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In the traditional construction of ecological security pattern, the minimum cost path is extracted as the ecological corridor based on the minimum cumulative resistance model, and the ecological nodes are identified manually. This method lacks the consideration of the exchange process of energy flow and information flow in the ecological process, resulting in a certain lack of ecological security pattern in structure and function. Therefore, an ecological security pattern construction method integrating CVOR-GWLR-Circuit model is proposed to solve the above problems by transforming natural background data into localized correction variables and adding them to the ecological security pattern evaluation model. Taking Yunnan Province as an example, firstly, the ecological security evaluation system of "Contribution, Vigor, Organization, Resilience" (CVOR) is constructed based on the importance of ecosystem services and ecosystem health, and the ecological security of Yunnan Province in 2020 is evaluated, and the ecological source areas are identified combined with nature reserves. Then, the ecological resistance surface was constructed by considering land use data and topographic factors, and the landslide sensitivity evaluation model was constructed based on geographically weighted logistic regression model (GWLR) to correct the basic resistance surface. Finally, the circuit theory model is used to extract the ecological corridor and construct the ecological security pattern in Yunnan Province. The ecological pinch points and barriers in the ecological corridor are diagnosed by the current density, so as to identify the width of the ecological corridor and identify the key areas of ecological protection and restoration. The results showed that the ecological sources area of Yunnan Province was about 69,417.78 km

Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China’s seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the “heart, window and name card” of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China.

Droughts in winter and spring are one of the most prominent natural disasters in the Yunnan Province in China. They occur frequently, with long durations and have a wide range of damage, which has a serious impact on social and economic development, as well as agricultural production and, therefore, strongly impacts the lives of the people living in the region. The traditional drought monitoring model does not take terrain into consideration, thereby affecting the comparative nature of results, as baseline conditions are not the same. Therefore, this study proposed a comprehensive drought monitoring model considering the influence of terrain factors to improve the evaluation effect. Firstly, based on NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measurement Mission (TRMM 3B43) data, vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (TRCI), and three terrain factors ground elevation (DEM), slope (SLOPE), aspect (ASPECT) were selected as model parameters. Then, a comprehensive drought monitoring model without considering terrain factors (Model A) and a comprehensive drought monitoring model of considering terrain factors (Model B) were constructed by using multiple linear regression models. Finally, the effects of the two models were evaluated by using standardized precipitation evapotranspiration index (SPEI) in southwest Yunnan Province, China, and model B was used to analyze the drought in winter and spring in the study area from 2008 to 2019. The results showed that (1) the correlation coefficient of model B was higher than that of model A in winter and spring and the standard error of model B was lower than that of model A. (2) The grade consistency rate of Model A and SPEI was 0.92 in winter and 0.33 in spring; the grade consistency between model B and SPEI was 0.83 in winter and 0.75 in spring, and therefore the monitoring effect of model B was more stable. (3) There were periodic droughts during the study period, and the degree of drought in spring was less than in winter. Medium and severe droughts were observed in winter. Thus, this study concluded that the effect of terrain has an important influence on the evaluation of droughts. The comprehensive drought monitoring model which considers topographic factors can effectively identify the occurrence of drought, and therefore provide significant input with regards to disaster prevention and mitigation policies in southwest Yunnan.

Establishing an effective forest fire forecasting mechanism is the premise of scientific planning and management of forest fires and forest fire prevention. In recent years, the forest fire prediction mechanism has been one of the key areas of concern for the government forestry management departments and forestry researchers. One of them, is logistic regression ( LR ). It is a relatively frequent prediction probability model used in forest fire prediction and prediction in China and abroad for the past few years. However, with the gradual deepening of research, it is found that the logistic regression model fails to fully consider the spatial non-stationary relationship between forest fires and driving factors, which leads to poor fitting effect and low prediction accuracy of the model. But its extended counterpart, the Geographically weighted logistic regression ( GWLR ) model, takes into account the spatial correlation between model variables, and effectively improves the fitting ability and prediction accuracy of the model. Therefore, this paper compares the ability of the logistic regression model and the geographically weighted logistic regression model in terms of fitting ability and prediction accuracy in order to obtain the ability of the two models to predict forest fires in Yunnan Province. In this paper, the samples were divided into 60% training samples and 40% test samples, and repeated sampling was carried out 5 times for training. Variables that appeared in the training model for 3 or more times were used to construct the final LR and GWLR models. Finally, the models with better fitting ability and higher prediction accuracy were used to classify the fire risks in Yunnan Province. The results show that the geographically weighted logistic regression model is superior to the logistic regression model in terms of fitting effect and accuracy. The geographically weighted logistic regression model is more suitable for the data structure of forest fires in Yunnan Province and has better prediction ability. The AUC value of the geographically weighted logistic regression model is 0.902, and the prediction accuracy is 82.7 %; The AUC value of logistic regression model was 0.891, and the prediction accuracy was 80.1%; Fully considering the spatial heterogeneity among model variables can, to some extent, predict forest fires more accurately. The fitting of the two models shows that the relative humidity, temperature, air pressure, sunshine hours, daily precipitation, wind speed, and other meteorological factors; Vegetation type; terrain factor; Population density, road network and other human activity factors become the cause of forest fires in Yunnan Province.

