Long-Term Changes in Water Clarity in Lake Liangzi Determined by Remote Sensing

remote sensing

Article Long-Term Changes in Water Clarity in Lake Liangzi Determined by Remote Sensing

Xuan Xu 1,2, Xiaolong Huang 1,2, Yunlin Zhang 2,* and Dan Yu 1,*

1 National Field Station of Freshwater Ecosystem of Liangzi Lake, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China; [email protected] (X.X.); [email protected] (X.H.) 2 Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, Jiangsu, China * Correspondence: [email protected] (Y.Z.); [email protected] (D.Y.)

Received: 24 August 2018; Accepted: 6 September 2018; Published: 10 September 2018

Abstract: Water clarity (via the Secchi disk depth, SDD) is an important indicator of water quality and lake ecosystem health. Monitoring long-term SDD change is vital for water quality assessment and lake management. In this study, we developed and validated an empirical model for estimating the SDD based on Landsat ETM+ and OLI data using the combination of band ratio of the near-infrared (NIR) band to the blue band and the NIR band. Time series data of remotely estimated SDD in Lake Liangzi were retrieved from 2007 to 2016 using the proposed models based on forty Landsat images. The results of the Mann–Kendall test (p = 0.002) and linear regression (R2 = 0.352, p < 0.001) indicated that the SDD in Lake Liangzi demonstrated a significant decreasing trend during the study period. The annual mean SDD in Lake Liangzi was significantly negatively correlated with the population (R2 = 0.530, p = 0.017) and gross domestic product (R2 = 0.619, p = 0.007) of the Lake Liangzi basin. In addition, water level increase and the flood have an important effect on SDD decrease. Our study revealed that anthropogenic activities may be driving factors for the long-term declining trend in the SDD. Additionally, floods and heavy precipitation may decrease the SDD over the short term in Lake Liangzi. A declining trend in the SDD in Lake Liangzi may continue under future intense anthropogenic activities and climate change such as the extreme heavy precipitation event increase.

Keywords: water clarity; Landsat; Lake Liangzi; anthropogenic activities; ﬂoods

1. Introduction Freshwater, which is mainly stored in lakes and reservoirs, is an essential resource for natural and human needs. However, the water quality of many lakes is becoming degraded, and people ﬁnd it necessary to monitor water quality change [1]. Traditional water quality monitoring is based on in situ measurements and laboratory analysis, which are accurate but time-consuming and expensive and cannot monitor an overview of large regions [2,3]. Satellite remote sensing is a powerful tool that has been widely used to assess spatial and temporal variations in water quality [4–6]. More importantly, satellite remote sensing is the only way to retrospectively view long-term variations due to many in situ water environment monitoring efforts only having been started in the past several years. Landsat series data have been widely used for monitoring surface water quality. Many reliable algorithms for Landsat data and water quality parameters have been developed, such as chlorophyll-a (Chl-a), total suspended matter (TSM), total nitrogen, and total phosphorus [3,7,8]. The Secchi disk depth (SDD) is a measure of water clarity that is widely accepted to indicate water quality conditions [9,10]. Water clarity is related to many other water quality parameters, such as TSM, Chl-a, and dissolved organic carbon and is a basic and important water quality parameter [11].

Remote Sens. 2018, 10, 1441; doi:10.3390/rs10091441 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 1441 2 of 15

Water clarity also can reflect essential information on light availability in aquatic ecosystems [12,13]. Therefore, it is important to monitor long-term SDD changes to benefit our understanding of lake ecosystems and lake management. A large number of studies have used Landsat data to retrieve SDDs, and most of them use the simple linear regression of single band or band ratios [14]. However, these retrieved algorithms of the SDD are empirical regional algorithms and cannot be used in Lake Liangzi directly. Thus, customized retrieval models of the SDD based on Landsat data in Lake Liangzi need to be developed and validated. The water quality of inland waters is determined by numerous factors, such as hydrologic conditions, phytoplankton, and anthropogenic activities [11,15]. The watershed human population and economic development in the area are also important factors of declining water quality with many direct and indirect effects [16]. The gross domestic product (GDP) provides an indicator of economic development. Most studies have focused on the effects of anthropogenic activities on water chemical parameters and trophic status [17,18]. Furthermore, the number of studies linking socioeconomic metrics to water quality are limited due to administrative units not holding jurisdiction over entire watersheds in most cases [1]. Alternatively, some extreme weather events, such as floods and heavy rainfall, may also strongly influence water quality. The immediate and short-term effects of floods and heavy rainfall on water quality are usually associated with elevated turbidity, total suspended matter and nutrient matter by inputting a large amount of domestic, industrial, and agricultural effluents into the lake [19–22]. However, little attention has been given to the effects of the relationship between anthropogenic activities and climate change on water clarity. Therefore, the main objectives of this study are to (1) develop empirical remote sensing models to retrieve SDDs in Lake Liangzi based on Landsat ETM+ and OLI images, (2) document the temporal and spatial variations in SDDs from 2007 to 2016 using the proposed estimation models, and (3) understand the long-term changes in lake water SDDs driven by anthropogenic activities and climate change.

2. Materials and Methods

2.1. Study Area Lake Liangzi (30◦50–30◦180N, 114◦210–114◦390E), which has a water surface area of 362.5 km2, a catchment area of 3260 km2, a mean water depth of 3.0 m, and a water volume of 12.65 × 108 m3, is the largest shallow lake in Hubei Province, historically, China. Lake Liangzi is a macrophyte-dominated lake in the middle and lower reaches of the Yangtze River but the macrophytes have degraded in the past few decades [23,24]. The mean water level of Lake Liangzi is 18.01 m. From 2007 to 2016, two major ﬂoods occurred in Lake Liangzi in 2010 and 2016. The maximum water levels of Lake Liangzi in 2010 and 2016 were 21.29 m and 21.49 m, respectively. Lake Liangzi is the backup potable water source of Wuhan. However, in recent years, some areas of Lake Liangzi have become lightly eutrophic [25].

2.2. Sampling Sites and Water Quality Parameters Measurements The in situ SDD observations were conducted monthly at predeﬁned sites in Lake Liangzi in 2016. Forty-ﬁve sampling sites were distributed uniformly across Qianjiangdahu, Manjianghu, and Gaotanghu (Figure1). The sampling vessel used could not reach the Zhangqiaohu and Niushanhu areas due to the steel cable enclosure between regions. Thus, there were no sampling sites in these two lake regions. For each season, 90 samples from every two surveys (Spring: April and May; Summer: July and August; Fall: September and November; Winter: January and February) that were close to the date of Landsat images were selected and then divided into two groups randomly. One group was used for calibration and the other for validation. A total of 180 samples (45 samples × 4 seasons) were used to develop the SDD remote estimate model and another 180 samples (45 samples × 4 seasons) were used for model validation. A standard 20 cm diameter Secchi disk was used to measure the SDD. The Secchi disk is a circular black and white disk that is placed by an observer into a water Remote Sens. 2018, 10, 1441 3 of 15 column until it disappears from view. When the disk is no longer visible, the depth is recorded as the

SDDRemote (Figure Sens. 20181)., 10, x FOR PEER REVIEW 3 of 15

Figure 1. Distributions of sample sites and hydrological station inin LakeLake Liangzi.Liangzi.

