Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future.
Digital preservation of cultural heritage is complex, which is due to the difficulty in representing the three dimensional space (e.g. from underwater or underground to ground) and the temporal dynamic interactions between natural processes and human activities. The development of multi-source detection techniques and multi-dimensional environmental reconstruction technologies provide possible solutions for the above problems. Thus, it is becoming a new hotspot within the research of digital preservation of cultural heritage. This paper firstly reviewed and compared different multi-source detection techniques. According to spatial hierarchy, the detected heritage can be divided into different types, including underwater cultural, near-surface cultural, and the indoor to outdoor landscape heritage. Also, these multi-source detection techniques were further classified and they can be selected for detecting a specific kind of heritage. Secondly, the technical methods of multi-dimensional environmental reconstruction were analyzed. Furthermore, the applications of these methods have been represented and the potential of these methods has been explored in the fields of archaeological cartography, information management of multi-source heterogeneous cultural heritage, three dimensional reconstruction and virtual remediation, and intelligent multimodal interactive presentation. Finally, the research prospect of digital preservation of cultural heritage was proposed from the perspective of multi-sensor collaborative observation, distributed management and sharing of multi-dimensional data, dynamic cognition and reconstruction of environments, and multimodal interactive presentation.
Extracting spatial relation from text documents in natural languages (news, journal, blog, social network etc.) is an important method of obtaining spatial information in the era of big data. Former methods of extracting spatial relation from Chinese characterized text only focused on the features of Chinese characters and phrases, which easily cause ambiguous matching. This paper presented a new rule-based method that integrates lexical, syntactic and semantic knowledge. The extracting rule in this method was composed of spatial words and syntactic dependences between these words, which jointly formed a tree structure. The tree nodes represent the spatial words and they were connected by syntactic dependences. Spatial words were the words that can be used to express spatial relations, which were subsequently classified into 6 categories: geographical entities, preposition, locative nouns, spatial predicate, metaphorical spatial nouns and assistant words. In the process of rule matching, finite automata was used to identify new spatial relation instances that satisfy the following two conditions: (1) same syntactic dependence structure with regard to the extracting rules; (2) similarity of the spatial words. The part-of-speech, semantic similarity were used to measure the consistency between spatial words. The experiment of extracting the direction relations from Encyclopedia of China shows that the accuracy and the recall rate of this method achieve 86.67% and 63.11% respectively, which is better than the former methods. Comparing with the former methods, the improvements of this method include: (1) the process of extracting rule generation does not require human intervention; (2) the ambiguous matching can be diminished by integrating syntactic dependence knowledge, which evidently promoted the performance of spatial relation identification.
Currently the method of geoscience mechanism and process simulation is using 2D or 3D finite element mesh to discretize geographical space, and then complete the calculation and simulation with relevant numerical calculation method. Therefore, the finite element mesh subdivision is the basis to carry out the learning mechanism and process simulation. Traditional grid-based hexahedron generating algorithm uses a standard orthometric grid to cover the entire research area, deletes the meshes that lie outside the area or are intersected at the boundaries, and then fills the space between the border and the grid area. As a result, the research area are fullfilled with regular meshes inside the area and irregular ones on the border. However, the geological research subjects are generally characterized by complex boundary and contain more spatial feature contraints. Meanwhile, geosciences analysis and computation require meshes of higher quaility. Therefore, the existing hexahedron generating algorithm can not generate meshes with preferable discretization for geological research subjects. The paper presents a modified grid-based hexahedron generating algorithm taking the advantages of the traditional grid-based algorithm for geosciences analysis, in order to meet the requirements of relevant geoscience researches for hexahedron mesh, and to promote further improvements to the geoscience simulation and the traditional GIS space analysis that are based on numerical methods. The algorithm generates the backbone grid by extracting the geometrical characteristics of the surface model, which takes the constraints such as internal pores and caves into consideration. At the same time, it builds some templates to handle the complex feature constraints, in order to keep the geometric shape of these feature constraints, and to achieve a smooth transition between the refined area of feature constraints and the unconstrained part. Then it uses mesh quality optimization algorithm to improve the mesh quality. In the end, it is proved practically by an example of the geographic model of Nanjing south railway station. The algorithm can produce high quality hexahedron meshes which not only keep the geometrical form of characteristic for the geoscience research objects, but also fullfill the requirements of geoscience analysis process simulation.
