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Song Qing
College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China

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Journal article
Published: 06 May 2021 in Sensors
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Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

ACS Style

Aru Han; Xiaoling Lu; Song Qing; Yongbin Bao; Yuhai Bao; Qing Ma; Xingpeng Liu; Jiquan Zhang. Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China. Sensors 2021, 21, 3220 .

AMA Style

Aru Han, Xiaoling Lu, Song Qing, Yongbin Bao, Yuhai Bao, Qing Ma, Xingpeng Liu, Jiquan Zhang. Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China. Sensors. 2021; 21 (9):3220.

Chicago/Turabian Style

Aru Han; Xiaoling Lu; Song Qing; Yongbin Bao; Yuhai Bao; Qing Ma; Xingpeng Liu; Jiquan Zhang. 2021. "Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China." Sensors 21, no. 9: 3220.

Journal article
Published: 12 March 2021 in International Journal of Remote Sensing
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Lakes at a global level have increasingly experienced algal blooms in recent decades, and it has become a key challenge facing the aquatic ecological environment. Remote sensing technology is considered an effective means of algal bloom detection. This study proposed a novel algal bloom detection index (ABDI) based on Sentinel-2 Multispectral Instrument (MSI) data. The ABDI was evaluated by application to Hulun Lake, China. Areas of algal bloom detected by the ABDI were consistent with those identified from visual interpretation maps [the coefficient of determination = 0.87; root-mean-square error = 0.67 km2; overall accuracy >98%; Kappa coefficient >0.88]. The ABDI was less sensitive to thin cloud and turbid water compared to the floating algae index (FAI), adjusted floating algae index (AFAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI). Algal bloom dynamics in relation to meteorological factors in Hulun Lake were analysed using time-series MSI data, which indicated that algal blooms occurred mainly in summer and were distributed in the near-shore waters. Temperature, precipitation, sunshine duration, and wind speed as well as human activities were found to influence spatio-temporal patterns of algal blooms. The results indicate that ABDI is applicable to the detection of algal blooms under a variety of environmental conditions occurring in other regions, such as in the Taihu, Dianchi, and Chaohu lakes and the Yellow Sea. The results of this study can provide an operational algorithm for the detection of algal blooms and environmental management.

ACS Style

Mengmeng Cao; Song Qing; Eerdemutu Jin; Yanling Hao; Wenjing Zhao. A spectral index for the detection of algal blooms using Sentinel-2 Multispectral Instrument (MSI) imagery: a case study of Hulun Lake, China. International Journal of Remote Sensing 2021, 42, 4510 -4531.

AMA Style

Mengmeng Cao, Song Qing, Eerdemutu Jin, Yanling Hao, Wenjing Zhao. A spectral index for the detection of algal blooms using Sentinel-2 Multispectral Instrument (MSI) imagery: a case study of Hulun Lake, China. International Journal of Remote Sensing. 2021; 42 (12):4510-4531.

Chicago/Turabian Style

Mengmeng Cao; Song Qing; Eerdemutu Jin; Yanling Hao; Wenjing Zhao. 2021. "A spectral index for the detection of algal blooms using Sentinel-2 Multispectral Instrument (MSI) imagery: a case study of Hulun Lake, China." International Journal of Remote Sensing 42, no. 12: 4510-4531.

Journal article
Published: 05 January 2021 in Sustainability
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An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

ACS Style

Aru Han; Song Qing; Yongbin Bao; Li Na; Yuhai Bao; Xingpeng Liu; Jiquan Zhang; Chunyi Wang. Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data. Sustainability 2021, 13, 432 .

AMA Style

Aru Han, Song Qing, Yongbin Bao, Li Na, Yuhai Bao, Xingpeng Liu, Jiquan Zhang, Chunyi Wang. Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data. Sustainability. 2021; 13 (1):432.

Chicago/Turabian Style

Aru Han; Song Qing; Yongbin Bao; Li Na; Yuhai Bao; Xingpeng Liu; Jiquan Zhang; Chunyi Wang. 2021. "Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data." Sustainability 13, no. 1: 432.

