Social media data are increasingly perceived as an important channel to record people’s perception by virtue of its large volume, availability and timeliness. Especially, some social media data are location-stamped, associating with the space in the city with human cognition. Thus, we can further manifest the sociocultural signature of places in a semantic way. In this paper, geo-tagged text data on Weibo were utilized to explore the hidden semantic characteristics of locations, with focus on semantic similarities among regions. Specifically, Latent Semantic Analysis (LSA) were introduced to transform the unstructured regional and semantic feature in social media into a cognition-friendly and deep-related vector. Then, spatial analysis method, including factor analysis, spatial correlation analysis and clustering analysis were employed to mining the hidden characteristics of locations. In terms of research results, different latent topics and their distribution across the city were uncovered. Similarity index of tested locations were then obtained by measuring their latent semantic features. Baidu-pedia entries were further used as empirical consensus and spatial autocorrelation analysis was employed to investigate urban functional hot-regions. Besides, spatial clusters were acquired by using K-MEANS method in latent semantic space. Its effectiveness was validated by the diversity of POI density among clusters. This study demonstrates how the semantic meaning of a space can be harvested through the analysis of crowd-generated content in social media, which is useful to capture the unique themes that shape a location and support urban planning.
According to the characteristics of geographic entities and the requirements of storage management in pan-spatial information system, a spatio-temporal data model has been proposed to efficiently express the dynamic changes of geographical entities and their associations. Firstly, geographical entities are abstracted into spatio-temporal objects consisting of ordered and seamless object segments series. On that basis, a three-tuple model, which includes spatial location, geometry morphology and attribute feature, is designed to represent object segment. Secondly, in the aspect of association of entities like spatial relationship and attribute relationship, an extended RDF(Resource Description Framework) model is adopted to support the formalized description. Finally, as mentioned before, the dynamic changes contain the changes of geographical entities and the change of the entities’ associations. On one hand, the change of geographical entities is considered to fall into one of three types: change of spatial location movement, change of geometry morphology transformation and change of attribute feature. On the other hand, the changes of entity association includes spatial relationship and attribute relationship changes. According to the snapshot/increment and equation/model, the methods introduced could represent the discrete and continuous changes of the geographical entities and the associations. The proposed spatio-temporal model data model can not only precisely express the dynamic changes of spatial characteristics and attribute characteristics of geographic entities, but also explicitly represent the various associations of geographic entities over space and attribute. Furthermore, with the model, the exploration and mining over the change regularities and inner relevance of geographic phenomenon is expected to have a better behavior in some way, which is significant for the prediction, decision and planning in our lives.
OpenStreetMap (hereinafter short for OSM) data quality evaluation only considered the one-sided evaluation index or single level assessment of the object. This paper takes the authoritative data of government planning agency as the reference data and designs a data quality assessment model of OSM road network at a multi-level and multi-granularity scale. This model is based on the method of fuzzy integrated evaluation and is the combination of quality evaluation index and spatial level. From three aspects: single target, group goals and overall goals, this paper builts the evaluation indexes system of data quality and sets the control of number of individual and group structure in one, then sovles the crucial technical problems, such as the determining of membership function and the optimal combination weight. This paper also achieves the combinations of macro and micro, global and local. The results show that OSM data qualities of the single level and the overall level are superior. The group level is the general. The qualities of the single target and most indexes of group goal are superior so that it’s consistent and logical that the overall level is superior. This shows the method is feasible and practical. Some industries which are based on VGI data source can use this empirical basis and reliable data quality evaluation method for reference.
