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.
Fires belong to one of the main disturbance factors and play an important role in various ecosystems. Burned area detection not only indicates the impact of fires on ecosystems, but also provides a scientific support for the global carbon cycle studies. Traditional burned scar area detection approach mainly depends on ground survey and measurements, which still has several defects, such as the heavy workload, high cost, low efficiency, and poor timeliness etc. By applying remote sensing technology to map the burned area can produce burn scar information with greater spatial and temporal scale and effectively avoid the above-mentioned problems. Currently, many methods aiming to map the burned area on remote sensing images have been developed, and various global burned area products which provide the consistent assessments of fire activity at the global scale are also available; however, the efficiency of their performances differs within various ecosystems. In this study, we developed an algorithm to map the burned scar area in an ecosystem transition zone by using the Moderate Resolution Imaging Spectroradiometer (MODIS) data. This algorithm was developed based on the Normalized Burned Ratio differencing (dNBR) and the vegetation coverage data. The NBR index was originally developed specifically for mapping burned areas, and recently it has been used in the assessment of burning severity. Firstly, we used the near red and shortwave infrared bands of MODIS Surface Reflectance products (MOD09A1) to calculate the NBR values. Then, the differenced NBR (dNBR) calculated from the NBR values for a composite period with the previous 8-day range was calculated. The frequency distribution of dNBR maximum value in the burned scar area and the unburned region was analyzed. Since the change of NBR values in regions with different vegetation coverage was different, the tree cover and herbaceous cover data provided by the MODIS Vegetation Continuous Fields product (MOD44B) were also used for setting up rules to extract the burned scar area. A case study was carried out in an ecosystem transition zone within the southeast Siberia, where forest, grassland, farmland and other different ecosystems coexist. Comparison of the burned area detected by this algorithm with the adoption of high resolution burned scar information from Landsat ETM+ imagery shows a high accuracy. And the result obtained using this algorithm was better than the one using the MODIS Combined Burned Area product (MCD45A1), with the kappa coefficient increased from 0.70 to 0.75. To make a better comparison, we set up rules with the same threshold values of dNBR to extract the burned scar area, but without the usage of tree cover or herbaceous cover data. We found that the use of tree cover data as well as the herbaceous cover data can reduce mistakes during the process and improve the accuracy of burned area extraction, with the kappa coefficient increased from 0.69 and 0.73 respectively to 0.75.
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.
