Ma J.,Changchun Institute of Technology |
Ma J.,Key Laboratory of Geo informatics of National Administration of Surveying |
Bi Q.,Jilin Province Water Resource and Hydropower Consultative Company |
Zhang J.,Sinohydro Bureau 11 Co. |
Zhou H.,Changchun Institute of Technology
Sensors and Transducers | Year: 2014
Urban expansion is a complex spatial transformation process over time. It converts various land-use types into urban lands, therefore gradually increase the size of urban areas. The research in simulation and prediction of urban expansion can provide us with two benefits: one, we would be able to grasp the pattern of the complex process of a city's expansion; and the other, we may be able to foresee a possible problem that may occur during expansion, and deal with it ahead of time to create the best suitable plan for the city's structure and layout. In our experiment, we designed a Visual Basic (VB) program, and applied binding conditions and random factors to simulate the process of urban expansion using Cellular Automaton (CA) as a model. This experiment was based on selected previous research, and used the city of Changchun as an example. Our results indicate that it is feasible to model urban expansion with the sprawling process of CA. © 2014 IFSA Publishing, S. L.
Yin X.,Wuhan University |
Song H.,Wuhan University |
Yang W.,Wuhan University |
Yang W.,Key Laboratory of Geo informatics of National Administration of Surveying |
And 2 more authors.
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2013
This work introduces an unsupervised classification framework based on ensemble partitioning for polarimetric synthetic aperture radar (PolSAR) data, which can automatically determine the number of categories. First, the PolSAR image is divided into patches by an over-segmentation method. Second, ensemble partitioning is performed on the patch based dataset to obtain an ensemble similarity matrix. Third, a self-tuning spectral clustering method is adopted to automatically find the number of categories and the classification results, which is finally smoothed by a Markov random field based method. The experimental results on PolSAR image show the effectiveness of this unsupervised classification method. © 2013 IEEE.
Liu Y.,Wuhan University |
Feng Y.,Wuhan University |
Zhao Z.,Wuhan University |
Zhang Q.,Wuhan University |
And 2 more authors.
Land Use Policy | Year: 2016
Forest loss and fragmentation, which generate various negative environmental and ecological consequences, have become widespread phenomena across the globe. Motivation to investigate the underlying drivers is essential for land use planning and policy decision making. This paper characterizes forest loss and fragmentation from 1979 to 2014 in the Ningbo region (China) using multitemporal satellite imageries and a set of landscape metrics (area-weighted mean patch area, edge density, area-weighted shape index, Euclidean nearest neighbor distance, effective mesh size and total area); and then quantifies the responsible socioeconomic drivers (economy, social activities, science and technology, culture and policy, demography) under different land use planning schemes (urban and non-urban) using multivariate linear regression. Results show that the two zones present identical trend of intensifying forest loss and fragmentation but differ in changing magnitude and speed. More specifically, forest loss and fragmentation in the non-urban planning zone occurs at a significantly higher pace and magnitude. For the urban planning zone, population pressure, economic growth and fruit consumption are the primary drivers of forest loss, while forest fragmentation is mainly driven by economic openness, cash crop consumption and environmental protection consciousness. For the non-urban planning zone, income increases, fruit consumption and infrastructure development are the primary drivers of forest loss, while infrastructure and tourism development are the major drivers of forest fragmentation. Besides, forest loss and fragmentation in the two zones are both heavily subjected to land use policy. The variance partitioning analysis highlights that the policy driver is the most influential one and economic driver also has strong effect on forest loss and fragmentation in the urban planning zone. For the non-urban planning zone, the influence of policy driver is the strongest and social activity is also very powerful. These results provide compelling evidence that land use planning fails to play an efficient role in protecting forest resources in the Ningbo region. The failure should be attributed to several issues associated with land use planning and forestry governance that widely exist in China. We finally propose some pertinent implications and suggestions for China's land use planning and forest policy. This study is believed to advance the understanding of the socioeconomic drivers of forest loss and fragmentation. It therefore provides some new insights in land use policy. © 2016 Elsevier Ltd.
Gao C.-C.,CAS Wuhan Institute of Geodesy and Geophysics |
Gao C.-C.,University of Chinese Academy of Sciences |
Lu Y.,CAS Wuhan Institute of Geodesy and Geophysics |
Zhang Z.-Z.,CAS Wuhan Institute of Geodesy and Geophysics |
And 6 more authors.
