As the next generation of geographic language, virtual geographic environments (VGE) play an important role in understanding and exploring geographic phenomenon and in discovering associations between geographic phenomena. The development of virtual reality (VR) technology has enhanced VGE, as it provides users with a more immersive experience. However, this new technology carries problems within the human-computer interaction because the traditional methods of interaction (such as a mouse and keyboard) are no longer deemed as efficient. Greater efficiency and natural interaction is critical to the accessibility of VGE systems which rely heavily on VR technology and the efficiency of understanding and exploring geographic phenomenon. In order to interact with VGE systems more efficiently and naturally, users are required to rely less on the traditional input devices and turn to more advanced motion capture technology. This technology can provide the VGE system with more detailed information about the users. It is indisputable that motion capture technology is more advantageous than traditional input technology in many aspects. However, there are some issues with motion capture technology when applied to a VGE system, which usually has several people using the system at the same time and conversing with each other. The problems that have arisen include occlusion (caused by other people blocking the signal), positioning drift, and limited capture accuracy. In general, existing motion capture devices have failed to solve these issues because they are based on the single mode which has some limitations. Motion capture devices usually capture the actions of only a single person; therefore it cannot satisfy the demands of VGE systems. To overcome this device limitation when applied in VGE, interaction requirements within the VGE system and its special motion capture requirements were analyzed in this study. A method was proposed based on using multi-mode to enable the capture of multi-person motion. The method used in this paper has a focus on how to merge motion data from several devices in different modes, for example merging devices based on inertial positioning technology with those based on optical location tracking technology. By doing this, it is possible to solve the current issues through integrating the advantages of devices in various motion capture modes. The motion capture framework is deliberately designed to make the motion capture system more accessible, providing opportunity to develop related technologies that can merge the various real-time data streams. Through this method, significant improvements were obtained in most aspects of motion capture and a prototype system was then developed to verify the viability and efficiency of this method.
The detailed information in super-resolution reconstruction of hyper-spectral image is usually lost after using the Maximum A Posteriori (MAP). To improve the quality of a reconstructed image, this paper presents a MAP block super-resolution reconstruction algorithm based on the prior Huber Markov Random Field (HMRF) model. Firstly, Principal Component Analysis (PCA) is used to obtain the main components for a given hyper-spectral image, and then the initial image is obtained by spline interpolation technique. By using main components from the PCA operation, the proposed algorithm can not only effectively reduce the usage of computation memory but also reserve most of the information from the image. After calculating the Q statistic of the initial image, it is found that stratifying the hyper-spectral image into several (e.g., seven in this study) spatial heterogeneities is an effective way to characterize the complexity of the hyper-spectral image. To this end, a suitable partitioning scheme for obtaining an optimal super-resolution reconstructed image is adopted after comparing the reconstructed results by using different blocks with different sizes. As a result, the domain of the hyper-spectral image is split into several sub-blocks. The HMRF model with an adaptive threshold is then established for each sub-block image, and an objective function is defined by combining the fidelity terms of the sub-block images. The objective function can be solved by using the gradient descent method to obtain the high resolution sub-block images, which are then combined with the interpolated secondary component images. Though some cross artifacts occur in the process, they can be removed by extending edge based methods. The effective extending edge-based method is also proposed in this paper. Finally, the final high resolution image can be obtained by using the inverse PCA operation. In order to verify the validity and the superiority of the proposed algorithm, we test the proposed algorithm, the representative Tikhonov-based algorithm, total variation-based algorithm, and the traditional HMRF model-based super-resolution reconstruction method with the simulated and real images, respectively. The testing results show that the proposed algorithm is superior to other methods in the peak signal-to-noise ratio (PSNR) and the Structure Similarity Image Measure (SSIM).The qualitative evaluation indicated that the proposed method could obtain more obvious edge structure and detailed information at the same time.