It is of great significance to analyze the threshold relationship between landscape pattern and water quality for watershed water environment treatment. However, previous studies did not consider the influence of spatial scale on threshold. Therefore, this study proposed the idea of the relationship between landscape pattern and water quality threshold considering the spatial scale effect to solve the above problems. Firstly, the percentage of landscape composition area under 9 spatial scales (riparian buffer zone and sub-basin) of 20 rivers entering the lake in Dianchi Lake Basin was extracted to identify the optimal spatial scale of landscape pattern and water quality by redundant analysis (RDA). Then, a variety of nonlinear regression models such as power regression, exponential regression, quadratic regression, and segmented regression are used to quantitatively detect the thresholds of landscape pattern and water quality. The results show that (1) the spatial scale has a significant influence on the threshold relationship between landscape pattern and water quality, and the total interpretation rate of landscape pattern on water quality is the largest at the buffer scale of 1100 m riparian zone, which is an effective buffer for river governance. (2) Different spatial scales have different effects on the threshold relationship between landscape pattern and water quality. In the nonlinear regression model of landscape pattern and water quality in the buffer zone of 1100 m riparian zone, the significance and R

Based on multi-temporal remote sensing images, the building land index (BAI) of Yuxi City in 1995, 2005 and 2015 was extracted. The spatial and temporal characteristics of urban expansion in Yuxi City in recent 20 years were studied by selecting the expansion rate, expansion intensity, dynamic attitude, expansion degree, compactness and fractal dimension.The results showed that :(1) From 1995 to 2015, the building land in Yuxi City has been in a state of rapid expansion, the area of building land increased from 131.21km2 to 416.66km2, expanding by 284.45km2.(2)The compactness of urban shape has been increasing from 0.033 to 0.058, an increase of 0.025. The overall spatial pattern was becoming more and more compact, and the fractal dimension and stability was constantly declining.The urban form was complex and in an unstable state. In the future, reasonable policies should be formulated to adjust the spatial distribution and expansion mode of building land to achieve sustainable development.

How to quantitatively measure and identify the characteristics of urban landscape expansion is an important scientific problem facing rapid urbanization. The Multi-order Adjacency Index (MAI) can be used to measure the degree of urban expansion at multiple consecutive points in time, and has significant advantages in characterizing urban landscape expansion. This paper takes Chenggong District of Kunming City as the research area, selects 6 remote sensing images (2005, 2010, 2013, 2015, 2017 and 2018) to divide 5 time periods, and uses MAI to analyze the change process of urban landscape pattern in Chenggong District of Kunming City. The experimental results show that: (1) Relying on the expansion of the original construction land is the main mode of urban expansion in Chenggong District; (2) The degree of urban expansion in Chenggong District decreases with the attenuation of the spatial distance between new and old patches; (3) Generally speaking , The urban expansion rate of Chenggong District has gradually slowed down, and the urban expansion pattern has become more compact.

This article uses 20 years of remote sensing image maps of Kunming City as the basis, and calculates the city’s impervious surface change, expansion intensity index, compactness change index and other indicators to analyze the dynamic changes of Kunming’s urban land in terms of time series and spatial form Perform monitoring and analysis. It also discusses the driving factors of urban land expansion in combination with the socio-economic development of the study area and the natural geographic environment. Research shows that: (1) From 2000 to 2020, the area of urban construction land in the cities under the jurisdiction of Kunming continues to increase, and the overall trend is steady. (2) The urban land expansion intensity of the built-up area of Kunming City showed an overall trend of increasing first and then decreasing, showing phased characteristics; (3) According to the calculation of the Richardson compactness index, it is known that from 2000 to 2020, Kunming City The intensity of urban land expansion and compactness is generally on the rise year by year.

