We proposed the line-segment-matching approach with the constraint of local point-line affine invariance, to seek the solution to the fracture in straight lines extraction, the inconsistency of image scale, and the weakness of the gray similarity constraint in the texture fracture. Firstly, the corresponding triangulation network is established by the corresponding points which are obtained using SIFT(Scale-Invariant Feature Transform) matching algorithm in the references and searching images, at the same time, the candidate straight lines are obtained by triangulation network which searching range is constrained in the process of straight lines matching; Secondly, the direction constraint is used to perform secondary screening for the candidate straight lines in order to filter the candidate straight lines with obvious error. It can not only get further filter results of triangulation network constraint, but also provide a solution for angle transformation which caused by image rotation in the process of straight lines matching. The direction angles of each characteristic point of the reference image and the searching image are calculated separately, and the angle histogram is established according to the statistical results. Angle difference corresponding to the maximum peak of two histograms is called rotation angle. Finally, the support regions with the center of target straight line segment in the reference image and the corresponding support region with the center of candidate straight line segment in the searching image are both determined, and then the matching points in the support region are determined, after that the matching points are divided based on the straight line, meanwhile the corresponding straight line is determined according to subregional constraint by the principle of point-straight line affine invariance. Using proposed algorithm to perform straight line matching experiments in the typical image pairs which are selected from the online public image database, and the experiment results show that the proposed algorithm has better robustness and it can obtain reliable straight line matching results. What's more, instability problems caused by many factors exist in other algorithms of straight lines matching are improved by our algorithm.
Contour line is used to express surface information through curve cluster. The degree of topography change can be reflected based on the similarity between multi-source contour data. Therefore, the similarity measurement of contour groups is an essential step in the map partial renewal, multi-source data merging and cartographic generalization of topographic maps. Previous measurement methods are mainly based on measuring the single topological feature or geometric feature. Due to the complexity of geospatial data and the diversity of geographic elements, the existing methods may not completely reflect the similarities and differences between multi-source data, which may cause inconsistencies in areas with intensive contours or extreme terrain changes and map boundaries in incremental renewal application. For this reason, the spatial similarity theory is introduced and the similarity structure of contour group is built. Through analyzing the relationship and mechanism of the topological relations and geometric features, the hierarchical structure of contour group similarity is constructed, and the mutual relationship and similarity measurement methods of each influencing factor are discussed. Based on the hierarchical structure, a mixed similarity measure model using topological relation tree and geometric similarity measures is proposed. In the mixed measure model, the weight coefficients are calculated based on the analytic hierarchy process. Simulated and real datasets experiments are used to verify the reliability and validity of the similarity measure model proposed in this paper. The experimental results show that: (1) The mixed similarity measure model can quantitatively describe the similarities and differences between contour data from different scales and sources. (2) According to the relationship between the mixed similarity measure results and the update thresholds, partial renewing is applied to the changing areas that meet the update requirements. The accuracy test shows that the proposed similarity measure method has a good validity and reliability.
Observations with high spatial resolution and frequency can better monitor land surface dynamics, such as urban heat island effect monitoring, normalized difference vegetation index, leaf area index assimilation. However, it is still difficult to acquire satellite data with high spatial and temporal resolution from one single satellite sensor. For example, Landsat TM/ETM+/OLI data at 30 m spatial resolution have been widely applied for various applications, however, their 16-day revisit-cycles limit their usage in dynamics monitoring; on the other hand, MODIS data can be widely used in land process monitoring at global or large-scale because of their daily revisit period, but the coarser spatial resolution of MODIS data cannot meet the fine-scale environment applications. The spatio-temporal fusion is an effective way to solve this trade-off problem. The spatio-temporal fusion methods can be grouped into linear model methods (STARFM, ESTARFM), unmixing methods (FSDAF, STDFA) and so on. These methods have been used to support various applications such as leaf area index assimilation, urban heat island effect monitoring, land surface temperature generating, crop types and dynamics mapping, land cover classification, and land surface water mapping. The GF-1 satellite was launched from China's Jiuquan Satellite Launch Center in April 2013. It carries two panchromatic cameras with pixel resolution of 2 m and two multi-spectral cameras with pixel resolutions of 8 m, and four wide-field view (WFV) cameras with 16 m pixel resolution. The WFV sensors capture ground features with four bands that cover the visible and near-infrared wavelength range and the swath width reaches 800 km when the four sensors are combined. The spatio-temporal fusion researches on GF-1 WFV are still insufficient, hence, in this study, we used four spatio-temporal fusion methods including STARFM, FSDAF, STDFA and Fit_FC to blend GF-1 WFV and MODIS data, and then the fusion validation and accuracies were analyzed for further researches.