The commercial value of the subway station is the embodiment of the value of urban commercial, which is based on the changing of urban transportation mode by subway. The scientific and quantitative evaluation for the subway station is an important aspect of the subway station construction. In this paper, the evaluation system of the commercial value of the subway station was first constructed from both two aspects: commercial grade and commercial scale. After that, the Guangzhou Metro Line 8 was taken as the research object to evaluate the commercial value that was based on this evaluation system. In the ranking of commercial grade of the subway station, not only five spatial factors but also two non-spatial factors were selected in order to establish the multi-factor evaluation system. In the evaluation of commercial area factor, which is one of the spatial factors, the spatial decay function of commercial land price is innovatively constructed based on the consistent distance decay characteristics of the benchmark land price of commercial land around the city-level and district-level business circles. After that, the decay function was converted into the evaluation of the value of distance decay factors of the commercial circle. This method effectively solves the problem that caused by experts' subjective opinion. In this way, the commercial grade of the subway station on Metro Line 8 was evaluated. After finishing both the spatial factor evaluation and the non-spatial factor check, it was found that the area from the Shangxiajiu Pedestrian Street to the Cultural Park had the highest commercial grade while the second level was distributed in the middle of the Liwan District. For the commercial scale evaluation, a model was constructed using the method of the commercial building area demand balance and a population size model which is based on the service radius. This model was constructed for the purpose that it can be used to estimate the commercial building area that will be required in the future. This paper using the model, taking the Chen Clan Academy Station of Metro Line 8 as an example, to estimate the commercial building area demand of the site. On one hand, the factor evaluation method based on the land price distance attenuation and quantitative evaluation proposed in this paper completes the relevant method system. On the other hand, the digital model that was newly constructed increases the degree of rationality and scientificity of commercial value evaluation method of subway stations.
Taxi traffic has always been regarded as a supplement to public transportation. However, this may be in part due to previous studies focusing on independent research of taxi and bus passenger flow. Research around the relationship between taxi and bus passenger flow has not yet been thoroughly investigated. Taxi passenger hotspots not only provide real-time understanding of urban traffic hotspots, but also guide taxi drivers and enable taxi companies to make effective dispatches. Taxi passenger hotspots tend to occur in areas where demand for transportation is high and in areas of intensive crowding. Bus passengers' IC card data can reflect real-time traffic demand within the city. This study used Xiamen Island taxi GPS trajectory data and public transportation system data, along with the kernel density estimation method and geographic weighted regression (GWR) model to analyze the OD (Origin-Destination) passenger flow in both morning and evening peak travel times. Results showed a significant spatial heterogeneity in the kernel density value of the taxi passenger O. However, within the same area, the impact of bus passenger O and bus passenger D on the taxi passenger O was found to be opposite; in various regions, the negative impact of bus passenger O on the taxi passenger O in areas with complex urban functional types, bus passenger O had a positive impact on the taxi O at a single function area, while the bus passenger D was just the opposite. Compared to the ordinary least squares (OLS) model, GWR provided a much better fit (with the goodness of fit increasing from 0.13 and 0.11 to 0.59 and 0.53 in the morning and evening peak hours, respectively). Results of this study could provide the basis to forecast the number of taxi passengers.
Public security is a significant issue in China currently. A prevention and control system was implemented in an attempt to effectively increase public security. Improving public security through controlling the placement of police stations is one of the ways to solve issues of urban crime in China. A method was proposed to optimize police station locations and the allocation of police members. This method uses qualitative principles, quantitative indexes, and the multi-objective models of the location-allocation of police stations, taking into account police resource constraints. The method was divided into three steps: (1) The existing police stations were streamlined using the minimum facility model; (2) The maximum coverage model was used to optimize the police stations in urban key areas; (3) The improved minimum impedance model was introduced for global optimization with consideration to social equity. Method parameters were defined and discussed, and police station location optimizaion was analyzed based on crime and building data. Results showed spatial heterogeneity in crime hotspots and building distributions. These results form the foundation of this paper's theory. Using Lanzhou to carry out empirical research, the results show that the proposed method can effectively reduce the degree of overlap and the average response time of police. The results were as follows: (1) The method can effectively improve the coverage of demand points and service areas. Additionally, it can effectively improve the workload of emergency police services, as the Gini coefficient of emergency response time decreased significantly. The degree of overlap in police service areas decreased by 17.2%, and the average response time decreased by 6.67 seconds. The coverage of high-demand points, coverage of key areas, and general area coverage increased by 7.25%, 3.00%, and 12.01%, respectively, and the Gini coefficient of response time decreased from 0.382 to 0.268; (2) Through the analysis of spatial heterogeneity of crime hotspots and building distribution, it was found that this method can support the allocation of security police members and the police members in charge of household registration. Using this approach, more security police members were allocated to the blocks with higher crime, and more household registration police members to the blocks with larger numbers of residents according to the housing area and the number of crimes. As a result, the number of police members vary between police stations, and the ratio of security police members to household registration police members in the same police station is also different.