Journal article
Published: 01 October 2020 in Journal of Coastal Research
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Cao, M.; Qing, S.; Du, Y.; Yuan, R.; Shun, B.; Hao, Y., and Zhao, W., 2020. Remote sensing classification of aquatic vegetation in Ulansuhai Lake based on discrete particle swarm optimization algorithm. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 176-186. Coconut Creek (Florida), ISSN 0749-0208.As the largest natural wetland in the same latitude on the earth, water ecological environment of Ulansuhai Lake, China is seriously threatened, and there is various aquatic vegetation spread over the lake. Remote sensing is considered to be an effective way to map the distribution of aquatic vegetation. Different from the method used in most of the previous studies, discrete particle swarm optimization (DPSO) algorithm was used to identify and classify emergent vegetation (EV), yellow algae (YA), submerged aquatic vegetation (SAV) and water in Ulansuhai Lake based on Landsat-8 Operational Land Imager (OLI) in this research. The classification results were validated by 284 investigation sites data and visual interpretation of Gao Fen 2 (GF-2) image. The results indicated that determination coefficient (R2) for EV, YA, SAV and water were greater than 0.91, root mean square error (RMSE) were less than 0.025 km2. Besides, the overall accuracy (95.4 %) and Kappa coefficient (0.93) of DPSO algorithm are superior to spectral index, unsupervised classification methods and supervised classification methods. In addition, DPSO algorithm to other regions (the Yellow Sea) and sensors (Sentinel-2) have been successfully applied, which further proves the applicability of DPSO algorithm. The research provides a new tool to assist people in locating and quantifying aquatic vegetation, so that purposeful actions can be taken to control the eutrophication of lake water and improve the water ecological environment.

ACS Style

Mengmeng Cao; Song Qing; Yuchunzi Du; Ruiqiang Yuan; Buri Shun; Yanling Hao; Wenjing Zhao. Remote Sensing Classification of Aquatic Vegetation in Ulansuhai Lake Based on Discrete Particle Swarm Optimization Algorithm. Journal of Coastal Research 2020, 102, 176 -186.

AMA Style

Mengmeng Cao, Song Qing, Yuchunzi Du, Ruiqiang Yuan, Buri Shun, Yanling Hao, Wenjing Zhao. Remote Sensing Classification of Aquatic Vegetation in Ulansuhai Lake Based on Discrete Particle Swarm Optimization Algorithm. Journal of Coastal Research. 2020; 102 (sp1):176-186.

Chicago/Turabian Style

Mengmeng Cao; Song Qing; Yuchunzi Du; Ruiqiang Yuan; Buri Shun; Yanling Hao; Wenjing Zhao. 2020. "Remote Sensing Classification of Aquatic Vegetation in Ulansuhai Lake Based on Discrete Particle Swarm Optimization Algorithm." Journal of Coastal Research 102, no. sp1: 176-186.

Journal article
Published: 01 October 2020 in Journal of Coastal Research
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Yue, Y.L.; Qing, S.; Diao, R.X., and Hao, Y.L., 2020. Remote sensing of suspended particulate matter in optically complex estuarine and inland waters based on optical classification. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 303-317. Coconut Creek (Florida), ISSN 0749-0208.Accurate suspended particulate matter (SPM) concentration retrieval across complex estuarine to inland waters from ocean color remote sensing reflectance (Rrs(λ)) faces challenges. In this paper, an optical classification-based SPM retrieval algorithm in optically complex estuarine and inland waters was proposed and tested in the Yellow River Estuary and Daihai Lake, China. Firstly, the in situ measured Rrs(λ) (n = 204) were classified into two optical water types with the method defined by Matsushita et al. (2015). Secondly, we designed several mathematical models and selected the optimal algorithm according to the goodness of fit. Optimal algorithms were developed for each water type to achieve accurate SPM retrieval. Through the construction of the optimal retrieval algorithm in each water type, the uncertainty of SPM retrievals has been reduced from 95 % to about 39 % compared with the algorithm without optical classification. The retrieval algorithm based on optical water classification was further applied to the Sentinel-2 MSI L2A data over the study area and produced reliable SPM maps. Independent validation with the in situ-satellite match-ups further demonstrates the algorithm's validity (uncertainty of about 47 %). In contrast, applications of other SPM retrieval algorithms resulted in less reliable SPM results with either unsatisfactory retrieval accuracy in class1 (the lowest value of r can reach 0.02). The optical classification, together with the optimal retrieval algorithm for each optical type, is proved to be a feasible way for SPM retrieval in high accuracy over optically complex waters.

