Jiangsu Center for Collaborative Innovation

Nanjing, China

Jiangsu Center for Collaborative Innovation

Nanjing, China
SEARCH FILTERS
Time filter
Source Type

Ma J.T.,Nanjing Normal University | Chen S.Z.,Nanjing Normal University | Chen S.Z.,Jiangsu Center for Collaborative Innovation | He Z.C.,Nanjing Normal University | Zhu X.T.,Nanjing Normal University
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2017

Pore confined groundwater is an important fresh water resource for human beings. Pore groundwater is stored in systems of loose sedimentary aquifers. The spatial structure of the aquifer (or aquifer group) determines not only the spatial distribution pattern of groundwater, but also has some influence on the groundwater quality. Based on hydro-geological data and groundwater quality monitoring data collected between 2005 and 2014in the Yancheng coastal plain in China, methods including GIS and ANOVA were used to study the relationship between groundwater quality and the third confined aquifer buried depth. The groundwater quality dynamic evolution characteristics are also analyzed. Based on the conclusions, some recommendations are proposed to the local water resources management department. Firstly, based on the hydrogeological data in the study area, the buried depth Digital Elevation Model(DEM) was constructed and divided into 10 grades. The correlation between water quality factors and the aquifer buried depth was analyzed using variance analysis. The least significant difference method was used to determine the influence of different grading intervals on water quality factors. The average value of the monitored data for each year and for each buried depth interval was counted and time varying process curves of water quality factor levels were plotted to show the spatial and temporal variation of water quality factors with aquifer depth. The results showed that:in the study area, the III confined aquifer buried depth is mostly located between -118.9 m and -85.45 m. Owing to the variety of groundwater hydrogeological condition and different buried depths, some correlation between typical groundwater quality and aquifer buried depth is presented:Mineralization degree, total alkalinity and total bacterial count show the highest correlation with groundwater buried depth (69.67%, 75.76% and 58.09% respectively). Correlation between total hardness factor and depth is moderate (49.18%). The potassium permanganate index is less influenced by the buried depth (35.2%). It was found that, the correlation between each factor and the groundwater buried depth in each buried depth grading interval showed significantly different dynamic evolution characteristics. In the area where the aquifer was deeply buried (-160.8 m to -99.12 m), the total hardness, degree of mineralization and total number of bacteria were significantly affected by the buried depth. The potassium permanganate index content only showed a significant difference with depth in specific depth intervals (between -99.12 m and -92.91 m and between -85.45 m and -75.09 m). In addition, water quality factors showed different spatial distribution characteristics:the area with higher potassium permanganate index and total bacteria is mainly located between Sheyang county and Huangsha Port. The highest mineralization factor content values were found in the middle and western regions, total alkalinity and total hardness factor varied little, the total alkalinity was slightly lower in the shallow buried area, and the total hardness showed some upward trend. © 2017, Ecological Society of China. All rights reserved.


Yan M.,Nanjing Normal University | Yan M.,Jiangsu Center for Collaborative Innovation | Liu J.,Nanjing Normal University | Liu J.,Jiangsu Center for Collaborative Innovation | Wang Z.,Nanjing Normal University
Atmosphere | Year: 2017

A reconstructed land use/land cover change (LUCC) dataset was used with the Community Earth System Model (CESM) to conduct a climate sensitivity analysis over the past two millennia.Compared to a controlled experiment conducted with the CESM, the LUCC showed significant biogeophysical effects on global climate on multi-decadal to centennial time scales. The global annual mean temperature and precipitation show clear decadal and multi-centennial scale oscillations when the LUCC effect was considered in the CESM simulation. With increased crop acreage and decreased natural vegetation over the past two millennia, the reflected terrestrial solar radiation has increased and the net terrestrial radiation has decreased, leading to a decrease in the global annual mean temperature. Global annual mean precipitation has also decreased along with decreased evaporation and atmospheric humidity. Our simulation suggests that LUCC mainly influences convective precipitation and has little influence on large-scale precipitation. The impact of LUCC has latitudinal and seasonal differences. The largest response of temperature to LUCC has occurred in the middle latitudes of the Northern Hemisphere (NH), while the largest precipitation response occurred at lower latitudes of the NH. The responses of temperature and precipitation to LUCC is stronger in winter and spring than in summer and autumn. © 2017 by the authors.