The DEM Terrain Description Error (Et) has a basic fact that even if the elevation sampling errors of DEM grid points are equal to zero, a terrain description error will still exist because of DEM simple matrix data format. With the development of earth observation technology, Et has become the main factor affecting the quality of DEM, especially when the DEM grid resolution is low. Traditional calculation models could not calculate the Et value of random points on terrain surface. A new calculation model of DEM terrain description error is presented in this paper for improving the traditional models. The new Et calculation model of any ground point considers the layout position of DEM grid which is established based on the mechanism of Et production. Statistic indexes of Et based on DEM are firstly presented. Then the layout scheme of DEM grid on different datum mark position is designed. At last, the Et value of any ground point considering the layout position of DEM grid is calculated using the new model of Et. 100 DEMs with 5、15、25、50、75、100 and 150 meters resolution for Loess hilly test area in Shannxi Province. The results show that: (1) the standard deviation, average value, extreme value, extreme difference and other metrics indexes of Et for each ground point in any DEM grid resolution of different physiognomy type areas could be calculated scientifically and effectively; (2) reconstruction methods of terrain surface based on DEM strongly influence the accuracy of Et model. Complete regularized spline function interpolation method based on a 4×4 search circle is tested for the optimal interpolation method.
Points cluster is a set of points which generally have its specific organizational structure. Gully is a self-contained class and important spatial entity, so the gully feature point cluster which expresses and describes gully’s morphology, structure and the key attributes have tight organizational structure, close spatial relationship and complete description of the properties. Establishing the spatial structure model of gully feature point cluster and applying the model to the gully features’ spatial analysis and application have important scientific significance. There are some important completed works. Firstly, based on analyzing the elements, spatial and structural characters of gully feature point cluster, the existing stored method of spatial data point information was analyzed. By using object-oriented modeling ideas, the gully feature point cluster data model was designed for describing gully’s topology and other spatial information based on hierarchical model and unstructured file storage. The gully feature point cluster data model was achieved by C#. And then, based on the point cluster file, the prototype system was researched and designed. Through using visual C# and ArcEngine, visualization (tree structure), spatial analysis (correlation structure), property characteristics analysis (statistics, and hydro-geomorphological analysis) and other basic functions of gully feature point cluster were realized initially. Finally, as a case study, the feature points retrospective sought, the processing time was comparatively analyzed and the time-consuming and point number relationship figure was drawn. The analysis verified the documents based on feature points cluster analysis with a more gully high processing efficiency. In this study, a new idea using point cluster model for gully analysis of expression was practiced. Established the point cluster prototype system orienting valley structure, morphology and properties analysis functions has some practical application value.
In recent years, with the rapid development of location-based services, significant changes have taken place on location maps which are considered as new map products in research context compared with the general map. Those changes expand location map’s research areas and scope. This paper is mainly divided into three parts. First of all, it analyzes the relationship between context theory and location map, and points out the significance of context theory in location services especially in the application of location map. Then, it develops the context model, which has two main problems in mobile services: one is that different context information is not relevant, the other is that it is difficult to realize the context inference. So we should analyze the role of activity information in context modeling according to its importance. Also it establishes the logical structure of the location map’s context model by using the formal description language “ontology”, proposing the three-layer model of location map which is based on active layer, researching the content and features of activity model, behavior model and context information model, and giving further analysis on the relationships between activity model and different behaviors as well as the relationships between behavior model and various context information. Finally, this paper gives an example about a user’s check-in behavior at the airport to validate the process and methods of location map’s context model that described above.
Location Based Service (LBS), with the support of GIS, is a thriving service for users related with coordinates received by wireless communication network or GPS. The trajectories composed with a set of received coordinates mainly express the character and habit of user’s behavior. Through analyzing and mining users’ trajectories, we will improve the efficiency of location based service. In this paper, firstly, the trajectories data including location coordinates and semantic fields are collected through GPS signal by the self-developed software installed in the terminal. The semantic fields contain the ID of user, current speed, nearby landmark and so on. Then the mistakes incorporated in raw trajectories due to the GPS instability should be filtered to enhance data accuracy. A method has been applied to filter the “jitter” points and to calculate the angle (angle threshold is 15°) and time interval (time threshold is 3s). Different from the conventional method that calculates mean value as the stay point’s coordinate directly, we divide the points in sub-trajectory into different groups based on semantic information. Afterwards, on the basis of the number of points in each group, we acquire weighted coordinate of the stay point. Finally, we match the stay points with POIs, which have ample information, like opening hours, special offers, etc., and then get a set of matched POIs around the stay point. In addition, through analyzing the interest and job of user, it could retrieve the more appropriate service and send it to user accordingly.
