Remote Sensing Applied to the Evaluation of Spatial and Temporal Variation of Water Quality in a Coastal Environment, Southeast Brazil

The monitoring of water quality in large coastal regions demands great analytical efforts through the collection of many samples, over long periods. Remote sensing is a reliable tool that can provide valuable information on the spatial and temporal variations of environmental parameters, particularly turbidity and chlorophyll a. The aim of the present research was to evaluate the spatial and temporal distribution of water quality from 2005 to 2017 along the north coast of São Paulo and its responses to the implementation of industrial developments and to variations in rainfall. Fifty-two MODIS images were used, showing concentrations of chlorophyll a and turbidity, in the dry season and wet season, from 2005 to 2017. The results showed that dilution processes (due to rainfall) control chlorophyll a concentrations. However, a notable increase in concentrations could be identified after the installation of some of the developments in the region, particularly roads and ports. Turbidity was also shown to be affected by dilution processes, and during the wet season this parameter presented lower values. No effect in the results of turbidity could be identified from the installation of roads or ports, showing that vegetation cover exerts an important control on the erosional processes.

River Water Quality Model Based on Remote Sensing Information Methods——A Case Study of Lijing River in Guilin City

River water quality models based on remote sensing information models are superior to pure water quality models because they combine the inevitability and risk of geographical phenomena and can take complex geographical characteristics into account. A water quality model for forecasting COD has been established with remote sensing in- formation modeling methods by monitoring and analyzing water quantity and water quality of the Lijing River reach which flows through a complicated Karst mountain area. This model provides a good tool to predict water quality of complex rivers. It is validated by simulating contaminant concentrations of the study area. The results show that remote sensing information models are suitable for complex geography. It is not only a combined model of inevitability and risk of the geographical phenomena, but also a semi-theoretical and semi-empirical formula, providing a good tool to study organic contaminants in complicated rivers. The coefficients and indices obtained have limited value and the model is not suitable for all situations. Some improvements are required.

Integrated Approach Using Remote Sensing and GIS for Assessment of Groundwater Quality and Hydrogeomorphology in Certain Parts of Tummalapalle Area, Cuddapah District, Andhra Pradesh, South India

The present work deals with the assessment of groundwater potential zones and their suitability for drinking in the severely drought affected villages of Vemula mandal of Cuddapah District, Andhra Pradesh. This study is based on remote sensing and GIS approach. In this approach the IRS P6 LISS III Data (23.5 m Spatial Resolution) with Path: 100;Row: 063 of Indian Remote Sensing Satellite, Resourcesat IRS-P6 LISS-III has been utilized to analyse the onscreen interpretation and delineated different geomorphological units, lithological formations and geological structures. By integrating the above said parameters the hydrogeomorphological map is prepared on 1:50,000 scale. The study area is characteristically occupied by the Papaghni and Chitravati group of rocks. In the present study, the lithological formations on the basis of their genesis have been classified as fluvial, denudational and structural. Majority of lineaments are trending in NE-SW and NW-SE directions. The fluvial landforms namely valley fill moderate and valley have good groundwater prospects while shallow weathered buried pediplain has moderate to poor groundwater prospects. The chemical quality parameters of groundwater samples have indicated that the water samples are found to be suitable for drinking, agricultural, and industrial purposes.

Effects of Ulva prolifera dissipation on the offshore environment based on remote sensing images and field monitoring data

