Yaolin Liu

Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals.

Soil contamination by heavy metals is an increasingly important problem worldwide. Quick and reliable access to heavy metal concentration data is crucial for soil monitoring and remediation. Visible and near-infrared reflectance spectroscopy, which is known as a noninvasive, cost-effective, and environmentally friendly technique, has potential for the simultaneous estimation of the various heavy metal concentrations in soil. Moreover, it provides a valid alternative method for the estimation of heavy metal concentrations over large areas and long periods of time. This paper reviews the state of the art and presents the mechanisms, data, and methods for the estimation of heavy metal concentrations by the use of visible and near-infrared reflectance spectroscopy. The challenges facing the application of hyperspectral images in mapping soil contamination over large areas are also discussed.

Comparison of MODIS and Landsat TM5 images for mapping tempo-spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve, China

Landsat has successfully been applied to map Secchi disk depth of inland water bodies. Operational use for monitoring a dynamic variable like Secchi disk depth is however limited by the 16‐day overpass cycle of the Landsat system and cloud cover. Low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) image captured twice a day could potentially overcome these problems. However, its potential for mapping Secchi disk depth of inland water bodies has so far rarely been explored. This study compared two image sources, MODIS and Landsat Thematic Mapper (TM), for mapping the tempo–spatial dynamics of Secchi disk depth in Poyang Lake National Nature Reserve, China. Secchi disk depths recorded at weekly intervals from April to October in 2004 and 2005 were related to 5 Landsat TM and 22 MODIS images respectively. Two multiple regression models including the blue and red bands of Landsat TM and MODIS respectively explained 83% and 88% of the variance of the natural logarithm of Secchi disk depth. The standard errors of the predictions were 0.20 and 0.37 m for Landsat TM and MODIS‐based models. A high correlation (r = 0.94) between the predicted Secchi disk depth derived from the two models was observed. A discussion of advantages and disadvantages of both sensors leads to the conclusion that MODIS offers the possibility to monitor water transparency more regularly and cheaply in relatively big and frequently cloud covered lakes as is with Poyang Lake.

Will the Three Gorges Dam affect the underwater light climate of Vallisneria spiralis L. and food habitat of Siberian crane in Poyang Lake

Almost 95% of the entire population of the Siberian crane (Grusxa0leucogeranus) winter in Poyang Lake, China, where they forage on the tubers of the submerged aquatic macrophyte Vallisneriaxa0spiralis. The Three Gorges Dam on the Yangtze River may possibly affect this food source of the Siberian crane by affecting the light intensity reaching the top of the V.xa0spiralis canopy. In this study, the photosynthetically active radiation at the top of the V.xa0spiralis canopy (PARtc) in Lake Dahuchi was modeled from 1998 to 2006, and the potential impacts of changes in water level and turbidity on the underwater light climate of V.xa0spiralis were analyzed. PARtc was calculated from incident irradiance while the losses due to reflection at the water surface, absorption, and scattering within the water column were taken into consideration. The results indicated significant differences in PARtc between years. Six years of water level and Secchi disk depth records revealed a seasonal switching of the lake from a turbid state at low water levels in autumn, winter, and spring to a clear state at high water levels during the monsoon in summer. The highest PARtc occurred at intermediate water levels, which were reached when the Yangtze River forces Lake Dahuchi out of its turbid state in early summer and the water becomes clear. The intended operation of the Three Gorges Dam, which will increase water levels in May and June, may advance the moment when Lake Dahuchi switches from turbid to clear. We suggest that this might increase production of V.xa0spiralis and possibly improve the food habitat conditions for wintering Siberian crane in Poyang Lake.

