Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

Nanjing, China

Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

Nanjing, China
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Yang B.,Wuhan University | Dong Z.,Wuhan University | Liu Y.,Wuhan University | Liang F.,Wuhan University | And 3 more authors.
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2017

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects. © 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)


Zhang C.,Beijing Normal University | Zhang C.,Chinese University of Hong Kong | Chen M.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Chen M.,Nanjing Normal University | And 4 more authors.
Ecological Modelling | Year: 2015

Geography investigates changes in physical structures and distributions of objects in spatiotemporal world, which are shaped by geographic process (geo-process). With extensive simulation models used to study geo-process, this paper examines the status of geo-process modeling (namely model-based simulation) for multidisciplinary geo-processes across scales in virtual geographic environments (VGEs). The conceptual framework of integrated modeling in VGEs is proposed with a review of specific issues, including model sharing and management, collaborative modeling and uncertainty analysis. The contribution of a model base in model reusability and modeling management, concerning input data, parameterization, and simulation output, is detailed. Finally, this paper concludes with a discussion of future research directions for holistic geo-process modeling. © 2015 Elsevier B.V.


Ma L.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Ma L.,Nanjing University of Information Science and Technology | Zheng G.,Nanjing University of Information Science and Technology | Eitel J.U.H.,University of Idaho | And 2 more authors.
Agricultural and Forest Meteorology | Year: 2016

Accurately determining woody-to-total area ratio (WTA) is a key step to indirectly retrieve leaf area index (LAI) from terrestrial laser scanning (TLS) data. In this work, we first collected both individual tree and forest plot point cloud data (PCD) from broadleaf and coniferous tree species and leaf characteristics using both side-lateral and full field-of-view TLS field setups with scan distances between 2.5 to 28 m. Using a local geometrical feature-based algorithm, the generated PCD were automatically classified into three different categories including photosynthetic canopy components, non-photosynthetic canopy components, and bare earth. To convert each classified point into a surface area, we then developed and validated a novel approach that considers sampling space, laser incidence angle, and leaf orientation information. The estimated surface areas from this approach showed strong agreements with validation datasets for single leaf (91.44%), photosynthetic (95.64%), and non-photosynthetic canopy components (89.60%) of an artificial tree and stems of an old-growth coniferous tree (93.53%), two individual broadleaf trees (98.31% and 97.46%) and a broadleaf forest plot (90.26%). By doing this, we computed the parameter WTA for an individual artificial tree (10.90%), an old-growth coniferous tree (29.97%), two individual broadleaf tree (14.83% and 4.27%) and four natural forest stands ranging from 7.74%–15.57%, respectively. The proposed method can effectively improve the accuracy of retrieving true LAI by removing the effects of woody components and converting each point into a surface area. © 2016 Elsevier B.V.


Chen Y.,Nanjing Normal University | Chen Y.,Jiangsu Provincial Key Laboratory of Materials Cycling and Pollution Control | Wang F.,Nanjing Normal University | Wang F.,Jiangsu Provincial Key Laboratory of Materials Cycling and Pollution Control | And 4 more authors.
Journal of Molecular Liquids | Year: 2016

As an agricultural waste, rice husk ash (RHA) has the potential to be a viable alternative adsorbent for the removal of tetracycline (TC) from aqueous solution. This study aims to explore its feasibility of RHA as a new adsorbent, and understand its adsorption mechanism for TC. Also assessed in this study are the influences of initial concentration of TC, adsorption time, solution pH and temperature on RHA adsorption performance. It is found that TC removal efficiency of RHA is related to the initial concentration of TC solution. TC concentration decreases sharply within the first 60 min in its adsorption process, and then only gradually, reaching the equilibrium within 600 min. RHA adsorption capacity is related to solution pH, temperature, and ion intensity, especially at a high pH value. A rise of 313 K in temperature caused the adsorption capacity to more than double. Furthermore, low-acid and high-alkaline solution can accelerate the adsorption of TC onto RHA. The highest adsorption capacity of 8.37 mg/g achieved in this study is much higher than other adsorbates reported in the literature, indicating the feasibility of RHA as a new adsorbent. The Langmuir isotherm model is the most reasonable in depicting the adsorption behavior. The pseudo second order model fits the experimental data nicely, suggesting a mostly physical and chemical control over the adsorption. Such findings can serve as a useful guide in expanding the applicability of RHA to new areas. © 2016


Zhang Y.,Suzhou University of Science and Technology | Deng H.,Nanjing Normal University | Xue H.-J.,Nanjing Normal University | Chen X.-Y.,Suzhou University of Science and Technology | And 3 more authors.
Catena | Year: 2016