Outbreaks of Ulva prolifera have continued in the South Yellow Sea of China(SYS)since 2007,becoming a serious marine ecological disaster.Large amounts of U.prolifera drift to the coast of the Shandong Peninsula to dissipate under the action of southeast monsoons and ocean surface currents.This causes serious harm to the ecological environment and economic activities of coastal cities.To investigate the impact of U.prolifera dissipation,this study extracted the spatiotemporal distribution of U.prolifera in the SYS from 2012 to 2020 based on the Google Earth Engine.The outbreak cycle of U.prolifera was determined by fitting analysis of outbreak time and coverage area through MATLAB.This study also looked at the effect of U.prolifera dissipation on water quality through field monitoring data.The results showed that the growth curve of the U.prolifera has a significant Gaussian distribution.The U.prolifera dissipates in Haiyang,China,in July and August every year and affects the offshore environment.Water quality parameters of seawater at different depths had significant differences after the U.prolifera dissipation.Changes in pH,chemical oxygen demand,nitrite nitrogen,nitrate nitrogen,ammonia nitrogen,chlorophyll a,total phosphorus,and suspended solids were more significant in surface seawater than in deeper water.Changes in the concentrations of dissolved oxygen and total nitrogen were more significant in the deep seawater(1.63 and 1.1 times higher than those in the surface seawater,respectively).The dissipation of U.prolifera releases a large amount of carbon and nitrogen into the seawater,which provides rich nutrients for phytoplankton and may cause secondary disasters such as red tide.These findings are useful for further understanding the rules of U.prolifera dissipation,as well as preventing and controlling green tide disasters.

Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water

Hyperspectral remote sensing offers an effective approach for frequent, synoptic water quality measurements over a large spatial extent. However, the optical complexity of case 2 water makes the water quality monitoring by remote sensing in estuarine water a challenge. The prime objective of this study was to develop algorithms for hyperspectral remote sensing of water quality based on in situ spectral measurement of water reflectance. In this study, water reflectance spectra R(λ) were acquired by a pair of Ocean Optic 2000 spectroradiometers during the summers from 2008 to 2011 at Patuxent River, a tributary of Chesapeake Bay, USA. Simultaneously, concentrations of chlorophyll a and total suspended solids (TSS), as well as absorption of colored dissolved organic matter (CDOM) were measured. Empirical models that based on spectral features of water reflectance generally showed good correlations with water quality parameters. The retrieval model that using spectral bands at red/NIR showed a high correlation with chlorophyll a concentration (R2 = 0.81). The ratio of green to blue spectral bands is the best predictor for TSS (R2 = 0.75), and CDOM absorption is best correlated with spectral features at blue and NIR regions (R2 = 0.85). These empirical models were further applied to the ASIA Eagle hyperspectral aerial imagery to demonstrate the feasibility of hyperspectral remote sensing of water quality in the optical complex estuarine waters.

Fuzzy Integral Based Information Fusion for Water Quality Monitoring Using Remote Sensing Data

To improve the monitoring precision of lake chlorophyll a (Chl-a), this paper presents a fusion method based on Choquet Fuzzy Integral (CFI) to estimate the Chl-a concentration. A group of BPNN models are designed. The output of multiple BPNN model is fused by the CFI. Meanwhile, to resolve the over-fitting problem caused by a small number of training sets, we design an algorithm that fully considers neighbor sampling information. A classification experiment of the Chl-a concentration of the Taihu Lake is conducted. The result shows that, the proposed approach is superior to the classification using a single neural network classifier, and the CFI fusion method has higher identification accuracy.

Evaluation of Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) for Water Quality Monitoring:A Case Study for the Estimation of Salinity

Landsat-5 Thematic Mapper(TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum(6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square(OLS) regression and Geographically Weighted Regression(GWR) based on in situ data of October 2009. Results show that the coefficient of determination(R^2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher(R^2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay(north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant(32 practical salinity units) towards the open sea.

Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6(Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0–5 m because of the influence of human activities. The inversion accuracy of 5–10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5–20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.

A spatial resolution effect analysis of remote sensing bathymetry

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.