Frontal Area Density (FAD) of urban buildings is one of the important parameters of urban morphology. Therefore, the quantitative analysis and its mapping play a significant role in the field of urban microclimate research. It helps climatologists and urban planners mark out the detected ventilation paths, which could improve the thermal conditions in the inner city. In order to determine an effective and reliable method of analyzing the distribution of urban FAD, we took Jinjiang city of Fujian province as an example. We selected the vector and the raster calculation model to simulate FAD. Considering computational efficiency, we analyzed the obtained results from various scales and land use types. Two computer models were developed based on GIS and geodatabase. Each calculation model has an advantage of its specific data type. Mean, maximum, minimum and standard deviation of FAD for some chosen sample areas in Qingyang subdistrict were calculated and there were significant differences between selected areas. The research shows that the vector model is more efficient than the raster model. At the urban scale, the simulations of the vector and raster models are both consistent to the distribution characteristics of buildings at the macro level. At the neighborhood scale, the results of the raster model are more in line with building distribution than the vector model. When the scale of the area is reduced, the differences between two models increase. The raster model is more stable than the vector model, and is less affected by the resolution. In different land use types, the mean FAD values in business districts and urban residential areas are higher than the others. In the raster model, the average difference between the two resolutions of urban residential area is the lowest. In the vector model, the average difference of the green space is lower than the other three land use types. Thus, the two models are applicable for sparse distribution areas, but for building dense regions, such as business districts and city residential areas, the raster model performs better.
In order to understand the existing drought variation, to unveil the alternation between wet condition and dry condition, and to predict the future of drought in the countries and regions along “The Belt and Road Initiatives”, we analyzed the pattern of periodical variation and the trend of drought under multiple temporal scales. Specifically, we used 0.5°×0.5° SPEI (Standardized Precipitation Evaporation Indices) data with two temporal scales of 12-month and 3-month from 1901 to 2013 for analysis. Linear trend, principal component analysis (PCA), Mann-kenndall (M-K), and wavelet analysis are employed to analyze the multi-temporal scale data. The results showed that the SPEI and the drought area increase slowly at both annual and seasonal（except summer）scales over the past 113 years. However, 60% of the research area is slowly getting wetter. Also, 25.38% of the research area had significant SPEI increased, and only 12.02% of the research area had significant SPEI decreased. Both M-K and PCA indicated that the low latitude area between 15°-35°, including North Africa, Arabian Peninsula and Iranian Plateau, has the most frequent drought occurrence. M-K and PCA also show that Russia, Kazakhstan, the India Peninsula, Mongolia, and China have significant seasonal drought discrepancies. The wavelet spectral analysis revealed that the main periods of the annual and seasonal scale have both similar and different features: investigation with a long-time scale (annual) presents general trends yet fails the significance test of the wetting and drying alternation pattern. Such alternation only presents significant patterns with short-time scale. Finally, it appears that the variation pattern of drought is significant with annual drought period of 2-4 a.
Biological diversity is the material basis for the survival and development of human being. Most of the species in China distribute in the priority areas of biodiversity conservation. How to protect ecosystems and biodiversity is an issue that needs to be solved for achieving sustainable development. Based on the land use and land cover data of 2010-2015, we analyzed the intensity and variation of the disturbance caused by human activities in thirty-two biodiversity conservation priority areas. The results showed that: (1) The human disturbance intensity in the biodiversity conservation priority areas was majorly at the slight and mild level in 2015. Fifteen biodiversity conservation priority areas were at the slight level, which covered 84.10% of the total area. Thirteen biodiversity conservation priority areas were at the mild level, which covered 6.65% of the total area. Regions of North China Plain and Loess Plateau and Middle-East China hilly and plain region were at the highest level of human disturbance. The mountains and canyons in the southwest, the desertification region of the Inner Mongolia and Xinjiang plateau and the Qinghai-Tibet Plateau were at the very low level of human disturbance. (2) Human disturbance indices of the most regions basically did not change from 2010 to 2015, which covered 84.54% of the total area. The area where human disturbance index increased was equal to that where human disturbance index decrease. Overall, the change of biodiversity conservation priority area was not obvious. The human disturbance index of the hilly area in South China and mountain and plains in the northeast experienced the highest growth and increased over 1%. The human disturbance index of North China Plain and Loess Plateau suffered the sharpest drop and decreased by 0.15%. (3) These regions with higher levels and larger variations of human disturbance degrees were mainly affected by the ecosystem disturbance caused by economic development and the intensive land use due to the increase of the demand for cultivated land, residential land and industry and mining land. Some ecological projects including Grain for Green project were favorable for reducing the ecosystem disturbance caused by human activities. Overall, human disturbance indices of thirty-two biodiversity conservation priority areas of China were very low and varied within a small range. Furthermore, regions with high human disturbance indices have comparatively concentrated distribution. These indicated that the implementation of the national ecological protection policies and measures played a good role in improving biodiversity protection.
