The contemporary development of science and technology reduced barriers to applying unmanned aerial vehicles (UAV) and remote sensors. Meanwhile, public participation triggered an explosive growth of innovative applications in the field of UAV remote sensing. Therefore, UAVs have become a common means of scientific research. Under some circumstances, the UAV remote sensing data can be used to substitute for the satellite remote sensing data. In this study, the authors firstly systematically summarized both of the features of times and characteristics of science and technology of UAV remote sensing. Then, the authors introduced several fundamental applications including earthquake relief, surveying and mapping of islands and reefs, Antarctic scientific expedition, accurate farmland management, etc. Thirdly, the authors put forward some future directions from the aspects of the stimulation of restructuring of remote sensing data supply, the promotion of comprehensive perspective in geography research as well as the necessity of planning of UAV remote sensing testing sites. In particular, a concept of UAV remote sensing data carrier was proposed.

ACM SIGSPATIAL conference is the international summit which highlights the inter-discipline between Geographical Information Science (GIS) and Computer Science (CS). The former name of this conference is ACM SIGSPATIAL GIS. Since 2014, the name of the conference has changed to ACM SIGSPATIAL. After 23 years' development (1993-2015), ACM SIGSPATIAL has taken a wide coverage among spatio-temporal mining, spatio-temporal modeling and algorithm, location-based service, map matching, parallel computing, navigation and trajectory analysis, etc. All the topics in ACM SIGSPATIAL represent the state-of-the-art and the-state-of-the-technique levels in current GIS and CS domains, showing important values both for academic and industrial researches. In this paper, we introduced ACM SIGSPATIAL 2015 in details, including the main conference, the workshop, and keynote speaking and the ACM SIGSPATIAL CUP. We analyzed the acceptance rate of ACM SIGSPATIAL 2015 in the main conference, including full papers, vision papers, and demo papers. We paid attention to the spatial distribution of the accepted papers, and projected the nationalities of all the first authors onto the map. We also drew the word cloud for the accepted papers based on the statistics of key words and abstracts. Furthermore, we classified the data types employed in these papers. These statistical data showed the hot topics in current GIS and CS researches. To better grab the key points of the presentations in the main conference, we clustered all the presentations into three directions: (1) the value of multi-source data, (2) the dominated priority of trajectory research, and (3) the rising of semantic analysis. We selected representative papers for each direction and reviewed them in details. The workshops in ACM SIGSPATIAL 2015 took 12 sessions, relating to mobile entity localization and tracking, privacy in GIS, emergency management on the use of GIS, geo-streaming, smart cities and urban analytics, big geospatial data analysis, LBSN service, mobile geographic information system, indoor spatial awareness and computational transportation science. UrbanGIS 2015 and EM-GIS 2015 were newly included in ACM SIGSPATIAL 2015. The scope of all the workshops covered the current hot topics in the research fields. The ACM SIGSPATIAL CUP was the special feature of this conference. This year's contest was about finding the shortest path under polygonal obstacle constraints. Computing shortest paths in real-time had become a necessity with the advent of online web services. It also became imperative to provide shortest paths under various constraints. Many online services now support a variety of constraints, including avoiding tolls and boarders to selecting favorite highways. Top three teams were invited to submit a four page paper for the ACM SIGSPATIAL CUP session. In this paper, we reported the work from the top team in details.As the premier annual event of the ACM Special Interest Group on Spatial Information, ACM SIGSPATIAL fosters interdisciplinary discussions and researches in all aspects of GIS. We hope to show the latest progresses in this buzzing field, and bridge the gap between GIS and CS.

The shortest path computation used in navigation system plays an important role in mobile Internet. Due to the increase of network scale and the moving terminal, the traditional serial algorithms for the calculation of the shortest path cannot meet the real-time requirements. The offline preprocessing technology is widely used in the shortest path computation. On the other hand, the increase of graph data scale will improve online query time for the shortest path query, so graph partition technology is used to partition road network graph data. The Arc-flags algorithm is a classic shortest path algorithm based on the preprocessing and graph partition technology, which provides efficient online shortest path query. Arc-flags have two main parts, one is graph partition and flags-setting algorithm and the other one is online query algorithm. Until now, existing research on Arc-flags algorithm mainly focus on the improvement of space and time-cost of preprocessing and comparison of the pros and cons of different network partitioning methods. However, the influence of the graph partitioning for Arc-flags algorithm is not analyzed in-depth. Our paper tested and analyzed the effect of different graph partitioning technology for Arc-flags algorithm in real road networks in many aspects, such as the pre-processing time, memory consumption and online query efficiency. The real road network data includes three public data sets: American New York City Road Network, American San Francisco Bay Area Road Network and American Northeast Road Network. In order to compare the effect of different graph partition technology, one graph partition tool Metis was used. We compared Arc-flags and simple Dijkstra algorithm. Arc-flags had much better performance on online query time. Also, we compared the results of Arc-flags based on different graph partition technology, the preprocessing time and the graph partition number was linear growth while the preprocessing time increased faster than the boundary point number. The graph partition number had little effect on online query time if the number arrived a large value. The searching range had little effect on online query time if the searching range reduced to a certain extent. If so, the main effect factor was memory access efficiency and so on, not the searching range. At last, we gave some reasonable graph partitioning suggestions according to our experimental results and analysis. We should use the best graph partitioning to partition road in order to reduce the boundary point number. Our research could provide some guidance to help the improve and use of the Arc-flags algorithm for shortest path algorithm in real navigation system.