Geography simulation model such as Cellular Automata (CA) is one of the most important tools for simulating and early warning the urban growth. The CA model can simulate urban sprawling accurately only when suitable conversion rules for every cell are achieved. Hence, the core of CA is to derive the conversion rules, and many researchers have been interested in discovering the rules. However, the conversion rules of traditional CA are mainly derived from historic samples, in which both changed samples and unchanged ones are considered for function fitting to retrieve parameters simultaneously. In this approach, it is assumed that if the urban sprawling occurred, samples were labeled as 1; otherwise, samples were accordingly labeled as 0. However, it will result in over fitting for the unchanged samples, because those samples with labels of 0 may have the potentiality to transform in future, especially for those located at the rural-urban fringe. Therefore, we proposed a gradient CA for simulating urban sprawling. In this model, whether or not urban growth would occur was determined by the developing probability instead of its developed or undeveloped status. Accordingly, the unchanged samples were set to the values ranging from 0 to 1. And in this research, the developing potentiality was estimated according to present planning maps. Compared with traditional CA, the gradient CA could avoid the over fitting problem for the unchanged samples to a certain degree. Moreover, the fitting objective was distinguished from traditional CA for its ability in retrieving conversion rules. In addition, particle swarm optimization algorithm was used to obtain the parameters of spatial indices. Finally, Guangzhou City, which locates in the Pearl River Delta of China, was chosen as the study area for model implementation and validation. In this case study, the spatial developing potentiality was allocated referring to the major function zone (MFZ) planning, because MFZ is currently one of the most significant planning policies for Chinese government to control the chaotic urbanization. In order to evaluate the model’s efficiency, a comparison analysis was carried out between the gradient CA and traditional CA. Global and local patterns of the simulation results were analyzed respectively in details. Results demonstrate that the model modified in this paper can perform efficiently and the overall accuracy of the model is greater than 70%, which can provide better and reasonable spatial scenarios for medium-and long-term urban planning.
Information visual analysis is one of the key technologies in big data. The advent of “big data” era promotes the development of visualization techniques and also brings changes to the traditional crime analysis. The crime visualization could offer assistance to crime analysis in practice. However, they are separated in application. The primary challenge that crime visualization faces is how to analyze data features’ heterogeneity, scale, timeliness and complexity. This problem can be resolved by applying visual analysis, which allows users to explore data of different types and dimensions, and to obtain more valuable information with high correlation through interactions. Public security data, in the “big data” era, is characterized by multi-source and heterogeneity, and multi-dimension and long temporal series. Based on the characteristics of the data and criminal analysis theory, this article mainly focuses on the visual content, the representing method and the interactive design of visual crime analysis combined with geo-visualization and information visualization technologies, such as Wordle, Story line, parallel coordinate and scatter plot matrices, etc. A series of topic-oriented visual analyses were proposed in this study, including visual analyses based on spatio-temporal trajectory data of serial crime, real-time criminal data, spatio-temporal data of criminal process, criminal time-series statistical data, descriptive crime texts, criminal multidimensional attribute data, and crime-related statistical data. Supports from criminal cases investigation, trend prediction, hotspot analysis and references of visualizing studies from other fields were also offered and discussed in this article.
