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Hou P.,Satellite Environment Application Center | Zhan Z.,Satellite Environment Application Center | Zhao Y.,Beijing Normal University
2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010 | Year: 2010

Lots of hydrological and substance exchange indicate that environmental management from watershed perspective is very important to improve the ability of self-regulation of ecological system and ecological health in watershed scale. This paper takes Huntai river watershed as study region to assess the ecological health with remote sensing and Pressure-State-Response (PSR) model. Firstly, county is taken as the assessment unit in spatial scale, the assessment indexes are established according to the ecological feature of this watershed, and extracted from satellite images. Secondly, indexes are normalized and weighted by the Principal Component Analysis (PCA), then ecological health is analyzed. © 2010 IEEE.


Lyu H.,Nanjing Normal University | Li X.,Chongqing Institute of Surveying and Planning for Land Resources and Housing | Wang Y.,Nanjing Normal University | Jin Q.,Nanjing Normal University | And 3 more authors.
Science of the Total Environment | Year: 2015

Fourteen field campaigns were conducted in five inland lakes during different seasons between 2006 and 2013, and a total of 398 water samples with varying optical characteristics were collected. The characteristics were analyzed based on remote sensing reflectance, and an automatic cluster two-step method was applied for water classification. The inland waters could be clustered into three types, which we labeled water types I, II and III. From water types I to III, the effect of the phytoplankton on the optical characteristics gradually decreased. Four chlorophyll-a retrieval algorithms for Case II water, a two-band, three-band, four-band and SCI (Synthetic Chlorophyll Index) algorithm were evaluated for three water types based on the MERIS bands. Different MERIS bands were used for the three water types in each of the four algorithms. The four algorithms had different levels of retrieval accuracy for each water type, and no single algorithm could be successfully applied to all water types. For water types I and III, the three-band algorithm performed the best, while the four-band algorithm had the highest retrieval accuracy for water type II. However, the three-band algorithm is preferable to the two-band algorithm for turbid eutrophic inland waters. The SCI algorithm is recommended for highly turbid water with a higher concentration of total suspended solids. Our research indicates that the chlorophyll-a concentration retrieval by remote sensing for optically contrasted inland water requires a specific algorithm that is based on the optical characteristics of inland water bodies to obtain higher estimation accuracy. © 2015 Elsevier B.V..


PubMed | Satellite Environment Application Center, Chongqing Institute of Surveying and Planning for Land Resources and Housing, National University of Singapore and Nanjing Normal University
Type: | Journal: The Science of the total environment | Year: 2015

Fourteen field campaigns were conducted in five inland lakes during different seasons between 2006 and 2013, and a total of 398 water samples with varying optical characteristics were collected. The characteristics were analyzed based on remote sensing reflectance, and an automatic cluster two-step method was applied for water classification. The inland waters could be clustered into three types, which we labeled water types I, II and III. From water types I to III, the effect of the phytoplankton on the optical characteristics gradually decreased. Four chlorophyll-a retrieval algorithms for Case II water, a two-band, three-band, four-band and SCI (Synthetic Chlorophyll Index) algorithm were evaluated for three water types based on the MERIS bands. Different MERIS bands were used for the three water types in each of the four algorithms. The four algorithms had different levels of retrieval accuracy for each water type, and no single algorithm could be successfully applied to all water types. For water types I and III, the three-band algorithm performed the best, while the four-band algorithm had the highest retrieval accuracy for water type II. However, the three-band algorithm is preferable to the two-band algorithm for turbid eutrophic inland waters. The SCI algorithm is recommended for highly turbid water with a higher concentration of total suspended solids. Our research indicates that the chlorophyll-a concentration retrieval by remote sensing for optically contrasted inland water requires a specific algorithm that is based on the optical characteristics of inland water bodies to obtain higher estimation accuracy.


Sun D.,Nanjing University of Information Science and Technology | Li Y.,Nanjing Normal University | Wang Q.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | And 3 more authors.
Hydrobiologia | Year: 2011

Our aim was to refine the optical classification of turbid waters in order to improve the performance of water color algorithms. Bio-optical measurements and sampling of optically active substances were performed in highly turbid lakes Taihu, Chaohu, and Dianchi, and in Three Gorges reservoir in China. Based on strong correlations between trough depths of remote sensing reflectance (Rrs(λ)) near 680 nm (denoted as TD680) and the ratios of inorganic suspended matter (ISM) to total suspended matter (TSM) concentrations, an empirical model was developed for water classification. In the 400-900 nm spectral range, different correlations between Rrs(λ), TSM and chlorophyll a (Chla) concentrations indicate discrepancies among the following ISM/TSM ranges: ISM/TSM ≤ 0.5, 0.5 < ISM/TSM < 0.8, and ISM/TSM ≥ 0.8. Corresponding findings support an important conclusion that only high ISM/TSM ratios, usually above 0.5, and using the more sensitive and stable near infrared spectral range (730-820 nm), can assure good performances of the TSM remote sensing algorithms. Meanwhile, the particulate absorption ap(λ) and scattering bp(λ) were strongly influenced by different ranges of ISM/TSM ratios. Typically the ap(675) peaks became more and more vague as ISM/TSM increased, and the bp(λ) values first decreased and then increased with a marked inflexion at ISM/TSM ≈ 0.5. The TD680 threshold values were derived to discriminate three types of turbid waters, i. e., Type 1 (TD680 ≥ 0.0082 sr-1), Type 2 (0.0082 sr-1 > TD680 > 0 sr-1), and Type 3 (TD680 ≤ 0 sr-1). This study provides a promising tool for identifying various types of highly turbid waters, and is significant for the development of class-based algorithms of water color remote sensing. © 2011 Springer Science+Business Media B.V.