Feasibility of estimating heavy metal concentrations in Phragmites australis using laboratory-based hyperspectral data—A case study along Le’an River, China

Abstract It is necessary to estimate heavy metal concentrations in plants for understanding the heavy metal contaminations and for keeping the sustainable developments of ecosystems and human health. This study, with the Le’an River and its two branches in Jiangxi Province of China as a case study, aimed to explore the feasibility of estimating concentrations of heavy metal lead (Pb), copper (Cu) and zinc (Zn) in Phragmites australis using laboratory-based hyperspectral data. 21 P. australis leaf samples were collected, and their hyperspectral data, chlorophyll concentration and Pb, Cu and Zn concentrations were measured within the laboratory. The potential relations among hyperspectral data, chlorophyll concentration and Pb, Cu and Zn concentrations were explored and employed to estimate Pb, Cu and Zn concentrations from hyperspectral data with chlorophyll concentration as a bridge. The results showed that the linear combination of normalized band depths at wavelengths 537 (green), 667 (red) and 747 (near infrared)xa0nm could explain 82% of the variation of chlorophyll concentration; the Pb, Cu and Zn concentrations were significantly and negatively related to the chlorophyll concentration, and the chlorophyll concentration could explain around 30% of the variations of Pb, Cu and Zn concentrations, respectively; and the absolute estimation errors for more than 80% estimations of Pb, Cu and Zn concentrations were less than 30%. We conclude that the laboratory-based hyperspectral data hold potentials in estimating concentrations of heavy metal Pb, Cu and Zn in P. australis . More sampling points and spectral characteristics-based methods should be collected and employed for improving the stabilities and accuracies of estimation models.

Absorption and backscattering coefficients and their relations to water constituents of Poyang Lake, China

The measurement and analysis of inherent optical properties (IOPs) of the main water constituents are necessary for remote-sensing-based water quality estimation and other ecological studies of lakes. This study aimed to measure and analyze the absorption and backscattering coefficients of the main water constituents and, further, to analyze their relations to the water constituent concentrations in Poyang Lake, China. The concentrations and the absorption and backscattering coefficients of the main water constituents at 47 sampling sites were measured and analyzed as follows. (1) The concentrations of chlorophyll a (C(CHL)), dissolved organic carbon (C(DOC)), suspended particulate matter (C(SPM)), including suspended particulate inorganic matter (C(SPIM)) and suspended particulate organic matter (C(SPOM)), and the absorption coefficients of total particulate (a(p)), phytoplankton (a(ph)), nonpigment particulate (a(d)), and colored/chromophoric dissolved organic matter (a(g)) were measured in the laboratory. (2) The total backscattering coefficients, including the contribution of pure water at six wavelengths of 420, 442, 470, 510, 590, and 700 nm, were measured in the field with a HydroScat-6 backscattering sensor. (3) The backscattering coefficients without the contribution of pure water (b(b)) were then derived by subtracting the backscattering coefficients of pure water from the total backscattering coefficients. (4) The C(CHL), C(SPM), C(SPIM), C(SPOM), and C(DOC) of the 41 remaining water samples were statistically described and their correlations were analyzed. (5) The a(ph), a(d), a(p), a(g), and b(b) were visualized and analyzed, and their relations to C(CHL), C(SPM), C(SPIM), C(SPOM), or C(DOC) were studied. Results showed the following. (1) Poyang Lake was a suspended particulate inorganic matter dominant lake with low phytoplankton concentration. (2) One salient a(ph) absorption peak was found at 678 nm, and it explained 72% of the variation of C(CHL). (3) The a(d) and a(p) exponentially decreased with increasing wavelength, and they explained 74% of the variation of C(SPIM) and 71% variation of C(SPM), respectively, at a wavelength of 440 nm. (4) The a(g) also exponentially decreased with increasing wavelength, and it had no significant correlation to C(DOC) at a significance level of 0.05. (5) The b(b) decreased with increasing wavelength, and it had strong and positive correlations to C(SPM), C(SPIM) and C(SPOM), a strong and negative correlation to C(CHL), and no correlation to C(DOC) at a significance level of 0.05. Such results will be helpful for the understanding of the IOPs of Poyang Lake. They, however, only represented the IOPs during the sampling time period, and more measurements and analyses in different seasons need to be carried out in the future to ensure a comprehensive understanding of the IOPs of Poyang Lake.