There has been a debate on the relationship between soil microbial diversity and soil resilience. Moreover, the key soil properties that drive soil resistance and resilience are seldom known. Therefore, we conducted an integrative study with the aim of investigating the effects of soil microbial diversity and abundance along with soil physiochemical properties on soil resistance and resilience. A total of 24 soil samples were collected throughout China from the north (Harbin, N45°45′56″; E126°38′42″) to the south (Xishuangbanna, N22°0′22″; E100°47′44″). The soil microbial diversity based on bacterial 16S rRNA gene fragments was determined using terminal restriction fragment length polymorphism. The 16S rRNA gene was quantified using real-time PCR. Soil physiochemical properties, including soil pH, total carbon and nitrogen concentrations; sand and clay proportions; and soil cation exchange capacity, were also determined. Soil resistance and resilience were determined by measuring the substrate induced respiration (SIR) rate one day and sixty days after the application of 100 mg kg− 1 Cu2 + perturbation, respectively. The results showed that there was no significant correlation between soil microbial diversity and soil resistance and resilience of SIR to Cu2 + perturbation. The resistance was positively correlated with soil pH, while the resilience was negatively correlated with the proportion of sand. Both correlations were significant (P < 0.05). Because the soil pH exhibited a spatial variation that decreased from north to south in China, the soil resistance showed a similar trend. The exponential and polynomial regression models were optimal for pH-resistance and sand proportion-resilience relationships, respectively. Our results suggest that soil microbial functional resistance and resilience is decided by soil properties that immobilize heavy metals rather than by microbial diversity and abundance. © 2016 Elsevier B.V.


Guo Y.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Li Y.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Li Y.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | And 2 more authors.
Guangxue Xuebao/Acta Optica Sinica | Year: 2015

A chlorophyll-a spectrum index (CSI) is built based on the linear spectral unmixing method using the endmembers selected from in situ spectra. The character of CSI is analyzed based on a dataset including 307 samples collected from the Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Georges Reservoir. Furthermore, a chlorophyll-a concentration (Cchla) estimation model is built and compared with traditional models in terms of noise immunity and sensor adaptability. The results show that: 1) CSI is a good indicator for Cchla. Two spectrum sets that are divided by fCSI=0 clearly show different spectrum characteristics; 2) for the hyperspectral dataset, the CSI algorithm gets similar performance to that of the three band algorithm (TBA) (their validation mean absolute percent errors are 0.332 and 0.330, respectively, root mean squares errors are 9.892 and 9.929, respectively); 3) the CSI algorithm is not sensitive to both unbiased and biased noise. Its accuracy is nearly independent on the unbiased noise. Meanwhile, the traditional three band algorithm is sensitive to both of the two kinds of noise; 4) the CSI algorithm is less sensitive to the band settings of the remote sensor than the traditional algorithms. The advantage is more obvious for wide band multispectral sensors. Compared with traditional Cchla semi-empirical models, the CSI algorithm is more stable and potential. © 2015, Chinese Laser Press. All right reserved.


Zhang J.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Zhang J.,State Key Laboratory Cultivation Base of Geographical Environment Evolution Jiangsu Province | Wang J.,Nanjing Normal University | Muller C.,Justus Liebig University | And 3 more authors.
Soil Biology and Biochemistry | Year: 2016

Nitrogen (N) transformation dynamics are often adapted to the prevailing climatic conditions and also in response to plant N uptake characteristics of species in natural ecosystems. Thus, the interplay between plant species preferential N uptake and soil N transformation characteristics is key to an optimized N use efficiency (NUE) and the understanding how N losses via denitrification, leaching or runoff can be minimized. However, despite the intimate connection between plant and soil N characteristics is well known, only a few quantitative studies are available that address these internal ecosystem connections on a mechanistic level. In this study, the N recoveries and N balances of cucumber, potato and rice, which differ in their preferential N-uptake, were investigated under different pH conditions (pH 4.9 and 7.8, respectively). N recoveries of applied 15N either as nitrate or ammonium in plant and soil were determined and N losses were calculated by 15N balance. The results indicate that not only the match of the applied dominant N form with the optimal preferential N-uptake of crop species, but also soil N transformation characteristics could significantly affect the recoveries and losses of applied 15N. Crops preferring ammonium took up more of the applied ammonium-N in the soil characterized by low N/M (nitrification rate/mineralization rate) ratios than in the soil with high N/M ratios. In contrast, crops preferring nitrate took up more applied ammonium-N in the soil with high N/M ratios than in the soil with low N/M ratios. A match exists between the applied N form with crop species preferential N uptake and that soil gross N transformation dynamics playing an important role in providing an essential support for the specific plant associated N use. It is the intimate connection between plant and soil N dynamics that is critical for an enhanced NUE with reduced N losses in monoculture agricultural systems. These observations can serve as a blueprint for the introduction of new crop species by taking into account site-specific soil and climatic conditions as well as known plant N-uptake characteristics. © 2016 Elsevier Ltd