Geo-entity relation recognition from rich web texts requires robust and effective keyword extraction method. Unsupervised learning methods attract more attention because they can capture dynamic variations of features in text and discover additional relation types. Frequency-based methods for keyword extraction have been extensively studied. However, the sparse distribution of geo-entity relations in web texts makes it difficult to directly apply frequency-based methods to geo-entity keyword extraction. This paper proposes a context enhanced keyword extraction method to solve this problem. Firstly, the contexts of geo-entities are enhanced to reduce the sparseness of terms, with context merging and semantic fusion. Secondly, two well-known frequency-based statistical methods (Domain Frequency and Entropy) are used to automatically build a large-scale corpus. Thirdly, the lexical features and their weights are statistically determined based on the corpus. Finally, all terms in the enhanced contexts are measured according to their lexical features and the most important terms are picked as keywords of geo-entity pairs. Experiments are conducted with large and real web texts. The results show that compared with the Document Frequency and Entropy methods, the presented method improved the precision by 41% and 36%, respectively. It also correctly generated additional 60% of keywords.

As WLAN getting more and more popular and pervasive, Wi-Fi based indoor localization is becoming a hot issue in research and application fields. Among various kinds of up-to-date indoor localization methods, fingerprint based methods are most widely used because of the good performance. However, the existing fingerprint based methods still have following three common problems: Firstly, fingerprint based methods require a vast amount of calibration work, which need huge human and time consumption both in offline and online phases. It makes the systems difficult to be applied in the practical applications. Secondly, the Wi-Fi signals in the environment change frequently, bringing the significant timeliness in collected data. So it cannot guarantee to provide a long term effective localization. Thirdly, the Wi-Fi access points change frequently in real scene. Thus, the feature dimensions of training data and testing data are unequal. The traditional algorithms cannot well handle the feature dimension changing problem caused by increase or decrease in APs’ number. To solve these problems mentioned above, we proposed a crowdsourcing based indoor localization method, including Semi-supervised ELM, Timeliness Managing ELM and Feature Adaptive Online Sequential ELM. We also developed an indoor localization platform. Applications show that our method can reduce human effort in data calibration and improve the model training speed. Moreover, our method can maintain the high location accuracy for a long time.

With the wide applications of information and communication technologies in port infrastructures and operations, huge volumes of maritime sensing data have been generated. These data come from various sources and demonstrate heterogeneous structures, providing us with new opportunities to understand port performance and regional economic development. In this paper, we introduce the recent work on port sensing and computation based on maritime big data. Specifically, by making use of ship GPS trajectories, ship attributes, port geographic information and port facility parameters, we can automatically estimate a set of metrics for the measurement and comparison of port performance. First, we can use ship GPS trajectories and port geographic information to detect the events of ships arriving at different ports and terminals. Second, we can use ship attributes and port facility parameters to estimate the cargo throughput of each arrived ship. Third, we can aggregate the ship arriving events and the cargo throughput in different terminals and ports to derive a set of port performance metrics, including ship traffic, port throughput, terminal productivity and facility utilization rate. Evaluation results using real-world maritime data collected in 2011. Results showed that these methods accurately estimated the port performance metrics. We also presented a case study in port of Hong Kong to showcase the effectiveness of our framework in port performance analysis.

As one of the most classical geography models, Huff model has been widely used in explaining and predicting the movement of human, goods, transport, and currency. In determining trade area for shopping centers, the size of the shopping center is the most commonly used parameter to reflect its attractiveness. However, the attractiveness of a shopping center is usually determined by many other factors, such as price, services, accessibility and environment. Therefore, using size as the measurement of shopping center attractiveness could cause misleading results. To obtain the actual attractiveness factors that should be used in Huff model, this study conducted a questionnaire on five representative shopping malls in one of the largest city in Shenzhen, China, and identified six factors using factor analysis. Then, we used principal component Logistic model to obtain their level of significances and corresponding weights. The results of this study could be helpful to parameter selection of Huff model and thus improve the accuracy of model prediction.

The automatic recognition of fold structure is one of the bases of tectonic interpretation, geomorphology classification and three-dimensional geological modeling. At present, most of the automatic recognition methods used for landform classification are based on the regular statistical unit. These methods, although effectively extract the characteristic landform by using image or terrain data, cannot recognize the tectonic landforms which combined the structural feature and topographical feature. As one of the most general tectonic landforms, fold landform has featured a symmetric repetitive spatial structure, which can be used to recognize the fold. To realize the automatic recognition of fold landform types, this research provides a method based on the spatial structure pattern matching. This method focuses on building scene models of fold structures by using Attributed Relational Graph (ARG) and identifying the fold landform types by defining different spatial structure patterns through the formal grammar. The implementation process is presented as follows. Firstly, extract the long strip scene that may contain the fold structure according to the principles used in choosing fold cores and section lines. Secondly, build and simplify the spatial structure model of the long strip scene by following the ARG approach. Thirdly, convert the ARG model into sentences, and classify the fold types with respect to different grammatical inferences of the sentences. If the sentences cannot be inferred by Anticline Grammar and Syncline Grammar, then it is not a fold. Fourthly, determine the fold landform types by checking whether the terrain containing the fold is a mountain or a valley. The result shows that the proposed method is capable for automatically recognizing the fold landform types in the northern Lushan area. It basically solves the problem in the auto-recognizing of fold landform types for mountainous area, and can be a supplementary reference to the traditional methods used for landform classification.

Nowadays, studies of factors influencing geographical spatial orientation ability mainly concentrate on gender whereas relationships of field cognitive style and spatial terminology with spatial orientation ability have rarely been studied. This study used eye tracking technology to explore the influences of the three individual variables on spatial orientation ability. 86 people participated in the experiments with an average age of 21 (SD=2.67). First, the test of embedded figures and a questionnaire survey were carried out to collect basic information of the participants including field cognitive style, gender and habitual spatial terminology. Next, the participants were asked to complete a series of spatial orientation missions of various complexity levels in a virtual 3-D environment. Through this process, participants’ eye movements were automatically recorded by eye tracker. Spatial orientation ability was assessed by both completed results and reaction time which represent orientation accuracy and efficiency, respectively. Statistical tests were applied to test the significance of differences among different groups. Results show that there is no significant difference in orientation accuracy and efficiency among participants with different field cognitive styles as well as those with different spatial terminologies. It is demonstrated that field cognitive style and spatial terminology have no significant influence on spatial orientation ability. Participants of different genders show a significant difference in orientation accuracy which indicates that gender difference have a significant influence on spatial orientation ability. Males outperform females in the orientation tasks.