Regional consumption is an important driving force for the development of economy. The evolution of regional consumption level not only manifests the time sequence variation, but also shows the spatial interaction. While traditional studies consider little of the space-time effect together, this paper characterizes the regional consumption level with the use of per capita consumption retail sales. On the basis of calculating regional consumption level gap, the mutation point of time series values for regional consumption level gap is detected. Furthermore, the change of consumption level in China is divided into two periods of 1978-1986 and 1996-2010. Then, by using the traditional Markov chain and space Markov chain method, we analyzed the characteristics of spatial and temporal evolution pattern for per capita consumption level within the two periods respectively. The results show that: (1) the change of regional consumption level in each study phase shows the phenomenon of “club convergence”. And both the regions with low and high consumption level regions are steadily evolving along with their original types. (2) The variation of regional per capita consumption level is affected by the background of consumption level in adjacent regions, and the convergence process is not spatially independent. Generally, if a region is adjacent to regions with low consumption level, it will suffer from negative influences; if adjacent to regions with high consumption level, it will help increase the number of regions with their consumption level shifting upward and reduce the number of regions with same level transfer. (3) The interactions of regional consumption level between different regions present an obvious east-west differentiation trend. The regions and their neighborhoods with high consumption level that simultaneously shifting upward are mainly distributed in the east of China. The regions and their neighborhoods with consumption level shifting downward are clustered in the west of China. In addition, the central China region indicates a stationary consumption level, while the consumption level of the adjacent regions presents a varied state of convergence.
Regional development depends on central cities and their hinterlands which supply resources for the economic activities of cities. Therefore, a reasonable delimitation for hinterland and the analysis of its spatial pattern are needed not only to present an approach for studies on urban economic region, but also provide the basis for regional decision making. Due to the significant advantages of a clear understanding of the interrelationship between city and its hinterland, as well as between city and city, the study on urban hinterland is becoming a highlight in regional research. However, to date, there is not an efficient and credible methodological system and techniques to identify the urban hinterland area in China. This research investigates the potential of a computerized identification method supported by geographic information techniques to provide a better understanding of the distribution of urban hinterland. It improves the traditional field models from two aspects, which are “composite nodality index” and “regional accessibility”, in order to delineate urban hinterland area more reasonably. The principal components analysis method along with the indicators system were used to calculate urban nodality index. With the application of raster cost weighted distance method and k-order data fields, this paper attempts to comprehensively measure the regional accessibility and the spatial field of 17 cities at the prefecture level in Henan Province. Furthermore, this paper delimits the urban hinterlands in 1991 and 2010 by using the hydrologic analysis model. At last, the dynamic evolution characteristic of urban hinterland area was investigated in three perspectives: area levels, spatial morphology and spatial relationship between the urban hinterlands and the administrative districts. The results indicate that the accessibility condition keeps improving and the average accessibility is 45.41 min and 33.03 in 1991 and 2010 respectively, which is improved by 12.38 min. Spatial filed have been increasing significantly from 1991 to 2010, and the spatial difference of spatial field appears to be remarkable. The whole pattern of the urban hinterland area in Henan Province has not changed much. The hinterland in Nanyang City had the largest increase in its area, while the hinterland area of Xinyang City shrinks most. With regard to the change ratio of urban hinterland area, Anyang City has the largest increasing ratio, while Luohe City had the largest decreasing ratio. Zhengzhou City has the largest deviation rate between its urban hinterland area and its administrative area.
Glaciers play an important role in the global climate system. Regular methods of analyzing glacier changes are not applicable in some areas of the region. The use of remote sensing (RS) techniques and GIS provides an efficient tool to analyze the status and the changes of glaciers. The study utilized Landsat TM and DEM data from 1990 to 2010, and analyzed the glaciers’ temporal and spatial distribution characteristics and changes in the South Mountain of Manas River Watershed by the method of object-oriented classification. And we discussed the reasons for these changes by making use of the temperature data of nearly 20 years (1987-2007). The results show that: (1) between 1990 and 2010, glaciers in the study area had retreated from 1442.32km2 to 710.54km2, and the overall area had decreased by 50.7%. (2) From 1990 to 2010, the main trend of the glacier change is retreating. Especially below the altitude of 4000m, the glacier area had decreased sharply, while above the altitude of 4000m, the change was relatively moderate. This phenomenon was much more obvious in the eastern area and relatively gentler in the western study area. (3) Since 1987, the increasing temperature was one of the main reasons for the continuous glacier retreatment.
