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Now showing items 1 - 16 of 31

  • Proximal and remote sensing techniques for mapping of soil contamination with heavy metals

    Shi, Tiezhu   Guo, Long   Chen, Yiyun   Wang, Weixi   Shi, Zhou   Li, Qingquan   Wu, Guofeng  

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  • Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice

    Shi, Tiezhu   Liu, Huizeng   Chen, Yiyun   Wang, Junjie   Wu, Guofeng  

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  • New spectral metrics for mangrove forest identification

    Shi, Tiezhu   Liu, Jue   Hu, Zhongwen   Liu, Huizeng   Wang, Junjie   Wu, Guofeng  

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  • Estimating leaf nitrogen concentration in heterogeneous crop plants from hyperspectral reflectance

    Shi, Tiezhu   Wang, Junjie   Liu, Huizeng   Wu, Guofeng  

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  • Monitoring Arsenic Contamination in Agricultural Soils with Reflectance Spectroscopy of Rice Plants

    Shi, Tiezhu   Liu, Huizeng   Wang, Junjie   Chen, Yiyun   Fei, Teng   Wu, Guofeng  

    The objective of this study was to explore the feasibility and to investigate the mechanism for rapidly monitoring arsenic (As) contamination in agricultural soils with the reflectance spectra of rice plants. Several data pretreatment methods were applied to improve the prediction accuracy. The prediction of soil As contents was achieved by partial least-squares regression (PLSR) using laboratory and field spectra of rice plants, as well as linear regression employing normalized difference spectral index (NDSI) calculated from fild spectra. For laboratory spectra, the optimal PLSR model for predicting soil As contents was achieved using Savitzky-Golay smoothing (SG), first derivative and mean center (MC) (root-mean-square error of prediction (RMSEP) = 14.7 mg kg(-1); r = 0.64; residual predictive deviation (RPD) = 1.31). For field spectra, the optimal PLSR model was also achieved using SG, first derivative and MC (RMSEP = 13.7 mg kg(-1); r = 0.71; RPD = 1.43). In addition, the NDSI with 812 and 782 nm obtained a prediction accuracy with r = 0.68, RMSEP = 13.7 mg kg(-1), and RPD = 1.36. These results indicated that it was feasible to monitor the As contamination in agricultural soils using the reflectance spectra of rice plants. The prediction mechanism might be the relationship between the As contents in soils and the chlorophyll-a/-b contents and cell structure in leaves or canopies of rice plants.
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  • Visible and near-infrared reflectance spectroscopy—An alternative for monitoring soil contamination by heavy metals

    Shi, Tiezhu   Chen, Yiyun   Liu, Yaolin   Wu, Guofeng  

    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. (C) 2014 Elsevier B.V. All rights reserved.
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  • Soil Organic Carbon Content Estimation with Laboratory-Based Visible-Near-Infrared Reflectance Spectroscopy: Feature Selection

    Shi, Tiezhu   Chen, Yiyun   Liu, Huizeng   Wang, Junjie   Wu, Guofeng  

    This study, with Yixing (Jiangsu Province, China) and Honghu (Hubei Province, China) as study areas, aimed to compare the successive projection algorithm (SPA) and the genetic algorithm (GA) in spectral feature selection for estimating soil organic carbon (SOC) contents with visible-near-infrared (Vis-NIR) reflectance spectroscopy and further to assess whether the spectral features selected from one site could be applied to another site. The SOC content and Vis-NIR reflectance spectra of soil samples were measured in the laboratory. Savitzky-Golay smoothing and log(10)(1/R) (R is reflectance) were used for spectral preprocessing. The reflectance spectra were resampled using different spacing intervals ranging from 2 to 10 nm. Then, SPA and GA were conducted for selecting the spectral features of SOC. Partial least square regression (PLSR) with full-spectrum PLSR and the spectral features selected by SPA (SPA-PLSR) and GA (GA-PLSR) were calibrated and validated using independent datasets, respectively. Moreover, the spectral features selected from one study area were applied to another area. Study results showed that, for the two study areas, the SPA-PLSR and GA-PLSR improved estimation accuracies and reduced spectral variables compared with the full spectrum PLSR in estimating SOC contents; GA-PLSR obtained better estimation results than SPA-PLSR, whereas SPA was simpler than GA, and the spectral features selected from Yixing could be well applied to Honghu, but not the reverse. These results indicated that the SPA and GA could reduce the spectral variables and improve the performance of PLSR model and that GA performed better than SPA in estimating SOC contents. However, SPA is simpler and time-saving compared with GA in selecting the spectral features of SOC. The spectral features selected from one dataset could be applied to a target dataset when the dataset contains sufficient information adequately describing the variability of samples of the target dataset.
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  • Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