Impacts of climate change and adaptation are of key concern of scientific research. Vast research results indicated that agricultural production and environment in Africa have been affected a lot by increased temperature and decreased precipitation caused by climate change. This study used the output of regional climate model HadGEM2 under Representative Concentration Pathways Scenario 4.5 (RCP 4.5) to analyze the temporal and spatial evolution of major climate factors including precipitation, solar radiation, annual average temperature, maximum temperature and minimum temperature. Our results indicated that the variation of the five climate variables at different periods showed obvious regional differences. (1) Compared with the base period of 1970-1999, precipitation increased during the three future periods and reached peak value in 2080s. The area of precipitation increase is mainly located in the latitude of 20 degrees, such as Niger, Chad, Libya, etc. and the maximum increase is around 4.5%. (2) The area of increased solar radiation is mainly located in north and south ends of Africa continents, especially in high altitude area, i.e. Atlas mountain and Plus plateau and the maximum increase is 0.04%. (3) Over the next 90 years, the annual average temperature, maximum temperature and minimum temperature are all increasing and reach the maximum value by 2080s, increasing 5 ℃,4.3 ℃,5.1 ℃ at 2020s, 2050s, 2080s, respectively. The temperature is significantly increased compared with the base period of 1970-1999, but increased less in the coastal area due to the cold current. The high increase of temperature might play negative role in agriculture production and regional security.

Vegetation dynamics and their coupled relations with ecology are current research hot spots in exploring how the terrestrial ecosystems respond to the climate systems. We have obtained the vegetation leaf area index on both sides for the expressway in the research area, based on the simulations which use the PROSAIL and TM images as the information source. We studied the dynamic changes of road vegetation growth status with LAI from aspects of time and space. The results are shown as follows. (1) The temporal change of vegetation growth pattern in the expressway region shows that: the temporal growth condition of the roadside vegetation is influenced heavily in the first 5-year period after a new expressway has opened. (2) The spatial change of vegetation growth pattern in the expressway region shows that: the spatial growth condition of the roadside vegetation is heavily impacted within the regions that are closer to the expressway, while the vegetation located far from the expressway is mildly affected. Generally, this research provides reliable basic data for guiding the vegetation restoration and protection.

Wind speed is a basic parameter of oceanography. It plays an important role in the interaction between ocean and atmosphere. Therefore, it is significant and necessary to obtain the wind datasets over the sea surface. However, due to the large area and complex condition, it is usually difficult to get the wind field data of South China to satisfy different demands in time. Conventional approaches, such as placing observation stations or buoys, are not only expensive but also dependent on the weather condition. Therefore it is urgently necessary to find other ways to get the wind datasets timely. ENSISAT ASAR, an all-weather and all-time microwave radar sensor, could collect the real-time and dynamic information over sea surface, which provides a new approach for researchers to acquire wind field datasets over sea surface, especially for the waters with complicated conditions, such as South China Sea. In this paper, the Gaussian-FFT method is firstly applied to retrieve the wind field of South China Sea based on ASAR image. At first, the FFT spectrum of ASAR image is acquired with the FFT algorithm. Secondly, a “cigar-shaped” two-dimensional (2-D) Gaussian function is fitted to the FFT spectrum to find the direction of wind streaks and further to obtain the wind direction which is perpendicular to it. In this experiment, the wind direction acquired from the ASAR image by the Gaussian-FFT algorithm also has a 180 ambiguity in direction. To resolve the 180 ambiguity, CCMP wind field datasets are taken into consideration to act as the wind field references. Besides, the wind direction computed with the Gaussian-FFT method is compared with the wind direction obtained by the Peak-FFT method. Then, the optimal wind direction (Gaussian-FFT wind direction) is input into the CMOD4 and CMOD5 models to compute the wind speed values respectively. Through comparing the wind field retrieval results with the CCMP datasets, we proved that it is valid to retrieve wind direction from ASAR image with Gaussian-FFT algorithm and it is achievable to obtain wind speed value over South China Sea with CMOD4 model. The approach used to obtain the wind field in this paper is of great significance to provide guidance to the wind field inversion in other waters of South China Sea, especially in areas that are lack of field observations. In addition, it is also critical for other researches whose specialties are related to oceanography, as this approach could offer vital wind parameters to these researches.

Land subsidence in Beijing plain is becoming increasingly acute. The causes of land subsidence are complicated, including artificial over-exploration of groundwater and building load as well as natural soil consolidation and the influence of active structure. Over-exploration of groundwater and building load are the two important driving factors in the land subsidence of Beijing Plain. What we have focused on is how regional scale building load should be extracted and evaluated. In this paper, the simplified plot ratio stands for the building load and the land subsidence information of the study area is measured by PS-InSAR technique. We get the uneven subsidence distribution under the same groundwater exploration by the way of GIS spatial analysis. Meanwhile, building height is extracted by the way of Shadow's Length Method with high-resolution optical images. At last, the relationship between building load and land subsidence is studied by spatial analysis and regression analysis. The main conclusions obtained are as follows: (1) Land subsidence in Beijing is very serious during 2003-2010, the percentage of 30mm/a-41.89mm/a area is 21.08%. (2) The uneven subsidence distribution under the same groundwater exploration is located in the central and northern Beijing as an H shape. (3) Shadow's Length Method can accurately estimate the plot ratio, which can be used to extract regional scale building load. (4) Land subsidence rate has a correlation with building plot ratio in similar geological condition and ground water level changing area but the correlation coefficient is small.