2.3. Satellite Data AcquisitionAcquisition and Process For the shapeshape andand sizesize ofof LakeLake Liangzi,Liangzi, LandsatLandsat seriesseries imageryimagery isis suitablesuitable forfor estimatingestimating water qualityquality because because of of its its 30 30 m mspatial spatial resolution resolution [26]. [26 A]. total A totalof forty of cloud-free forty cloud-free Landsat Landsat Level 1T Level images 1T images(24 Landsat (24 Landsat ETM+ ETM+and 16 and Landsat 16 Landsat OLI OLIimages) images) from from 2007 2007 to to2016 2016 were were downloaded downloaded from from thethe Geospatial Data Cloud (http://www.gs (http://www.gscloud.cncloud.cn). ).First, First, Landsat Landsat ETM+ ETM+ and and OLI OLI data data were were rescaled rescaled to totop top of ofthe the atmosphere atmosphere (TOA) (TOA) radiance radiance values values usin usingg the the radiometric radiometric calibration calibration coefficients coefficients in the metadata filefile (MTL file)file) viavia thethe ENVIENVI 5.35.3 softwaresoftware (Boulder,(Boulder, CO,CO, USA).USA). TheThe MODTRAN4MODTRAN4 radiationradiation transfertransfer codecode whichwhich isis incorporatedincorporated in thethe FLAASHFLAASH modulemodule hashas beenbeen widelywidely usedused inin inlandinland waterwater bodies [27[27–29].–29]. Thus, Thus, the the atmospheric atmospheric correction correctio inn this in studythis study was based was onbased the MODTRAN4 on the MODTRAN4 radiation transferradiation calculations transfer calculations from the FLAASH from the module.FLAASH module.

Remote Sens. 2018, 10, 1441 4 of 15

2.4. Water Level, Rainfall, Air Temperature, Population, and GDP Data The daily water level of Lake Liangzi was collected from of the hydrological station of Liangzi (Figure1), which belongs to the Hubei Provincial Department of Water Resources (http://219.140.162. 169:8800/rw4/report/fa07.asp). The water level data were collected from January 1, 2007 to December 31, 2016. The rainfall and air temperature data from three meteorological stations near Lake Liangzi, including Jiayu (29.55◦N, 113.58◦E), Jiangxia (30.21◦N, 114.2◦E), and Huangshi (30.14◦N, 115.02◦E), were collected from 1957 to 2016. These data can be downloaded from the China Meteorological Data Sharing Service System (http://cdc.nmic.cn). The population and GDP of the Lake Liangzi basin from 2007 to 2016 were collected from Hubei Statistics Bureau (http://www.stats-hb.gov.cn/info/iIndex. jsp?cat_id=10055).

2.5. Statistical Analysis and Accuracy Assessment Previous studies showed that the log-transformed water quality parameter relationship to Landsat images would be better than the relationships between the original data [30,31]. Thus, the SDD data were log-transformed to obtain stronger corrections to the Landsat data in this study. The Pearson correlation between the log-transformed in situ water clarity data and band reﬂectance of Landsat ETM+ and OLI was conducted to determine which spectral band, or band ratio, was most suitable for predicting the SDD. Levels are reported as signiﬁcant if p < 0.05. Then, simple and multiple regression models were constructed to develop the best prediction algorithms. The root mean square error (RMSE) between the in situ and estimated values was used to assess the accuracy of the algorithms. The RMSE was calculated with the following equation: s 2 ∑n (X − X ) RMSE = i=1 obs,i model,i (1) n where n represents the number of samples, and Xobs and Xmodel represent the in situ observed and estimated values, respectively. All data analyses were conducted using SPSS 19.0 (SPSS Incorporated, Illinois, USA). To assess variation trends in the water SDD in Lake Liangzi, we conducted a Mann–Kendall test using Kendall.exe (which can be downloaded from http://pubs.usgs.gov/sir/ 2005/5275/downloads/).

3. Results

3.1. In Situ SDD Characteristics Table1 summarizes the characteristics of in situ SDD of Lake Liangzi for calibration and validation across each season in 2016. The SDD dataset for calibration ranged from 0.25 to 1.10 m, with a mean (± standard deviation) of 0.63 ± 0.16 m, and the dataset for validation ranged from 0.25 to 1.15 m, with a mean of 0.64 ± 0.10 m in the aggregated dataset (Table1).

Table 1. Statistics of the in situ water Secchi disk depth (SDD) for calibration and validation in Lake Liangzi.

Calibration SDD Dataset (m) Validation SDD Dataset (m) Min Max Mean ± SD Min Max Mean ± SD Spring (April and May) 0.45 0.90 0.63 ± 0.10 0.40 1.00 0.66 ± 0.13 Summer (July and August) 0.25 0.65 0.44 ± 0.09 0.25 0.60 0.42 ± 0.10 Fall (September and November) 0.45 1.10 0.73 ± 0.12 0.50 1.15 0.75 ± 0.12 Winter (January and February) 0.35 1.00 0.73 ± 0.13 0.40 1.00 0.72 ± 0.13 Total dataset 0.25 1.10 0.63 ± 0.16 0.25 1.15 0.64 ± 0.17 Remote Sens. 2018, 10, 1441 5 of 15

3.2. Algorithm Development and Validation The results of the Pearson correlation coefficients showed negative correlations between the log-transformed SDD and preprocessed Landsat spectral bands. The blue band, near-infrared (NIR) band and the ratio of the NIR to the blue band in the Landsat images showed a strong negative association with the SDD (R = −0.815, −0.855, and −0.925, respectively). To develop the predictive SDD algorithm, we constructed empirical models using simple and multiple linear regression models relating log-transformed in situ data and reflectance values of Remote Sens. 2018, 10, x FOR PEER REVIEW 5 of 15 selected bands and band ratios (Table2). The multivariate linear regression model with reflectance ofof NIR NIR band band and and ratioratio of the the NIR NIR to to blue blue bands bands showed showed the highest the highest coefficient coefficient of determination of determination (R2 = (R20.860)= 0.860) for forpredicting predicting ln(SDD) ln(SDD) (Table (Table 2, Figure2, Figure 2). 2).

TableTable 2. The2. The estimated estimated ln(SDD) ln(SDD) algorithms algorithmsand and statisticalstatistical parameters of of Lake Lake Liangzi Liangzi (n (n = =180). 180).