Random forest classification has become an effective method in remote sensing classification of machine learning. It is of great significance to combine the Landsat satellite data and random forest method to obtain long time series data in the complex terrain areas and to explore its land use/land cover change. Based on the multi-spectral data of landsat8 OLI satellite, this paper adopted the random forest classification method to classify the land use types of Huangshui basin complex topography areas in Qinghai province. According to the characteristics of complex terrain areas, the study area was divided into different geographical regions. The topographic parameters were then selected, and the optimal feature collection was constructed by extracting spectral and texture information of Landsat8 data. The objective of this papers was to explore the applicability of random forest methods in land use classification on the complex topographic regions. The results showed that RFC classification with the landsat8 OLI data can be well used to obtain the land use types in the Huangshui basin. The combination of spectral, topographic, and texture information performed differently in different areas. In the middle and high mountain areas, the combination of spectral and topographic information can obtain the best results in the random forest classification with the overall accuracy of 91.33% and Kappa coefficient of 0.886. In the shallow mountain areas and valley plain, however, the random forest classification can obtain the best results by combining spectral, topographic, and texture information with the overall accuracy of 92.09% and 87.85% and Kappa coefficient of 0.902 and 0.859, respectively. Using the random forest algorithm to optimize the selection of texture feature combination can extract the land use type information quickly and ensure its accuracy. Random forest classification combined multi-source information can be used effectively to classify land use types, which can provide some enlightenment and reference values for the renewal of land use status and the development of social economy in the study area.
Affected by social institutional transformation and climate change, Kazakhstan is the most significant country with ecological degradation and grass-livestock contradiction in Central Asia. Over the past century, the distinct characteristics of various grassland ecosystems have changed due to agricultural reclamation, changes in grazing patterns, and climate change in Kazakhstan. Therefore, it is important to study the process and mechanisms of grassland degradation in Kazakhstan in order to understand the responses of grassland ecosystems to climate change and human activities in Central Asia. These findings may also support regional ecological sustainable development in the construction of green silk roads. Ecological change research is based on the land cover statistics. However, there are significant differences between the current widely-used global data sets, leading to uncertainty in the understanding of ecological variation and the simulation of future change. This study compared the similarities and differences of grassland distribution using five types of global land cover data (UMD 1992-1993, MCD12Q1 2001, GLC 2000, CCI-LC 2000, Glob Cover 2005). Grassland type identification, consistency of spatial distribution and the cause of spatial distribution variation were used to provide the basis for selection of land cover datasets in Kazakhstan. Results showed that: ① the primary cause of differences in grassland definitions were differing remote sensing data sources, ancillary data, classification methods, verification methods, and data within the five data sets. The MCD12Q1 data had the largest difference in grassland distribution area; ② the area of grassland distribution overlaps within the five data sets (complete consistency) or within the four data sets (high consistency) accounted for only 39.66% of the total, which were mainly located in the typical grassland and part of the semi-desert grassland. The spatial consistency gradually decreased from the inside to the outside around the typical grassland distribution zone. An inconsistent zone within the five data sets accounted for 26.78%, mainly located in the desert grassland; ③ CCI-LC2000 data had the highest areas of overlap compared to other types of data. There were 76% of the grassland overlapped with areas of complete consistency or high consistency in the five data sets. In the inconsistent areas, the most easily confused land cover types were mainly rainfed cropland, irrigated cropland, mosaic cropland and natural vegetation, bare areas and shrub land.