Global land cover data plays an important role in climate change research, geographical conditions monitoring and ecological environment protection. It' s of great significance to produce and evaluate the global land cover data at a specific spatial scale. In 2014, the National Geomatics Center of China (NGCC) produced GlobeLand30 of the remote sensing mapping product with the world’s highest 30 m resolution. In this paper, the 1:100 000 land use data of Henan Province was used as the reference data to validate global land cover data of GlobeLand30, GlobCover2001 and MCD12Q1. The accuracy assessment and comparative analysis of these data were conducted with three methods, including spatial statistics, area relevance and consistency, and confusion matrix. The results show that the three land cover products have a good consistency for description of land forms with the reference data, and the area relevance is higher than 0.9. Cropland and forestland are the main land cover types, followed by grassland, water body and artificial surface, but the classified land has different area in these products. By evaluating accuracy of the three land cover products, the overall accuracy and Kappa coefficient of GlobeLand30 are the highest, followed by MCD12Q1 and those of GlobCover2009 are the lowest. In terms of specific land type, although cropland and forestland have high precision in these products, the accuracy of grassland classification is poor. The producer accuracy of water body and artificial surface in GlobeLand30 is much higher than the other two products, but the difference of the user accuracy is small. The three land cover products show the spatial confusion especially in forestland, grassland and cropland with the reference data. The confusion degree of GlobeLand30 is lower than the other two kinds of data. This paper illustrates that GlobeLand30 has higher accuracy than other products and it will play a more and more important role in many fields. Not only can the methods and conclusions in this paper pave the way for further research in other areas, but also they can have great significance on promoting the application and value of GlobeLand30. Moreover, because of the spatial resolution of GlobeLand30 is much higher than other land cover products, the use of GlobeLand30 for further application and research is the focus in the next step. In addition, there are a lot of remote sensing images, vector data, and other multi-source data and how to improve the quality of the global land cover data is one of the problems that should be considered.

Snow cover is one of the most active natural components on Earth’s surface. The variability of snow phenology has a major impact on water cycle, climate change, environment and human activities. The Qinghai-Tibetan Plateau has a wide range of seasonal snow cover, and its accumulation and rapid meltdown can affect the regional and global climate change. Studying the snow variability in the Qinghai-Tibetan Plateau is therefore important. In this study, the MODIS snow product and IMS snow-ice product were used. Firstly, the Terra and Aqua satellite images were combined to reduce the proportion of cloud pixels. Secondly, the temporal combinations were employed to further reduce the cloud pixels. Finally, the processed MODIS snow product and IMS were fused to produce the daily cloud-free snow product of the Qinghai-Tibetan Plateau from 2002 to 2012. Then, the snow-covered days (SCD), snow cover start (SCS) and snow cover end (SCE) dates were calculated for each hydrological year, and their spatial and temporal variations in different eco-geographical regions were analyzed. The correlations among the SCS, SCE and climate factors were also investigated. The results show that the distribution of snow cover over the Qinghai-Tibetan Plateau was very uneven. The longest SCD, totalized to be more than 200 days, occurred in the Himalayas, Karakoram, Nyainqentanglha Mountains and the Pamirs Plateau. Up to 18.1% of the area of SCS showed a significantly advanced trend, which mainly occurred in the Golog-Nagqu high-cold region and the southern Qinghai high-cold region; while 8.5% of the area showed a slightly delayed trend. Up to 23.2% of the area of SCE was delayed, occurring mainly in the central and eastern Tibetan Plateau; while only 6.9% of the area showed an advanced trend. The SCS and SCE were greatly affected by temperature and precipitation, but showed different spatial patterns and evolution trends in different ecological zones. Generally, the higher temperature delayed the SCS and advanced the SCE, but more precipitation led to the earlier SCS and the later SCE.

Data provenance is an important reference factor of data reliability evaluation and important research content of geospatial data ontology. Taking consideration of provenance, an important research object of geospatial data, we constructed a geospatial data provenance conceptual model based on systemic analysis of the meaning of geospatial data provenance. Based on it, we put forward geospatial data Provenance-Ontology concepts system and the formalization method for constructing geospatial data Provenance-Ontology. Finally, we take the data materials in “special work of the science and technology basic work” as an example. Based on Provenance-Ontology library, using RDF to link geospatial data and D3.js to achieve the data provenance visualization. The result shows that data linking based on Provenance-Ontology can effectively solve the problem of the nonstandardization in the description of data provenance information. It can support geospatial data semantic retrieval, intelligent recommendation and other applications. It also provides new ideas for geodata sharing and data linking.

Parameter optimization is an effective means for the accurate estimation of ecosystem model parameters and the reduction of the uncertainty in model predictions. We proposed a method for parameter optimization of the ecosystem model, which is based on the Bayesian machine learning and called No-U-Turn-Sampler (NUTS). As an efficient means of parameter optimization, NUTS uses a recursive algorithm to build a set of candidate points to obtain the posterior information of the parameters. If the constraint condition of “Non-U-Turn” is met, subtrees will be built to update parameters. Otherwise, “the optimal” set of parameters from current sample will be recorded, and then the next sampling begin to run until enough samples are taken. This algorithm avoids sampling redundancy caused by random walk and thus improves the efficiency of parameter optimization. Taking the carbon flux simulations of the Qianyanzhou subtropical coniferous plantation as an example, we implemented the parameter inversion of the carbon flux (Net Ecosystem Exchange, NEE) model using the NUTS method based on the Pymc3 framework. The comparison between the inversion results of NUTS and Metropolis-Hastings (MH) shows that the sampling frequency reduces about 85%, and the optimization efficiency increases about 3 times when the parameter values of the NUTS algorithm reaches convergence. The uncertainties of the seven parameters estimated by NUTS in the two NEE models are reduced by 10%-53% compared to MH. The NEE simulation improved significantly, with the R² between the simulated values and the observed values increased by 23% and 17%, respectively and the RMSE decreased by 3% and 4%, respectively. In sum, the NUTS parameter optimization method proposed in this paper provides an efficient approach for the parameter optimization in ecosystem modeling.