AlgorithmsAlgorithms R2R 2 p p ln(SDD) = 28.16 × RBlue − 72.38 × RNIR − 0.531 0.646 <0.001 ln(SDD) = 28.16 × RBlue − 72.38 × RNIR − 0.531 0.646 <0.001 ln(SDD)ln(SDD) = −6.781 = − × 6.781(RNIR/R× Blue(RNIR) + /R2.023Blue ) + 2.023 0.806 0.806 <0.001 <0.001 ln(SDD)ln(SDD) = −8.266 = − ×8.266 (RNIR×/R(RBlueNIR) +/R 1.863Blue )× + R 1.863Blue + ×2.386RBlue + 2.386 0.813 0.813 <0.001 <0.001 ln(SDD)ln(SDD) = −8.753 = − ×8.753 (RNIR×/R(RBlueNIR) +/R 5.223Blue )× + R 5.223NIR + ×2.552RNIR + 2.552 0.860 0.860 <0.001 <0.001

RBlueRBlueand and RNIR RNIRrepresent represent the atmosphericallythe atmospherically corrected corrected reﬂectance reflectance values ofvalues the blue of bandthe blue and band near-infrared and near- band in theinfrared Landsat band images in the respectively. Landsat images The best respective ﬁt algorithmly. The is shown best fit in algorithm bold. is shown in bold.

1.4 1.4 a b 1:1 line 1.2 y = 10.54*exp(4.89x) 1.2 R2 = 0.661 R2 = 0.860 RMSE = 0.118m 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 Estimated SDD (m) SDD Estimated

In situIn measured SDD (m) 0.2 0.2

0.0 0.0 -0.8 -0.7 -0.6 -0.5 -0.4 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 -8.753*(R /R )/5.223 + R In situ measured SDD (m) NIR Blue NIR FigureFigure 2. The2. The SDD SDD algorithms algorithms for for ( a(a)) the the calibrationcalibration of in in situ situ measured measured SDDs SDDs and and Landsat Landsat reflectance reﬂectance

andand (b) ( theb) the validation validation of of Landsat-estimated Landsat-estimated SDDs SDDs with with in in situ situ measured measured SDD.SDD. RRBlueBlue andand R RNIRNIR representrepresent thethe atmospherically atmospherically corrected corrected reﬂectance reflectance values values of the of bluethe blue band band and near-infraredand near-infrared band inband the in Landsat the images,Landsat respectively. images, respectively.

ToTo assess assess the the performance performance of the of selectedthe selected SDD S estimationDD estimation models, models, regressions regressions between between the Landsat the estimatedLandsat SDD estimated and independent SDD and independent in situ measured in situ me SDDasured were SDD conducted. were conduc Theted. estimated The estimated SDD retrieved SDD byretrieved the selected by algorithmsthe selected showed algorithms a highly showed signiﬁcant a highly linear significant correlation linear with correlation the in situ with measured the in situ SDD (R2measured= 0.661) (FigureSDD (R22). = In0.661) addition, (Figure the 2). in In situ addition, measured the andin situ estimated measured data and were estimated distributed data were along thedistributed 1:1 line demonstrating along the 1:1 line the demonstrating consistency (Figure the co2nsistency). The RMSE (Figure of 2). the The validation RMSE of results the validation between theresults measured between and the retrieved measured SDDs and were retrieved 0.118 SDDs m (Figure were2 0.118). The m results (Figure of 2). validation The results indicate of validation that the indicate that the selected models can be used to retrieve SDD. selected models can be used to retrieve SDD.

3.3.3.3. Variations Variations in in SDD SDD The developed SDD estimated algorithms were applied to forty Landsat ETM+ and OLI images The developed SDD estimated algorithms were applied to forty Landsat ETM+ and OLI images to retrieve SDD values in Lake Liangzi from 2007 to 2016. Then mean and standard deviation values to retrieve SDD values in Lake Liangzi from 2007 to 2016. Then mean and standard deviation values of of the SDD were extracted to explore variation trends. The results of the Mann–Kendall test indicated that the SDD in Lake Liangzi showed a significant declining trend during the study period (Z = −3.069, p = 0.002) (Table 3). The results of the linear regression indicated that the SDD in Lake Liangzi showed a significant declining trend from 2007 to 2016 (R2 = 0.352, p < 0.001) (Figure 3). The mean seasonal SDD was highest in summer, with a value of 0.89 m, and lowest was in spring, with a value of 0.71 m, during the study period (Figure 4). However, the SDD in Lake Liangzi did not present a significant seasonal variation (p = 0.201).

Remote Sens. 2018, 10, 1441 6 of 15 the SDD were extracted to explore variation trends. The results of the Mann–Kendall test indicated that the SDD in Lake Liangzi showed a significant declining trend during the study period (Z = −3.069, p = 0.002) (Table3). The results of the linear regression indicated that the SDD in Lake Liangzi showed a significant declining trend from 2007 to 2016 (R2 = 0.352, p < 0.001) (Figure3). The mean seasonal SDD was highest in summer, with a value of 0.89 m, and lowest was in spring, with a value of 0.71 m, during the study period (Figure4). However, the SDD in Lake Liangzi did not present a significant seasonalRemote Sens. variation 20182018,, 1010 (,, pxx FOR=FOR 0.201). PEERPEER REVIEWREVIEW 6 6 of of 15 15

TableTable 3. Mann–Kendall 3.3. Mann–KendallMann–Kendall test testtest of ofof water waterwater SDD SDDSDD variation variationvariation trends trentrends inin LakeLake Liangzi during during the the study study period. period.

Tau CorrelationTauTau CorrelationCorrelation Coefﬁcient CoefficientCoefficient S SS ZZ Z p Valuep Value Whole lakeWhole lake −0.392 −−0.3920.392 −67 −−6767 −−3.0693.0693.069 0.0020.002 0.002 NiushanhuNiushanhu −0.480 −−0.4800.480 −82 −−8282 −−3.7523.7523.752 <0.001<0.001 <0.001 ManjianghuManjianghu −0.345 −−0.3450.345 −59 −−5959 −−2.6902.6902.690 0.0070.007 0.007 GaotanghuGaotanghu −0.415 −−0.4150.415 −71 −−7171 −−3.2603.2603.260 0.0010.001 0.001 QianjiangdahuQianjiangdahu −0.421 −−0.4210.421 −72 −−7272 −−3.2963.2963.296 0.0010.001 0.001 − − − ZhangqiaohuZhangqiaohu 0.450 −−0.4500.450 77 −−7777 −−3.5243.5243.524 <0.001<0.001 <0.001

2.22.2 2.02.0 2 1.81.8 SDDSDD == -0.11*Time+0.998-0.11*Time+0.998 RR == 0.3520.352 p << 0.0010.001 1.61.6 1.41.4 1.21.2 1.01.0 SDD (m) SDD (m) 0.80.8 0.60.6 0.40.4 0.20.2 0.00.0 SpSp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W Sp Sp Su Su F F W W 20072007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 Time FigureFigureFigure 3. Long-term3.3. Long-term trend trend in in Landsat Landsat retrieved retrieved seasonal seasonal meanmean SDD of of Lake Lake Liangzi Liangzi during during the the study study period.period. Sp: Sp: spring; spring; Su: Su: summer; summer; F: F: fall; fall; W: W: winter. winter.