With the development of urbanization, the problem of "big city disease" is becoming more and more prominent in recent years. Since the strategy of polycentric city has been proposed, polycentric characteristics of city has become one of the most important issue in revealing spatial structure of a city. Due to the rough resolution of traditional census data, most studies have not get an insight into the fine structure of city centers and lacked empirical researches. With the widely application of LBS services, most trajectory data of human activities have been recorded to provide a profile of daily urban dynamics in real time. This information has opened up a new way to analyze the mechanism of polycentric city. Based on the reason, this study proposes a new method to identify the multi-centers in Beijing within rings, and analyzes the spatial-temporal characteristics and interactions in each center, using sina micro blog check-in data and taxi GPS data with POI data. In our study, groups of centers with different social functions can be identified from the clusters of residents' activities. From these results, 4 city centers, 16 district centers and 45 community centers have been identified within 5th rings in Beijing. The district centers can be divided into 4 groups, including cultural and recreational center, business center, education center and transportation center. And the representativeness for each types of centers are Sanlitun, Guomao, Beijing Normal University and Beijing West Railway Station. The community centers can be divided into 9 groups, including political center, residential center, administration center and business-residential center. The representativeness for each types of centers are Xidan, Wudaokou, Wanliu, Qianmen, Zhongguancun, Tianmen square, Chaoyang District Government, Beijing West Railway Station and Yonganli Community. The residential clusters can be observed in some daily hours. Number of residential clusters for district centers are commonly higher than number for community centers, while number of residential clusters in workdays area higher than that in holidays. For district centers, there are 4 classes of common cluster pattern in workdays and 5 classes of common cluster pattern in holidays. For community centers, there are 5 classes of common cluster pattern in workdays and 3 classes of common cluster pattern in holidays, respectively.
：Hand, foot and mouth disease (HFMD) is a common infectious childhood disease. In recent years, the number of cases of HFMD in China has increased rapidly, and has received increasing attention. Although there are many related studies, only a few studies focus on the spatiotemporal heterogeneity of HFMD incidence and quantify the association between meteorological factors, socioeconomic variables, and HFMD incidence. Geodetector and Bayesian space-time hierarchical models were applied to analyze the spatiotemporal heterogen-eity of the HFMD incidence from 2009 to 2013 within the Beijing-Tianjin-Tangshan region. These were used to quantify the determinant power of meteorological factors, socioeconomic variables, and the interactions between two of these factors. The Geodetector method has the axiom that if an explanatory variable (x) determined an explained variable (y), the explained variable would exhibit a spatial distribution similar to that of the explanatory variable. This method has been widely used to measure the determinant power of potential explanatory variables. The Bayesian space-time hierarchical model has the potential to show the spatiotemporal variation of a geographic phenomenon. The results showed that: (1) the highest incidence of HFMD occurred in late spring and summer (May to July), and the lowest incidence occurred in winter (December to February). (2) Spatial heterogeneity existed. In particular high risks areas were mainly concentrated in areas of high economic development. The population density and proportion of the tertiary industry determinants, play a lead role in contributing to the spatial heterogeneity of HFMD incidence (q values of 0.35 and 0.28, respectively, as calculated by GeoDetector). (3) The main meteorological factors affecting the temporal heterogeneity of HFMD incidence were average temperature, cumulative precipitation, and relative humidity (with a determinant powers calculated by GeoDetector of 0.38, 0.27 and 0.13, respectively). Additionally, the interactions were greater than the independent effects between socioeconomic variables or meteorological factors. For example, the interaction of average temperature and relative humidity, average temperature and precipitation, average temperature and wind speed were 0.43, 0.40 and 0.42, respectively. The interaction of population density and proportion of the tertiary industry was 0.55. This result presented the strongest correlation with HFMD incidence. Temperature and relative humidity were also dominant factors influencing the spatiotemporal transmission of HFMD, along with areas of high economic development with high population density. This study provides a theoretical basis for the prevention and control of HFMD by detecting the spatiotemporal heterogeneity of the HFMD incidence and quantifying the impact factors within the study region.