Application of Multivariate Model Based on Three Simulated Sensors for Water Quality Variables Estimation in Shitoukoumen Reservoir,Jilin Province,China

This study applied a multivariate model based on three simulated sensors to estimating water quality variables in Shitoukoumen Reservoir,Changchun City,Jilin Province,China,including concentration of total suspended matter,concentration of chlorophyll-a and non-pigment matter absorption.Two field campaigns for spectra measurements with a total of 40 samples were carried out on June 13 and September 23,2008.The in-situ spectra were recalculated to the spectral bands and sensitivities of the instruments applied in this paper,i.e.Landsat TM,Alos and P6,by using the average method.And the recalculated spectra were used for estimating water quality variables by the single model and multivariate model.The results show that the multivariate model is superior to the single model as the multivariate model takes the combined effects of water components into consideration and can estimate water quality variables simultaneously.According to R2 and RMSE,Alos is superior to other sensors for water quality variables estimation although the precision of non-pigment matter absorption inversion performed the second.

Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study

Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.

Introduction to the airborne marine surveillance platform and its application to water quality monitoring in China

China Marine Surveillance Force was equipped with modern aerial equipments for marine lawexecute with the advantage of functioning agilely at a large scale of surveillance coverage, providing powerful all-round safeguard, which is of benefit to the harmonious and sustainable development of coastal economy. Onboard the planes, three kinds of remote sensing sensors have been installed, including a marine airborne multi-spectrum scanner （MAMS）, an optical-electronic platform, and an airborne hyper-spectral system AISA＋. The specifications of remote sensing platforms were introduced briefly first, then examples of water quality monitoring by airborne remote sensing were presented, including the monitoring in coastal suspended material, oil-spill and abnormal warm water, etc.

Spectral Geometric Triangle Properties of Chlorophyll-A Inversion in Taihu Lake Based on TM Data

The main objective of this study was to develop and validate the applicability of the Area Chlorophyll-a Concentration Retrieved Model (ACCRM), Height Chlorophyll-a Concentration Retrieved Model (HCCRM), Angle Chlorophyll-a Concentration Retrieved Model (AgCCRM), and Ratio Model of TM2/TM3 (RM) in estimating the chlorophyll-a concentration in Case II water bodies, such as Taihu Lake in Jiangsu Province, China. Water samples were collected from 23 stations on the 27th and 28th of October, 2003. The four empirical models were calibrated against the calibration dataset (samples from 19 stations) and validated using the validation dataset (samples from 4 stations). The regression analysis showed higher correlation coefficients for the ACCRM and the HCCRM than for the AgCCRM and the Ratio Model;and the HCCRM was slightly superior to the ACCRM. The performance of the ACCRM and the HCCRM was validated, and the ACCRM underestimated concentration values more than the HCCRM. The distribution of chlorophyll-a concentrations in Taihu Lake on October 27, 2003 was estimated based on the Landsat/TM data using the ACCRM and the HCCRM. Both models indicated higher chlorophyll-a concentrations in the east, north and center of the lake, but lower concentrations in the south. The accuracy of results obtained from the HCCRM and the ACCRM were also supported by the validation dataset. The study revealed that the HCCRM and the ACCRM had the best potential for accurately assessing the chlorophyll-a concentration in the highly turbid water bodies.

Hyper-spectrum models for monitoring water quality in Dianshan Lake,China

The correlation between water quality parameters and hyper-spectral reflectance is studied with models established for each parameter and applied in Dianshan Lake, in the upstream of the Huangpu River running through Shanghai, China. Models are for dissolved oxygen (DO in mg/L): R720/R680 = 20.362×(R720/R680)2?31.438×(R720/R680)+19.156; for turbidity (NTU): R*714.5 = 206.07× (R*714.5)2?582.5×R*714.5 + 423.24; and for total phosphorus (TP in mg/L): R*509.5 = 16.661× (R*509.5)2?32.646×R*509.5+16.116. The R2 values are 0.770 8, 0.660 4 and 0.738 7, respectively, showing strong positive relationships. The models were then applied to assess water quality of different times. Results are quite satisfactory for some samples.