    Shi, Tiezhu   Wang, Junjie   Chen, Yiyun   Wu, Guofeng  

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  • Response to “Visible and near-infrared reflectance spectroscopy is of limited practical use to monitor soil contamination by heavy metals” by Philippe C. Baveye

    Shi, Tiezhu   Chen, Yiyun   Liu, Yaolin   Wu, Guofeng  

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  • Application of Spectrally Derived Soil Type as Ancillary Data to Improve the Estimation of Soil Organic Carbon by Using the Chinese Soil Vis-NIR Spectral Library

    Liu, Yi   Shi, Zhou   Zhang, Ganlin   Chen, Yiyun   Li, Shuo   Hong, Yongshen   Shi, Tiezhu   Wang, Junjie   Liu, Yaolin  

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  • Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

    Yang, Chao   Wu, Guofeng   Ding, Kai   Shi, Tiezhu   Li, Qingquan   Wang, Jinliang  

    Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.
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  • Prediction of total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy

    Liu, Yaolin   Jiang, Qinghu   Shi, Tiezhu   Fei, Teng   Wang, Junjie   Liu, Guilin   Chen, Yiyun  

    Visible/near-infrared (Vis/NIR) spectroscopy has been proven to be an effective technique for soil total nitrogen (TN) content estimation in the laboratory conditions. However, the transferability of this technique from laboratory study to field application is complicated by soil moisture effects. This study aims to compare the performance of four spectral transformation strategies, namely, Savitzky-Golay (SG) smoothing, SG smoothing followed by first derivative (FD), orthogonal signal correction (OSC), and generalized least squares weighting (GLSW), in the removal of soil moisture effects on TN estimation. The spectral transformations were applied on 8 sets of spectral reflectance measured from 62 soil samples at 8 moisture levels. The air-dried set was used for partial least squares regression (PLSR) calibration, whereas the other seven sets with moisture gradients were used for external validations. Results show that the SG-PLSR model cannot be transferred from the air-dried samples to the samples with moisture gradients. The FD-PLSR model showed fair TN prediction performance, with five out of seven residual prediction deviations (RPD) that are greater than 1.4. Both OSC-PLSR and GLSW-PLSR had good transferability to the moist samples. More specifically, the GLSW-PLSR model (mean of R-pre(2) = 0.718, root mean square error for prediction [RMSEP] = 0.262, and RPD = 1.885) outperformed the OSC-PLSR model (mean of R-pre(2) = 0.695, RMSEP = 0.277, and RPD = 1.780). The results demonstrate the value of OSC and GLSW in eliminating the effects of moisture on TN estimation, and the GLSW-PLSR is recommended for a better Vis/NIR estimation of TN content under different soil moisture conditions.
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  • Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

    Liu, Yaolin   Jiang, Qinghu   Fei, Teng   Wang, Junjie   Shi, Tiezhu   Guo, Kai   Li, Xiran   Chen, Yiyun  

    The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r(Pre)(2) = 0.66, RMSE = 2.78 g.kg(-1), residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350-800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350-1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard-Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content.
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  • Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: A remote sensing perspective

    Yang, Chao   Zhang, Chenchen   Li, Qingquan   Liu, Huizeng   Gao, Wenxiu   Shi, Tiezhu   Liu, Xu   Wu, Guofeng  

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  • New spectral metrics for mangrove forest identification

    Shi, Tiezhu   Liu, Jue   Hu, Zhongwen   Liu, Huizeng   Wang, Junjie  

    This study proposed three spectral metrics, namely spectral match degree (SMD), normalized difference mangrove index (NDMI) and shortwave infrared absorption depth (SIAD), to enhance the separability between mangrove forests and terrestrial vegetation in remote sensing imagery. The Landsat 8 OLI image of an interest area in Beilunhekou National Nature Reserve was used to test the spectral metrics. The derived spectral metrics and raw band reflectance data were classified using a support vector machine classifier. Mangrove forest maps were then identified from the classified images. Identification accuracies were compared and evaluated by determining the user's accuracy (UA), producer's accuracy (PA), overall accuracy (OA) and by conducting McNemar's test. Results showed that the use of spectral metrics (UA =3D 85%, PA =3D 94%, OA =3D 95%) outperformed the use of raw band reflectance data (UA =3D 72%, PA =3D 82%, OA =3D 90%). McNemar's test confirmed that the spectral metrics were significantly better than the raw band reflectance data (Z =3D 4.63, p < 0.05). Therefore, the proposed spectral metrics could improve the accuracy of mangrove forest identification.
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  • Quantitative Phosphoproteomics Reveals System-Wide Phosphorylation Network Altered by Spry in Mouse Mammary Stromal Fibroblasts

    Shi, Tiezhu   Yao, Linli   Han, Ying   Hao, Piliang   Lu, Pengfei  

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