Terrain position is the basic morphologic feature on the surface of the Earth. The classification and extraction of terrain position have been widely applied in many research fields such as landform evolution, digital soil mapping and landscape ecological mapping. Proposed by Kang X et al. (2016), the multi-scale Geomorphons method maps terrain position by recognizing the morphology of each interest cell in a DEM according to its relative altitudes within the neighboring window. Multi-scale Geomorphons method can avoid the shortnesses of other classificaton methods, which are caused by different terrain attributes and a single analysis scale. However, there are still some drawbacks in the multi-scale Geomorphons method. For example, the classification results are fragmented and the domain of the analysis scale is difficult to determine. To solve the above problems, this paper presents a new method to classify terrain position, which is based on object-oriented segmentation and multi-scale Geomorphons. First of all, we propose an approach of determining the domain of optimal analysis scale of the multi-scale Geomorphons method. Then, the multi-scale segmentation and classification methods are constructed according to the initial terrain position data via the multi-scale Geomorphons method. At last, the presented method is evaluated by the experimental data of the DEM with 5 m resolution in the loess plateau region of northern Shaanxi. The experimental results show that: (1) the method of mean change-point analysis can effectively solve the problem which is difficult to determine the domain of the analysis scale of the multi-scale Geomorphons method. The domain of optimal analysis scale of the sample area is 5×5 to 33×33 cells. (2) The layer of each terrain position type with the value 0 for non-type cells and 255 for type cells is suitable for multi-scale segmentation. The parameters (i.e. scale, weight of shape and weight of compactness) for multi-scale segmentation have deep influence on segmentation results. There is optimal segmentation parameters for a experimental region. There is optimal segmentation parameter for an experimental region. (3) Comparing with the multi-scale Geomorphons method, the classification results of the present approach are more integrity and reasonable in the aspects of morphology correspondence and geological interpretation.
Affected by the differentiation of regional functions, the urban residents' activities presents specific timing characteristics. Different traveling patterns could indicate differences in regional functions. Meanwhile, the interactive features of regional function can be reflected by the spatial interaction activities of residents' trips. The advent of the big data era makes the individual geographical big data represented by the GPS data feasible to reflect residents' trip characteristics from the micro perspective. In this paper, the individual geographical big data and data mining method are employed to study the diversities and connections of urban regional functions under the perspective of residential perception. The study area enclosed by the Sixth Ring Road in Beijing is divided into regular grids, for the convenience of extracting timing characteristics of residential activities from 3 months' GPS data on taxis. Specifically, the cluster analysis based on expectation maximization algorithm, the point of interest and the daily traveling characteristics of residents are used to identify functional regions into six types, such as residential districts and commercial entertainment districts. Finally, the spatial interaction characteristics between functional areas are analyzed from two dimensions of distance and time, revealing that functional complementation weakens the influence of distance on the spatial interaction strength and the functional interaction indicates significant temporal differences.
Urban sprawl is a common problem in the process of rapid urban development. Due to differences in economic systems and other aspects, the characteristics and driving factors of urban sprawl in China are quite different from those in Western countries. The urban sprawl in China is accompanied by the disorderly development of land in the process of rapid urbanization. Based on the summary of domestic and international theoretical and empirical research, this paper chooses the elasticity of urbanized land to urbanized population to characterize the urban sprawl. This article analyzes the urban sprawl of 214 prefecture-level municipal districts from 1996 to 2014, and summarizes the following characteristics of its sprawl tendency: stable sprawl, adjustable sprawl, steady and high-speed sprawl, steady and low-speed sprawl. We summarize the following characteristics of its sprawl intensity: no sprawl, general spread and highly sprawl. In addition, we construct the panel model to explore the driving factors of urban sprawl from the aspects of the industrial structure, the government expenditure, the education development level, the traffic development level, the globalization level and market-oriented level. The results are as follows: (1) the issue of urban sprawl has a path dependence. Under the guidance of the development program with rapid economic growth, the high level of sprawl will continue for the rest of the year. (2) In the urban development mode oriented by economic construction, the development of secondary sector will significantly aggravate the urban spread. However, by promoting the development of the tertiary sector, local governments can attract foreign investment to optimize the structure of economic development and alleviate the problem of urban sprawl. (3) The influence of road construction, marketization level and college education level on the spread of cities is small or insignificant. Finally, we summarize the types, drivers and path dependence of urban sprawl and put forward policy proposals to control the urban sprawl from the perspectives of industrial structure and development.