Chinese Journal of Geophysics (Acta Geophysica Sinica) | Year: 2015
As a critical component of the cryosphere, the Antarctic Ice Sheet (AIS) has strong connection with the sea level change and global climate change. Accurate quantification of the current spatial and temporal mass changes of AIS is very important to improve our understanding and prediction of its response and contribution to global change. The Gravity Recovery and Climate Experiment (GRACE) mission has provided new and useful observations to detect AIS mass balance since its launch in March 2002. There are significant differences among the GRACE estimates of the total mass change. The big difference is due in part to considerable uncertainty in the accuracy of glacial isostatic adjustment (GIA) signals, and also due to use of different time spans, different versions of GRACE products and different GRACE post-processing methods. Using 124 monthly GRACE gravity field solutions of Release 5 (RL05) produced at the Center for Space Research (CSR) of the University of Texas, Austin, spanning the interval from January of 2003 through December of 2013, the mass balance of AIS is estimated by two post-processing ways: the optimizing averaging kernel method (also named VW) and the combined filter method (the first step is called P5M11 decorrelation filter to remove correlated noise by fitting and subtracting a fifth-order polynomial to even and odd coefficient pairs at spherical harmonic orders eleven and above, the second involves smoothing with a 250 km Gaussian filter). A detailed error analysis is provided including consideration of leakage-in, leakage-out, and errors in modeling mass variations of the atmosphere, ocean and GIA. In addition, a statistical model selection criterion is employed in computation of trends from mass variation time series, and the impact of K1 tidal alias is analyzed. The results reveal that during 2003-2013, the total mass of the ice sheet decreased significantly at change rates of -163±50, -129±41 and -81±27 Gt/a for three GIA models: GW13, IJ05, W12a. There was a distinct region with mass loss in the Amundsen Sea Embayment of West Antarctic ice sheet and the Northern Antarctic Peninsula, while an increasing mass gain was concentrated in the Dronning Maud Land and the Enderby Land of East Antarctic ice sheet. Furthermore, we use hypotheses testing and information criteria evaluation to select the best trend model fitting together with sinusoidal functions of annual (365.0-d) and semi-annual (181.0-d) signals and the S2 (161.0-d), K1 (2725.4-d) and K2 (1362.7-d) tidal aliases. We found that K1 tidal alias has a potential to falsify the acceleration estimates. Although it is not good enough to confirm the K1 tidal alias based on an eleven-year time-series, the impact of K1 tidal alias deserves further notice. By comparing the quantities of total mass balance computed by the two different processing methods and three different GIA models in the Antarctica, we find that the differences are less than 15 Gt/a between two processing methods, but the largest difference is about 80 Gt/a between different GIA models. The analysis of the uncertainty of GRACE's estimation of AIS mass balance indicates that the largest source of error is the GIA correction. Our results indicate that during January 2003 to December 2013 the contribution of AIS to sea level rise was about +0.34±0.11 mm/a. Significant mass loss increases were limited to the basin that contains Pine Island Glacier along the Amundsen Sea coast of West Antarctica. During the analyzed time period, the total mass acceleration was -8±10 Gt/a2, equivalent to +0.02±0.03 mm/a2 sea level rise. Results of analysis point to the conclusion that when using a given GRACE data set with same error correction, the differences of total mass changes are not highly dependent on which post-processing strategies to be used but on the different GIA models. Therefore, a more accurate GIA model is the key for determining Antarctic ice mass change from GRACE in the present and future. ©, 2015, Science Press. All right reserved.
Zhu C.-D.,Chinese Academy of Sciences |
Zhu C.-D.,University of Chinese Academy of Sciences |
Lu Y.,Chinese Academy of Sciences |
Shi H.-L.,Chinese Academy of Sciences |
And 8 more authors.
