributed areas to have better insights about the latent patterns of involved individuals in a highly dynamic state of the most active country in OSM, Germany. A logistic regression model (LRM) is applied to discover the potential correlations between dependent and independent variables. The findings explain that the areas with high population density, middle level of education, high income, high rate of overnight stays, high number of foreigners, and residents aged from 18 to 69 are more likely to be involved in OSM. Furthermore, the degree of dynamism in OSM is a function of proximity to built-up areas. Finally, concluding remarks concerning the independent variables and model sensitivity are presented.