Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

Measuring total nitrogen (TN) and total phosphorus (TP) is important in managing heavy polluted urban waters in China. This study uses high spatial resolution IKONOS imagery with four multispectral bands, which roughly correspond to Landsat/TM bands 1–4, to determine TN and TP in small urban rivers and lakes in China. By using Lake Cihu and the lower reaches of Wen-Rui Tang (WRT) River as examples, this paper develops both multiple linear regressions (MLR) and artificial neural network (ANN) models to estimate TN and TP concentrations from high spatial resolution remote sensing imagery and in situ water samples collected concurrently with overpassing satellite. The measured and estimated values of both MLR and ANN models are in good agreement (R2 > 0.85 and RMSE 0.86 and RMSE < 0.89). Results validate the potential of using high resolution IKONOS multispectral imagery to study the chemical states of small-sized urban water bodies. The spatial distribution maps of TN and TP concentrations generated by the ANN model can inform the decision makers of variations in water quality in Lake Cihu and lower reaches of WRT River. The approaches and equations developed in this study could be applied to other urban water bodies for water quality monitoring.

[1]  J. Kämäri,et al.  Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland. , 2001, The Science of the total environment.

[2]  Min Shao,et al.  City clusters in China: air and surface water pollution , 2006 .

[3]  Vladimir M. Krasnopolsky,et al.  A Neural Network Multiparameter Algorithm for SSM/I Ocean Retrievals , 2000 .

[4]  M. Kishino,et al.  Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data , 2005 .

[5]  D. Chalikov,et al.  New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model , 2005 .

[6]  M. Bauer,et al.  A procedure for regional lake water clarity assessment using Landsat multispectral data , 2002 .

[7]  M. Bauer,et al.  A 20-year Landsat water clarity census of Minnesota's 10,000 lakes , 2008 .

[8]  Kaishan Song,et al.  Estimation of chlorophyll‐a concentration and trophic states for inland lakes in Northeast China from Landsat TM data and field spectral measurements , 2008 .

[9]  M. Ashton,et al.  Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .

[10]  A. Melesse,et al.  Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network , 2012, Water, Air, & Soil Pollution.

[11]  Minghua Zhang,et al.  Monitoring and modeling dissolved oxygen dynamics through continuous longitudinal sampling: a case study in Wen‐Rui Tang River, Wenzhou, China , 2012, Hydrological Processes.

[12]  R. Hecky,et al.  Total nitrogen, total phosphorus, and nutrient limitation in lakes and oceans: Is there a common relationship? , 2000 .

[13]  B. Cohen.  Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability , 2006 .

[14]  Semih Ekercin,et al.  Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in Istanbul, Turkey , 2007 .

[15]  H. Siegel,et al.  Correction of bottom influence in ocean colour satellite images of shallow water areas of the Baltic Sea , 2001 .

[16]  Alex Marshall,et al.  State of world population 2007: unleashing the potential of urban growth , 2007 .

[17]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[18]  T. Kutser,et al.  Telespectrometrical estimation of water transparency, chlorophyll-a and total phosphorus concentration of Lake Peipsi , 1995 .

[19]  JiR. öå Kopàcöek,et al.  Factors governing nutrient status of mountain lakes in the Tatra Mountains , 2000 .

[20]  Yunpeng Wang,et al.  Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data. , 2004, The Science of the total environment.

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

[22]  Susanna T. Y. Tong,et al.  Modeling the relationship between land use and surface water quality. , 2002, Journal of environmental management.

[23]  Yan-li Qiao,et al.  [Inversion of the lake total nitrogen concentration by multiple regression kriging model based on hyperspectral data of HJ-1A]. , 2011, Guang pu xue yu guang pu fen xi = Guang pu.

[24]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

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

[26]  Nathan J. Heinert,et al.  Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery , 2003 .

[27]  Ping Shi,et al.  Retrieval of oceanic chlorophyll concentration using support vector machines , 2003, IEEE Trans. Geosci. Remote. Sens..

[28]  Luoheng Han,et al.  Estimating and mapping chlorophyll‐a concentration in Pensacola Bay, Florida using Landsat ETM+ data , 2005 .

[29]  Philip A. Townsend,et al.  A MODIS approach to predicting stream water quality in Wisconsin , 2013 .

[30]  Lin Li,et al.  Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources , 2012, Environmental Monitoring and Assessment.

[31]  J. Qi,et al.  Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data , 2010 .

[32]  Soon-Jin Hwang,et al.  Landscape ecological approach to the relationships of land use patterns in watersheds to water quality characteristics , 2009 .

[33]  J. Qi,et al.  Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery , 2013 .

[34]  M. He,et al.  Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters , 2003 .

[35]  Cornelius T. Leondes,et al.  Neural network systems techniques and applications , 1998 .

[36]  Lei Kun.  Monitoring the surface water quality of Taihu Lake based on the data of CBERS\|1 , 2004 .

[37]  Dennis Normile,et al.  China's Living Laboratory in Urbanization , 2008, Science.

[38]  Wenzhi Liu,et al.  Influences of watershed landscape composition and configuration on lake‐water quality in the Yangtze River basin of China , 2012 .

[39]  Minghua Zhang,et al.  Spatial and temporal variations of nitrogen pollution in Wen-Rui Tang River watershed, Zhejiang, China , 2011, Environmental monitoring and assessment.

[40]  H. Paerl,et al.  Coastal marine eutrophication: Control of both nitrogen and phosphorus is necessary , 2008, Proceedings of the National Academy of Sciences.

[41]  Bai Zhang,et al.  Water quality monitoring using Landsat Themate Mapper data with empirical algorithms in Chagan Lake, China , 2011 .

[42]  David P. Hamilton,et al.  Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand , 2011 .

[43]  Ana C. Teodoro,et al.  Retrieving TSM Concentration From Multispectral Satellite Data by Multiple Regression and Artificial Neural Networks , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China , 2013, Environmental Science and Pollution Research.

[45]  Ni-Bin Chang,et al.  Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models , 2013 .

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

[47]  Deyong Sun,et al.  A Unified Model for Remotely Estimating Chlorophyll a in Lake Taihu, China, Based on SVM and In Situ Hyperspectral Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.