The problem of urban traffic congestion has become a serious problem in the development of many cities in the world. To solve this problem, pan-spatial information system provides a new way of solving urban traffic congestion by multi-granularity abstracting, multi-scale modeling and multi-level comprehensive analysis of dynamic and complex traffic jam processes. In reality, the process of traffic congestion is usually accompanied by the dissemination of traffic warning information. Accordingly, when the competition occurs, which is generated by traffic congestion and the spreading of warning information in different network layers, the interplay between traffic congestion and warning information plays an important role. Thus, in order to study the interplay between information spreading and traffic congestion spreading, we constructed a multiplex network with road intersections or sites to analyze the interplay between information spreading and traffic congestion spreading. Firstly, we considered the effect of the surrounding nodes and proposed an improved SIS model. Then, based on the improved SIS model, we used the method of state transition probability to study the competing spreading processes of multiplex network. Finally, using the Monte Carlo method, we analyzed and simulated the traffic congestion threshold in both homogeneous network and heterogeneous network. This study indicates that the process of traffic congestion depends on dynamics of warning information spreading through transport network.

With the sustained and rapid growth of car ownership, the parking problem in urban areas has become increasingly serious. Estimating parking spaces and parking potential in such areas is of great importance for government departments to make parking planning and ease parking contradiction. However, the current survey method of parking spaces and parking potential under the framework of statistical data collection and field investigation is difficult to reflect the supply situation of parking facilities in a timely and comprehensive way. Using GIS technologies, a method of estimating parking spaces and parking potential in urban areas is proposed based on four kinds of data sources: the monitoring data of national geographical conditions, the thematic data from the department of transport and geomatics, and the aerial remote sensing image with high resolution. Firstly, ground parking lots and roads, impervious surface in residential districts, government agencies, enterprises, and public institutions are extracted by using monitoring data of national geographic conditions. Then, the estimation model of the ground parking spaces is constructed with parking lot shape indices, and combining with the survey data of accessory parking space, the total number of parking spaces is estimated. Finally, the estimation model of the curb parking potential is defined with constraint conditions of the road width. Also, the estimation method of the impervious surface parking potential based on shape indices is designed. Taking urban built-up areas in Wuhan as a case, this study estimates and evaluates the actual and potential supply capacity of parking spaces, obtains the basic parking gap, and presents suggestions for easing parking contradiction. The experiment results show that this method has a preferable estimation accuracy which is greater than 82.6% over 15 typical parking lots sampled in the studied area, and is suitable for urban parking spaces and parking potential estimation. Overall, this study can provide an effective method of applying monitoring data of national geographic conditions to estimate urban parking resources and make parking planning in a scientific way.

It is important to acquire the amount and the spatial distribution features of permanent population accurately, which can be used to clarify the development of social state. Thus, it would enhance the capacity of population management. Currently, population census data is mainly collected in administrative regions, making it difficult to describe the spatial distribution features of population in cities. Moreover, the precision decreases when using night light data to regress population, and it is clearly affected by roads, public service facilities and the lights of the cities. Therefore, it is necessary to improve the precision of population regression. This study takes Shanghai as the study area because it is one of the national center cities and faced with huge population pressure along with the rapid urbanization processes. Two types of data sources are involved in the study, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP -VIIRS) night light data and township-level permanent population census data. We extracted the night light data in commercial and residential land in order to mitigate the influence of roads and the lights of the city. Results showed that the correlation coefficient between summation of night light data and amount of permanent population was improved from 0.7032 to 0.8026. Further, we used a spatial regression model to derive the permanent population of Shanghai in 2013, and found that the relative error is 10.57%. Finally, we corrected the results in partition. Experimental results of high precision can be achieved when spatial regression model was used to regress permanent population. Moreover, the gridding results of permanent population can make up the shortcoming of low spatial resolution of traditional statistical data, and describe the circle feature and real distribution of permanent population with more details.

Now, there are still many land border territory disputes causing local wars or unstableness between countries. Under the current international situation, negotiation is the most effective method of solving territory disputes and it needs lots of demarcation technology. Current demarcation methods cannot fully safeguard unilateral resource interest and it costs lots of time. Thus, we proposed a new solution using neighborhood expansion method (NEM). Firstly, the disputed area was split using hexagon where related information such as resource reserve was mapped, and benefit density (BD), which is the comprehensive evaluation value of related resources in each grid, was calculated and disposed. Secondly, the disputed area was initially divided using NEM under the guide of regional integrity, BD and bilateral agreed area ratio, where most hexagons with higher BD were assigned to the related country. Thirdly, the single-source optimal path algorithm based on hexagon was improved to solve the optimal path from non-enclave to enclave caused during the initial segmentation of disputed area. The ascriptions of all the enclaves were determined once again based on some rules. Finally, the integrity of unilateral region was optimized, the gap between the unilateral area and the agreed area was reduced to the extent smaller than the area of single complete grid using NEM. The disputed zone was split accurately according to the agreed area ratio. Tests were made to compare our method with the other one using genetic algorithm based on simulated data, different hexagon sizes and agreed area ratios. The results indicated that our method owns the following characteristics: (1) it can correctly assign bilateral agreed never-lost regions and impenetrable areas like ethnic settlements; (2) the disputed area can be divided fast and precisely according to agreed area ratio; (3) it can fully safeguard unilateral resource interest. These features indicate that our method is effective and reliable and it can provide important reference and guide for one-side delimitation.