An integrated measurement and modeling methodology for estuarine water quality management

This paper describes research undertaken by the authors to develop an integrated measurement and modeling methodology for water quality management of estuaries. The approach developed utilizes modeling and measurement results in a synergistic manner. Modeling results were initially used to inform the field campaign of appropriate sampling locations and times, and field data were used to develop accurate models. Remote sensing techniques were used to capture data for both model development and model validation. Field surveys were undertaken to provide model initial conditions through data assimilation and determine nutrient fluxes into the model domain. From field data, salinity re- lationships were developed with various water quality parameters, and relationships between chlorophyll a concentrations, transparency, and light attenuation were also developed. These relationships proved to be invaluable in model development, particularly in modeling the growth and decay of chlorophyll a. Cork Harbour, an estuary that regularly experiences summer algal blooms due to anthropogenic sources of nutrients, was used as a case study to develop the methodology. The integration of remote sensing, conventional fieldwork, and modeling is one of the novel aspects of this research and the approach developed has widespread applicability.

Geospatial Evaluation for Ecological Watershed Management: A Case Study of Some Chesapeake Bay Sub-Watersheds in Maryland USA

Geospatial technology is increasingly being used for various applications in environmental management as the need for sustainable development becomes more evident in today’s rapidly-developing world. As a decision tool, Geographic Information system (GIS) and Global positioning System (GPS) can support major decisions dealing with natural phenomena distributed in space and time. Such is the case for land use/cover known to impact ecosystems health in very direct ways. Our study examined one such application in managing land use of some sub-watersheds in the eastern Shore of Maryland, USA. We conducted a 20-year historical land use/cover evaluation using Landsat-TM remotely sensed images and GIS analysis and water monitoring data acquired during the period by Maryland Department of Natural Resources, including sewage discharge of some municipalities in the area. The results not only showed general trends in land use patterns, but also detailed dynamics of land use-land cover classes, impact on water quality, as well as other useful information for guiding both terrestrial and aquatic ecosystems management decisions of the sub-watersheds. The use of this technology for evaluating trends in land use/cover on a decade-by-decade basis is recommended as standard practice for managing ecosystem health on a sustainable basis.

Satellite-observed significant improvement in nearshore transparency of the Bohai Sea during pollution control

The Bohai Sea(BS)is the unique semi-closed inland sea of China,characterized by degraded water quality due to significant terrestrial pollution input.In order to improve its water quality,a dedicated action named“Uphill Battles for Integrated Bohai Sea Management”(UBIBSM,2018–2020)was implemented by the Chinese government.To evaluate the action effectiveness toward water quality improvement,variability of the satelliteobserved water transparency(Secchi disk depth,Z_(SD))was explored,with special emphasis on the nearshore waters(within 20 km from the coastline)prone to terrestrial influence.(1)Compared to the status before the action began(2011–2017),majority(87.3%)of the nearshore waters turned clear during the action implementation period(2018–2020),characterized by the elevated Z_(SD)by 11.6%±12.1%.(2)Nevertheless,the improvement was not spatially uniform,with higher Z_(SD)improvement in provinces of Hebei,Liaoning,and Shandong(13.2%±16.5%,13.2%±11.6%,10.8%±10.2%,respectively)followed by Tianjin(6.2%±4.7%).(3)Bayesian trend analysis found the abrupt Z_(SD)improvement in April 2018,which coincided with the initiation of UBIBSM,implying the water quality response to pollution control.More importantly,the independent statistics of land-based pollutant discharge also indicated that the significant reduction of terrestrial pollutant input during the UBIBSM action was the main driver of observed Z_(SD)improvement.(4)Compared with previous pollution control actions in the BS,UBIBSM was found to be the most successful one during the past 20 years,in terms of transparency improvement over nearshore waters.The presented results proved the UBIBSM-achieved remarkable water quality improvement,taking the advantage of long-term consistent and objective data record from satellite ocean color observation.