1.61.6 1.41.4 a b 1.41.4 1.21.2 1.21.2 1.01.0 1.01.0 0.80.8 0.80.8 0.60.6 SDD (m) SDD SDD (m) SDD (m) SDD 0.60.6 SDD (m) 0.40.4 0.40.4

0.20.2 0.20.2

0.00.0 0.00.0 20072007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 SpringSpring Summer Summer Fall Fall Winter Winter Year Season FigureFigureFigure 4. 4.4.The TheThe (a ()(aa annual)) annualannual mean meanmean SDD SDDSDD and andand ( ((bb))) seasonalseasonalseasonal mean mean SDDSDD SDD inin in LakeLake Lake Liangzi.Liangzi. Liangzi.

TheThe SDD SDD of small of small lake bayslake wasbays lower was thanlower the than center thethe of lakecentercenter (Figure ofof lakelake5). The(Figure(Figure SDD 5).5). of QianjiangdahuTheThe SDDSDD ofof regionQianjiangdahu was usually region higher was than usually other regionshigher than in Lake othe Liangzirr regionsregions (Figures inin LakeLake5 LiangziandLiangzi6). The (Figures(Figures SDD of55 andand Niushanhu 6).6). TheThe wasSDD usually of Niushanhu lower than was other usually regions lowe (Figuresrr thanthan otherother5 and regionsregions6). The (Figures(Figures SDD of 55 ﬁve andand lake 6).6). TheThe regions SDDSDD experienced ofof fivefive lakelake regionsregions experiencedexperienced aa decliningdeclining trendtrend thethe samesame asas ththee wholewhole lakelake (Table(Table 3,3, FigureFigure 6).6). Furthermore,Furthermore, thethe SDDSDD ofof GaotanghuGaotanghu andand NiushanhuNiushanhu decreaseddecreased mumuchch moremore andand muchmuch fasterfaster thanthan otherother regionsregions (Figure(Figure 6).6).

Remote Sens. 2018, 10, 1441 7 of 15

Remote Sens. 2018, 10, x FOR PEER REVIEW 7 of 15 a declining trend the same as the whole lake (Table3, Figure6). Furthermore, the SDD of Gaotanghu and NiushanhuRemote Sens. 2018 decreased, 10, x FOR PEER much REVIEW more and much faster than other regions (Figure6). 7 of 15

FigureFigureFigure 5. 5.Maps 5.Maps Maps of of theof the the averaged averaged averaged SDDSDD SDD retrievedretrievedretrieved fromfrom LandsatLandsat Landsat images images images from from from 2007 2007 2007 to to2016. to 2016. 2016.

NiushanhuNiushanhu 1.2 1.2 ManjianghuManjianghu GaotanghuGaotanghu QianjiangdahuQianjiangdahu 1.01.0 ZhangqiaohuZhangqiaohu

0.8 0.8SDD (m) SDD (m)

0.60.6

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Time Time

Figure 6. Long-term trend in Landsat retrieved annual mean SDD of five regions in Lake Liangzi FigureFigure 6. 6.Long-term Long-term trend trendin inLandsat Landsat retrievedretrieved annual mean mean SDD SDD of of five ﬁve regions regions in in Lake Lake Liangzi Liangzi during the study. duringduring the the study. study.

Remote Sens. 2018, 10, 1441 8 of 15 Remote Sens. 2018,, 10,, xx FORFOR PEERPEER REVIEWREVIEW 8 8 of of 15 15

3.4.3.4. Relationships Relationships of of Water Water Clarity Clarity with with Population Population and and GDP GDP TheThe population population of of the the Lake Lake Liangzi Liangzi basin basin experienced experienced a a slow slow but but significant significant growth growth from from1.288 1.288 2 millionmillion people people in in 2007 2007 to to 1.322 1.322 million million people people in 2016in 2016 (R2 (=R2 0.942, == 0.942,0.942,p < p 0.001; < 0.001; Figure Figure7a). The7a). GDPThe GDP of the of Lakethethe LakeLake Liangzi LiangziLiangzi basin basinbasin showed showedshowed significantly significantlysignificantly rapid rapidrapid growth growthgrowth from 21.38 fromfrom billion 21.3821.38 billionbillion yuan in yuanyuan 2007 inin to 20072007 94.60 toto billion 94.6094.60 2 yuanbillion in yuan 2016 in (R 22016= 0.980, (R2 == p 0.980,0.980,< 0.001; p < Figure0.001; Figure7b). To 7b). evaluate To evaluate the relationship the relationship between between the SDD the andSDD anthropogenicand anthropogenic activities, activities, linear regressionslinear regression of thes SDD of the with SDD population with popu andlationlation GDP wereandand conducted.GDPGDP werewere Significantconducted. negative Significant correlations negative correlations were observed were when observedobserved comparing whenwhen comparingcomparing the annual thethe mean annualannual SDD meanmean in Lake SDDSDD 2 2 Liangziinin LakeLake with LiangziLiangzi population withwith populationpopulation (R2 = 0.530, ((R2p === 0.530,0.530, 0.017 p < = 0.05; 0.017 Figure < 0.05;8 a)Figure and GDP 8a) and ( R2 GDP= 0.619, (R2 =p= 0.619,=0.619, 0.007 p <= 0.007 0.01; Figure< 0.01;8 Figureb). 8b).

1.341.34 120120 a b 1.33 2 2 1.33 R2 = 0.942 100100 R2 = 0.980 p < 0.001 p < 0.001 1.321.32 p < 0.001 8080 1.311.31 6060 1.301.30 4040 1.291.29 GDP (billion RMB) Population (million) (million) Population GDP (billion RMB) Population (million) (million) Population 20 1.281.28 20

1.27 1.27 00 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 20072007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 Year Year Year FigureFigure 7. 7.The TheTheincrease increaseincreasein inin( (a(a))) population populationpopulation and andand ( b((b))) GDP GDPGDP in inin the thethe Lake LakeLake Liangzi LiangziLiangzi basin basinbasin from fromfrom 2007 20072007 to toto 2016. 2016.2016.