Coal mine accidents bring huge loss of life and property to the country and the people. Grasping the occurrence laws of coal mine accidents and the influencing factors of regional coal mine safety is helpful to the safety of coal mine production. This paper tries to research on the coal mine accidents using the theory and method of GIS. Based on the spatial autocorrelation and spatiotemporal evolution model, we analyze the temporal and spatial characteristics of coal mine accidents in China from 2006 to 2015 and the influencing factors and relative risk of provincial coal mine safety. The results are as follows: there is obvious spatial autocorrelation of coal mine accidents in China, but the self-correlation is declining with the improvement of coal mine safety production situation in each province. The space-center evolution model of coal mine accidents is expressed by the spatial center of gravity transition curve. The coal mine accident center was shifted to the Northeast in the past ten years, which indicated the improvement of coal mine safety production situation in the southwestern provinces is more obvious than that in the east. The provinces and autonomous regions with frequent occurrence of major accidents tend to have a low coal mine million tons of mortality due to the strong implementation of regulatory measures. Also, the provinces and autonomous regions with the low coal mine production capacity always have a high death rate per million-ton coal. The Bayesian spatial model based on explanatory variables is established to evaluate the relative risk of coal mine accidents in different provinces and autonomous regions. The relative risk of coal mine safety production has an obvious spatial law and is quite different in different provinces and regions in China. The provinces with relatively high risk are mainly located in southern region, such as Hubei and Fujian. Provinces with lower risk are mainly located in the eastern region, such as Shanxi and Inner Mongolia.
Poverty is a common problem during the development of human society, and is also one of grand challenge to achieve sustainable development for the developing countries. Rural poverty is a major problem in the process of building a well-off society in a all-around way in China. Thus, the identification and the measurement of poverty are premise and basis of the policies of poverty eradication and poverty alleviation under implementing the new regional development. From a geographical view of county scale, we select the major influencing factors of poverty from common natural and social factors to build an evaluation index system based on spatial poverty and its related theory. First, we use Pearson correlation analysis to differentiate the poverty leading factors and poverty elimination factors. Then, we use GIS and BP Neural Network to simulate Natural Impoverishing Index (NII) and Social Economic Poverty Alleviation Index (SEPAI). We compute Poverty Pressure Index (PPI) combining natural impoverishing index and social economic poverty alleviation index, and explore the spatial distribution characteristics of poverty, revealing spatial pattern of poverty and its differentiation mechanism. We put scientific and reliable theoretical foundation to poverty alleviation and development of rural regions in China. The results show that the natural factors, such as NPP, slope, elevation, terrain, are the major impoverishing index for the study area. The social economic factors, such as the public revenue, household saving, fixed assets, are the main factors to alleviate and eliminate poverty. From the view of spatial distribution, the higher NII were mainly distributed in the west and north of China, especially in Tibet plateau and the northwest of Xinjiang, but the lower NII counties were located in the east and south with the characteristics of zonal distribution of latitude and longitude. SEPAI is positively correlated with the local economic development level in the spatial distribution. The coefficient of variation of SEPAI in provinces are significantly higher than that of NII. The poverty distribution pattern of PPI show a tendency of "large dispersion, small aggregation" by dividing Heihe: Baise Line. The characteristics of PPI represents a globally strong spatial dependence with a Moran's I coefficient of 0.33. The poverty-stricken counties identified in this paper have a high coincidence with the national key poverty alleviation counties.