China's rapid urbanization has caused a large number of migrants to move to the city, which has also led to housing shortages. Rapid access to fine-scale house price distribution data plays a very important role in urban housing management, government decision-making, and urban economic model analysis. The availability of data and limitations of existing models make only a few studies involving the mapping of house price distribution at the microscale. By combining house price data with remote sensing images, this study builds a remote sensing image features mining model based on Convolutional Neural Network (CNN) and Random Forest (RF). The proposed CNN-based model in this paper can be applied for accurate and reasonable microscopic mapping of house prices without introducing auxiliary geospatial variables. Only using the house prices data and remote sensing images, we successfully carry out the house prices mapping with the precision of 5 meters in the downtown area of Wuhan city. By comparison with the results generated by the other three traditional mining techniques (including A: using spatial datasets extracted from auxiliary geographic dataset only, B: using original features extracted from high-resolution remote sensing images only, C: using original features extracted from high-resolution remote sensing images and auxiliary geographic dataset), the results show that the proposed CNN-based model has the highest house price simulation accuracy (R2=0.805), at least 23.28% higher than the fitting accuracies of the traditional methods (A: R2=0.592, B: R2=0.0.434, C: R2=0.653). Moreover, based on the fine-scale house price map, this study further analyzes the spatial heterogeneity distribution of housing prices in the downtown area of Wuhan city. By comparing the partial and overall similarity of the simulated house price distribution map calculated via the perceptual hash algorithm, the results also demonstrate that the house prices distribution of Wuhan city has remarkable fractal characteristics. The micro-scale house price data obtained in this study can provide a basis for microeconomics and fractal research in the urban economics. Meanwhile, this study also provides a brand-new research method for micro-scale economic analysis and resource optimization of large cities in China.
Accurately quantifying spatial pattern of built-up areas is of great significance to analyzing the ecological and environmental impacts of the built-up landscape and planning for regional development. In this paper, we used the Global Urban Footprint (GUF) data with 12 m spatial resolution in 2012 to analyze the spatial pattern of built-up areas in China at three scales, i.e., the national, economic zone and urban agglomeration scales. Specifically, we chose six landscape metrics, i.e., total area of the built-up area, percentage of the built-up area of the landscape, number of patches, patch density, landscape shape index and mean Euclidean nearest-neighbor distance to measure spatial pattern of the built-up areas. Then, we explored the relationship between spatial pattern of built-up area and socioeconomic variable at different scales. The results showed the 12 m GUF dataset can delineate the built-up area in China with higher accuracy and more details, compared to previous coarse resolution datasets. The built-up areas reached 1.73×105 km2 in 2012, accounting for 1.81% of the total land area in China. At the economic zone scale, more than half of built-up areas concentrated in three economic zones, the Northern Coastal region, the Middle Reaches of the Yellow River and the Eastern Coastal region. From the perspective of the spatial pattern of the built-up areas, the fragmentation of built-up areas was highest at the urban agglomeration scale. The mean patch density at the urban agglomeration scale were 3.66 and 1.62 times as large as those at the national and economic zone scales. The results of correlation analysis indicated that population and economic level played important roles in influencing the spatial pattern of built-up areas. The number of patches and the degree of fragmentation for built-up areas increased with the amount of urban residents, gross domestic product and investment in the fixed assets. The correlation coefficients between these two sets of measurements ranged from 0.55 to 0.94 (P<0.05). In the future, we should make place-based plans to solve the fragmentation of the built-up areas and to promote a rational development of built-up areas in China.