1.4 1.4 a b 2 2 R2 = 0.530 R2 = 0.619 1.2 1.2 p = 0.017 1.2 p = 0.007

1.0 1.0

0.8 0.8 SDD (m) (m) SDD 0.8 (m) SDD 0.8 SDD (m) (m) SDD SDD (m) (m) SDD

0.6 0.6

0.4 0.4 1.28 1.29 1.30 1.31 1.32 1.33 20 40 60 80 100 Population (million) GDP (billion RMB) Population (million) GDP (billion RMB) FigureFigure 8.8. CorrelationsCorrelations of of the the SDD SDD with with (a)) populationpopulation (a) population andand ((b and)) GDP.GDP. (b) ErrorError GDP. barsbars Error representrepresent bars represent SDDSDD standardstandard SDD standarddeviations. deviations. 3.5. Relationships between Water Clarity and Air Temperature, Water Levels, and Rainfall 3.5. Relationships between Water Clarity and Air Temperature, Water Levels, and Rainfall From 2007 to 2016, the monthly maximum, minimum, and mean water levels were 21.11 m, From 2007 to 2016, the monthly maximum, minimum, and mean water levels were 21.11 m, 16.99 16.99 m, and 18.25 m, respectively (Figure9a). To evaluate the relationship between the SDD and water m, and 18.25 m, respectively (Figure 9a). To evaluate the relationship between the SDD and water level,level, rainfallrainfall amount,amount, andand air air temperature, temperature, linear linear regressions regressions were were conducted. conducted. TheThe results results showed showed level, rainfall amount, and air temperature, linear regressions were conducted.2 The results showed that the SDD was significantly negatively correlated with water level (R 2= 0.524, p = 0.018 < 0.05; thatthat thethe SDDSDD waswas significantlysignificantly negativelynegatively correlatedcorrelated withwith waterwater levellevel ((R2 == 0.524,0.524, p = 0.018 < 0.05; Figure 10a). During the ten years, two floods occurred in 2010 and 2016. When floods occurred, the lake Figure 10a). During the ten years, two floods occurred in 2010 and 2016. When floods occurred, the SDD decreased. The lake SDD decreased from 0.78 m to 0.6 m during the flood of 2010 and decreased lakelake SDDSDD decreased.decreased. TheThe lakelake SDDSDD decreased from 0.78 m to 0.6 m during the flood of 2010 and from 0.65 m to 0.49 m during the flood of 2016 (Figure3). The monthly maximum water levels in 2010 decreased from 0.65 m to 0.49 m during the flood of 2016 (Figure 3). The monthly maximum water and 2016 were 20.75 m and 21.11 m, respectively, corresponding to monthly rainfall amounts of 586 levelslevels inin 20102010 andand 20162016 werewere 20.7520.75 mm andand 21.1121.11 m,m, respectively,respectively, correspondingcorresponding toto monthlymonthly rainfallrainfall mm and 549 mm, respectively (Figure9). The rainfall amount during month of multiple floods were amounts of 586 mm and 549 mm, respectively (Figure 9). The rainfall amount during month of 586 mm and 549 mm, which means that a monthly rainfall amount above 500 mm may lead to a flood multiple floods were 586 mm and 549 mm, which means that a monthly rainfall amount above 500 mm may lead to a flood in Lake Liangzi. Thus, we conductedconducted frequencyfrequency statisticsstatistics ofof thethe monthlymonthly

Remote Sens. 2018, 10, 1441 9 of 15

Remote Sens. 2018, 10, x FOR PEER REVIEW 9 of 15 in Lake Liangzi. Thus, we conducted frequency statistics of the monthly rainfall greater than 500 mm fromrainfall 1957 greater to 2016. than The 500 results mm indicatedfrom 1957 that to the2016. frequency The results of heavy indicated rainfall that or the ﬂoods frequency increased of heavy from 1956rainfall to 2016 or floods (Figure increased 11). from 1956 to 2016 (Figure 11).

22 a 21

18 Water Level (m)

16 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Time 600 b 500

400

300

200 Rainfall amount (mm) 100

0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Time 35

30 c

10 Air temperature (℃) temperature Air 5

0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Time FigureFigure 9. 9.Variation Variation in in ( a()a monthly) monthly mean mean water water levels, levels, ( b(b)) monthly monthly rainfall rainfall amounts, amounts, and and ( c()c) monthly monthly meanmean air air temperature temperature in in Lake Lake Liangzi Liangzi from from 2007 2007 to to 2016. 2016.

Remote Sens. 2018, 10, 1441 10 of 15 Remote Sens. 2018, 10, x FOR PEER REVIEW 10 of 15

1.4 1.4 a b R2 = 0.524 R2 = 0.203 1.2 R2 = 0.524 1.2 R2 = 0.203 1.2 p = 0.018 1.2 p = 0.192

1.0 1.0 SDD (m) (m) SDD SDD (m) (m) SDD 0.8 0.8 SDD (m) (m) SDD SDD (m) (m) SDD 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4 1000 1200 1400 1600 1800 2000 17.4 17.6 17.8 18.0 18.2 18.4 18.6 18.8 19.0 1000 1200 1400 1600 1800 2000 Water level (m) Rainfall amount (mm) 1.4 c R2 = 0.283 1.2 R2 = 0.283 1.2 p = 0.114

1.0

SDD (m) (m) SDD 0.8

0.6

0.4 16.5 17.0 17.5 18.0 18.5 Air temperature (℃) Air temperature (℃) FigureFigure 10. 10.Correlations Correlations of of the the SDD SDD with with (a(a)) water water level, level, ( b(b)) rainfall rainfall amount, amount, andand ((cc)) airair temperature.temperature. ErrorError bars bars represent represent SDD SDD standard standard deviations. deviations.

Frequency (times) 1

0 1957 1967 1977 1987 1997 2007 2017 Year (1957-2016) Year (1957-2016) FigureFigure 11. 11.Long-term Long-term trendstrends inin the the frequency frequency of of heavy heavy rainfall rainfall from from 1957 1957 to to 2016 2016 in in Lake Lake Liangzi. Liangzi. 4. Discussion 4. Discussion 4.1. Predictive SDD Algorithm for Landsat Imagery 4.1. Predictive SDD Algorithm for Landsat Imagery Landsat series data provide a historical data set covering almost 40 years and Landsat ETM+ Landsat series data provide a historical data set covering almost 40 years and Landsat ETM+ and and OLILandsat data haveseries been data widelyprovide used a historical to estimate data water set covering quality almost parameters, 40 years including and Landsat the SDD ETM+ [8, 32and]. OLI data have been widely used to estimate water quality parameters, including the SDD [8,32]. PreviousOLI data studies have been have widely shown used that theto estimate blue and wa redter bands quality of parameters, Landsat were including successfully the SDD correlated [8,32]. Previous studies have shown that the blue and red bands of Landsat were successfully correlated to toPrevious water clarity studies [ 9have,33,34 shown]. In addition,that the blue the and band red ratios bands of of red Landsat and blue were may successfully be the most correlated suitable to water clarity [9,33,34]. In addition, the band ratios of red and blue may be the most suitable relationshipwater clarity to retrieve[9,33,34]. SDDs In addition, [31,35]. However,the band inra ourtios results,of red theand red blue band may of Landsatbe the didmost not suitable show relationship to retrieve SDDs [31,35]. However, in our results, the red band of Landsat did not show good correlations to SDD in Lake Liangzi. Zhao et al. (2011) considered that band selection for retrieving SDDs may depend on the image quality and limnological properties of a water body [34].