Dengue fever (DF) is a rapidly spreading vector-borne viral disease that is widely prevalent in some tropical and subtropical regions. In recent years, the increasingly serious DF epidemic has formed a high incidence area in southern China and has posed a definite threat to China's public health security. DF is mainly affected by the complex environmental conditions and socio-economic factors in the region. Thus, exploring the influencing factors on the spatial distribution of DF by spatial geographic models and predicting the prevalence of DF epidemic are important bases for effective prevention and control of DF. Base on the socioeconomic data (such as land use data of urban, village, forest, farmland, grass, wetland, water area, construction area and population density data) and the spatial data of DF cases from 2010 to 2014 in the Pearl River Delta area (PRD), the Land Use Regression (LUR) model was constructed to analyze the impact of social and economic factors on the spatial distribution of DF epidemic within a range of 1~10 km buffer zones using 500 sample points. In addition, the land use data in 2030 predicted by the SLEUTH model, and the population density in 2030 obtained from the population prediction model were collected to reveal the risk of the DF epidemic in 2030. The results found that DF was significantly correlated with population density (R2=0.779), grass (R2= -0.473), urban (R2=0.818), forest (R2=-0.642) and farmland (R2=-0.403) within the buffer zone of 10 km, 7 km, 10 km, 2 km and 1km respectively. The LUR model with these five variables possessed the satisfactory capability of predicting the spatial distribution of DF with the adjusted R2=0.796 and an appropriate F value of 390.409 (P<0.01). The overall result of the model is good with the fitting accuracy of 0.7101 between the predicted values and the measured values. And the leave-one cross test results show that the model has a relative root mean square error of 0.7046. Further more, the accuracy of land use data in 2030 simulated by the SLEUTH model is good (the kappa coefficient is 0.849) as well with the expanded urban areas. They are mainly distributed in Shenzhen, Dongguan and the border areas of Guangzhou-Foshan, which also have relatively high risk of DF epidemic in 2030. In summary, LUR model can predict the spatial distribution of DF epidemics with high accuracy, which provides supports in methodology for local hygienic authorities to prevent and control DF epidemics.
Knowledge on the spatio-temporal pattern of economy development can effectively help inform policy on economy. Most current studies on spatio-temporal pattern in economic development mainly rely on statistical data. However, these statistical data have disadvantages such as lacking of consistently statistical standard and low spatial resolution. These shortcomings prevent the use of statistical data to accurately describe the real pattern of economic development. Nightlight data covers the most surface on the earth, and it is available with free of charge. Moreover, the nightlight data is highly related to socio-economic activities, so it can be used as a proxy variable to study human activities. Based on the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) nightlight data, three methods including gravity center, standard deviation ellipse, and local Moran'I were used in this study to explore the spatio-temporal pattern in Chinese economic development at different scales. The results showed that: (1) Chinese economic gravity center moved to the southeast from 2003 to 2013, but the moving distance was reduced year by year. These results indicated that there existed an economic gap between eastern region and inland region, but the gap was reduced gradually in these periods. The ellipse's extent of standard deviation in Chinese economy expanded, but its oblateness decreased from 2003 to 2013. This implies that the total volume of Chinese economy continued to rais, however, the spatial pattern became locally aggregated gradually. Besides, the direction angle of Chinese economy's standard deviation ellipse deflected to the east in these periods, which agrees with the result that the economic gravity center moved to the southeast. (2) High-high and low-low clustered areas were the two most obvious features of Chinese economy.
Xinjiang region is of strategic significance to China and Central Asia. This study aimed to effectively combine different data sources and classification systems to mitigate the lack of their interoperability regarding spatial distribution of land cover data. For this purpose, three types of land cover data were included. They were the visual interpretation of land use status in 2010 remote sensing monitoring data, GlobeLand30, and GlobCover2009. Four methods including category similarity analysis, category confusion analysis, confusion matrix analysis, and spatial consistency analysis were used to evaluate their accuracies and consistencies. We expect that this study would provide recommendations for the applicability of land cover data in the arid region of northwest China. The results showed that the three types of land cover data exhibited a good consistency for describing land cover categories in Xinjiang, with similarity higher than 0.9. Particularly, bare land identification demonstrated the highest consistency, followed by grassland, cultivated land, and forest. About 95% of the land area in Xinjiang showed a relatively high consistency, and the overall accuracy for land cover data ranged from 64.11% to 72.57%. Data from the group of visual interpretation/GlobCover2009 demonstrated the lowest accuracy, followed by the group of GlobeLand30/GlobCover2009. The group of visual interpretation data/GlobeLand30 had the highest accuracy, but it still had room for improvement. These results demonstrated that using the same satellite sensor plays an integral role in enhancing the accuracy of evaluation results. Moreover, classification systems, classification methods, spatial resolution, and satellite passage time used would also have a huge impact on the accuracy of evaluation results. In order to solve this problem more effectively, multi-source remote sensing data integration technology or deep learning will become more promising for accurately interpreting remote sensing image data in the near future, for further improving data accuracy in global land cover mapping and application fields. Depending on the distinctive landscape pattern of Xinjiang region, this research analyzed the accuracy of three different kinds of data for different land cover categories to provide reliable information which shall be proved to be useful in resource development, environment protection and sustainable development of Xinjiang. Additionally, it initiated a framework for providing basic data for China’s significant development strategy "the Belt and Road". Moreover, the results demonstrated the better performance of GlobeLand30 in accuracy assessment. As compared to other land cover data within the same category, the GlobeLand30 data is overwhelming in spatial resolution.