Performance of Landsat TM in ship detection in turbid waters

Abstract The visible and near infrared bands of Landsat have limitations for detecting ships in turbid water. The potential of TM middle infrared bands for ship detection has so far not been investigated. This study analyzed the performance of the six Landsat TM visible and infrared bands for detecting dredging ships in the turbid waters of the Poyang Lake, China. A colour composite of principal components analysis (PCA) components 3, 2 and 1 of a TM image was used to randomly select 81 dredging ships. The reflectance contrast between ships and adjacent water was calculated for each ship. A z-score and related p-value were used to assess the ship detection performance of the six Landsat TM bands. The reflectance contrast was related to water turbidity to analyze how water turbidity affected the capability of ship identification. The results revealed that the TM middle infrared bands 5 and 7 better discriminated vessels from surrounding waters than the visible and near infrared bands 1–4. A significant relation between reflectance contrast and water turbidity in bands 1–4 could explain the limitations of bands 1–4; while water turbidity has no a significant relation to the reflectance contrast of bands 5 and 7. This explains why bands 5 and 7 detect ships better than bands 1–4.

Comparison of MODIS-based models for retrieving suspended particulate matter concentrations in Poyang Lake, China

Abstract Suspended particulate matter (SPM) is a key parameter describing water quality, and developing the retrieval model of SPM concentration ( C SPM ) is fundamental for obtaining the spatiotemporal information of C SPM and further for understanding, managing and protecting aquatic ecosystems. This study aimed to compare moderate resolution imaging spectroradiometer (MODIS)-based C SPM retrieval models in order to find the optimal model for improving the C SPM estimation in Poyang Lake. The C SPM measurements on 27 September 2007 and their coincident MODIS Terra image were used to calibrate retrieval models with the least-squares technique. The C SPM measurements on 31 August 2012 and the MODIS Terra image on 30 August 2012 were applied to validate the calibrated models, and the correlation coefficient ( r ) between the measured and estimated C SPM values, the root mean square error (RMSE) and relative root mean square error (RRMSE) of estimation as well as the model bias evaluation result were compared to determine the optimal model for estimating the C SPM values of Poyang Lake from MODIS images. Model calibration showed that, after two samples were removed, the exponential models of blue, green and red band, the linear model of infrared band, the cubic model of red band as well as the exponential model of red minus infrared band explained about 92%, 88%, 90%, 89%, 90% and 76% of the variation of C SPM , respectively; while model validation indicated that, after removing two samples, the exponential models of blue and green band got biased C SPM estimations, the agreement between the measured and estimated C SPM values was not very high ( r xa0=xa0 r xa0=xa00.87, RMSExa0=xa022.1xa0mg/l, RRMSExa0=xa052.8%). We concluded that the exponential model of red minus infrared band obtained stable C SPM estimation and was the optimal model for C SPM estimation in this study, and more independent datasets should be obtained to further validate our finding for improving the C SPM estimation in Poyang Lake.

Using remotely sensed suspended sediment concentration variation to improve management of Poyang Lake, China

Abstract This study applied Moderate-Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2010 to obtain and analyze the spatiotemporal variation of suspended sediment concentration (SSC) and discussed factors affecting it in Poyang Lake, China. Results showed that (1) the mean SSC was lower in the south, higher in the north, and moderate in the central lake region; (2) the mean SSC in the south was lower than or close to 20 mg/L, with no clear annual trend; (3) the mean SSC in the north was slightly higher than 20 mg/L in 2000 and increased from 2001, with the highest value >60 mg/L in 2006; (4) the mean SSC in the central lake region, except for 2009, ranged from 20 to 40 mg/L and had an annual pattern similar to that in the southern lake region; (5) for the entire lake, the mean SSC declined from January to March, increased from September to December, and fluctuated from April to August; and (6) several higher SSC values were found in the central or southern lake regions. The spatiotemporal variation of SSC was controlled by natural and human factors, in which dredging was dominant. Limiting the area of dredging and reducing dredging intensity would decrease SSC and maintain sustainable development of Poyang Lake. Remote sensing can obtain the spatiotemporal information of some water quality parameters, which will help managers understand the lake dynamics and mechanisms to make better decisions for lake management.

Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake, China

Suspended particulate matter (SPM) is a dominant water constituent of case-II waters, and SPM concentration (CSPM) is a key parameter describing water quality. This study, using Landsat 8 Operational Land Imager (OLI) images, aimed to develop the CSPM retrieval models and further to estimate the CSPM values of Dongting Lake. One Landsat 8 OLI image and 53 CSPM measurements were employed to calibrate Landsat 8-based CSPM retrieval models. The CSPM values derived from coincident Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) images were compared to validate calibrated Landsat 8-based CSPM models. After the best stable Landsat 8-based CSPM retrieval model was further validated using an independent Landsat 8 OLI image and its coincident CSPM measurements, it was applied to four Landsat 8 OLI images to retrieve the CSPM values in the South and East Dongting Lake. Model calibration results showed that two exponential models of the red band explained 61% (estimated standard error (SE) = 7.96 mg l–1) and 67% (SE = 6.79 mg l–1) of the variation of CSPM; two exponential models of the red:panchromatic band ratio obtained 81% (SE = 5.48 mg l–1) and 77% (SE = 4.96 mg l–1) fitting accuracy; and four exponential and quadratic models of the infrared band explained 72–83% of the variation of CSPM (SE = 5.18–5.52 mg l–1). By comparing the MODIS- and Landsat 8-based CSPM values, an exponential model of the Landsat 8 OLI red band (CSPM = 1.1034 × exp(23.61 × R)) obtained the best consistent CSPM estimations with the MODIS-based model (r = 0.98, p < 0.01), and its further validation result using an independent Landsat 8 OLI image showed a significantly strong correlation between the measured and estimated CSPM values at a significance level of 0.05 (r = 0.91, p < 0.05). The CSPM spatiotemporal distribution derived from four Landsat 8 images revealed a clear spatial distribution pattern of CSPM in the South and East Dongting Lake, which was caused by natural and anthropogenic factors together. This study confirmed the potential of Landsat 8 OLI images in retrieving CSPM and provided a foundation for retrieving the spatial distribution of CSPM accurately from this new data source in Dongting Lake.

Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data—A case study along Le’an River, China

It is necessary to estimate heavy metal concentrations within soils for understanding heavy metal contaminations and for keeping the sustainable developments of ecosystems. This study, with the floodplain along Le’an River and its two branches in Jiangxi Province of China as a case study, aimed to explore the feasibility of estimating concentrations of heavy metal lead (Pb), copper (Cu) and zinc (Zn) within soils using laboratory-based hyperspectral data. Thirty soil samples were collected, and their hyperspectral data, soil organic matters and Pb, Cu and Zn concentrations were measured in the laboratory. The potential relations among hyperspectral data, soil organic matter and Pb, Cu and Zn concentrations were explored and further used to estimate Pb, Cu and Zn concentrations from hyperspectral data with soil organic matter as a bridge. The results showed that the ratio of the first-order derivatives of spectral absorbance at wavelengths 624 and 564 nm could explain 52% of the variation of soil organic matter; the soil organic matter could explain 59%, 51% and 50% of the variation of Pb, Cu and Zn concentrations with estimated standard errors of 1.41, 48.27 and 45.15 mg·kg−; and the absolute estimation errors were 8%–56%, 12%–118% and 2%–22%, and 50%, 67% and 100% of them were less than 25% for Pb, Cu and Zn concentration estimations. We concluded that the laboratory-based hyperspectral data hold potentials in estimating concentrations of heavy metal Pb, Cu and Zn in soils. More sampling points or other potential linear and non-linear regression methods should be used for improving the stabilities and accuracies of the estimation models.