Remote Sens. 2018, 10, 1441 11 of 15 good correlations to SDD in Lake Liangzi. Zhao et al. (2011) considered that band selection for retrieving SDDs may depend on the image quality and limnological properties of a water body [34]. In our study, the NIR band and band ratio of the NIR to blue band showed strong negative correlations to the SDD, which was used for predicting the SDD in Lake Liangzi. The NIR band was strongly absorbed by water, and the reﬂectance of the NIR was probably suspended matter reﬂection. The blue band was dominated by the absorbing effects of phytoplankton and detritus, and dividing by the blue band serves to normalize the brightness in the NIR band [2]. Thus, the band and band ratio selection of the SDD algorithm in our study was reasonable. In addition, the dataset used to develop the algorithm included all seasons (spring, summer, fall, and winter) covering a large variability of SDD from 0.25 m to 1.10 m. These characteristics indicated that the developed empirical model was acceptable for predicting the SDD in Lake Liangzi.

4.2. Potential Factors for Long-Term Changes in SDD Our results of the Mann–Kendall test and linear regression demonstrate that Lake Liangzi is experiencing a declining trend in SDD, with a relatively high SDD value of 1 m in 2007 and a low value of 0.6 m in 2016 (Table3, Figures3 and4). Previous studies showed that the decline in SDD may be attributed to more intensive agriculture land use [33], increased phytoplankton [11,36], and increased total suspended matter [37]. The water quality of lakes is seriously affected by anthropogenic activities in the watershed, such as pollutant emissions, agricultural nonpoint source pollutants, and domestic sewage, which could be associated with human population growth and economic development [18]. In this study, human population and GDP in the lake basin were used to elucidate the effects of anthropogenic activities on the decreased SDD in Lake Liangzi. Our results showed that the SDD was significantly negatively correlated with the population and GDP in the watershed. The population in the watershed was growing slowly from 1.288 million people to 1.322 million people during the ten years of the study. The population in the watershed increased with growing demand for water resources for domestic use, agriculture, and industry, which resulted in the accumulation of contaminants flowing into lakes [15]. The GDP in watershed was growing rapidly during the ten years. The rapid growth of the GDP resulted in more resource input and more intensive human influence on the lake [17]. These may result in increased nutrients input and lake eutrophication, which would decrease the lake SDD. These indicate that increased human activity intensity in the watershed may contribute to the SDD decrease in Lake Liangzi. Furthermore, with the fast growing rate of GDP, the SDD in Lake Liangzi may be further reduced. The SDD in Lake Liangzi did not show significant correlations with rainfall amount and air temperature (p = 0.192 and p = 0.114, respectively, Figure 10b, c). However, the SDD was significantly negatively correlated with water levels in Lake Liangzi (Figure 10a). High water levels could introduce terrestrial phosphorus and nitrogen from flood land areas, which could fuel algal populations [21]. Furthermore, high water levels could also cause light limitations to submerged macrophytes and reduce their effects in maintaining the water’s clarity [38]. In addition, flood events and heavy rainfall may have great impacts on water quality and aquatic ecosystems [20,21]. The mean SDD during the flood years (2010 and 2016) was significantly decreased compared to that of the previous year. This indicates that floods decrease the water clarity in Lake Liangzi during the short term. Flooding could introduce significant amounts of suspended matter and nutrients from land [19]. High inflows in floods and heavy precipitation events could bring in higher concentrations of colored dissolved organic materials, and these all could reduce water SDD in the lake [39]. Therefore, a negatively but nonsignificant linear relationship was found between SDD and rainfall amount. Furthermore, some studies found that heavy precipitation may induce river plumes and the frequency of heavy precipitation in past decades was increased [22]. Our results indicate an increase in the frequency of heavy precipitation (monthly rainfall amount >500 mm) in the Lake Liangzi basin (Figure 11). These results suggest that the frequency of flood events and heavy Remote Sens. 2018, 10, 1441 12 of 15 precipitation in the Lake Liangzi basin may increase in future decades. The SDD in Lake Liangzi may be further decreased in the future if these trends continue. There are some other factors that may also be contributing to the SDD decrease in Lake Liangzi. Some regions of Lake Liangzi are used for the aquaculture of fish and crabs [40]. Aquaculture activities could increase the concentration of nutrients in water, causing eutrophication and reducing water SDD [41]. In addition, some regions of Lake Liangzi were polluted with the discharge of toxic wastewater which could kill macrophytes [25]. Macrophytes can help in stabilizing the sediment and keeping water clarity for shallow Lake Liangzi.

4.3. Implications of Decreasing SDD for Ecosystem Evolution A declining trend in the SDD of Lake Liangzi may continue with the growth of the human population, economic development, and climate change such as the extreme precipitation events increase. Decreases in SDD will reduce the available light entering the water column [42]. In addition, the temperature of the surface water may increase, and that of the deep water may decrease due to more solar radiation being absorbed at the water surface [43]. For a macrophyte-dominated lake, the macrophytes, especially submerged macrophytes, play a key role in maintaining healthy aquatic ecosystems and providing ecological service in lakes [44]. The available light and temperature decrease in the water column, especially at lake bottom, will reduce the biomass, growth rate, and photosynthetic capacity of submerged macrophytes and may result in the degradation of submerged macrophytes and a state shift from clear macrophyte-dominated status to turbid phytoplankton-dominated status [24,45]. Additionally, the cover and biomass of ﬂoating plants (e.g., Eichhornia crassipes, Lemna minor, and Hydrocharis dubia) may be enhanced due to the increased temperature of surface water and trophic status induced by anthropogenic activities [46]. The implications of a decrease in SDD are not limited to macrophytes but could also affect phytoplankton. A decreasing SDD may affect the critical depth of phytoplankton communities and reduce primary production [47]. The increased temperature of surface water due to global warming and SDD decrease may affect the dominated community and phenophase of phytoplankton [48,49].

5. Conclusions In this study, we developed an empirical model to retrieve the SDD in Lake Liangzi based on Landsat ETM+ and OLI data and validated its accuracy. Subsequently, the temporal and spatial variations of the SDD in Lake Liangzi were derived from Landsat data from 2007 to 2016. During the study period, the SDD in Lake Liangzi presented a significant decreasing trend. The SDD was significantly and negatively correlated to the human population and GDP in the lake basin. Anthropogenic activities may be the driving factors for the decreasing SDD trend over long timescales, and flood events may affect the SDD in the short term. A decreasing SDD may lead to the degradation of submerged macrophytes and a state shift in the lakes. A declining trend in the SDD in Lake Liangzi may be continued under future anthropogenic activities and climate change. Remote sensing imagery can be used to increase the current knowledge of water quality and develop management strategies for lake conservation.