As an important part of regional ecology, the thermal environment of urban agglomeration has become a research hot topic in recent years. How to choose the thermal environment quantification mehod for the complex geomorphological features of urban agglomeration has been a difficult technical problem to be solved. Based on this, This study proposes a solution to multi-sample, nonlinear, non-stationary and high-dimensional function fitting. The calculation method is established, and the thermal environment surface model of Beijing-Tianjin-Hebei urban agglomeration based on support vector machine (SVM) is established to reveal the temporal and spatial morphological changes of the thermal environment of urban agglomeration. The results show: ①that the SVM model has theoretical and practical feasibility in describing the spatial distribution of the thermal environment of urban agglomerations with multi-core and multi-land-use types. It can optimize the differences locally through the Gaussian Kernel Function according to the overall spatial distribution of the thermal environment, and minimize the impact of default values on the fitting results of the model. Comparing with the control method, the spatial distribution pattern of heat island in urban agglomerations with complex geomorphologic features can be simulated with higher accuracy. ② In the process of fitting the surface of SVM model, accuracy and the time of fitting are important indexes to measure the results, and original image resolution is the decisive influencing factor. ③ In 2003-2013, the most obviously change regions of urban heat island effect are Beijing and Tianjin. The heat island area of the two cities increased by 7091 km square and 4196 km square, respectively. The spatial trend was developing continually year by year, and the trajectory of gravity center of the heat island had obvious spatial and temporal variations. Beijing's urban heat island is characterized by uneven growth in the southeast and slow growth in the west, while Tianjin's urban heat island is characterized by the expansion of city center to the surrounding areas. This study further enriches the quantitative methods of urban thermal environment assessment, and can provide quantitative and visual decision supports for urban agglomeration planning, urban construction, environmental protection and regional sustainable development practically.
Understanding the spatial pattern and dynamic processes of vegetation changes and their causes is one of the key topics in research on global change of terrestrial ecosystems. Characterized by vulnerable alpine vegetation, which is sensitive to external disturbance, the Qinghai-Tibet Plateau is one of the ideal areas for studying the response of vegetation to climate change. It is necessary to investigate the impacts of climate change on vegetation in a short synthetic period because of the intense climate variations in the Qinghai-Tibet Plateau. Previous studies have not sufficiently investigated NDVI change comparisons between various periods and the persistence of NDVI trends. In this study, we investigated monthly vegetation dynamics in the Qinghai-Tibet Plateau and their relationships with climatic factors over 15 progressive periods of 18-32 years starting in 1982. This was accomplished by using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data. The NDVI time-synthesis method of each season masks the trends of NDVI variations within the single month. Except for August, vegetation increased in other six months, with a significant increase occurring in April-July and September. The increase rate of NDVI in most months decreased significantly with the extension of the period, indicating that the increasing trend of NDVI slowed down. At pixel scale, the regions with significant changes (including both increase and decrease) in NDVI showed increasing trends in most months, but the range of significant decreases in NDVI expanded faster than that of significant increases. Vegetation activities in the Qinghai-Tibet Plateau are generally controlled by temperature changes, but the dominant climatic factors affecting vegetation are varied in different months and regions. The vegetation activities in April and July were mainly promoted by temperature and sunshine hours, and those in June and September were controlled by temperature, and in August were mainly affected by precipitation. The emergence of long time series NDVI data sets provides a precondition for application of nested time series to study the trend analysis of vegetation growth and change. The persistence of the trend of vegetation activity may help to visualize the process of vegetation change, understand the vegetation response to climate change, and to predict thevegetation growth trend. It is inferred that the increases of NDVI in the future tend to be more moderate in general, but areas with significant pixel-scale changes in NDVI tend to increase in most months.