Potential Evapotranspiration (PET) is one of the important factors in the study of evapotranspiration (ET) and local water resources. Accurate PET dataset is crucial for improving our understanding of basin-scale hydrology, agriculture and earth sciences. In this study, ET data measured at 12 stations during the period of 2000-2009 were used to simulate the PET in Heihe River Basin by using a new surface modeling method, so called High Accuracy and high Speed Method (HASM), which has been successfully used in the construction of Digital Elevation Model (DEM) and the studies of ecosystem changes. The relationship between PET and climatically/topographical variables was explored, and a polynomial regression model for PET was developed. The stepwise regression method was used to find an optimal subset of influence factors for PET. Finally, polynomial regression and residual interpolation using HASM were employed to develop a gridded ET dataset for Heihe River Basin. The simulation results of our method were compared with other potential evapotranspiration datasets including the "Simulated forcing dataset of 3km/6 hour in 1980-2010 in Heihe River Basin" and the interpolation results by using Kriging, IDW and Spline. Results showed that HASM exhibited good performance in ET simulation. Accuracy tests revealed that HASM was superior to other datasets. Therefore, HASM can be considered as an alternative and accurate method for PET interpolation in Heihe River Basin. As the basic geographical data, PET produced by HASM can be used for other applications.
The increase in the frequency and intensity of extreme heat events (EHEs), potentially associated with climate change in the near future, highlights the importance of heat health risks assessment, which is an important starting point for the reduction of heat-related mortality and sustainable development. However, there is a spatiotemporal mismatch between hazard data (at pixel-level) and exposure data (at census unit level) in heat health risks assessment. Based on multisensor remote sensing data and demographic and socioeconomic statistical data, we used a human settlement index to assess heat exposure. Heat health risks and its driving factors were spatially explicitly assessed and mapped at the 250 m × 250 m pixel-level across the Yangtze River Delta region (YRD). The visual inspection suggests that the high risks areas were mainly distributed in urbanized areas of YRD, including the downtown of Shanghai, Changzhou, Hangzhou, Ningbo, Wuxi, Jiaxing and Taizhou, which was mostly driven by the high human exposure and heat hazard index. In less-urbanized cities and the suburban and rural areas of mega-cities, the heat health risks come second. Even though the human exposure index was low in other less-developed areas, the heat health risks in those areas were high due to the high heat hazard and human vulnerability index. It's of great importance to identify the driving factors of high heat health risks to provide science-based support for adaptation strategies and emergency planning.
Urban heat island effect has been widely concerned as a typical climatic feature of urban area in recent years. Understanding the spatio-temporal evolution and the causes of the formation of urban heat island are of great significance to ease the thermal environment of the city and improve the comfortability for human settlement. Firstly, we retrieved the land surface temperature of Beijing downtown based on Landsat thermal infrared images in 2005, 2010 and 2016. The mean-standard deviation method was used to divide the land surface temperature to obtain multi-grade heat island intensity for analyzing the evolution law and spatial pattern of urban heat island. Secondly, four typical ground feature types, impervious surface, vegetation, bare soil and water body, were extracted. Also, the transfer information of each heat intensity in different years and the relevant thermal landscape pattern indices were calculated. Then, according to the distance from the center of the city, we divided the downtown into 30 ring buffers and analyzed the area ratios of ground feature types and the information of heat island intensity in each ring buffer. Finally, based on the statistical data of each ground feature type and the distance from the downtown, the relationship between the influence factor and the heat island intensity is established. Also, the influence of the ground feature types and the change of the distance on the urban heat island is comprehensively analyzed. The results showed that the overall heat island intensity is increasing every year in Beijing downtown. The high grade of heat island intensity of the thermal patch area gradually expanded. The diversity of the thermal landscape types is decreasing with time. Impervious surface has a great impact on the heat island intensity. The strength of heat island intensity become greater with the percentage growth of impervious surface. The strength of heat island intensity decreases gradually with the increasing distance from the center of the city.