Aiming at the problem that the fuzzy c-means (FCM) algorithm cannot effectively segment remote sensing images with noise, an algorithm of remote sensing image clustering based on Kernel Fuzzy C-Means (KFCM) clustering with local spatial information is proposed in this paper. Firstly, all pixels of a remote sensing image are mapped into a high-dimensional feature space through the kernel function. Different contributions of each feature vector to the clustering results are fully taken into consideration as well. Thus the influence of noise on the clustering results is greatly reduced and the high-dimensional non-clustered data can be divided nonlinearly. Then, the useful features of the remote sensing image are optimized by non-linear mapping. Next, according to the correlation between adjacent pixels, a space function is used to redefine the fuzzy membership of the pixels. Additionally, the local spatial information of pixels is introduced into the FCM algorithm and the pixels are clustered within the high-dimensional feature space by applying the above-mentioned FCM algorithm based on local spatial information. Accordingly, the clustering results are more accurate. Because of the introduction of local spatial information of pixels, the proposed algorithm can be directly applied to the original remote sensing image without filtering preprocesses and its robustness is adequately strong. A large number of experiments are performed and the results show that the proposed remote sensing image clustering algorithm based on KFCM with local spatial information has stronger noise reduction capabilities and can obtain better homogeneous regions. Therefore, the clustering effect of remote sensing image can be further improved. It is superior to the existing algorithms of remote sensing image clustering such as FCM algorithm, Fuzzy Local Information C-Means (FLICM) algorithm and KFCM algorithm. The proposed algorithm lays a good foundation for the next step of high-spatial-resolution remote sensing image processing.
Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium resolution spatial satellites such as Landsat and Satellite Pour l'Observation de la Terre (SPOT), especially in pluvial regions. A solution for fine-scale vegetation research is to blend the data from both high temporal resolution sensors (e.g., MODIS) and moderate ground resolution satellites (e.g., Landsat) to generate synthetic observations with characteristics of both. In recent decades, several approaches have been proposed to enhance the temporal frequency of high-resolution spatial satellite observations. However, there is a lack of application research of those methods, especially in South China where the climate is complex and the region is scattered with broken terrain. In order to evaluate the application ability of spatial and temporal image fusion models in South China, five spatial and temporal image fusion models were assessed in this paper. The five models are LORENZO model, LIU model, statistical model, STARFM and ESTARFM. Using the Landsat-ETM+ and MODIS data, the five methods were tested in an area near the Nanjing city of Jiangsu Province. QualitativeE:\app:ds:qualitativeevaluationE:\app:ds:evaluation and quantitativeE:\app:ds:quantitativeevaluationE:\app:ds:evaluation methods were used to evaluate the similarity between the simulated images and the real Landsat ETM+ images. Results showed that except Lorenzo model, the other models were able to produce synthetic images very similar to the actual observed images with a correlation coefficients r of higher than 0.6. The more information, such as distance, temporal and spectral information, isused in the image fusion, the synthetic fusion image could better reflect the detailed features of land surface.
Co-registration error between two land use maps based on different dates can cause a considerable overestimation of the land use/cover change. Even a small amount of misregistration markedly reduces the accuracy of land cover change estimates. Without relevant information about misregistration, existing methods cannot work effectively to detect and eliminate the false changes caused by misregistration. In this paper, we propose a methodology (Symmetric Theory) from the viewpoint of the relationship between original land use polygon and the changed polygons to detect the false change caused by misregistration. Symmetric Theory presents that the area of ‘changing from’ and ‘changing to’ polygons overlaid from the original polygon is symmetric in some degree, if true change polygons are eliminated from the changing polygons. Based on this theory, an automated detecting model is designed and developed. A case study was conducted using this method based on two land cover maps from 1980 and 2000, and their simulated misregistration maps for Naiman County, Tongliao City, Inner Mongolia, China (a total area of 8137.6 km2). This study shows that this method can effectively discriminate the spurious land cover changes from true land cover changes with false change detection accuracy ranging from 75.0% to 87.4%, true change detection accuracy ranging from 71.2% to 93.8%, and overall detection accuracy ranging from 73.3% to 92.7%. However, with the image shifts from half to ten pixels (15m to 300m), the ability of detecting false changes decreases with the increase of image misregistration. And when using this method, the SI threshold should be set as 0.2-0.4. If no relevant knowledge is mentioned, 0.3 is the best choice.