Using big data to analyze the spatial pattern of urban service facilities has become a new research hotspot, and catering industry is a typical representative of urban service industry. Therefore, it is of great significance to study the spatial layout of urban catering industry through open source big data. The restaurants in a city can be abstracted as point objects in the geographical study , and clustering analysis is a classical data mining method that quantificationally identifies geographical clustering among objects. In this paper, Beijing is selected as the research area, and the data of 153 895 restaurants in Dianping.com are obtained by using web crawler technology. The density-based CFSFDP clustering algorithm (clustering by fast search and find of density peaks) is adopted here to analyze the geographical clustering characteristics of catering industry in terms of spatial distribution density and per capita consumption level. This approach, which is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively larger distance from points with higher densities, can recognize clusters regardless of their shape and the dimensionality of the space in which they are embedded, so more accurate spatial analysis results can be obtained. The results show that: (1) the spatial pattern of Beijing restaurants is imbalanced, which generally presents the characteristics of multi-center spatial distribution, and the agglomeration degree of restaurants decreases with the distance increase from the main urban area which are regarded as the core. Besides, the restaurants hot spots mainly circles around important business centers, tourist attractions as well as residential areas, and extends along the traffic line evidently. (2) The catering stores with different per capita consumption levels have the characteristics of hierarchical system. That is to say, there are the number of high-grade restaurants is few, and mainly concentrated in the commercial centers, financial centers and famous tourist attractions in Dongcheng district, Xicheng district, Chaoyang district and Haidian district, while the number of middle and low-grade restaurants is much more and their spatial distribution are more scattered. (3) The density and price of restaurants accords with consumption level of consumers. At the same time, this paper also analyses the factors influencing the spatial distribution pattern of catering clusters by combining the two indicators of spatial agglomeration characteristics and consumption level, in order to provide useful reference of urban commercial spatial layout for the government planning departments.
With the advancement of urbanization, natural land cover has been continuously replaced by impervious surface which resulted in the phenomenon of Urban Heat Island (UHI). UHI can lead to serious negative effects on urban ecology and residents' health, so is of great significance to study the corresponding spatial pattern and dynamic change. Based on 3 summer Landsat images acquired in 2001, 2007 and 2016 of Wuhan, this paper retrieved Land Surface Temperature (LST) using Radiative Transfer Function (RTF) method and verified the results by MOD11A1 which is the daily LST product of MODIS. Furthermore, LST grade and UHI ratio index (URI) were calculated to analyze the corresponding spatial-temporal variation. We also explored the relationship between LST and impervious surface. Globally, the multiple linear regression method was applied to compare the heating effect of impervious surface with the cooling effect of vegetation and water. Locally, we used Geographically Weighted Regression (GWR) to analyze the spatial-temporal variation of the heating effect of impervious surface combined with topographic data. The results indicated that: ① RTF method is suitable for retrieving LST in the study area. URI of Wuhan ascended from 0.42 in 2001 to 0.54 in 2007, and then descended to 0.51 in 2016. However, the areas with high temperature are still expanding; ② The multiple linear regression achieved a desirable fitting accuracy with R^2 being 0.910 because it covered the impact of 3 land cover types on LST simultaneously. Overall, the heating effect caused by impervious surface in Wuhan is stronger than the cooling effect caused by vegetation, but weaker than the cooling effect caused by water; ③ From 2001 to 2016, the distribution of areas with high heating effect of impervious surface showed a trend from "single center" to "multi-center". The original single center which is located in the center of city expanded to multiple center areas covering the districts near the Third Ring Road such as Hanyang Zhuankou Industrial Area, Qingshan Industrial Area, Yangluo Open Economic Zone and Dongxihu District. Therefore, the UHI phenomenon in Wuhan is still serious in summer. The heating effect of impervious surface is intensifying in suburb areas. So urban planners should pay more attention to these areas to mitigate the heat stress.
In this paper, we propose a methodology to reconstruct the individual-tree models from Terrestrial Laser Scanning (TLS) point clouds via skeleton-based optimization. The proposed method is a data-driven method, and in theory, it can generate any geometric shapes of different tree species. Mathematically, the salient points reflected from trunks and remarkable branches have been successfully recognized by using statistical analysis method. Then, we organize the raw points via an undirected graph, from which the initial skeleton of individual tree is created by using the Minimum Spanning Tree (MST) algorithm under the constraint of salient points. The initial skeleton tree models are further enhanced and refined through a series of optimizations, i.e., point density adjustment and branch smoothing. The tree skeletal structure is inflated into a tree model by simultaneous combination of a robust cylinder fitting method and allometric model. The tree leaves are finally properly added into the tree models, thereby enhancing the photorealistic representation of the geometric tree models. Various experiments on different tree species captured at Nanjing Forestry University show that the proposed methodology is insensitive to the point density and data missing, and meanwhile can generate meaningful and accurate individual geometric tree models. In addition, it is to be found that our modeling tree algorithm based on salient points and sampling strategy can reach the optimal computational efficiency, compared to that only using the salient points or the original laser scanning tree points as inputs. This enhancement in the efficiency can significantly expand our current individual tree modeling into the reconstruction of large-scale scanning scenes.