In recent years, the frequent occurrence of waterlogging in China has been one of the serious urban diseases. Spatial pattern of urban impervious surface density is an important factor affecting the waterlogging. This paper aims to provide a new method to optimize the spatial pattern of impervious surface in order to reduce urban waterlogging by the integration of SCS-CN model and the Ant colony algorithm. Firstly, the density of urban impervious surface was estimated by remote sensing images through the method of linear spectral mixing modeling. Secondly,the CN value was corrected by using the Williams formula. Then, the modified SCS-CN model was used to calculate the surface runoff. Thirdly,according to the goal of minimizing runoff coefficient, the spatial pattern of impervious surface of 18 runoff plots was optimized by Ant colony algorithm. Fourthly, landscape pattern indices were used to analyze the spatial pattern of impervious surface. The results show that: in rainfall reappearing periods of 1 year, 5 years, 10 years, 20 years, 50 years and 100 years, the optimized impervious surface pattern could reduce the runoff coefficient by 21.19%, 19.58%, 19.38%, 18.93%, 18.41% and 17.25%, respectively. Based on the experimental results above, this research puts forward three suggestions for the optimization of urban renewal. ① Increase the area of grassland, garden, trees and other vegetation types to reduce the high impervious surface area which can be further divided into patches of lower levels of impervious surface. ② Gather low and medium-to-low types of impervious surface to increase the connectivity and the medium-to-high levels of impervious surface. ③ Increase the quantity and density of patches in each runoff plot and reduce the degree of spread and aggregation.

As a preparatory section for urban development, urban fringe area is not only a transitional area for both urban and rural areas, but also the most unstable region for urban development. In general, the primary task of urban fringe region study is to execute the spatial recognition and boundary division of urban fringe area. With the increased development of Geographic Information Systems (GIS) and satellite imagery technique, the definition of urban fringe area is becoming increasingly convenient and feasible. Owing to the existing one-fold or relatively complicated problems in extracting research indexes, it is of great significance to select better discrimination parameters in demarcating urban fringe. Based on SPOT-5 and Landsat-5 TM remotely sensed images and other corresponding auxiliary data, this study firstly constructed an evaluation framework and the index system of urban fringe area from the perspectives of physics, landscape and population, using the impervious surface coverage and the degree of landscape disturbance as primary indicators, and the population density data as auxiliary indexes. Then, the spatial range of the urban fringe of Xi'an city in 2010 was further quantified in this study by performing the computation of information entropy and mutation detection methods. The final results obtained are as follows: (1) The selected indexes show distinctive spatial signatures. The extraction model of urban fringe, which is based on the variability characteristics of both urban and rural areas, is practicable. Besides, the selected indicators are more scientific and accurate, which is of great significance in this paper. (2)Xi'an city takes on a clear ring and wedged structure of urban core area, urban fringe area, and rural hinterland. The extension of urban fringe area is primarily promoted by traffic facilities and policy orientation. This study can provide theoretical support and scientific basis for other relevant researches on urban fringe area.

Coastline change detection is critical for analyzing the rise of sea levels, coastal erosion, harbor siltation, wetland ecological resources, and the offshore environment. Satellite remote sensing technology has a wide application and plays an indispensable role in coastline monitoring. The Pearl River Estuary is one of city groups with the high density population and the most developed economy in China. With the consistent increase of the reclamation and coastal zone exploitation, the coastline changes in the Pearl River Estuary are dramatic. In this paper, a set of Landsat images from 1973 to 2015 were collected to detect the coastline evolutions in the Pearl River Estuary. Firstly, the coastlines were divided into 8 categories and extracted with the aid of remote sensing and geographic information system (GIS) technologies. In addition, the spatiotemporal evolution characteristics of coastline length, categories, and spatial changes were analyzed during the study period. A coastline utilization index was proposed to determine the impact of human activities. Finally, the driving factors of coastline changes were discussed. The results are as follows: ① The total length of coastlines in the Pearl River Estuary increased by 135.46 km, which was equivalent to a growth of 3.15 km per year. The artificial coastline increased significantly, with a net increase of 315.94 km in length. The natural coastline constantly declined, with the most decrease in mud coastline. The change intensity of the coastline length showed remarkable periodicity. It was slow before 1990, peaked from 1990 to 2000, and then weakened after 2000. ② The coastline category was changed from natural coastline to artificial coastline in the study period. The natural coastline was the main coastline category before 1990, but the artificial coastline took the lead position thereafter. Among all coastline categories, the proportion of the construction coastline changed most dynamically, which increased from 7.09% in 1973 to 46.49% in 2015. ③ During the period of 1973-2015, the coastline showed a prevailing trend of advancing seaward, reaching an annual rate of 39.10 m. The seaward extension rate had significant difference in different area. The greatest extension speed appeared on the coastline between Jiti outlet and Hutiao outlet. The seaward extensions of the coastlines between Modao outlet and Jiti outlet, and between Jiao outlet and Hongqi outlet, were remarkable, too. Other regions had an advancing seaward but with a small magnitude. ④ In the past 40 years, the coastline utilization index grew stably. The growth rate increased markedly from 1973 to 1995 and changed gently after 1995. The coastline utilization index in the east coast of the Lingding Sea occupied the largest increasing extent because more and more natural coastline had been artificialized. ⑤ The coastlines in the Pearl River Estuary are affected mainly by human activities, such as outlet renovation, coastal zone construction, and sea farming. Environmental conditions, demographic and economic growth, as well as policies are important driving forces of coastline changes. This study will provide scientific support for the coastline change detection, coastal zone management and sustainable development in the coastal area.

Image mosaicking is an important part of remote sensing image processing. It plays a vital role in the analysis of trans-regional remote sensing images. In order to solve the problems of low utilization rates of the nodes and frequent data I/O in the traditional parallel algorithms of remote sensing images, we proposed a parallel mosaicking algorithms based on self-defined RDD (Resilient Distributed Datasets), in which the Spark distributed memory computing framework has been used. In this paper, we take full advantage of the Spark, which is conducive to the processing of iterative data, and build remote sensing images parallel mosaic processing model through the operation of the Spark RDD. Firstly, according to the logical separability and data independence of the Fourier transform and inverse Fourier transform in the phase correlation method, we improved the traditional phase correlation method by executing a single instruction on multiple nodes, which are executed parallel in the cluster. We did so to improve the image overlapping region estimation multi-node parallel computation in the algorithm. Then, we override the compute and getPartitions methods in RDD and self-define the RDD for remote sensing image processing. Meanwhile, we used the three key steps of the image mosaicking, including overlapping region estimation, image registration and image fusion, which are the transformation-type operators of the self-defined RDD. These transformation-type operators do not perform calculations in the process of parallel mosaicking, until the final mosaicking image is required to be written to disk or file system. Thus, reducing the time consumption in the process of image parallel mosaicking. Finally, the parallel processing of image mosaicking is realized by calling the operators of self-defined RDD with the method of implicit conversion, compared with the parallel mosaicking algorithm based on MPI. The experimental results show that the parallel mosaicking algorithm of massive remote sensing image based on self-defined RDD can effectively improve the image mosaicking efficiency of large data volume on the basis of guaranteeing the image mosaicking effects.