Wang B.J.,Nanjing University | Ju W.,Nanjing University | Ju W.,Jiangsu Center for Collaborative Innovation
Remote Sensing | Year: 2017

Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY) models the effects of leaf biochemical concentrations on reflectance spectra on the basis of Melamed theory, which has several limitations. These are: (1) the radiation components are not treated satisfactorily; (2) the directional changes of both particle and sublayer scattering ratios are not considered; and (3) the boundary constraint which makes needle leaves different from broadleaves is not included. Proofs of these limitations as well as theoretical improvements are given in this study. Global sensitivity analysis (SA) of three models: the original LIBERTY, our improved LIBERTY (LIBERTYim) and The optical PROperties SPECTra model (PROSPECT) suggests that compared with LIBERTY, the global reflectance and transmittance of LIBERTYim are more sensitive to diametrical absorbance αd-a parameter related to leaf biochemistry. Moreover, the global reflectance and transmittance of LIBERTYim and PROSPECT had similar sensitivity patterns to the input variables, demonstrating indirectly the validity of our improvements over LIBERTY. However, neither LIBERTY nor LIBERTYim considers boundary constraints, which limits their applications in modelling needle leaf optical properties. We introduced a particle string model, which might be used to simulate needle leaf optical properties in the future. © 2017 by the authors.


Lu M.,Nanjing Normal University | Lu M.,Jiangsu Center for Collaborative Innovation | Cheng J.,Guangxi Teachers Education University | Jin C.,Nanjing Normal University | Jin C.,Jiangsu Center for Collaborative Innovation
Sustainability (Switzerland) | Year: 2017

Sustainable development has become a main concern of governments at a variety of levels. Assessing ecological assets, which is significant for the sustainability of human society, plays an important role in measuring the performance of local governments. Using Deqing County in Zhejiang Province as a case study, this paper adapts a county-level indicator assessment of ecological assets and quantifies these indicators using high-resolution data sets. The resulting value of ecological assets in Deqing County accounts for 24.85% of its GDP, which is much higher than other published case studies across China. Through contrasting per capita valuation of ecological assets and per capita enterprise taxation at township level, this paper has classified all townships into four categories, each of which has varied implications for the local development strategy from the perspective of sustainability. This study implies that the integration of the valuation of ecological assets into the measurement of political performance at the lowest township level enables the provision of quantitative evidence to enhance sustainable development at local (county) level. © 2017 by the authors.


Zhang X.,China University of Mining and Technology | Chen B.,China University of Mining and Technology | Chen B.,Jiangsu Center for Collaborative Innovation | Fan H.,China University of Mining and Technology | Zhu D.,China University of Mining and Technology
Canadian Journal of Remote Sensing | Year: 2016

The main objective of this study is to develop a multifeature soil moisture retrieval method based on the principal component analysis (PCA) dimensionality reduction technique. RADARSAT-2 data were used to compute the backscattering coefficients and polarimetric variables. The optimal input features for soil moisture retrieval were selected by means of PCA dimensionality reduction and least root mean square error (RMSE) criterion. The support vector regression (SVR) model was used to estimate soil moisture content. The results indicated that the optimal features extracted by the PCA dimensionality reduction showed high correlation with soil moisture content. The RMSE, R2 (determination coefficient) and mean relative error (MRE) were (1.4 vol.%, 0.73, 18.2%) and (1.6 vol.%, 0.66, 15.6%) over the low grass cover areas A and B, respectively. For the bare soil areas A and B, the statistic results were (1.3 vol.%, 0.76, 12.1%) and (1.6 vol.%, 0.72, 14.9%), respectively. This case study confirmed the potential of the developed approach to estimate soil moisture over the low grass cover and bare soil areas. © CASI.