Author Contributions: Conceptualization, X.X., Y.Z. and D.Y.; Data curation, X.H.; Methodology, X.X.; Resources, X.H.; Software, X.X. and X.H.; Validation, X.H.; Writing—original draft, X.X.; Writing—review & editing, Y.Z. Funding: This work was jointly supported by the Project funded by the China Postdoctoral Science Foundation (BX20180320), the National Natural Science Foundation of China (41621002 and 41771514), the Key Program of the Chinese Academy of Sciences (ZDRW-ZS-2017-3-4), and the Key Program of Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS2017GH03). Acknowledgments: We thank the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences for providing the Landsat data. Conﬂicts of Interest: The authors declare no conﬂict of interest. Remote Sens. 2018, 10, 1441 13 of 15

References

1. Zhou, Y.Q.; Ma, J.R.; Zhang, Y.L.; Qin, B.Q.; Jeppesen, E.; Shi, K.; Brookes, J.D.; Spencer, R.G.M.; Zhu, G.W.; Gao, G. Improving water quality in China: Environmental investment pays dividends. Water Res. 2017, 118, 152–159. [CrossRef][PubMed] 2. Bonansea, M.; Rodriguez, M.C.; Pinotti, L.; Ferrero, S. Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero Reservoir (Argentina). Remote Sens. Environ. 2015, 158, 28–41. [CrossRef] 3. Li, Y.; Zhang, Y.; Shi, K.; Zhu, G.; Zhou, Y.; Zhang, Y.; Guo, Y. Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery. Sci. Total Environ. 2017, 599, 1705–1717. [CrossRef][PubMed] 4. Shi, K.; Zhang, Y.L.; Zhou, Y.Q.; Liu, X.H.; Zhu, G.W.; Qin, B.Q.; Gao, G. Long-term MODIS observations of cyanobacterial dynamics in lake taihu: Responses to nutrient enrichment and meteorological factors. Sci. Rep. 2017.[CrossRef][PubMed] 5. Zhang, Y.L.; Giardino, C.; Li, L.H. Water optics and water colour remote sensing. Remote Sens. 2017, 9, 818. [CrossRef] 6. Zheng, Z.; Li, Y.; Guo, Y.; Xu, Y.; Liu, G.; Du, C. Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting lake, China. Remote Sens. 2015, 7, 13975–13999. [CrossRef] 7. Tebbs, E.J.; Remedios, J.J.; Harper, D.M. Remote sensing of Chlorophyll-a as a measure of cyanobacterial biomass in Lake Bogoria, a hypertrophic, saline-alkaline, flamingo lake, using Landsat ETM. Remote Sens. Environ. 2013, 135, 92–106. [CrossRef] 8. Zhang, Y.; Zhang, Y.; Shi, K.; Zha, Y.; Zhou, Y.; Liu, M. A Landsat 8 OLI-based, semianalytical model for estimating the total suspended matter concentration in the slightly turbid Xin’anjiang Reservoir (China). IEEE J. 2017, 9, 398–413. [CrossRef] 9. Mccullough, I.M.; Loftin, C.S.; Sader, S.A. Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity. Remote Sens. Environ. 2012, 123, 109–115. [CrossRef] 10. Olmanson, L.G.; Brezonik, P.L.; Finlay, J.C.; Bauer, M.E. Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes. Remote Sens. Environ. 2016, 185, 119–128. [CrossRef] 11. Shi, K.; Zhang, Y.; Zhu, G.; Qin, B.; Pan, D. Deteriorating water clarity in shallow waters: Evidence from long term modis and in-situ observations. Int. J. Appl. Earth Obs. 2018, 68, 287–297. [CrossRef] 12. Shang, S.L.; Lee, Z.; Shi, L.H.; Lin, G.; Wei, G.M.; Li, X.D. Changes in water clarity of the Bohai Sea: Observations from MODIS. Remote Sens. Environ. 2016, 186, 22–31. [CrossRef] 13. Xu, X.; Yang, L.; Huang, X.; Li, Z.; Yu, D. Water brownification may not promote invasions of submerged non-native macrophytes. Hydrobiologia 2018, 817, 215–225. [CrossRef] 14. Matthews, M.W. A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. Int. J. Remote Sens. 2011, 32, 6855–6899. [CrossRef] 15. Juma, D.; Wang, H.T.; Li, F.T. Impacts of population growth and economic development on water quality of a lake: Case study of Lake Victoria Kenya water. Environ. Sci. Pollut. Res. 2014, 21, 5737–5746. [CrossRef] [PubMed] 16. Duan, H.T.; Ma, R.H.; Xu, X.F.; Kong, F.X.; Zhang, S.X.; Kong, W.J.; Hao, J.Y.; Shang, L.L. Two-decade reconstruction of algal blooms in China’s Lake Taihu. Environ. Sci. Technol. 2009, 43, 3522–3528. [CrossRef] [PubMed] 17. Cheng, X.; Chen, L.D.; Sun, R.H.; Kong, P.R. Land use changes and socio-economic development strongly deteriorate river ecosystem health in one of the largest basins in China. Sci. Total Environ. 2018, 616, 376–385. [CrossRef][PubMed] 18. Huang, C.C.; Wang, X.L.; Yang, H.; Li, Y.M.; Wang, Y.H.; Chen, X.; Xu, L.J. Satellite data regarding the eutrophication response to human activities in the plateau lake Dianchi in China from 1974 to 2009. Sci. Total Environ. 2014, 485, 1–11. [CrossRef][PubMed] 19. Goransson, G.; Larson, M.; Bendz, D. Variation in turbidity with precipitation and flow in a regulated river system-river Variation in turbidity with precipitation and flow in a regulated river system-river Göta Älv, SW Sweden. Hydrol. Earth Syst. Sci. 2013, 17, 2529–2542. [CrossRef] Remote Sens. 2018, 10, 1441 14 of 15

