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Song K.S.,Chinese Academy of Sciences | Song K.S.,Indiana University - Purdue University Indianapolis | Li L.,Indiana University - Purdue University Indianapolis | Tedesco L.,The Wetlands Institute | And 3 more authors.
Journal of Environmental Informatics

The present study is focused on remote quantification of total suspended matter (TSM) for turbid inland waters. In situ remote sensing reflectance (Rrs) and TSM at 863 stations over 10 inland water bodies from China, Australia, and USA were collected and examined. Four empirical regression models based on sensitive reflectance bands (SB), derivatives (SD), the band ratio proposed by Doxaran et al. (2002; Rrs850/Rrs550: DM), and optimal band ratios (OBR) were examined to estimate TSM. The performance varies due to TSM concentration and the Chl-a: TSM ratio. The four models perform well when the water bodies are dominated with non-algal particles at high TSM concentration and yielded higher accuracy (R2 ranged from 0.83 to 0.91) with both DM and OBR models, while the OBR model outperformed other models when waters are dominated by phytoplankton. Our findings also indicate that phytoplankton in the water column affects the band ratio algorithm for TSM estimates. When data from all water bodies are considered collectively, the OBR model (R2 = 0.92) marginally outperforms the other three models (0.89, 0.87, and 0.88 for DM, SB, and SD, respectively). Future studies should be undertaken to analyze the influence of phytoplankton abundance on water-leaving signals for TSM estimates. The results of the present study also need further analyses to gain a more in-depth understanding of inherent optical properties for optically active constituents (OACs), such as absorption and backscattering to interpret the observed variations. © 2014 ISEIS All rights reserved. Source

Song K.,Indiana University - Purdue University Indianapolis | Song K.,CAS Changchun Northeast Institute of Geography and Agroecology | Li L.,Indiana University - Purdue University Indianapolis | Tedesco L.,The Wetlands Institute | And 2 more authors.
Chinese Geographical Science

This study examined the spatiotemporal dynamics of colored dissolved organic matter (CDOM) and spectral slope (S), and further to analyze its sources in three productive water supplies (Eagle Creek, Geist and Morse reservoirs) from Indiana, USA. The results showed that he absorption coefficient aCDOM(440) ranged from 0.37 m–1 to 3.93 m–1 with an average of 1.89 ± 0.76 m–1 (±SD) for the aggregated dataset, and S varied from 0.0048 nm–1 to 0.0239 nm–1 with an average of 0.0108 ± 0.0040 nm–1. A significant relationship between S and aCDOM(440) can be fitted with a power equation (S = 0.013 × aCDOM(440)–0.42, R2 = 0.612), excluding data from Geist Reservoir during high flow (12 April 2010) and the Morse Reservoir on 25 June 2010 due to a T-storm achieves even higher determination coefficient (R2 = 0.842). Correlation analysis indicated that aCDOM(440) has strong association with inorganic suspended matter (ISM) concentration (0.231 < R2 < 0.786) for each of the field surveys, and this trend followed the aggregated datasets (R2 = 0.447, p < 0.001). In contrast, chlorophyll-a was only correlated with aCDOM(440) in summer and autumn (0.081 < R2 < 0.763), indicating that CDOM is mainly from terrigenous sources in early spring and that phytoplankton contributed during the algal blooming season. The S value was used to characterize CDOM origin. The results indicate that the CDOM source is mainly controlled by hydrological variations, while phytoplankton originated organic matter also closely linked with CDOM dynamics in three productive reservoirs. © 2015, Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag Berlin Heidelberg. Source

Song K.,Chinese Academy of Sciences | Song K.,Indiana University - Purdue University Indianapolis | Wang Z.,Chinese Academy of Sciences | Li L.,Indiana University - Purdue University Indianapolis | And 4 more authors.
Journal of Environmental Management