Wohlfart C.,Company for Remote Sensing and Environmental Research SLU | Liu G.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Huang C.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Huang C.,Jiangsu Center for Collaborative Innovation | Kuenzer C.,German Aerospace Center
Remote Sensing | Year: 2016

The Yellow River Basin is one of China's most densely-populated, fastest growing and most dynamic regions, with abundant natural resources and intense agricultural production. Major land policies have recently resulted in remarkable landscape modifications throughout the basin. The availability of precise regional land cover change information is crucial to better understand the prevailing dynamics and underlying factors influencing the current processes in such a complex system and can additionally serve as a valuable component for modeling and decision making. Such comprehensive and detailed information is lacking for the Yellow River Basin so far. In this study, we derived land cover characteristics and dynamics from the complete last decade based on optical high-temporal MODIS Normalized Differenced Vegetation Index (NDVI) time series for the whole Yellow River Basin. After filtering and smoothing for noise reduction with the use of the adaptive Savitzky-Golay filter, the processed time series was used to derive a large variety of phenological and annual metrics. The final classifications for the basin (2003 and 2013) were based on a random forest classifier, trained by reference samples from very high-resolution imagery. The accuracy assessment for all 18 thematic classes, which was based on a 30% reference data split, yielded an overall accuracy of 87% and 84% for 2003 and 2013, respectively. Major land cover and land use changes during the last decade have occurred on the Loess Plateau, where land and conservation reforms triggered large-scale recovery of grassland and shrubland habitat that had been previously covered by agriculture or sparse vegetation. Agricultural encroachment and urban area expansion are other processes influencing the dynamics in the basin. The necessity for regionally-adapted land cover maps becomes obvious when our land cover products are compared to existing global products, where thematic accuracy remains low, particularly in a heterogeneous landscape, such as the Yellow River Basin. The basin-wide novel land cover and land use products of the Yellow River Basin hold a large potential for climate, hydrology and biodiversity modelers, as well as river basin and regional governmental authorities and will be shared upon request. © 2016 by the authors.


Ming D.,China University of Geosciences | Zhou T.,BGP Inc. | Wang M.,Nanjing Normal University | Wang M.,Jiangsu Center for Collaborative Innovation | Tan T.,China University of Geosciences
Journal of Applied Remote Sensing | Year: 2016

Land cover classification based on remote sensing imagery is an important means to monitor, evaluate, and manage land resources. However, it requires robust classification methods that allow accurate mapping of complex land cover categories. Random forest (RF) is a powerful machine-learning classifier that can be used in land remote sensing. However, two important parameters of RF classification, namely, the number of trees and the number of variables tried at each split, affect classification accuracy. Thus, optimal parameter selection is an inevitable problem in RF-based image classification. This study uses the genetic algorithm (GA) to optimize the two parameters of RF to produce optimal land cover classification accuracy. HJ-1B CCD2 image data are used to classify six different land cover categories in Changping, Beijing, China. Experimental results show that GA-RF can avoid arbitrariness in the selection of parameters. The experiments also compare land cover classification results by using GA-RF method, traditional RF method (with default parameters), and support vector machine method. When the GA-RF method is used, classification accuracies, respectively, improved by 1.02% and 6.64%. The comparison results show that GA-RF is a feasible solution for land cover classification without compromising accuracy or incurring excessive time. © 2016 Society of Photo-Optical Instrumentation Engineers (SPIE).


Zhang S.,Nanjing University | Liu Y.,Nanjing University | Liu Y.,Jiangsu Center for Collaborative Innovation | Yang Y.,Nanjing University | And 2 more authors.
Journal of Great Lakes Research | Year: 2016

