Remote sensing of water quality based on HJ-1A HSI imagery with modified discrete binary particle swarm optimization-partial least squares (MDBPSO-PLS) in inland waters: A case in Weishan Lake

Abstract Remote sensing has been recognized as an effective tool to monitor water quality in inland waters. An adaptive model coupled with a modified discrete binary particle swarm optimization algorithm utilizing the catastrophe strategy and partial least squares (MDBPSO-PLS) was developed to retrieve the water quality indexes (chlorophyll a (chl-a), total suspended matter (TSM), and turbidity) in Weishan Lake. Based on the selective bands for water quality retrieval, the proposed MDBPSO algorithm was compared with original DBPSO and Genetic Algorithm (GA) in order to validate the feasibility and efficiency of the proposed algorithm. The comparison results indicated that MDBPSO with the catastrophe strategy could avoid the premature convergence phenomenon of DBPSO algorithm. The hyperspectral data from HJ-1A Hyperspectral Imager (HSI) selected by MDBPSO were utilized to establish the PLS model by correlating the spectral data with measured water quality parameters. Then the established PLS model was compared with the PLS model established with full spectral data and measured water quality parameters. MDBPSO-PLS showed the better performance in retrieving the above-mentioned three parameters in Weishan Lake. The comprehensive errors between measured and retrieved parameters in MDBPSO-PLS (chl-a (R2 = 0.97, CE = 3.16%), TSM (R2 = 0.97, CE = 5.84%), and turbidity (R2 = 0.97, CE = 7.40%)) were lower than those in PLS model (chl-a (R2 = 0.55, CE = 18.71%), TSM (R2 = 0.49, CE = 49.50%), and turbidity (R2 = 0.50, CE = 50.46%). The three water quality parameters were mapped by using MDBPSO-PLS. Our observations indicated that the MDBPSO-PLS model showed the significantly higher retrieval accuracy than the PLS model and could provide realistic information on the distribution of chl-a, TSM, and turbidity in Weishan Lake based on HJ-1A HSI.

[1]  Alexander A Gilerson,et al.  Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. , 2010, Optics express.

[2]  R. Lunetta,et al.  Barriers to adopting satellite remote sensing for water quality management , 2013 .

[3]  Jun Liu,et al.  Random DBPSO algorithm application in the Optimal Test-sequencing Problem of complicated electronic system , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[4]  David Riaño,et al.  Retrieval of Fresh Leaf Fuel Moisture Content Using Genetic Algorithm Partial Least Squares (GA-PLS) Modeling , 2007, IEEE Geoscience and Remote Sensing Letters.

[5]  Desire L. Massart,et al.  Genetic algorithms (GA) applied to the orthogonal projection approach (OPA) for variable selection , 2004 .

[6]  Xiaoling Chen,et al.  Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China , 2017 .

[7]  Menghua Wang,et al.  Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS, MERIS and GOCI satellite data , 2014 .

[8]  Annick Bricaud,et al.  Estimation of new primary production in the Benguela upwelling area, using ENVISAT satellite data and a model dependent on the phytoplankton community size structure , 2008 .

[9]  F. Gohin,et al.  Satellite-derived parameters for biological modelling in coastal waters: Illustration over the eastern continental shelf of the Bay of Biscay , 2005 .

[10]  K. Ali,et al.  Application of a partial least-squares regression model to retrieve chlorophyll-a concentrations in coastal waters using hyper-spectral data , 2016, Ocean Science Journal.

[11]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[12]  Kusum Deep,et al.  A Modified Binary Particle Swarm Optimization for Knapsack Problems , 2012, Appl. Math. Comput..

[13]  Quan Sun,et al.  ractional vegetation cover estimation in arid and semi-arid environments using J-1 satellite hyperspectral data , 2012 .

[14]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[15]  Riccardo Leardi,et al.  Application of genetic algorithm–PLS for feature selection in spectral data sets , 2000 .

[16]  C. Cao,et al.  Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation , 2014 .

[17]  Shuai Li,et al.  Remote sensing of chlorophyll-a concentration for drinking water source using genetic algorithms (GA)-partial least square (PLS) modeling , 2012, Ecol. Informatics.

[18]  Peter Fearns,et al.  Detecting trend and seasonal changes in bathymetry derived from HICO imagery: A case study of Shark Bay, Western Australia , 2014 .

[19]  Junsheng Li,et al.  Assessment of water constituents in highly turbid productive water by optimization bio-optical retrieval model after optical classification , 2014 .

[20]  C. Giardino,et al.  Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission , 2015 .

[21]  Zhe Fan,et al.  The optimal retrieval of ocean color constituent concentrations based on the variational method , 2013 .

[22]  Zhongfeng Qiu,et al.  Detection of Total Phosphorus Concentrations of Turbid Inland Waters Using a Remote Sensing Method , 2014, Water, Air, & Soil Pollution.

[23]  Deyong Sun,et al.  A semi-analytical approach for detecting suspended particulate composition in complex turbid inland waters (China) , 2013 .

[24]  Zulu Zhang,et al.  Vegetation Dynamic Changes of Lake Nansi Wetland in Shandong of China , 2011 .

[25]  Jiankun Hu,et al.  A new binary hybrid particle swarm optimization with wavelet mutation , 2017, Knowl. Based Syst..