ACS Style

Yalei Yue; Song Qing; Ruixiang Diao; Yanling Hao. Remote Sensing of Suspended Particulate Matter in Optically Complex Estuarine and Inland Waters Based on Optical Classification. Journal of Coastal Research 2020, 102, 303 -317.

AMA Style

Yalei Yue, Song Qing, Ruixiang Diao, Yanling Hao. Remote Sensing of Suspended Particulate Matter in Optically Complex Estuarine and Inland Waters Based on Optical Classification. Journal of Coastal Research. 2020; 102 (sp1):303-317.

Chicago/Turabian Style

Yalei Yue; Song Qing; Ruixiang Diao; Yanling Hao. 2020. "Remote Sensing of Suspended Particulate Matter in Optically Complex Estuarine and Inland Waters Based on Optical Classification." Journal of Coastal Research 102, no. sp1: 303-317.

Journal article
Published: 01 February 2020 in Ecological Indicators
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Global lakes have suffered from algae blooms and loss or expansion of aquatic vegetations in recent decades. Remote sensing is considered as an effective approach to monitor aquatic vegetations and algae blooms. However, individual spectral index is unable to separate them due to the similarity in spectral features. In this paper, spectral characteristics analyses were conducted to find suitable spectral indices for distinguishing emergent vegetation, submerged aquatic vegetation and floating yellow algae in a complex aquatic environment, Ulansuhai Lake, China. It was found that near infrared band was appropriate to extract open water, and short-wave infrared band was suitable for extracting emergent vegetation, whereas the green and red bands were the characteristic spectra for distinguishing submerged aquatic vegetation and yellow algae. Hence, we firstly used the normalized difference vegetation index (NDVI) to extract open water, and the emergent vegetation spectral index (EVSI) to extract emergent vegetation. Then, a new developed macroalgae index (MAI) was used for distinguishing submerged aquatic vegetation and yellow algae. The applicability of the spectral indices was tested against both in situ measurements and Landsat-8 Operational Land Imager (OLI) data. The results indicated that the combination of these spectral indices was an effective method to separate aquatic vegetations and yellow algae. The proposed method was then applied to time-series Landsat images for investigating the seasonal and inter annual dynamics of aquatic vegetations and yellow algae and their responses to air temperature in the Ulansuhai Lake. The results show that emergent vegetation area increased from May to its maximum in July. The submerged aquatic vegetation area gradually increased from May to its maximum coverage in August, and decreased from late September. The yellow algae appeared in late May, and reached its maximum area in June or July, and disappeared in October. The long-term variation analyses showed that emergent vegetation area increased from 1986 to 2014, and was decreasing from 2014. The area of submerged aquatic vegetation increased during 1986–2008, and sharply decreased from 2009 to 2013, followed by a significant increasing from 2014. The yellow algae bloom mainly outbroke during the period of 1998 to 2010. We also found that the yellow algae area was more sensitive to short term mean temperature, while the area of emergent vegetation was sensitive to longer timescale of temperature. The submerged aquatic vegetation area had no significant correlation with air temperature. Besides, our study also indicated that the MAI concept can be extendable and applicable to other high-resolution satellite sensors (e.g., GF-2 PMS) and other regions with different algae blooms (e.g., Yellow Sea).

ACS Style

Song Qing; Runa A; Buri Shun; Wenjing Zhao; Yuhai Bao; Yanling Hao. Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986–2018. Ecological Indicators 2020, 112, 106073 .

AMA Style

Song Qing, Runa A, Buri Shun, Wenjing Zhao, Yuhai Bao, Yanling Hao. Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986–2018. Ecological Indicators. 2020; 112 ():106073.

Chicago/Turabian Style

Song Qing; Runa A; Buri Shun; Wenjing Zhao; Yuhai Bao; Yanling Hao. 2020. "Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986–2018." Ecological Indicators 112, no. : 106073.