Hou P.,Satellite Environment Application Center | Wang Q.,Satellite Environment Application Center | Cao G.,Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites | Wang C.,Satellite Environment Application Center | And 2 more authors.
Journal of Geographical Sciences | Year: 2012

Terrestrial ecosystem and climate system are closely related to each other. Faced with the unavoidable global climate change, it is important to investigate terrestrial ecosystem responding to climate change. In inland river basin of arid and semi-arid regions in China, sensitivity difference of vegetation responding to climate change from 1998 to 2007 was analyzed in this paper. (1) Differences in the global spatio-temporal distribution of vegetation and climate are obvious. The vegetation change shows a slight degradation in this whole region. Degradation is more obvious in densely vegetated areas. Temperature shows a general downward trend with a linear trend coefficient of -1.1467. Conversely, precipitation shows an increasing trend with a linear trend coefficient of 0. 3896. (2) About the central tendency response, there are similar features in spatial distribution of both NDVI responding to precipitation (NDVI-P) and NDVI responding to AI (NDVI-AI), which are contrary to that of NDVI responding to air temperature (NDVI-T). Typical sensitivity region of NDVI-P and NDVI-AI mainly covers the northern temperate arid steppe and the northern temperate desert steppe. NDVI-T typical sensitivity region mainly covers the northern temperate desert steppe. (3) Regarding the fluctuation amplitude response, NDVI-T is dominated by the lower sensitivity, typical regions of the warm temperate shrubby, selui-shrubby, bare extreme dry desert, and northern temperate meadow steppe in the east and temperate semi-shrubby, dwarf arboreous desert in the north are high response. (4) Fluctuation amplitude responses between NDVI-P and NDVI-AI present a similar spatial distribution. The typical sensitivity region mainly covers the northern temperate desert steppe. There are various linear change trend responses of NDVI-T, NDVI-P and NDVI-AI. As to the NDVI-T and NDVI-AI, which are influenced by the boundary effect of semi-arid and semi-humid climate zones, there is less correlation of their linear change tendency along the border. There is stronger correlation in other regions, especially in the NDVI-T in the northern temperate desert steppe and NDVI-AI in the warm temperate shrubby, selui-shrubby, bare, extreme and dry desert. © 2012 Science China Press and Springer-Verlag Berlin Heidelberg.


Du C.-G.,Nanjing Normal University | Li Y.-M.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | Zhu L.,Satellite Environment Application Center | Lu H.,Nanjing Normal University
Huanjing Kexue/Environmental Science | Year: 2016

The TP concentration is an important index of water quality and an important influencing factor of eutrophication and algae blooms. Remote sensing technology has advantages of wide scope and high time limited efficacy. Monitoring the concentration of TP by satellite remote sensing is important for the study of water quality and eutrophication. In situ datasets collected during the three times of experiments in Taihu Lake between 2013 and 2014 were used to develop the TP inversion model based on GOCI data. The GOCI data in spring, summer, autumn and winter in 2014 were selected to analyze the time and space changes of TP concentration in Taihu Lake. The results showed that the TP algorithm was built up based on the variables, which was to use the eight band combination of GOCI data as variable, and build model using Multi factor linear regression method. The algorithm achieved more accurate TP estimation with R2=0.898, MAPE=14.296%, RMSE=0.026 mg·L-1. Meantime, a analysis on the precision of the model by using the measured sample points and the synchronous satellite images with MAPE=33.642%, 22.551%,RMSE=0.076 mg·L-1, 0.028 mg·L-1 on August 5, 2014 and October 24, 2014. Through the analysis of the 30 images on the four days of the four seasons, it showed that the absolute concentration of total phosphorus was different in different seasons. But temporal and spatial distribution of total phosphorus concentration was similar in the morning and afternoon. In spatial distribution, the TP concentration in Meiliang Bay, Zhushan Bay, Gonghu Bay, Xiaomei Port and Changdou Port in the southwest coast was at a continuously high position. The TP concentration change in different regions was influenced by wind direction, wind speed and other factors. The TP concentration highest in the morning, and then gradually decreased, this phenomenon reflected that the TP concentration was affected by temperature and light. © 2016, Science Press. All right reserved.