The lower reaches of the Tarim River is a region with fragile ecology and significant environmental problems in an arid area of Western China. The local ecological environment has experienced tremendous degradation over the decades, due to water resource deficits from human activities and climate change. In the year 2000, the Tarim River Basin Administration Bureau (TBAB) initiated the Ecological Water Conveyance Project (EWCP) in an attempt to restore the downstream ecosystem. By the end of 2017, 18 water delivery projects had been completed, totaling 6.3 billion m3. Understanding the effect of water delivery on the ecological environment in the lower reaches of the Tarim River is of environmental importance. This paper monitors and analyses regional environmental changes and ecological responses before and after water conveyance from surface water (lakes, rivers and wetlands), groundwater and vegetation cover, using multi-source remote sensing and long-term series data. Wetland information and updated maps were produced using thematic map plaque knowledge transfer technology. Regional wetlands mapping and area change statistics were then completed for the years 1990, 1995, 2000, 2005, 2010 and 2015. Finally, regional wetland and vegetation change characteristics were analyzed pre-EWCP period (before 2000) and post-EWCP (after 2000). Results indicate that: (1) Pre-EWCP period (1990-2000), the regional ecological environment in the lower reaches of the Tarim River continued to deteriorate (almost half of the swamp wetlands in the basin disappeared and the regional vegetation coverage decreased significantly). (2) Post-EWCP period (2000-2017), the regional ecological environment has been significantly improved. This was indicated by a “V” type reversal in the lakes and wetlands of the basin. In addition, the areas containing medium and sparse vegetation coverage had a significant increasing trend, and the ecological environment of wetland was gradually restored. Furthermore, the long-term dry and shut-off state of the downstream river channel has been changed. In particular, the Tail Lake (Taitema Lake) gradually regained its vitality after 30 years of becoming dry (area reached 147.87 km2 in Aug. 2017). This research showed that the EWCP played a key role in the rescue and control of the ecological environment in the lower reaches of the Tarim River and hence, the regional ecological environment is gradually recovering. These findings are critical for environmental effect assessments by the EWCP and future ecological restoration effort in the lower reaches of the Tarim River. They may also provide useful technical support and decision-making references for ecological engineering construction and performance evaluation.
Land surface temperature (LST) and fractional vegetation coverage （FVC） are important indicators of ecological environment changes. Studying the temporal and spatial variations of LST and FVC as well as their interaction in Fujian Province are of great significance to the evaluation of ecological environment construction and improvement of regional ecological environment. In this study, the temporal and spatial variations of LST in Fujian Province and the interaction between LST and FVC are analyzed, based on the reconstruction time series data of MODIS 11A2 LST and 13Q1 NDVI from 2001-2015. The results showed that: (1) The overall LST in Fujian Province presented a slight downward trend from 2001 to 2015, and the downward trend of LST is more pronounced after 2010. The spatial distribution of LST and FVC had a good negative correlation consistency, which implies the LST value is lower in the higher area while the LST is higher in the lower FVC area. (2) LST is negatively correlated with FVC, DEM and latitude. And their negative correlation was increased or decreased regularly with the change of months in a year .The negative correlation between FVC and LST was higher in summer and became lower in winter with the correlation coefficient reduced from 0.7 to 0.4. (3) The decreasing trend of LST with the increase of FVC is piecewise linear and has an obvious "FVC inflection point". In front and behind "FVC inflection point", the decreasing trends of LST with the increase of FVC are "slowly first and fast afterwards" in summer and "fast followed by slow "in winter. Moreover, the difference of LST decreasing rate with the increase of FVC becomes smaller in spring and autumn. In summer, when FVC is greater than 0.4, the LST can reduce about 0.77 °C with FVC value increase 0.1, and the cooling effect is about twice as much as that when FVC is less than 0.4. Therefore, if we want to effectively reduce LST in summer, we should make the surface vegetation cover more than 40%。Only in this way can vegetation play a better role in cooling. (4) From January to August, the negative correlation of FVC on LST has a lag, and vegetation change has a greater impact on the spatial and temporal distribution of the next month's LST. This study has a certain significance for the construction and evaluation of ecological environment in Fujian Province, and provide an important reference for the development of vegetation to suppress regional high temperature.