[26]  Sadiq M. Sait,et al.  Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits , 2013, Appl. Soft Comput..

[27]  Vittorio E. Brando,et al.  Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality , 2003, IEEE Trans. Geosci. Remote. Sens..

[28]  K. K. Satapathy,et al.  Variability of nutrients and phytoplankton biomass in a shallow brackish water ecosystem (Chilika Lagoon, India) , 2009, Limnology.

[29]  A. L. Robertson,et al.  Using a partial least squares (PLS) method for estimating cyanobacterial pigments in eutrophic inland waters , 2009, Optical Engineering + Applications.

[30]  Qinghua Hu,et al.  Discrete particle swarm optimization approach for cost sensitive attribute reduction , 2016, Knowl. Based Syst..

[31]  David Doxaran,et al.  Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery , 2010 .

[32]  Chen Yang,et al.  The seasonal and spatial variations of phytoplankton community and their correlation with environmental factors in a large eutrophic Chinese lake (Lake Chaohu) , 2014 .

[33]  Xiaohan Liu,et al.  Lake Topography and Wind Waves Determining Seasonal-Spatial Dynamics of Total Suspended Matter in Turbid Lake Taihu, China: Assessment Using Long-Term High-Resolution MERIS Data , 2014, PloS one.

[34]  Stephanie C. J. Palmer,et al.  Remote sensing of inland waters: Challenges, progress and future directions , 2015 .

[35]  D. Doxaran,et al.  Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations , 2002 .

[36]  N. Oppelt,et al.  Remote sensing for lake research and monitoring – Recent advances , 2016 .

[37]  Qiao Wang,et al.  Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes. , 2015, The Science of the total environment.

[38]  R. Leardi,et al.  Genetic algorithms applied to feature selection in PLS regression: how and when to use them , 1998 .

[39]  R. Vincent,et al.  Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie , 2004 .

[40]  Wonkook Kim,et al.  Evaluation of chlorophyll retrievals from Geostationary Ocean Color Imager (GOCI) for the North-East Asian region , 2016 .

[41]  Maoguo Gong,et al.  Discrete particle swarm optimization for high-order graph matching , 2016, Inf. Sci..

[42]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[43]  B. Nechad,et al.  Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters , 2010 .

[44]  R. Naiman,et al.  Freshwater biodiversity: importance, threats, status and conservation challenges , 2006, Biological reviews of the Cambridge Philosophical Society.

[45]  Anne Lyche Solheim,et al.  Sustaining recreational quality of European lakes: minimizing the health risks from algal blooms through phosphorus control , 2013 .

[46]  Ronghua Ma,et al.  Distribution and incidence of algal blooms in Lake Taihu , 2014, Aquatic Sciences.

[47]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[48]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[49]  Martin Kernan,et al.  Assessing the ecological status in the context of the European Water Framework Directive: where do we go now? , 2014, The Science of the total environment.

[50]  D. Hering,et al.  Drivers and stressors of freshwater biodiversity patterns across different ecosystems and scales: a review , 2012, Hydrobiologia.

[51]  Els Knaeps,et al.  A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters , 2015 .

[52]  T. Moore,et al.  An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. , 2014, Remote sensing of environment.

[53]  M. Bonansea,et al.  Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina) , 2015 .

[54]  K. Ali,et al.  Multivariate approach for chlorophyll-a and suspended matter retrievals in Case II type waters using hyperspectral data , 2013 .

[55]  F. Gohin,et al.  Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western–northwestern Black Sea coastal zone , 2013 .

[56]  S. Ramachandran,et al.  Entropy based Binary Particle Swarm Optimization and classification for ear detection , 2014, Eng. Appl. Artif. Intell..

[57]  C. Sotin,et al.  Mapping microphytobenthos biomass by non-linear inversion of visible-infrared hyperspectral images , 2005 .

[58]  B. Moss,et al.  Cogs in the endless machine: lakes, climate change and nutrient cycles: a review. , 2012, The Science of the total environment.

[59]  Lin Li,et al.  Using hyperspectral remote sensing to estimate chlorophyll‐a and phycocyanin in a mesotrophic reservoir , 2010 .

[60]  M. Matthews.  A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters , 2011 .

[61]  Lin Li,et al.  Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model , 2013 .

[62]  Emiliana Valentini,et al.  Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake , 2015 .

[63]  R. Bukata.  Retrospection and introspection on remote sensing of inland water quality: “Like Déjà Vu All Over Again” , 2013 .

[64]  Kaishan Song,et al.  Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs , 2012, Water, Air, & Soil Pollution.

[65]  M. Matthews,et al.  An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters , 2012 .

[66]  David Gilvear,et al.  Mapping macrophytic vegetation in shallow lakes using the Compact Airborne Spectrographic Imager (CASI) , 2010 .

[67]  Lin Li,et al.  Hyperspectral retrieval of phycocyanin in potable water sources using genetic algorithm-partial least squares (GA-PLS) modeling , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[68]  Martti Hallikainen,et al.  A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland , 2007 .

[69]  R. Bukata,et al.  Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery , 2010 .

[70]  Lin Li,et al.  Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA-PLS) modeling. , 2012, The Science of the total environment.

[71]  Francis Gohin,et al.  Annual cycles of chlorophyll- a , non-algal suspended particulate matter, and turbidity observed from space and in-situ in coastal waters , 2011 .