Detecting breakpoints plays an important role in plotting and analyzing time series of the changing characteristics such as firing, logging, diseases and insect pests in vegetation. It is a useful technique of extracting the significant information in time series data. We focused on the method of Detecting Breakpoints and Estimating Segments in Trend (DBEST). We studied the detection of vegetation breakpoints by using vegetation fractional coverage (VFC) data which is derived from MODIS NDVI remote sensing images ( 250 m) from 2000 to 2015 in Changting County of Fujian Province. In order to determine if the results of breakpoints detection are reasonable, the primary experiment is to test the applicability of DBEST method by using the VFC data of various changing types in time series. We select several samples of time series data which covered the key water and soil erosion conservation area. The vegetation changes more frequently in this area for conducting the break-points detection experiments. We make an accuracy evaluation of changing time and changing types by using the temporal trajectories and Landsat remote sensing images of every point. We find that DBEST is suitable for VFC time series data of Changting, by using the default first and second level-shift-thresholds (θ₁ = 0.1, θ₂ = 0.2) which indicated that DBEST could define the changing level of VFC, but the duration-thresholdφ should be adjusted according to the study area and the type of time series data (we setφ=3). Those parameters have weak influences on the accuracy of breakpoints positions, but have more effects on the changing types of breakpoints. On the whole, the excessive intervention is not necessary for detecting vegetation in DBEST. However, through a lot of experiments we believe that the threshold of the changing magnitude can be modified by our own need to gain a satisfying results. Finally, we set β = 0.2 to fit our own research targets. The precision of the changing time is 92%, greater than the changing types (80%), indicating that DBEST method works well in extracting the important changing information for VFC time series. Meanwhile, the experimental results are broadly consistent with the varying conditions of the local vegetation.

Using multi-source remote sensing data to extract impervious surface information is an important and active research direction. The present study integrated spatial and spectral information from nighttime light data and Landsat TM remote sensing images to automatically extract the coverage information of Impervious Surface Area (ISA), given that in the previous studies, manual selection of impervious surface samples was usually needed for model training. In the present method, firstly, ISA concentrated urban areas were located according to the distribution of nighttime lights. Thus, the ISA spectral characteristics of the local scale in the urban area parts were more clear and obvious compared to the whole-image scene scale. Meanwhile, for the urban exterior, there were mostly non-ISA pixels, therefore the soil samples which were easily confused with ISA were extracted from the urban exterior, and the general spectral features of these samples on this image were calculated. These features could be utilized to distinguish ISA pixels from urban areas. Thus, highly reliable ISA and non-ISA samples were automatically selected from urban area and urban exterior, respectively. Secondly, ISA from urban areas was extracted by an iterative classification process. For the iterative classification process, new samples from the previous extraction results were collected and then added to the following classification process, to make the features of the ISA samples more representative of different types of ISA coverage. Then, ISA samples of urban area were selected from the extraction results, combined with the non-ISA samples of the urban exterior. A sample set was formed to classify the urban exterior. Lastly, the classification results were integrated to complete the whole image. An experiment with this method was completed. DMSP/OLS nighttime light images and Landsat5 TM images of the Syracuse, USA were chosed. 84 urban areas were extracted and the detection accuracy rate was above 95% compared to the Openstreet map. Two urban areas with high and low ISA density from the detection results were selected as the test areas. Automatic selection of ISA and non-ISA samples were performed to the TC transform feature bands of the Landsat5 TM images. The overall accuracy and kappa coefficients of sample selection in urban areas were 92.45% and 0.76, respectively, and 96.52% and 0.85 in urban exterior. For the results extracted by decision tree classifier, the average overall accuracy and Kappa coefficient were 88.23% and 0.63 in the urban areas; 78.6% and 0.54 in the urban exterior. These results are superior to manual methods. This is because the approach of automatic samples selection was more capable of obtaining samples of mixed pixel types compared to manual samples selection. Moreover, the representativeness of samples in spatial distribution and spectral characteristics was better since the iterative classification process was introduced. It suggests an automated classifcaltion workflow is achieved by the proposed method, and this method is reliable and effective for both urban area and urban exterior. In further researches, it could be expected that the ISA extraction accuracy could be improved by optimizing classification characteristics (e.g. adding space features) and improving classification algorithms.