The study of optimum population size based on urban comprehensive carrying capacity has always been a hot issue in population geography. With the accelerating of China's urbanization, the population is gathering continuously in the city while environmental pollution, traffic jams and other "city diseases" have become increasingly prominent. City sustainable development is restricted by social economic factors and natural resources factors which is an important guarantee to maintain the population quantity in the range of carrying capacity. The concept of UCC (Urban Carrying Capacity) provides a powerful theoretical basis and quantitative means for government planning and calculating the optimum population size. As the political, economic and cultural center of China, the shortage of resources, traffic jam and deterioration of ecological environment are becoming more and more serious in Beijing. It's significant to determine the scale of optimum population for guiding the population floating and environment protection. However, the research results about optimum population scale of Beijing are few. Based on comprehensive carrying capacity, this paper constructed an appropriate population measurement model and synthetically analysed the scale of optimum population and carrying population of Beijing from 2004 to 2014. Results indicated that: (1) The scale of ecological carrying population which is in a state of population overload has an increasing trend; (2) Economic carrying population scale has been decreasing for 15 years. The output value of tertiary industry contributed most which reached 65.15% in 2014; (3)Resources carrying population scale is in a state of fluctuation while its overload rate increased year by year. The contribution of land resources carrying capacity is greater than that of water resources which reached 79.91% in 2014; (4) From 2004 to 2014, the optimum population size of Beijing decreased from 20.31 million to 15.51 million and start a demographic deficit in 2008. Considering its contribution factor, resource constraint has become the main factor hindering the development of Beijing, and the economic carrying capacity is the main driving force to maintain the appropriate population size. The conclusions of this study can provide a reference for the formulation of rational population management decisions and urban sustainable development planning.
Classification and regression tree (CART) algorithm was used to extract the impervious surface percentage (ISP) of Beijing six ring within the city in 2001 and 2011 based on QuickBird high-resolution images, Landsat TM and night light data. The method is suitable for typical temperate semi-arid climate area. We classified the ISP as three groups. The ISP region for 10%~60% is defined as a low-density region, 60%~80% is defined as a medium-density area and for the rest area which the ISP is more than 80% is high-density area. Meanwhile, based on the Landsat TM, the land surface temperature of 2001 and 2011 were retrieved by using the single window algorithm. The study area has been designated as a region within six rings of Beijing. According to the characteristic of Beijing urban layout, this paper analyzed the development trend of ISP in different ring road and its correlation with land surface temperature from 2001 to 2011. In order to seek an effective way to improve the ecological environment in Beijing and mitigate the urban heat island effect. The results are as follows: (1) The change of ISP in Beijing urban area mainly concentrate in the low-density area. In comparison with the low-density area, the ISP of middle-densitiy area and high-density area does not change so much. Due to the vigorous city construction within the fifth ring road from 2001 to 2011, the whole change of ISP is not obvious. The variation mainly concentrates in the region between the fifth ring road and the sixth ring road, in which the growth of low density area was significant, while the growth in the middle and high density area was mainly in the east part. From the above results, it can be concluded that the development within the fifth ring road and sixth ring road develops rapidly and the range of city construction continued in recent years. (2) Compared with 2001, the land surface temperature of the central areas in Beijing in 2011 increased dramatically, and the aggregation extent of high-temperature region is more evident. The temperature difference increased significantly between the region within the fourth ring road and the surrounding areas. (3) By comparing the average surface temperature of each density area in 2001 and 2011 we find that compared with 2001, the differences in land surface temperature in 2001 also increased between different ISP categories and the urban heat island effect was more and more remarkable. (4) In both 2001 and 2011, there is a positive correlation between the land surface temperature and the ISP in every ring road region of Beijing urban areas. In the regions between the fourth and sixth ring road, the land surface temperature and the ISP shares a similar change trend. In the regions with ISP between 10% and 20%, the rising rate of land surface temperature is obviously higher than other regions. In the regions with ISP higher than 20%, the rising rate of surface temperature decreases, and the change tends is uniform.