One of the polarized remote sensing advantages is its insensitivity to the land surface reflection, and now it has been widely used for aerosol properties retrieval from satellite and ground-based remote sensing. As to aerosols, the polarized remote sensing is only sensitive to small particles; however, there are different understandings of the small particle size to which extend the polarized remote sensing is sensitive. For example, some studies take fine mode aerosols as small particles, but the maximum radius of fine mode aerosol varies between 0.4~1.0
At present, more and more economic developed areas are facing serious land subsidence problems with increasing coverage and intensity, which prevents the application of sustainable development in these areas. Thus, the land subsidence problems should be controlled urgently. PS-InSAR technology, as one of the most important tools to detect land subsidence, has played a significant role in land subsidence monitoring. However, multi-track PS-InSAR results must be connected to obtain the large scale subsidence map when an extended land subsidence survey area is required, since spaceborne SAR image from only one track is too limited in scan range to cover the whole area. This paper analyzes the processing chain of PS-InSAR and points out two key problems when connecting multi-track PS-InSAR results from adjacent tracks. First, many PS from adjacent tracks are physically different, which is mainly caused by different incidence angles and coherences of adjacent tracks. Therefore, it is hard to find the tie point in the overlapped area. Second, there is a subsidence offset between two adjacent tracks according to the overlapped area, which is attributed to the physical differences of PS and the processing parameters of PS-InSAR, such as the reference point, the temporal coverage and so on. Among these factors, impacts caused by different incidence angles and reference offset are emphasized. Two new methods, Block Method and Interpolation Method, are proposed in this paper to calculate the differences between subsidence velocities from adjacent tracks caused by the physical differences of PS in the overlapped area. This can avoid the difficulty of finding tie point in the overlapped area. Moreover, subsidence velocities are adjusted to mitigate the impacts from different incidence angles. Finally, based on the existing software, a complete procedure for multi-track PS-InSAR subsidence results connection is demonstrated. At the end of this paper, ENVISAT ASAR datasets of Pearl Delta River in Guangdong province are processed and analyzed in experiments. Experimental results show that different incidence angles could affect the land subsidence from adjacent tracks, so it is necessary to correct the differential subsidence caused by the incidence difference, especially when multiple tracks are involved. Block Method and Interpolation Method are all effective in calculating the differences of subsidence velocities, and Block Method was superior to Interpolation Method through statistical analyses. Meanwhile, different incidence angles and reference offset are effectively mitigated to obtain the conformed subsidence velocities using these two methods. Consequently, PS-InSAR subsidence velocities from two ENVISAT tracks are connected effectively, which shows the effectiveness of the processing procedure proposed in this paper.
Variations in characteristics of urban surface energy are known to represent the urban ecosystem through relationships between its composition, function and feedback that influence the surface energy balance and lead to distinct urban energy distribution. To quantify the distinct urban energy distribution and furthermore to establish a standard ecosystem assessment indicator, the interpretation and comparison of the international "livable cities" are illustrated in this study. Spatial and temporal variations in the composition, function and feedback of the urban ecosystem are analyzed, and present us with following results: (1) the underlying impacts on the urban surface energy distribution are due to the differences among urban architecture (e.g. shape, volume), urban planning schemes (e.g. avenue, community), and thermal admittance (e.g. albedo, open space); (2) canopy complexity in the surrounding environment, between buildings, in city parks and other open spaces are essential to the surface energy balance. The international "livable cities" show the similarity that there are large-scale and low-density residential areas around the inner city (or metropolitan areas) with medium surface energy values as buffer and transition zones from urban to non-urban areas; (3) land surface energy change in urban open space is greater than metropolitan areas, and the proportional distribution of urban open space shows higher ratio of low-medium surface energy in the international "livable cities"; and (4) high surface energy areas are displayed with relatively smaller and more scattered pattern. Knowledge of the quantification of the surface energy and ecosystem assessment indicator are necessary for a better understanding of urban surface energy balance and are imperative for urban planning schemes.