兰州市南北两山土壤水分遥感反演及植被需水量估算

探究西北干旱区土壤水分和植被需水量动态变化特征,可为生态恢复不同阶段所需水资源量及水资源优化配置提供科学依据。以兰州市南北两山为研究区,基于Sentinel-2 L2A和Landsat 8 OLI遥感影像,结合实测土壤0~10 cm的111个数据,分别构建垂直干旱指数(Perpendicular Drought Index,PDI)、改进型垂直干旱指数(Modified Perpendicular Drought Index,MPDI)和植被调整垂直干旱指数(Vegetation-adjusted Perpendicular Drought Index,VAPDI)土壤水分反演模型,并采用4种模型指标定量决定系数(R^(2))、平均绝对误差(MAE)、平均相对误差(MRE)、均方根误差(RMSE)对模型反演的效果进行精度评价,选出最优的土壤水分反演模型并结合土壤水分限制系数,与研究区2019年林地、草地和耕地植被面积的空间数据、各站点生长季内的参考作物蒸散量,构建植被生态需水量模型,厘清研究区内土壤水分、植被需水量时空变化特征。结果表明:(1)2种数据源下的PDI、MPDI、VAPDI和实测数据之间均有着不同程度的线性负相关性,其中R^(2)分别为0.37、0.64和0.59,从评价指标的结果来看,MPDI的土壤水分回归模型的拟合决定系数最高,2种遥感数据反演的土壤水分空间分布格局具有一致性。(2)分辨率高的Sentinel-2L2A土壤水分反演更加精细,土壤水分整体呈波动增长趋势,多时段土壤水分的平均值为23.27%,呈现出降低再增加然后下降,总体增幅为74.07%。(3)兰州市南北两山4—10月植被需水量月均值也呈现先增加后下降的趋势,与土壤水分含量变化具有一致性,4—10月中7月植被需水量最大,为3.98×10^(7)m^(3),10月植被生态需水量最小,为0.97×10^(7)m^(3)。随着环境绿化工程的实施,兰州市南北两山从只能生长耐旱草本和低矮灌木的植物,逐步形成以多种类结合的群落结构。本研究可为兰州市南北两山土壤水资源合理利用及植被恢复提供参考。

基于Sentinel-2多光谱遥感影像的小浪底水质反演

多光谱遥感技术可根据遥感波段信息反演水质参数,降低监测成本,提高监测速度和质量,为大范围水环境监测提供了一种新的方法。通过分析小浪底水库的Sentinel-2多光谱影像以及采样点实测水质数据,建立了最佳光谱波段的水质参数反演模型,对小浪底水库的化学需氧量(COD)、总磷(TP)、总氮(TN)和氨氮(NH_3-N)进行了遥感反演,验证了反演模型的精确度和稳定性,并反演了各水质参数的空间分布规律。结果表明:在4种水质参数反演模型中,COD模型精确度和稳定性最高,其次是TP、TN,最低的是NH_3-N,水库出水口和部分边缘COD质量浓度较高,水库中心TN、TP和NH_3-N质量浓度高于边缘处。

基于多源遥感数据与模型对比的冬小麦土壤含水量区域监测研究

实时、精准的土壤水分含量监测是农业用水管理的基础,探究冬小麦土壤水分反演的最优模型对于提高农业用水效率和可持续发展均具有重要的意义。本研究以河南省鹤壁市浚县冬小麦种植区域的土壤水分含量为研究对象,采用无人机遥感数据、卫星遥感数据、田间采样数据,分别运用温度植被干旱指数模型、水云模型和改进的水云模型3种方法,进行土壤含水量反演对比分析与最优模型选择。结果表明,3种方法中10 cm深度的反演精度均高于20 cm,且R^(2)均大于0.4。其中采用改进的水云模型方法在10 cm深度的R^(2)为0.7055、RMSE为0.0209,20 cm深度的R^(2)为0.5069、RMSE为0.0271,优于水云模型和温度植被干旱指数的反演效果。因此,改进的水云模型是一种适合用于麦田土壤水分反演的方法,它能够提供较高的反演精度。