Chinese Journal of Geophysics (Acta Geophysica Sinica) | Year: 2015
The High Asia is the largest glacierized region over low-and mid-latitudes. During the last decade, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided valuable data for monitoring glacial mass changes in High Asia. The new released GRACE RL05 data is used to estimate the glacial mass changes in High Asia from April 2002 to July 2013. After removing glacial isostatic adjustment (GIA) and hydrological contributions from GRACE RL05 data, the least square fitting in the spectral domain and iteration in the spatial domain are used to separate the equivalent water mass changes in 17 mascons over High Asia and 15 mascons over the plains of northern India, and quantify more reliable mass changes in High Asia during the period 2002 to 2013. The impacts of groundwater signal leakage from the plains of northern India on GRACE estimates are discussed in detail based on 17 mascons over High Asia. The equivalent water mass changes in mascons are obtained in the spectral and spatial domain. The equivalent water height changes estimated from spectral and spatial domain show a good agreement in the spatial distribution. The largest equivalent water height change trends of Tianshan, Pamirs and Kunlun shan, Himalaya and Karakorum, Inner Tibet Plateau estimated from spectral and spatial domain are -1.7 cm·a-1 and -2.1 cm·a-1, -1.5 cm·a-1 and -2.0 cm·a-1, -10.9 cm·a-1 and -16.3 cm·a-1, 2.2 cm·a-1 and 2.6 cm·a-1, respectively. The average glacial mass change trends of Tian Shan, Pamirs and Kunlun Shan, Himalaya and Karakorum, Inner Tibetan Plateau are -2.8±0.9 Gt/a, -3.3±1.5 Gt/a, -9.9±2.1 Gt/a, 5.0±0.8 Gt/a, respectively. The average glacial mass change trend of High Asia is -11.0±2.9 Gt/a. The groundwater in the plains of northern India shows obviously mass loss at rate of -35.0±4.2 Gt/a. Without considering the leakage effects from the plains of northern India, the average glacial mass change trends of four sub regions in High Asia are -2.7±1.0 Gt/a, -1.5±1.5 Gt/a, -15.7±1.8 Gt/a, 5.0±0.8 Gt/a, respectively, and average glacial mass change trend of High Asia is -14.9±2.7 Gt/a. The spatial pattern of the glacial mass changes in High Asia is dominated by increases in the inland of the Tibetan Plateau, and by decreases at the margin, respectively. And the largest mass loss occurs at the margin of the southeastern Tibetan Plateau. The glacial mass change trends of Tian Shan, Pamirs and Kunlun Shan, Himalaya and Karakorum, Inner Tibetan Plateau are -2.8±0.9 Gt/a, -3.3±1.5 Gt/a, -9.9±2.1 Gt/a, 5.0±0.8 Gt/a, respectively. The average glacial mass change trend of High Asia is -11.0±2.9 Gt/a. The groundwater signal leakage from the plains of northern India is the key factor to study the estimations of the glacial mass changes in High Asia with GRACE data, and have significant impact on estimates in Pamirs and Kunlun Shan, Himalaya and Karakorum. The impact of such leakage can be effectively corrected by the spectral and spatial domain methods. Because GRACE senses the total mass change, the estimations of the glacial mass changes can be affected by various model errors. It is necessary to have a long and continuous measurement of satellite gravity, and combine the satellite gravity data with other space geodesy techniques to understand the mass change mechanism in High Asia. ©, 2015, Science Press. All right reserved.
Liu T.,Lanzhou Jiaotong University |
Liu T.,Gansu Provincial Engineering Laboratory for National Geographic State Monitoring |
Yan H.,Lanzhou Jiaotong University |
Yan H.,Gansu Provincial Engineering Laboratory for National Geographic State Monitoring |
Zhai L.,Key Laboratory of Geo Informatics of National Administration of Surveying
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015
Multi-criteria evaluation (MCE) method has been applied much in groundwater potential mapping researches. But when to data scarce areas, it will encounter lots of problems due to limited data. Digital Elevation Model (DEM) is the digital representations of the topography, and has many applications in various fields. Former researches had been approved that much information concerned to groundwater potential mapping (such as geological features, terrain features, hydrology features, etc.) can be extracted from DEM data. This made using DEM data for groundwater potential mapping is feasible. In this research, one of the most widely used and also easy to access data in GIS, DEM data was used to extract information for groundwater potential mapping in batter river basin in Alberta, Canada. First five determining factors for potential ground water mapping were put forward based on previous studies (lineaments and lineament density, drainage networks and its density, topographic wetness index (TWI), relief and convergence Index (CI)). Extraction methods of the five determining factors from DEM were put forward and thematic maps were produced accordingly. Cumulative effects matrix was used for weight assignment, a multi-criteria evaluation process was carried out by ArcGIS software to delineate the potential groundwater map. The final groundwater potential map was divided into five categories, viz., non-potential, poor, moderate, good, and excellent zones. Eventually, the success rate curve was drawn and the area under curve (AUC) was figured out for validation. Validation result showed that the success rate of the model was 79% and approved the method's feasibility. The method afforded a new way for researches on groundwater management in areas suffers from data scarcity, and also broaden the application area of DEM data.