As an important part of forestry economy and an important physiological structure of photosynthesis, tree canopy is also an important forest parameter and stand factor in the process of forest inventory such as growth monitoring and tree species identification. Accurate extraction of tree crown information can effectively provide support for forest inventory. With the development of remote sensing technology, the disadvantages of traditional forest survey methods are obvious, and they are gradually developing towards the direction of combining remote sensing information extraction with traditional forest inventory. In order to improve the accuracy of remote sensing information extraction, people began to use high spatial resolution remote sensing images, combined with computer automation technology, to develop remote sensing information automatic extraction methods. As the demand of the automatic extraction of remote sensing information continuously strengthen, based on high spatial resolution remote sensing data in Chuzhou HuangFu Mountain tree farm field as the study area, we proposed a method that combination of Gabor wavelet and morphology of canopy extraction. First we extracted texture features by Gabor filter, K-means clustering analysis was used to extract dense forest area from the texture feature by PCA (Principal Component Analysis) extracted broadleaf forest region, based on the morphological theory to reduce image noise, and used the prospect foreground markers of the watershed method extract individual tree crown. After comparing with the artificial interpretation canopy information found that the canopy precision automation method extracted in the dense forest area, segmentation accuracy is 79.59%, F measure reached 79.00%, and can accurately provide individual tree crown information, it has a certain practical significance of the development of forestry economic survey technology.
With the development of rural economic construction, the pollution problem caused by domestic waste and industrial solid waste has become increasingly prominent, which has become a key problem to restrict the construction of the new rural developing and ecological civilization. At present, the investigation and statistics of informal solid waste in rural areas mainly depend on the reports of departments of each township step by step, and the workload is large. So based on high-resolution remote sensing images, this paper combines Deep Learning model with Conditional Random Field model to the study of rural solid waste extracting, and explores a recognition and extraction model of rural solid waste based on Deep Convolution Neural Network. Due to the solid waste in images is characterized by small size, distribution ,fragmentation and so on, in order to improve the efficiency, the model is divided into two parts: Recognition and Extraction. In the first part, a Full-connected Convolution Network (CNN) is used to identify and judge solid wastes quickly, and the image blocks include the interesting regions are screened. In the second part, Conditional Random Field model (CRF) is added to the traditional Full Convolution Neural Network (FCN) to extract boundary of solid waste and improve the overall segmentation accuracy.According to the relevant reports about solid waste of some rural areas in Anhui and Shanxi province and the field inspection by the urban and rural planning and management center of the Ministry of Housing and Urban-Rural Construction, Compared with the test results of the model in this paper,the results show the recognition accuracy is 86.87%,the shape extraction accuracy is 89.84%,and the Kappa coefficient is 0.7851. So the recognition and extraction accuracy of the paper's method is proved to be superior to the traditional methods. At the same time, this method has been gradually applied to the investigation of informal solid waste in countryside in Chengdu, Lanzhou, Hebei and other provinces, and achieved satisfactory results.
As an important ecological resource in coastal zones, tidal flats are of great significance for environmental protection and regional sustainable development. Additionally, tidal flats are sentinels of regional and global changes and thus have recently become a focus of academic research. However, the accessibility of tidal flats is poor because tidal waters periodically inundate them. This makes it difficult to employ traditional measurements to monitor such highly dynamic environments. Accordingly, remote sensing observation has become a potential choice. Yet, remote sensing observations also face great challenges because of influences from imaging modes, atmospheric conditions, and tide conditions. For this research, we selected the Yangtze River Delta, which is covered by a single full scene of Landsat image, as the study region, specifically including parts of the Jiangsu coast and the Yangtze River estuary zone. A collection of the Landsat images with the lowest tidal level in each year from 1975 to 2017 was selected as the main data sources. Meanwhile, to extract tidal flat data, we choose the waterline as the outer boundary and the vegetation line or reclamation dyke as the inner boundary. Then, ArcGIS software was used to acquire and analyze the temporal and spatial variability of the tidal flat data. The results show the following: (1) The tidal flat area in the study region has generally decreased since 1975. The area remained generally stable before 1990s, but after 1990s, the decline was evident. The tidal flat areas along the north of the Yuantuojiao Point and the Yangtze River estuary zone reached their maximums around 1995, at 1101.2 km2 and 1495.5 km2, respectively, but were 649.5 km2 and 1043.4 km2 by 2017. This indicates an overall decreasing rate of 21.7 km2/a. (2) Reduced riverine sediment, reclamation, and estuarine engineering projects, such as the Deep-water Navigation Channel project, are the main controlling factors in tidal flat shrinkage in the study region. The results provide a holistic perspective on the evolution of the tidal flats in the Yangtze River Delta, which may be helpful for coastal zone management and planning.