Urban green coverage is an important part of urban ecosystem. Reasonable green coverage layout can improve urban environment and improve urban human suitability. In this paper, urban residential land in the Land Use/Land Cover（LUC）data in 2005, 2010 and 2015 were chosen as the urban area. Based on Landsat 5 and GF-1 remote sensing images, we used the support vector machine classification method to acquire the urban green coverage data of Beijing in 2005, 2010 and 2015. We also got the normalized difference vegetation index (NDVI) at the same time based on the MODIS and GF-1. Then, the rate of urban area green coverage, uniformity degree of green coverage and normalized difference vegetation index were considered as the evaluation indexes. We analyzed the spatial pattern and dynamic changes of Beijing urban green coverage at the scale of 1km raster and district. This study indicated that: (1) three indexes were consistent in two dimensions of space and time, raster and district. This reflected that the green space layout and the quality of greenness and the actual effect had been improved when green areas increased. (2) From 2005 to 2015 in the past 10 years in Beijing,, the urban area of green coverage increased from 518.93 km2 to 1405.54 km2, the rate of green coverage increased from 39.9% to 49.13%, the uniformity degree of green coverage increased from 0.598 to 0.653, and the vegetation greenness increased from 0.42 to 0.5. (3) There were obvious differences in time and space between urban greenness and overlay construction in Beijing. Greenness of the downtown was slow and the effect was not obvious. The results of major greenness were mainly concentrated in the urban fringe and suburban counties. The improvement process of urban greenness mainly occurred in 2005 - 2010.
Protecting traditional villages has gradually become the consensus of the whole society due to the announcement of the fourth batch of protection lists in China. Traditional villages are the product of the long-term interaction and organic fusion between human and natural environment. Such natural and ecological environments as the surrounding farmlands, mountains, forests and ponds maintain the production and living activities of traditional villages. However, against the backdrop of rapid urbanization after reform and opening up, the ecological environment around the villages is being threatened by increasingly serious erosion because of continuous urban and rural constructions. Urban and rural constructions also bring great challenges to the protection of traditional villages. We first define the concept of ecological erosion in traditional villages. After applying the theory of erosion dynamics, we analyze four ecological erosion ways, including self-ecological erosion, external ecological erosion, sudden ecological erosion and ecological erosion restoration. We also put forward four theoretical models of ecological erosion evolution. At the same time, according to analysis of the satellite images of ecological patches in different years, we take the traditional villages in the Pearl River Delta as the research case, analyze the ecological erosion based on various times and areas, discuss the evolvement law of ecological erosion and its temporal and spatial evolution characteristics in traditional villages under rapid urbanization. We summarize four models of ecological erosion in traditional villages, including "urban construction exterior invasion mode", "village construction expansion model", "road traffic occupation mode" and" comprehensive development model". As an effort to enrich and deepen the researches on traditional villages from ecological perspective, we provide an important reference for the protection and planning of traditional villages.
With the development of society, more and more attention is paid to the changes and rules of human economic activities. Nighttime light data is much concerned as a data set that reflects the economic activities of mankind. The traditional statistical methods make the study of socio-economic indicators difficult, while the strong correlation between nighttime light data and socio-economic data provides a convenient and fast data source for this kind of research. DMSP/OLS data offers the possibility of socio-economic research at a global scale, and the long time series and high spatial resolution of DMSP / OLS data is suitable for urban expansion study. This study used CitesSpace software to analyze the knowledge mapping of DMSP / OLS nighttime light data from 1997 to 2017, in order to comb the research hot spots and evolution route at home and abroad. We explore the research difficulties and provide the direction for later researchers. The literatures of "Web of Science" core dataset were chosen in this study by means of co-character, the co-occurrence network of key words and the co-citation network. The results are as follows: (1) The most active countries, institutions and authors are the United States, Chinese Academy of Sciences (CAS) and Elvidge, respectively. The research of DMSP/OLS data started in the United States. CAS did numerous research later, but some of the research lacked international influence. Elvidge is a pioneer in this filed, and conducted a lot of in-depth study on socio-economic estimation and urban expansion. (2) Socio-economic estimation (population, population density and electricity) and urban expansion have been the focus and frontier of the research. (3) The current difficulty is how to reduce the blooming effect and saturation effect on the research precision. (4) The research subjects have strong interdisciplinary, involving geography, science and technology of surveying and mapping, applied economics, sociology etc. Therefore, the future research trends are mainly reflected in three aspects: the optimization of data processing methods, the expansion of research fields and the deepening of the existing research results.