Spartina alterniflora is one of the major invasive species putting a high pressure to the native Phragmites australis in the coastal wetland in Chongming Island, Shanghai. Both species grow up together and result in extensive transitional zones. Spartina alterniflora and Phragmites australis have differences in physiology. The former prefers to live in a high-salt environment and it distributes closer to the sea. In the transitional zones, the two species mix in different proportion along the direction from the sea to the land and grow competitively with each other. Their growth in the transitional zones reflects the intensity of competition. Moreover, the change of the location of transitional zones reflects the dynamic process of the invasion of Spartina alterniflora. Thus, the transitional zone plays a key role in the study of dynamic change of wetland ecosystem. However, it is difficult to extract such information precisely by remote sensing because of the similar spectra of two species and complex composition of the transitional zone. They have similarities in both spectra and physiological and ecological characteristics because both of them are gramineous plants. In addition, the two species in transitional zones mix with different proportions in different regions, so the composition of the transitional zones is complex. There is little related research focusing on the transitional zones so far. This paper presents a comprehensive extraction method. Firstly, we combine different phenology with spectral characteristics to narrow the scope of the appropriate indicators down to reduce the workload. Secondly, we also consider location difference of the land and the sea. Analyzing the spectral characteristics along the direction from the sea to the land, it will be more intuitive for the spectral characteristics of vegetation in the transitional zones as well as the relationship between the change of mixing ratios and spectral characteristics. Finally, we determine extracting indicators and threshold by actual measured dataset. Remote sensing is an important measure for monitoring wetland ecosystem. Extracting transitional zone requires high-resolution remotely sensed images because the width of transitional zone in our research is narrow and transitional zone contains many patches which is due to many complex factors such as underwater micro-topography differences. This study selected appropriate multi-spectral remotely sensed data from GF-1 as research object through analyzing the canopy spectral differences between Spartina alterniflora and Phragmites australis in different time. Also, this study extracted transitional zone successfully in the study area which is an intertidal area located in the northeastern part of Chongming Island. The results indicate that different indicators should be used in different time. We select near-infrared band reflectance for spring and red band reflectance for autumn The near-infrared band reflectance of vegetation in transitional zone is lower than the other two pure species regions in spring while the red band reflectance is higher than the other two pure species regions in autumn. The scale of transitional zone has obvious difference in the two growing stages, the width of transitional zone in autumn is narrower than that in spring and the location moves towards the direction of Phragmites australis regions. The difference reflects the competitive situation in different seasons, objectively. Competitive edge of Spartina alterniflora is more evident in autumn.

Being an important part of the green space system, urban grassland has played a significant role in landscaping environment, regulating microclimate and preventing soil from erosion. Therefore, it is of great importance to monitor the health status of urban grassland timely and efficiently. Remote sensing technique has been widely used for assessing vegetation growth status for decades. Numerous studies have found that red edge indices are closely related to the important biochemical parameters of green plants. Thus, they can be regarded as important indicators for monitoring health status of vegetation. However, there is no explicit conclusion about which index is more suitable for monitoring the health status of urban grasslands among the existing red edge indices. The European Sentinel-2A satellite was successfully launched in late June 2015, aiming to replace and improve the old generation of satellite sensors of high resolution (i.e. Landsat and SPOT), with improved spectral capabilities. The multispectral instrument (MSI) of Sentinel-2 has made available a set of 13 spectral bands ranging from visible (VIS) and near infrared (NIR) to shortwave infrared (SWIR), featuring four bands at 10 m, six bands at 20 m, and three bands at 60 m of spatial resolution. In comparison to the previous sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region centered at 705, 740 and 783 nm, providing an opportunity for assessing red-edge spectral indices for monitoring the health status of urban grasslands. For this reason, the main objective of this paper is to find a red edge index that is more suitable for evaluating the growth status of urban grassland based on Sentinel-2A sensor data. Taking the urban grasslands in Fuzhou and Xiamen cities, Southeastern China, as examples, we firstly investigated the spectral responsive characteristics of grasslands in different health status using Sentinel-2A images dated on June 23, 2016 and August 22, 2016, respectively for Fuzhou and Xiamen. On this basis, six red edge indices related to grassland chlorophyll content were then selected to test their efficiency in detecting grassland health status. These are the red edge position (REP), the terrestrial chlorophyll index (MTCI), the normalized difference red edge index (NDRE1), the novel inverted red-edge chlorophyll index (IRECI), the red-edge chlorophyll index (CI_red-edge) and the modified chlorophyll absorption ratio index (MCARI2). Furthermore, independent sample T test and Euclidean distance methods were employed to evaluate the performance of the selected indices in the detection of grassland health status. Results showed that the six red edge indices had different performances. They have different degrees of sensitivity to the changes of grassland health status. In general, the IRECI was the most sensitive to the grassland health status among the six indices in the two study areas. The index can reveal significant differences in the numerical range and mean values between grasslands with different health status. The overall accuracy of the index is greater than 85% with a kappa coefficient exceeding 0.8 both in Fuzhou and Xiamen cases. The NDRE1 and MCARI2 indices ranked the second and third, while the other three indices were unable to effectively detect the health status of the grasslands. Accordingly, the IRECI is the optimal red edge index for evaluating the grassland health status using Sentinel-2A imagery.

The Beijing-Tianjin high-speed railway is the first high-speed railway in China. The stability of the geological environment is crucial for the safe operation of the high-speed railway. Land subsidence especially uneven subsidence will probably cause the deformation of the roadbed and bridge, which may affect the safety of high-speed railway operation. Therefore, it has very important significance for land subsidence monitoring along the high-speed railway. Interferometric synthetic aperture radar (InSAR) is an effective way for monitoring land subsidence with high precision. Based on 45 high-resolution TerraSAR-X images acquired from 2010 to 2015, the Permanent Scatter Interferometry (PS-InSAR) is empolyed to obtain land subsidence information along Beijing-Tianjin high-speed railway in Beijing section. The results indicate that there exist different spatial distributions of the land subsidence along the high-speed railway, the annual subsidence rate from Beijing south railway station to Shilihe interval is less than 10 mm/a, and from Shilihe to Shibalidian interval the annual subsidence rate ranges from 10 to 40 mm/a. And the maximum annual subsidence rate reaches 90mm/a from Yizhuang station to the east interval. The comprehensive analysis of static-dynamic loadings and hydrogeological data can help to understand the causes of land subsidence along high-speed railway. Over-exploitation of groundwater is the main factor of land subsidence in the study area, and the combination of dynamic and static loadings have certain influence on land subsidence. To some extent, the land subsidence along the high-speed railway is controlled by the Nanyuan-Tongzhou fault and the Jiugong fault, and most parts of the land subsidence are located in the Daxing uplifted belt with thick clay layer.