The Longshoushan uranium metallogenic belt in Gansu Province is an important uranium metallogenic belt in China. Jiling uranium deposit is a representative alkali-metasomatism type uranium deposit in Longshoushan metallogenic belt. There are a wide variety of alterations in Jiling deposit. And these alterations have close relation to the uranium mineralization. The airborne hyperspectral technique can be used to obtain the surface alteration, structure and lithology distribution information of Jiling uranium deposit from a macroscopic perspective, which provides a basis for the uranium and polymetallic mineral exploration in Jiling deposit and its adjacent area. In this article, CASI/SASI/TASI airborne hyperspectral remote sensing techniques have been applied to the identification of hydrothermal alterations in Jiling alkali-metasomatism type uranium deposit as well as its adjacent district in Longshoushan area, Gansu Province. A variety of alteration minerals have been identified including alkali-feldspar, hematite, tremolite, medium-Al sericite, kaolinite, quartz and so on. These minerals are closely related to the alkali-metasomatism hydrothermal action in Jiling deposit. Besides, comprehensive analysis on alteration mineral, structure and lithology information in Jiling uranium deposit has been made. Study shows that the alteration minerals such as alkali-feldspar, tremolite, medium-Al sericite and quartz separately represent the different stages of hydrothermal alteration process in Jiling uranium deposit and its adjacent area, namely the early alkali-metasomatism stage, the middle neutral-metasomatism stage and the late acid-metasomatism stage. The main channel for alkali-metasomatism hydrothermal action is the composite of regional unconformity surface, deep and large faults, and contact zones of different lithologic units. The uranium mineralization zone in Jiling deposit is controlled by Malugou fault. In the uranium mineralization zone, tremolite, medium-Al sericite, and silicification are evident. And these alteration minerals have close relation to alkali-metasomatism in Jiling deposit. According to the airborne hyperspectral remote sensing charateristics in Jiling deposit, the main prediction criteria for the prospecting of alkali-metasomastism type uranium deposits in the Longshoushan Mountain are proposed. These criteria are of great significance for the prediction of new favorable uranium exploration areas and the new evaluation of old uranium mineralization stations and anomalies in the Longshoushan area.
The land use structure and its spatio-temporal evolution characteristics within cities can not only reflect the level of urban economic development, but also predict the future development space and development potential of the city and even directly affect the suitability of the urban human settlement environment. There are significant differences in the internal land use structure of cities in different stages of development and different regions. In this paper, we used object-oriented multi-scale segmentation algorithm to carry out long-term remote sensing monitoring on the internal land cover structure of Wuhan main urban area based on the Landsat TM/OLI remote sensing images of 1990, 2000, 2010 and 2018. Then, the land use patterns in Wuhan were monitored, and the characteristics of land use structure changes during the 1990, 2000, 2010 and 2018 were further discussed. The research shows that: the internal land-use structure in the main urban area of Wuhan city is dominated by impervious surface, vegetation, and waterbody during the recent 28 years. The land use change pattern mainly reflects the transformation of vegetation and waterbody into impervious surface. From 1990 to 2018, impervious surface is the main type of land use in the study area, which increased from 329.73 km2 in 1990 to 466.69 km2 in 2018, which increased by 136.96 km2. The vegetation and waterbody area decreased from 332.74 km2, 318.26 km2 in 1990 to 320.46 km2, 196.39 km2 in 2018 respectively, which decreased by 12.28 km2 and 121.87 km2 respectively; bare land area is too small, and it's change is not as significant as the first three types of land changes. This study can provide reference for urban functional land mapping, urban land efficiency analysis and comprehensive evaluation of urban ecological environment quality.