20. Khan, S.J.; Deere, D.; Leusch, F.D.L.; Humpage, A.; Jenkins, M.; Cunliffe, D. Extreme weather events: Should drinking water quality management systems adapt to changing risk profiles? Water Res. 2015, 85, 124–136. [CrossRef][PubMed] 21. Murshed, M.F.; Aslam, Z.; Lewis, R.; Chow, C.; Wang, D.S.; Drikas, M.; van Leeuwen, J. Changes in the quality of river water before, during and after a major flood event associated with a La Niña cycle and treatment for drinking purposes. J. Environ. Sci. 2014, 26, 1985–1993. [CrossRef][PubMed] 22. Zhang, Y.L.; Shi, K.; Zhou, Y.Q.; Liu, X.H.; Qin, B.Q. Monitoring the river plume induced by heavy rainfall events in large, shallow, Lake Taihu using MODIS 250 m imagery. Remote Sens. Environ. 2016, 173, 109–121. [CrossRef] 23. Xie, D.; Zhou, H.; Zhu, H.; Ji, H.; Li, N.; An, S. Differences in the regeneration traits of potamogeton crispus turions from macrophyte- and phytoplankton-dominated lakes. Sci. Rep. 2015, 5, 12907. [CrossRef] [PubMed] 24. Zhang, Y.L.; Jeppesen, E.; Liu, X.H.; Qin, B.Q.; Shi, K.; Zhou, Y.Q.; Thomaz, S.M.; Deng, J.M. Global loss of aquatic vegetation in lakes. Earth Sci. Rev. 2017, 173, 259–265. [CrossRef] 25. Liu, G.H.; Li, E.H.; Yuan, L.Y.; Li, W. Occurrence of aquatic macrophytes in a eutrophic subtropical lake in relation to toxic wastewater and fish overstocking. J. Freshw. Ecol. 2008, 23, 13–19. [CrossRef] 26. Lee, Z.; Shang, S.; Qi, L.; Yan, J.; Lin, G. A semi-analytical scheme to estimate secchi-disk depth from Landsat-8 measurements. Remote Sens. Environ. 2016, 177, 101–106. [CrossRef] 27. Kaufman, Y.J.; Tanré, D.; Remer, L.A.; Vermote, E.; Chu, A.; Holben, B. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res. Atoms. 1997, 102, 17051–17067. [CrossRef] 28. Liu, D.; Fu, D. Atmospheric correction of Hyperion imagery over estuarine waters: A case study of the Pearl River Estuary in southern China. Int. J. Remote Sens. 2017, 38, 199–210. [CrossRef] 29. Zhang, M.; Hu, C.; English, D.; Carlson, P.; Muller-Karger, F.E.; Toro-Farmer, G.; Herwitz, S.R. Atmospheric correction of Aisa measurements over the Florida keys optically shallow waters: Challenges in radiometric calibration and aerosol selection. IEEE J. 2015, 8, 4189–4196. [CrossRef] 30. Brezonik, P.; Menken, K.D.; Bauer, M. Landsat-based remote sensing of lake water quality characteristics, including Chlorophyll and colored dissolved organic matter (CDOM). Lake Reserv. Manag. 2005, 21, 373–382. [CrossRef] 31. Sriwongsitanon, N.; Surakit, K.; Thianpopirug, S. Influence of atmospheric correction and number of sampling points on the accuracy of water clarity assessment using remote sensing application. J. Hydrol. 2011, 401, 203–220. [CrossRef] 32. Tan, W.X.; Liu, P.C.; Liu, Y.; Yang, S.; Feng, S.N. A 30-year assessment of phytoplankton blooms in Erhai Lake using Landsat imagery: 1987 to 2016. Remote Sens. 2017, 9, 1265. [CrossRef] 33. Olmanson, L.G.; Bauer, M.E.; Brezonik, P.L. A 20-year Landsat water clarity census of Minnesota’s 10,000 lakes. Remote Sens. Environ. 2008, 112, 4086–4097. [CrossRef] 34. Zhao, D.; Cai, Y.; Jiang, H.; Xu, D.; Zhang, W.; An, S. Estimation of water clarity in Taihu Lake and surrounding rivers using Landsat imagery. Adv. Water Resour. 2011, 34, 165–173. [CrossRef] 35. Urbanski, J.A.; Wochna, A.; Bubak, I.; Grzybowski, W.; Lukawska-Matuszewska, K.; Ł ˛acka,M.; Sliwińska,S.;´ Wojtasiewicz, B.; Zaj ˛aczkowski,M. Application of Landsat 8 imagery to regional-scale assessment of lake water quality. Int. J. Appl. Earth Obs. 2016, 51, 28–36. [CrossRef] 36. Fleming-Lehtinen, V.; Laamanen, M. Long-term changes in secchi depth and the role of phytoplankton in explaining light attenuation in the Baltic Sea. Estuar. Coast. Shelf. Sci. 2012, 102, 1–10. [CrossRef] 37. Capuzzo, E.; Stephens, D.; Silva, T.; Barry, J.; Forster, R.M. Decrease in water clarity of the southern and central North sea during the 20th century. Glob. Chang. Biol. 2015, 21, 2206–2214. [CrossRef][PubMed] 38. Cao, N.; Lee, H.; Jung, H.C.; Yu, H.W. Estimation of water level changes of large-scale Amazon wetlands using ALOS2 ScanSAR differential interferometry. Remote Sens. 2018, 10, 966. [CrossRef] 39. Hongve, D.; Riise, G.; Kristiansen, J.F. Increased colour and organic acid concentrations in Norwegian forest lakes and drinking water–a result of increased precipitation? Aquat. Sci. 2004, 66, 231–238. [CrossRef] 40. Zhang, K.; Dong, X.H.; Yang, X.D.; Kattel, G.; Zhao, Y.J.; Wang, R. Ecological shift and resilience in China’s lake systems during the last two centuries. Glob. Planet. Chang. 2018, 165, 147–159. [CrossRef] Remote Sens. 2018, 10, 1441 15 of 15

41. Alongi, D.M.; Chong, V.C.; Dixon, P.; Sasekumar, A.; Tirendi, F. The influence of fish cage aquaculture on pelagic carbon flow and water chemistry in tidally dominated mangrove estuaries of peninsular Malaysia. Mar. Environ. Res. 2003, 55, 313–333. [CrossRef] 42. Fee, E.J.; Hecky, R.E.; Kasian, S.E.M.; Cruikshank, D.R. Effects of lake size, water clarity, and climatic variability on mixing depths in Canadian Shield lakes. Limnol. Oceanogr. 1996, 41, 912–920. [CrossRef] 43. Strock, K.E.; Theodore, N.; Gawley, W.G.; Ellsworth, A.C.; Saros, J.E. Increasing dissolved organic carbon concentrations in northern boreal lakes: Implications for lake water transparency and thermal structure. J. Geophys. Res. Biogeosci. 2017, 122, 1022–1035. [CrossRef] 44. Scheffer, M.; Carpenter, S.; Foley, J.A.; Folke, C.; Walker, B. Catastrophic shifts in ecosystems. Nature 2001, 413, 591–596. [CrossRef][PubMed] 45. Scheffer, M.; Hosper, S.H.; Meijer, M.L.; Moss, B.; Jeppesen, E. Alternative equilibria in shallow lakes. Trends Ecol. Evol. 1993, 8, 275–279. [CrossRef] 46. Villamagna, A.M.; Murphy, B.R. Ecological and socio-economic impacts of invasive water hyacinth (Eichhornia crassipes): A review. Freshw. Biol. 2010, 55, 282–298. [CrossRef] 47. Leach, T.H.; Beisner, B.E.; Carey, C.C.; Pernica, P.; Rose, K.C.; Huot, Y.; Brentrup, J.A.; Domaizon, I.; Grossart, H.P.; Ibelings, B.W.; et al. Patterns and drivers of deep Chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification. Limnol. Oceanogr. 2018, 63, 628–646. [CrossRef] 48. Gobler, C.J.; Doherty, O.M.; Hattenrath-Lehmann, T.K.; Griffith, A.W.; Kang, Y.; Litaker, R.W. Ocean warming since 1982 has expanded the niche of toxic algal blooms in the north Atlantic and north Pacific oceans. Proc. Natl. Acad. Sci. USA 2017, 114, 4975–4980. [CrossRef][PubMed] 49. Henson, S.A.; Cole, H.S.; Hopkins, J.; Martin, A.P.; Yool, A. Detection of climate change-driven trends in phytoplankton phenology. Glob. Chang. Biol. 2018, 24, E101–E111. [CrossRef][PubMed]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).