In the past five decades, the wetlands in the Muleng-Xingkai Plain, Northeast China, have experienced rapid shrinkage and fragmentation. In this study, wetlands cover change and agricultural cultivation were investigated through a time series of thematic maps from 1954, and Landsat satellite images representing the last five decades (1976, 1986, 1995, 2000, and 2005). Wetlands shrinkage and fragmentation were studied based on landscape metrics and the land use changes transition matrix. Furthermore, the driving forces were explored according to socioeconomic development and major natural environmental factors. The results indicate a significant decrease in the wetlands area in the past five decades, with an average annual decrease rate of 9004 ha/yr. Of the 625,268 ha of native wetlands in 1954, approximately 64% has been converted to other land use types by 2005, of which conversion to cropland accounts for the largest share (83%). The number of patches decreased from 1272 (1954) to 197 (1986) and subsequently increased to 326 (2005). The mean patch size changed from 480 ha (1954) to 1521 ha (1976), and then steadily decreased to 574 ha (2005). The largest patch index (total core area index) indicates wetlands shrinkage with decreased values from 31.73 (177,935 ha) to 3.45 (39,421 ha) respectively. Climatic changes occurred over the study period, providing a potentially favorable environment for agricultural development. At the same time population, groundwater harvesting, and fertilizer application increased significantly, resulting in wetlands degradation. According to the results, the shrinkage and fragmentation of wetlands could be explained by socioeconomic development and secondarily aided by changing climatic conditions. © 2012 Elsevier Ltd. Source

Song K.,Indiana University - Purdue University Indianapolis | Song K.,Chinese Academy of Sciences | Li L.,Indiana University - Purdue University Indianapolis | Tedesco L.,The Wetlands Institute | And 6 more authors.
Environmental Science and Pollution Research

Nuisance cyanobacterial blooms degrade water resources through accelerated eutrophication, odor generation, and production of toxins that cause adverse effects on human health. Quick and effective methods for detecting cyanobacterial abundance in drinking water supplies are urgently needed to compliment conventional laboratory methods, which are costly and time consuming. Hyperspectral remote sensing can be an effective approach for rapid assessment of cyanobacterial blooms. Samples (n = 250) were collected from five drinking water sources in central Indiana (CIN), USA, and South Australia (SA), which experience nuisance cyanobacterial blooms. In situ hyperspectral data were used to develop models by relating spectral signal with handheld fluorescence probe (YSI 6600 XLM-SV) measured phycocyanin (PC in cell/ml), a proxy pigment unique for indicating the presence of cyanobacteria. Three-band model (TBM), which is effective for chlorophyll-a estimates, was tuned to quantify cyanobacteria coupled with the PC probe measured cyanobacteria. As a comparison, two band model proposed by Simis et al. (Limnol Oceanogr, 50(11): 237-245, 2005; denoted as SM05) was paralleled to evaluate TBM model performance. Our observation revealed a high correlation between measured and estimated PC for SA dataset (R2 = 0.96; range: 534-20,200 cell/ml) and CIN dataset (R2 = 0.88; range: 1,300-44,500 cell/ml). The potential of this modeling approach for imagery data were assessed by simulated ESA/Centinel3/OLCI spectra, which also resulted in satisfactory performance with the TBM for both SA dataset (RMSE % = 26.12) and CIN dataset (RMSE % = 34.49). Close relationship between probe-measured PC and laboratory measured cyanobacteria biovolume was observed (R2 = 0.93, p < 0.0001) for the CIN dataset, indicating a stable performance for PC probe. Based on our observation, field spectroscopic measurement coupled with PC probe measurements can provide quantitative cyanobacterial bloom information from both relatively static and flowing inland waters. Hence, it has promising implications for water resource managers to obtain information for early warning detection of cyanobacterial blooms through the close association between probe measured PC values and cyanobacterial biovolume via remote sensing modeling. © 2013 Springer-Verlag Berlin Heidelberg. Source

Song K.,CAS Changchun Northeast Institute of Geography and Agroecology | Song K.,Indiana University - Purdue University Indianapolis | Li L.,Indiana University - Purdue University Indianapolis | Tedesco L.P.,The Wetlands Institute | And 3 more authors.
ISPRS Journal of Photogrammetry and Remote Sensing

Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (Rrs) were measured. A partial least squares-artificial neural network (PLS-ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the Rrs with PC concentration. Our results indicate that the PLS-ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS-ANN model resulted in a high coefficient of determination (R2) for CIN dataset (R2=0.92, R: 0.3-220.7μg/L) and SA (R2=0.98, R: 0.2-13.2μg/L). In comparison, the TBM model yielded an R2=0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R2=0.86; SA: R2=0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS-ANN model still achieved good performance (OLCI: R2=0.84; Hyperion: R2=0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R2=0.65, Hyperion: R2=0.70). Based on the results, the PLS-ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R2=0.81; SA: R2=0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Source

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