Poyang Lake, an important wetland in the Ramsar Convention List, is the largest freshwater lake in China and an essential component of the Yangtze River system. The lake is increasingly experiencing serious water crises including seasonal desiccation, decreased wetland area, and water shortages, all of which are closely related to progressive changes in the lake's topography over recent years. Atime-series of bottom topography would contribute to our understanding of the lake's evolution during the past several decades. However, quality bathymetric data for Poyang Lake are scarce owing to the highly dynamic and turbid nature of its water. To resolve this limitation, we used a total of 146 medium-resolution satellite images to build annual and quasi-annual bottom topography maps of Poyang Lake during the period from 2000 to 2010 based on the well-established waterline method. Our results show that: (1)the average elevation of the lakebed relative to sea level has decreased by 14.4 cm/yr. from 2000 to 2010; and (2)the observed annual changes in the lakebed elevation were well correlated (r = 0.84) with measured changes in the lake's annual net sediment flux. The observed trends may be attributed to the impacts of human activities, especially the operation of the Three Gorge Dams, frequent sand mining, and the implementation of a large water conservancy project. This decade-long quantitative understanding of the lake's evolution and bottom topography elevations might assist both researchers and local policymakers in ecological management, wetland protection, and lake navigation safety. © 2016 International Association for Great Lakes Research.


Shi K.,CAS Nanjing Institute of Geography and Limnology | Zhang Y.,CAS Nanjing Institute of Geography and Limnology | Zhu G.,CAS Nanjing Institute of Geography and Limnology | Liu X.,CAS Nanjing Institute of Geography and Limnology | And 7 more authors.
Remote Sensing of Environment | Year: 2015

We have developed and validated a robust empirical model for estimating the concentrations of total suspended matter (TSM) in Lake Taihu (China), a large turbid inland water body. This model was generated using Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) medium-resolution (250m) data gathered from 2003 to 2013 and in situ data collected from a number of cruise surveys. A strong significant correlate relationship between the in situ TSM data and the atmospherically corrected MODIS-Aqua remote sensing reflectance at the 645nm band (Rrs(645)) was found (R2=0.70, p<0.001, n=150). From these data, a local TSM model was developed for Lake Taihu. Long-term TSM distribution maps retrieved from the MODIS-Aqua data demonstrated marked temporal and spatial variations. Temporally, significant lower TSM was found in summer and autumn than in winter and spring (p<0.005, t-test). The significant seasonal variability could be attributed to sediment resuspension due to changes in the wind speed between different seasons. Lake Taihu also experiences large inter-annual variations that are primarily caused by changes in wind force over the region. In particular, the TSM in Lake Taihu from 2006 to 2008 was relatively lower than in other years, which could be explained by the lower mean wind speed during these years compared to the other years. Spatially, the TSM in the Open area, especially in the southern part of this region, was consistently higher than in other sub-regions of Lake Taihu. The coverage of submerged aquatic vegetation (SAV) generally characterized East Lake Taihu as a region with a relatively lower TSM. Lake topographic conditions, SAV, and runoff discharge jointly contributed to the spatial variations in TSM. © 2015 Elsevier Inc.


Ke W.,Nanjing Normal University | Ke W.,Jiangsu Center for Collaborative Innovation | Yuan Y.,Nanjing Normal University | Zhang X.,Nanjing Normal University | Shao J.,Nanjing Normal University
Progress In Electromagnetics Research M | Year: 2016

As an emerging wireless localization technique, the electromagnetic passive localization without the need of carrying any device, named device-free passive localization (DFPL) technique has drawn considerable research attention. The DFPL technique detects shadowed links in the monitored area and realizes localization with the received signal strength (RSS) measurements of these links. However, the current RSS-based DFPL techniques have two major challenges: one is that the RSS signal is particularly sensitive to noise, and the other is that it needs a sufficient number of nodes to provide enough RSS measurements of wireless links to guarantee good performance. To overcome these problems, in this paper we take advantage of compressive sensing (CS) theory to handle the spatial sparsity of the DFPL problem for reducing the number of nodes required by DFPL systems and exploit the frequency diversity technique to deal with the problem of the RSS sensitivity. Meanwhile, inspired by the fact that the target's movement is continuous and that the target's current location must be around the last location, we add prior information on the support region into the sparse reconstruction process for enhancing sparse reconstruction performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance. © 2016, Electromagnetics Academy. All rights reserved.

Loading Jiangsu Center for Collaborative Innovation collaborators
Loading Jiangsu Center for Collaborative Innovation collaborators