In this study, a time series of MODIS -Aqua images were used to retrieve the atmospheric aerosol optical depth (AOD) of Xiamen City. Then, the spatio-temporal variations of AOD in different months of the year 2014/2015 except May and September were analyzed in respect of quantity, spatial configuration and structure, respectively. The analysis was implemented by integrating multiple indexes related to spatial autocorrelation and landscape metrics. The results showed that distribution of AOD had obvious spatial and temporal variations in the study area. The low-AOD patches were dominant over forests, while middle/high-AOD patches were dominant over built-up areas. The AOD showed significant seasonal variations, which increased in spring and summer and decreased in autumn and winter. The spatial clustering pattern of AOD, characterized by the indexes related to spatial autocorrelation, showed that low-low (LL) clustering pattern was in the northern mountains of the mainland, while high-high (HH) and high-low (HL) clustering patterns were in the newly developed northeast urban area of Xiamen Island and the urban areas of the mainland satellite towns along the coast. In terms of landscape metrics, high-AOD patches increased from autumn and winter to spring and summer, accompanied by an increase of both complexity and heterogeneity of the landscape structure. It is a useful tool to unfold more thorough, detailed and comprehensive description of the characteristics of AOD variation by using the multi-index analysis. Results of this study can help to assess the impact of aerosol on environment and climate, and provide decisions supporting for the sustainable development of cities.
The application of remote sensing technology to study the response and recovery of forest vegetation in burned area can provide scientific basis for quick and accurate disaster prevention and mitigation. We focus on the forest fire occurred in Victoria, Australia from February 7, 2009 to March 14, 2009, which was the most serious forest fire in Victoria. In order to analyze the characteristics of change and recovery of forest vegetation indices in response to forest fire, we first used Landsat5 TM data of pre-fire and post-fire to extract the burned area of Victoria in Australia in 2009 and calculate the burn severity based on the Differential Normalized Burn Ratio (dNBR). We analyzed the effects of forest fire with different burn severity on vegetation using the anomaly value of Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) of Global Land Surface Satellite product(GLASS). Based on the time series data of LAI and FAPAR, we analyzed the vegetation recovery characteristics affected by forest fire with different burn severity. The results showed that LAI, FAPAR can reflect the damage effects of forest fire on vegetation and tract the recovery process of vegetation well. The LAI and FAPAR values decreased rapidly after the forest fire. The greater the burn severity, the higher the decreasing amplitude. The maximum decreasing amplitude of LAI and FAPAR of high burn severity area were 1.2, 1.3 times higher than that of low burn severity area and medium burn severity area, respectively. The LAI and FAPAR values increased over a period of time after the forest fire and restored to normal levels in 2-3 years. The recovery time of LAI and FAPAR is related to fire scale, burn severity, the natural conditions of the burned area, the growth of the original vegetation and other factors. For example, one year after the forest fire in Victoria, the decreasing amplitude of LAI and FAPAR in small burned area was 4.8% of the big burned area. The recovery time of LAI and FAPAR in high burn severity area and big burned area was 1 year or 2 years later than that of low burn severity area and small burned area. This study analyzed the characteristics of the change of the vegetation indices (LAI and FAPAR) of vegetation affected by the forest fire and summarized the recovery characteristics of damaged vegetation in Victoria, which can provide scientific basis for formulation of the measures of forest fire recovery.
In recent years, flood disasters have emerged successively, so extracting the disaster range and studying detection of the change of water body are of great significance to the scientific and effective management of flood disasters. Synthetic Aperture Radar (SAR) has the ability of penetrating clouds and generating ground information and it can collect data from large areas under any weather conditions. SAR has become an important means of natural disasters observation. Sentinel-1A is an important part of satellite in ESA's Copernicus Programm for monitoring environment. Therefore, discussions of the application of Sentinel-1A SAR data to the monitoring of flood disaster is meaningful. The traditional pixel-based methods have limitation in image speckle noise suppression, so the object-oriented method is introduced in this study. This study takes Sentinel-1A SAR images of the pre-flood, flooding and after-flood periods as data source. We introduce the object-oriented classification software - eCognition 9.0 to establish an extraction process of flooding area. Firstly, we use the SNAP software to preprocess the Sentinel-1A SAR images. Then, to account for objects features, we conduct experiments using multi-scale segmentation of eCognition 9.0, comparative analysis of the experimental results and we use the prior knowledge to determine the optimal segmentation scale. Based on the assigned class function of eCognition 9.0, we analyze the VV polarization mean feature to extract flooding area. Meanwhile, we use DEM data to remove the hill-shade in water extraction. Using the methods above, the detection of flood dynamic changes of different periods and flood submerge area extracted in the Huixian karst wetland in Guangxi was realized. Maps of the detection of flood changes in the pre-flood, flooding and after-flood periods were plotted. At last, we compared the traditional pixel-based water extraction method (Unsupervised classification and OSTU threshold segmentation). This research shows that Sentinel-1A SAR data has a great application potential in flood detection field. Compared with the traditional pixel-based method, the object-oriented method can effectively avoid the “salt and pepper phenomenon”, have high precision in flooding area extraction and can improve the utilization of spatial information.