Environmental problems caused by sewage sludge are the major limiting factor for its large-scale application. Based on weighted linear combination (WLC), an environmental risk control method for land use of urban sludge was established. Also, an environmental risk control scheme of urban sludge application was proposed in Beijing. Environmental effects of different factors of sludge application were quantified: soil heavy metal concentration (0.22) > land use type (0.17) ≈ soil type (0.17) ≈ natural rainfall (0.17) > slope (0.13) > distance from natural water (0.09) > distance from urban residents (0.05). High-suitable area for sludge application in Beijing is mainly located in the junction of Pinggu and Shunyi district, central region of Changping and Yanqing district, and mountain forest land in the eastern part of Fangshan district. The usage area of high-suitable region is 2033 km2. Moderate-suitable application area is located in southwest and southeast regions, where land types are woodlands and drylands. The usage area of moderate-suitable region is 5079 km2, and usage area of low-suitable area is 380 km2. Low-suitable area is located in Shijingshan, northeast of Mentougou, southwest of Fangshan, and north of Pinggu. The prohibited area is 8916 km2 and it is mainly distributed in urban areas, suburbs of the city, and some other districts such as Yanqing, Huairou and Miyun.
The urban built-up area boundary is important basic information for urban studies, and is also the premise of the implementation of urban function space layout, the implementation of boundaries control. Accurate extract urban built-up area for urban construction, management and research has important guiding significance, but also reflects the city's comprehensive economic strength and the level of urbanization, one of the important indicators.The DMSP/OLS night light data has been widely used in the extraction of urban built-up areas. But due to the effects of saturated, diffuse, and low resolution problems, it is still a huge challenge to rely on the DMSP/OLS NTL mapping the urban built-up areas. In order to overcome the limitations of the data source itself, In this study, the application of hierarchical expert knowledge analysis, multi-source data extraction of the thematic information layer by layer into the extraction process, the construction of urban built-up area for the level of expert knowledge model to achieve the city built-area refinement extraction. The urban index （VANUI） was constructed by combining 250 m MODIS NDVI data with 1 km DMSP/OLS data. Based on the administrative boundary, the statistical area of the area is divided into the administrative boundary of each prefecture-level city, and the optimal segmentation threshold of each administrative unit VANUI feature image is calculated according to the regional segmentation method, so as to obtain 250 m urban boundary space information range. Meanwhile, Due to the low spatial resolution of the DMSP/OLS luminous data and the narrow range of light and light values, there is still a large gap between the optimal segmentation threshold and the built-up area. Therefore, this study proposed the maximum autocorrelation double threshold extraction method. The 30m Landsat 5 NDVI data were fused to obtain the maximum autocorrelation quadratic NDVI threshold in each 30m seed region by multi-scale segmentation of the regional threshold segmentation. According to the maximum autocorrelation threshold of each potential built-up area, each potential built-up area is revised one by one, and finally 30m urban built-up area is obtained. This paper takes the Beijing-Tianjin-Hebei region as the research area, the experimental results show that the total precision of extracting urban built-up area by multi-source remote sensing cooperative method is 92.9%, and it has higher validity and reliability in spatial distribution and statistical data. The results show that the results of the urban built-up area extracted by this method are not only the overall accuracy, but also the spatial extent of the visual interpretation, and the relative error of the statistical area in each prefecture-level city is small, which verifies the reliability and validity of the method in spatial distribution and statistical data, and avoids the error caused by subjective threshold selection. DMSP/OLS data can be used not only for urban area extraction, but also for the intensity and scope of human activities. Therefore, in the follow-up study, based on the identification of urban built-up area boundary, combined with the quantitative analysis of luminous data and evaluation of urban development area outside the expansion trend and internal dynamic changes for the DMSP/OLS luminous data to give full play to its effectiveness, Economic and historical values play a positive role in promoting.