Vegetation phenology changes is one of the most direct and sensitive indicators of global climate change and it has become the focus problem of the word studies. The Qinghai-Tibetan Plateau is a unique geographical unit covered by alpine vegetation types so that it is very important to study the remote sensing monitoring model of these vegetation types’ phenology. Firstly, Based on MODIS Normalized Difference Vegetation Index (NDVI) data from 2003 to 2012, we reconstructed the long-term time-series datasets through the combination of Inverse Distance Weighted Interpolation and Savitzky-Golay fitting method. After filtering, the obvious noise is removed but the detail information of vegetation growth is kept well so that the time-series curve is definitely suitable for the extraction of phenology paramethers. Then, we studied the extraction models of the typical vegetation phenology in the Qinghai-Tibetan Plteau with dynamic threshold value method, biggest change slope method and logistic curve fitting method. We compared and analyzed the monitoring results based on the nearly ten years NDVI dataset using the relationship between vegetation growing characteristics and daily mean temperature and then selected the dynamic threshold value method as the best model for typical vegetation phenology extraction in the Qinghai-Tibetan Plateau. Finally, we extracted the phenology information of grassland in the plateau with dynamic threshold value model. After the analysis of nearly ten years vegetation phenology, the results showed that the alpine grassland in the plateau experienced the trend of start of season (SOS) advancing (the ratio is 0.248d/a) as the end of season (EOS) following a more complex rule. The andvanced SOS manily caused by the rise of spring temperature and the influence of precipation is not significant. What’s more, the vegetation phenology and variation trends in the plateau showed obvious spatial distribution rule from the southeast to the northwest.
The term “afforestation” in this paper generally represents all kinds of non-crop vegetation in crop-dominated plain area, including but not limited to the vegetation in crop land (e.g., orchard, herb, nursery garden, fast-growing and high-yielding timber, farmland shelterbelt, and artificial turf), residential green, urban vegetation landscapes, trees planted alongside the roads and rivers, and the wind break and sand fixation forest. Accurate and timely information about afforestation in the plain region is useful because it can reflect the degree of agricultural diversity and the environmental health. However, getting the information about afforestation using remote sensing is hindered by some factors: afforestation are spatially scattered, temporally and spectrally overlapped with some crop lands. In addition, as a land use type, afforestation in plains often shares common spaces with other types of land use, such as croplands and roads. As a result, there is little, if any, data about afforestation in plains, let alone the afforestation change monitoring. As a follow-up to UN Millennium Ecosystem Assessment, the Chinese government is currently conducting a similar assessment at the provincial level, covering years of 2000-2005-2010, and using the traditional (FAO) land cover/land use system. It is beyond the expectation that the “forest” - as the major indicator of a good environment, in the province such as Henan, has only experienced a negligible increase. This is simply because the recent afforestation in plains was classified as other land cover types. With the advent of Chinese satellite HJ-1 A/B in September 2008, there may be a chance to extract information about afforestation in plains since its CCD sensor has Red and NIR channels, with a 30m spatial resolution and a 4-day temporal resolution. To test this potentiality, three steps were taken to extract afforestation in this study: (1) extract the plain information using 2010 Land Cover Map; (2) eliminate the double-crop (winter wheat) land using NDVIJune data when all the wheat had been harvested; (3) eliminate the single-crop land using NDVIApril data. Afforestation was extracted by calculating ((NDVIApril≥0.22)∩(NDVIJune≥0.35)). Validation was conducted using correlation with the statistics of 26 major cities, resulting in a significant R2=0.9166. The algorithm performed well in the wheat region, yet it did poor in the rice and rice-wheat transition regions, which are mainly distributed in the southern part of Xinyang. Aside from Xinyang, afforestation area extracted from HJ-1 is 6909.8 km2. This accounts for 20.53% of the total forest in Henan Province. Future study should make full use of HJ-1 high temporal resolution data by conducting zoning according to the climate and the soil.
Based on the multi-sensors remote sensing data of Landsat TM acquired in 1986 and 1994, Landsat ETM+ acquired in 2000, CBERS-02 CCD acquired in 2005, and ALOS AVNIR-2 acquired in 2009, the wetlands in Fuzhou coastal zone were classified using stratified decision rules. The coastal wetlands were divided into two categories: natural wetlands and artificial wetlands, from which the natural wetlands include five classes, while the artificial wetlands include three classes. The spatial patterns and dynamic changes of coastal wetlands from 1986 to 2009 were analyzed. The main changes of coastal wetlands happened during 1994 to 2005, and there were only a few changes before 1994 and after 2005. From 1986 to 2009, the total area of wetlands had decreased. The area of natural wetlands had decreased during the whole period, and was mainly transformed into aquacultures and non-wetlands area such as construction lands. The area of artificial wetlands had decreased before 2000 and increased after 2000, in which the area of aquacultures had increased very rapidly, and were mainly transformed from non-wetlands area, paddy fields and natural wetlands. The spatial pattern and dynamic change of coastal zone was jointly affected by local policies adjustment, the driving of economic interests and population increase. The temporal wetlands changes were closely related to the local policies.