Urbanization is taking place at an unprecedented rate around the world, particularly in China in the latest two decades. The effects of the intensive land-use / land-cover changes on urban surface temperatures and the consequences of these changes to human health are becoming progressively larger problems. Fuzhou, which is the capital city of Fujian province, is located in the coastal area of southeastern China. It has experienced a significant change of urban thermal environment during this period, and was recently named as one of the “new four furnace cities” in China. To study the process of changes in the thermal environment of Fuzhou city in the past 22 years, three Landsat images acquired in the years of 1994, 2003 and 2016, respectively, were used. HUTS is a widely used thermal sharpener method, which uses Normalized Difference Vegetation Index (NDVI) and surface albedo (α) to downscale the spatial resolution of thermal infrared data. It was applied to obtain LST images of higher spatial resolution (30 m) in the study area. The three downscaled LST images were then normalized, rescaled and overlaid to produce difference images to find out the changes of the thermal environment between different dates. Furthermore, by introducing simulations of different landscape patterns, these changes were evaluated and analyzed from the perspective of landscape ecology. The research results showed that, in 1994-2016, the high temperature area was increased from 35.75 km2 to 184.11 km2 with the city expansion. High temperature patches were expanded from city center to suburbs. On the other hand, the area and density of the high temperature patches were remarkably declined with the urban renewal. The Urban-Heat-Island Ratio Index (URI) rose up from 0.39 to 0.52, indicating that the urban heat island effect in the study area has been enhanced. Overall, the thermal environment of main urban area in Fuzhou has changed greatly in the past 22 years. Aggregation of high temperature patches was mitigated in Gulou, Taijiang and southern Jin'an Districts. Meanwhile, the temperature increased significantly in most area of Cangshan District, Mawei District and Minhou County due to rapid urbanization.
Gannan region is located in the southern Jiangxi Province, China. Gannan region includes 2 districts and 15 counties in Ganzhou. It has hilly land resources and its climate conditions are benefit to plant citrus. With the support and guidance from local government, Gannan region has experienced the boom of citrus planting and become the largest citrus production region in China over the past decades. Despite the economic success, the rapid and extensive citrus orchard expansion has brought great concern about ecological consequences. It is necessary to map citrus orchard for understanding the effects of citrus expansion. The objective of this study is to map the citrus orchard in 1990, 1995, 2000, 2005, 2010 and 2016 in Gannan Region. An image composite method was applied and total 2140 tiles of Landsat historical images were employed to generate seasonal images with lowest cloud composite at the pixel level. Random Forest classifier was used to classify multiple dimensional features from spectral, spatial and topographic domains. The image composite and classification were implemented with Google Earth Engine (GEE) platform. Results showed that: (1) GEE can effectively execute complex workflows of remote sensing data processing and information digging. (2) Lowest cloud composite at the pixel level is a reliable method of producing clear seasonal images for the region influenced by cloud and rain frequently. (3) Random forest classifier was suitable for mapping citrus orchard with an average overall accuracy (OA) of 93.15% and a Kappa coefficient of 0.90. (4) The citrus orchard has expanded extensively with the area from 9.77 km2 in 1990 to 2200.34 km2 in 2016. Citrus orchard was becoming clustered especially in Xunwu, Xinfeng and Anyuan and was mainly converted from forest, bush and cropland.