Lyu H.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | Wu C.,Satellite Environment Application Center | Zhu L.,Satellite Environment Application Center | And 3 more authors.
Ecological Informatics | Year: 2013

With the rapid development of the economy in recent years, massive algal (blue-green algae in particular) blooms have often observed in Chinese eutrophic lakes. The concentration of the cyanobacterial pigment phycocyanin (PC), an accessory pigment unique to freshwater blue-green algae, is often used as a quantitative indicator of blue-green algae in eutrophic inland waters. The purpose of this study was to evaluate the semi-analytic PC retrieval algorithm proposed by Simis et al. and to explore the potential to improve this PC algorithm so that it is more suitable for eutrophic lakes, such as Taihu Lake. In this paper, we recalculated the correction coefficients γ and δ to calculate the absorptions of chlorophyll-a at 665nm and the absorptions of phycocyanin at 620nm in terms of in situ measurements and observed that the values of these coefficients differed from the values used by Simis et al. and Randolph et al. The two coefficients are site dependent due to the different bio-optical properties of lakes. We also observed that the specific PC absorption at 620nm apc*(620) decreases exponentially with an increase in PC concentrations. Therefore, a non-linear power-function of apc*(620), instead of a constant value of apc*(620) as used by Simis et al., was proposed for our improved PC retrieval algorithm in Taihu Lake, yielding a squared correlation coefficient (R2) of 0.55 and a root mean square error (RMSE) of 58.89μg/L. Compared with the original PC retrieval algorithm by Simis et al., the improved retrieval algorithm has generally superior performance. In evaluating the limitation of the PC retrieval algorithms, we observed that the ratio of the total suspended solids to phycocyanin can be used as a primary measure for retrieval performance. Validation in Dianchi Lake and an error analysis proved that the improved PC algorithm has a better universality and is more suitable for eutrophic lakes with higher PC concentrations. © 2013 Elsevier B.V.


Li S.,Nanjing Normal University | Lai Z.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | Wang Z.,Nanjing Normal University | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

Distributed hydrological modeling plays an important role in water resource management and regional non-point source pollution assessment. The Soil and Water Assessment Tool (SWAT) is a popular modeling tool for understanding regional hydrological processes. However, the general approach based on the SWAT model was only applicable to the mountain and hilly dominated area. There is no effective way to modeling the hydrologic process in plain river network regions, which is characterized by large flat areas, consisted of many lakes and artificially hydrological polders, and intersected stream networks, etc. The existing methods cannot effectively extract the channels in flat and pit areas, parallel channels or discontinuous rivers and the definition error of the catchment areas. To overcome these problems, we developed a novel method for modeling the distributed spatial discretization of the plain river network area based on the SWAT model. There are three key techniques are discussed: making the rings and crossed rivers to dendritic stream networks by cutting the river ways shortly, restoring the distribution of water between reaches by transferring water from one reach to another one on the basis of flow rate of each reach and simulating the exchange of water inside and outside of the polders according to the scheduled rules of the polder areas by adding a 'virtual reservoirs' within the SWAT model. In this paper, the typical plain river network region located in western Taihu watershed was chosen as the study area, and a large number of basic geographic data such as topography, soil, climate and land use were collected and parameterized. The modeling procedures were used to simulate the monthly runoff of the area of western Taihu Lake from the year of 2008 to 2010, and the applicability of the method to the plain river network region was also verified. The simulated results matched mostly well to the observed data of Rongdengqiao, Hujiawei, and Yixing hydrological stations. The calculated Nash-Sutcliffe efficiency coefficient and correlation coefficient of three hydrological stations were 0.84, 0.80, 0.67 and 0.94, 0.95, 0.93, respectively. It indicated that our developed framework for the SWAT model was practical and capable of representing the hydrological processes in the plain river network regions.


Lyu H.,Nanjing Normal University | Zhang J.,Nanjing Normal University | Zha G.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | Li Y.,Nanjing Normal University
International Journal of Remote Sensing | Year: 2015

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern. © 2015 Taylor & Francis.


Guo Y.,Carbon Control | Li Y.,Carbon Control | Zhu L.,Satellite Environment Application Center | Liu G.,Carbon Control | And 2 more authors.
Remote Sensing | Year: 2015

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method to produce fused images that maintain both spectral and spatial information from the original images. Images from Environmental Satellite 1 (HJ1) and Medium Resolution Imaging Spectrometer (MERIS) were used in this study to validate the method. An improved UBF (IUBF) algorithm is established by selecting a proper HJ1-CCD image band for each MERIS band and thereafter applying an unsupervised classification method in each sliding window. Viewing in the visual sense-the radiance and the spectrum-the results show that the improved method effectively yields images with the spatial resolution of the HJ1-CCD image and the spectrum resolution of the MERIS image. When validated using two datasets; the ERGAS index (Relative Dimensionless Global Error) indicates that IUBF is more robust than UBF. Finally, the fused data were applied to evaluate the chlorophyll a concentrations (Cchla) in Taihu Lake. The result shows that the Cchla map obtained by IUBF fusion captures more detailed information than that of MERIS.

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