Xiong L.,Nanjing Normal University | Xiong L.,University of Wisconsin - Madison | Tang G.,Nanjing Normal University | Tang G.,State Key Laboratory Cultivation Base of Geographical Environment Evolution Jiangsu Province | And 4 more authors.
International Journal of Geographical Information Science | Year: 2016

Residual upland planation surfaces serve as strong evidence of peneplains during long intervals of base-level stability in the peneplanation process. Multi-stage planation surfaces could aid the calculation of uplift rates and the reconstruction of upland plateau evolution. However, most planation surfaces have been damaged by crustal uplift, tectonic deformation, and surface erosion, thus increasing the difficulty in automatically identifying residual planation surfaces. This study proposes a peak-cluster assessment method for the automatic identification of potential upland planation surfaces. It consists of two steps: peak extraction and peak-cluster characterization. Three critical parameters, namely, landform planation index (LPI), peak elevation standard deviation, and peak density, are employed to assess peak clusters. The proposed method is applied and validated in five case areas in the Tibetan Plateau using a Shuttle Radar Topography Mission digital elevation model (SRTM DEM) with 3 arc-second resolution. Results show that the proposed method can effectively extract potential planation surfaces, which are found to be stable with different resolutions of DEM data. A significant planation characteristic can be obtained in the relatively flat areas of the Gangdise–Nyainqentanglha Mountains and Qaidam Basin. Several vestiges of potential former planation areas are also extracted in the hilly-gully areas of the western part of the Himalaya Mountains, the northern part of the Tangula–Hengduan Mountains, and the northeastern part of the Kunlun–Qinling Mountains despite the absence of significant topographical features characterized by low slope angles or low terrain reliefs. Vestiges of planation surfaces are also identified in these hilly-gully upland areas. Hence, the proposed method can be effectively used to extract potential upland planation surfaces not only in flat areas but also in hilly-gully areas. © 2016 Informa UK Limited, trading as Taylor & Francis Group


Wang C.,Jiangsu University | Pei X.,State Key Laboratory Cultivation Base of Geographical Environment Evolution Jiangsu Province | Yue S.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Wen Y.,Nanjing Normal University
Wetlands | Year: 2016

Vegetation and soil are important factors in coastal wetland landscape evolution. This paper investigates the relationship between the aboveground biomass of Spartina alterniflora and soil factors of varying settling ages in Yancheng, China using correlation analysis and principal component analysis. The results indicate the following: (1) Soil factors varied significantly with different settlement ages of S. alterniflora that expanded toward the land and sea. Soil bulk density decreased with settlement age and was lowest for growth period IV (10 – 16 year old sites) whereas an opposite trend was shown for soil moisture. Soil salinity and soil nutrients were highest for growth period III (6 – 10 year old sites) (2) Principal component analysis demonstrated that soil bulk density, moisture and salinity are the main soil factors that drive landscape evolution in S. alterniflora marshes. (3) There was a significant positive correlation between S. alterniflora biomass and the organic matter and bulk density of soil (p < 0.05). Results showed that the invasion and settlement of S. alterniflora in the coastal wetland of Yancheng are changing the physical and chemical properties of the coastal wetland soil. This study has contributed to an understanding of wetland succession in the coastal landscape. © 2016 Society of Wetland Scientists


PubMed | Nanjing University and Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
Type: | Journal: Scientific reports | Year: 2016

Oil and gas exploration in the South China Sea (SCS) has garnered global attention recently; however, uncertainty regarding the accurate number of offshore platforms in the SCS, let alone their detailed spatial distribution and dynamic change, may lead to significant misjudgment of the true status of offshore hydrocarbon production in the region. Using both fresh and archived space-borne images with multiple resolutions, we enumerated the number, distribution, and annual rate of increase of offshore platforms across the SCS. Our results show that: (1) a total of 1082 platforms are present in the SCS, mainly located in shallow-water; and (2) offshore oil/gas exploitation in the SCS is increasing in intensity and advancing from shallow to deep water, and even to ultra-deep-water. Nevertheless, our findings suggest that oil and gas exploration in the SCS may have been over-estimated by one-third in previous reports. However, this overestimation does not imply any amelioration of the potential for future maritime disputes, since the rate of increase of platforms in disputed waters is twice that in undisputed waters.

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