Yunnan border area is an important geographic location. It is composed of 56 counties in 8 municipalities. Among which, 25 counties are adjacent to Laos, Myanmar, and Vietnam. Land use and nighttime light data were used in this study to explore the spatial pattern of GDP based on the spatialization of GDP in the Yunnan border area. This study was expected to inform policy on reducing economic gaps between regions and promoting regional common development. The land use data was used to spatially fit the Gross Domestic Product (GDP) from the first industry, and the DMSP/OLS nighttime light data was used to fit GDP from the second and third industries. The fitting results were summed up to realize the spatialization of total GDP in the border area of Yunnan province from 1992 to 2013. Based on this, the spatial difference of GDP in the Yunnan border area was analyzed. The results showed that: (1) The land use data could be well used to model the GDP from the first industry, with goodness of fit (R2) being greater than 0.82 in each year and overall relative error being less than 1.12%. The nighttime light data and the classification regression method were used to fit the GDP from the second and third industries. The maximum relative error of fitting was 6.404%, and the fitting accuracy of the sum of the two industries was satisfactory with the maximum relative error being only 4.241%; (2) The 22-phase GDP data of the Yunnan border area was positively correlated in space, presenting an obvious clusters; (3) The distribution of GDP cluster in the county was characterized by High-High values (HH) and Low-Low values (LL). The distribution of Low-High and High-Low values was scattered with no regularity. The clustered high values of GDP were concentrated in Kaiyuan, Mengzi, and other counties, while the clustered low values of GDP were concentrated in Luchun, Ximeng, and other counties; (4) The economic gap between counties in the Yunnan border area gradually increased from 1992 to 1996 followed by a decrease trend afterward. The spatial correlation effect showed a fluctuation of increase and decrease; (5) Results of three-dimensional interpolation in the Yunnan border area presented a topographical pattern of “depression-hill-flat-peak” from the northwest to the southeast. The counties in the southeast corner of the border area such as Jianshui, Gejiu and Kaiyuan and other counties in the Honghe municipality, had the highest GDP. The “hill” terrain was mainly concentrated in Tengchong, Baoshan city, and the southernmost Jinghong area. The terrain of “depression-flat” was mainly distributed in the counties such as Gongshan and Fugong in the northwest corner of the border area, the counties in the southwest corner of Ximen and Menglian, and in the central area, such as Luchun and Jiangcheng counties.
With the Landsat-8 Operational Land Imager (OLI) images over Xishuangbanna acquired during the dry seasons of 2014 and 2018 , the Change Rate of Normalized Burn Ratio (CRNBR) algorithm (reported in our previous study), along with two masks of Landsat-derived natural forest and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), were used to detect and obtain the geographical information of the mature rubber plantations during this period. The spatial and temporal change pattern of mature rubber plantations were detected and analyzed by means of spatial and statistical analyses using the ArcGIS 10.5 platform. Results showed that: (1) Mature rubber plantations were mainly distributed in areas with well hydrothermal conditions and moderate to low elevations in the south of Xishuangbanna. Jinghong City had the largest area of mature rubber plantations, followed by Mengla County and Menghai County accordingly. These plantations were typically concentrated within the towns of Menglong, Yunjinghong, and Gasa of Jinghong City, and Guanlei, Mengbang, and Mengman of Mengla County. (2) The declining area of mature rubber plantations were initially reported in Xishuangbanna from 2014 to 2018 (at a reduction of approximately 17.27%). In particular, Mengla County had the largest reduction in mature rubber plantations (19.10%), while Menghai County had the least (2.70%). (3) The stable plantation areas of mature rubber were predominantly located in the towns of Menglong, Gasa and Jingha of Jinghong City, and Guanlei, Mengbang and Mengla of Mengla County. The increase in mature rubber plantation areas were mainly concentrated in the towns of Menglong, Gasa and Yunjinghong in Jinghong City, Guanlei, Mengla and Mengban in Mengla County, and the town of Daluo in Menghai County. The reduction area was mainly evident in the towns of Mengyang, Gasa and Menglong of Jinghong City, and Mengbang of Mengla County and Mengban in Mengla County. This study can provide a new understanding of the current status of rubber plantation in Xishuangbanna.