As one of the important outcomes of the National Geographic Census of China, the land cover classification reveals the information of both natural and artificial coverage elements, including vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various artificial structures. Obviously, it mainly focuses on profiling the natural characters of the land surface with temporal and distribution attributes, which has an obviously different classification system from other scene classification applications. In recent years, more and more high-resolution remote sensing platforms become available, it is possible to update and evaluate land cover classification quickly with the advantage of huge volume of data and more frequent data updates. Meanwhile, in practice we face with more and more challenges of the huge data. In this paper, we propose a novel approach for evaluating the land cover classification results by combining the object-oriented method with the Deep Convolutional Neural Network (D-CNN) model. With deeper structure and wilder receptive field, the deep neural network has the capability of abstract description from low-level features, and the deep learning has become one of the latest development trends in the artificial neural network field. The deep learning shows a completely different possibility in many fields, and it has been applied to the speech recognition, image recognition, information retrieval and so on. The newly-developed method of image recognition based on deep leaning has been preliminarily verified in the scene classification field. Traditionally, the land cover classification method is established on the pixel-based classifying. The latest improved method of the object-oriented classification frame has been proposed, but this new frame is hard to be achieved because of the lack of supports from efficient methods and algorithms. Nowadays, the deep neural network provides us an effective tool to achieve the object-oriented classification by clipping image spots from original images and inputting the clipped image spots to D-CNN. The D-CNN model can convolute and pool the image spots to realize the object-oriented classification of the land cover. By the combination of the object-oriented classification with the deep learning, the proposed method can extract more and better abstract features than the pixel-based approach, while the pixel-based method requires more manual interventions. When applying the deep learning method to land cover classification recognition, the prepared image spots as appropriate inputs will be automatically scored to its belonging classes. Thus, the score represent the degree of membership of the image spot matching to the corresponding class. By fine-tuning the D-CNN, we can obtain a new approach of judging the quality of the samples, in order to assure the reliability of the proposed approach. The fine-tuned D-CNN is required to be sufficiently robust, and we verify its robustness in the following experiment by employing the AlexNet. The experimental results show that the image spots of arable land and building can be recognized with the membership degree of 99.95% and 99.41%, but those of woodland and water area can be recognized only with the membership degree of 62.73% and 43.59%. Obviously, the proposed model can achieve the promising reliability that is related to the qualified and sufficient data set of the image spots which is used for fine-tuning of the net. The reason for poor robustness of the fine-tuned AlexNet in classifying the woodland and water area may be the insufficient size of data-set of these two classes. It shows that a fine-tuned deep convolutional neural network as a new model can be utilized in evaluating the land cover classification with high reliability.
Plantation refers to the land of perennial woody and herbaceous plants that are planted to collect fruit and leaves, including the land used for nursery. Plantations in southern China is widely distributed, and the types are mixed and diversified. As a result, it is difficult to obtain the information of the garden distribution, which has caused great difficulties for agricultural management. In order to improve the classification accuracy of the remote sensing images of the plant species, using Landsat8 OLI data, we took the random forest algorithm to construct object-oriented plantation classification rule set based on data fusion and feature optimization. This was used to classify the banana, citrus, grape, livistona chinensis, phoenix dactylifera, carica papaya, hylocereus undatus and so on, which are typically planted in southern China. At the same time, we compared the classification effects of Bayesian classification, K nearest neighbor classification, support vector machine method, decision tree classification method and random forest classification, and it verified the applicability of the object-oriented random forest approach for the classification of the garden types in medium-resolution images. Then, based on the clustering matrix of the classification results of random forest classification and the feature distance matrix between two categories, we analyzed the reasons that affect the classification accuracy of the garden by combining the image conditions and field survey results. Finally, we compared the characteristic difference of typically easy-to-mix land before and after data fusion, and evaluated the effect of data fusion on the classification of garden. The results show that: after the data fusion, the difference of the characteristics of water body and aquatic vegetation is reduced, and the spectral difference of livistona chinensis and phoenix dactylifera is reduced in some bands. Although, the data fusion can improve the image spatial resolution, to a certain extent, it weaken the spectral differences of objects. Thus, it would affect the classification effect based on spectral information. The plant morphology and spectral characteristics of the plantation land are close to each other, the planting period of the plantation land is intertwined and there are important factors that affect the classification accuracy of typical garden in southern China. Based on the medium resolution images, the object-oriented random forest algorithm for plantation classification can reach 88.05% and Kappa coefficient of 0.87, which can effectively distinguish the typical plantation land types in South China. The classification results of object-oriented stochastic forest algorithm show that the area of various plantations in the study area: banana covers an area of 700.2 hectares, citrus covers an area of 981 hectares, livistona chinensis covers an area of 81 hectares, phoenix dactylifera covers an area of 68.04 hectares, carica papaya covers an area of 93.24 hectares, hylocereus undatus covers an area of 167.4 hectares, and grape covers an area of 16.2 hectares. For the classification accuracy of the coverage types, the random forest classification was higher than that of the other four algorithms except grape and hylocereus undatus. The random forest algorithm has certain advantages in classification accuracy, reliability and stability, and can provide scientific basis for crop growth monitoring and planting management.