Based on multi-temporal remote sensing images, the vegetation information of Yuxi City in 1995, 2005 and 2015 was extracted by using the normalized difference vegetation index ( NDVI ), and the pixel dichotomy model was used to calculate the vegetation coverage ( FVC ). The spatial and temporal characteristics of vegetation coverage area distribution in Yuxi City in recent 20 years were discussed by selecting the mean value of vegetation coverage and the transfer matrix of vegetation coverage grade area. The results showed that the area of middle and high vegetation coverage in Yuxi was more than 50 %, and the overall vegetation coverage was improved in from 1995 to 2015. In terms of spatial distribution, the overall vegetation coverage shows that the vegetation coverage in the northeast urban agglomeration area and both sides of the western valley is low, while the vegetation coverage in the southwest mountainous area is large. In the future, reasonable policies should be formulated to rationally use natural resources to enhance urban vegetation coverage, optimize land use structure, and realize the healthy and sustainable development of Yuxi City.

Considering the complex natural and human environmental factors in Yunnan Province, based on the meteorological, land use, DEM and MOD13A3 vegetation datas of four different years in 2005, 2010, 2015 and 2019 in Yunnan Province, 15 evaluation indexes including soil erosion intensity, landscape diversity index, net primary productivity of vegetation, annual temperature, annual precipitation and biological abundance were selected by using GIS and remote sensing technology combined with SRP model. The principal component analysis method was used to comprehensively and quantitatively study the spatial and temporal distribution characteristics of ecological vulnerability in Yunnan Province from 2005 to 2019. The results showed that: (1) From 2005 to 2019, the overall ecological vulnerability of Yunnan Province showed a trend of continuous improvement. The area of severely and extremely vulnerable areas decreased by 2833.09 km2 compared with 2005, and the improvement trend in 2015-2019 was the most obvious. The area of moderately, severely and extremely vulnerable areas decreased from 27015.66 in 2015 to 1325.68 km2 in 2019, and the proportion of slightly vulnerable areas increased from 88.3% in 2015 to 99.6%. (2) The severely and extremely vulnerable areas in Yunnan Province are mainly distributed in the central, northwest and northeast of Yunnan Province. The highest degree of ecological vulnerability is in the central Yunnan Province, and the extremely vulnerable area is about 443.8 km2. (3) Moderately vulnerable areas in Yunnan Province are mainly concentrated in southeast Yunnan; (4)The slightly vulnerable areas in Yunnan Province are mainly distributed in southwest Yunnan, accounting for 22.6% of the whole province.

With the rapid development of information technologies such as the Internet, the Internet of Things and cloud computing, network big data is widely used in various fields of society, and making good use of network big data is great significance to the sustainable development of cities. Based on the network big data as the data source, this paper analyzes the spatial distribution and spatial-temporal pattern of house prices in the main city zone of Kunming through the Getis-Ord Gi* index and Kriging method, and quantifies the spatial-temporal changes of house prices through statistical analysis and spatial overlay analysis. The result shows that in hot and cold spot analysis, hot spot (high house price agglomeration area) is surrounded by cold spot (low house price agglomeration area), and Chenggong District is the cold spot dominant area. The house price in the main city zone of Kunming forms two high-value areas in the spatial distribution, and presents a pile-shaped ring structure with a central high price and low price in circumference. During the study period, the overall house price fluctuations were small, showing a pattern of north high and south low. The results of statistical analysis and spatial overlay analysis show that there is a significant difference in house prices among various administrative districts. The high-value clusters and low-value clusters in house prices are randomly distributed in various administrative regions. The price changes in Chenggong District are the most significant, and Panlong District has the smallest change.

To explore temporal and spatial evolution of NDVI and its response relation with Climate in Kunming, Yunnan Province, it is is of great significance to the ecological environmental protection and ecological safety construction. Based on the Landsat data of Kunming City from 2010 to 2018 and the meteorological data of Kunming City during the same period, this paper uses the method of pixel dichotomy, correlation analysis and other methods to analyze the temporal and spatial evolution of vegetation coverage and its response to climate change in the past 9 years. The results show that: (1) From 2010 to 2018, Kunming's average annual NDVI showed an upward trend, with an increase of 0.017/year. In 2018, the area covered by high vegetation was as high as 54%, and the ecological environment of Kunming City was significantly improved. (2) The change of NDVI in Kunming is insignificantly positively correlated with annual precipitation and insignificantly negatively correlated with annual average temperature, but has a higher correlation with annual average temperature and is more sensitive to temperature factors. (3) The correlation between NDVI change and elevation differentiation is significant within the elevation zone of 619 ~ 2642m in Kunming city, NDVI increases with the increase of elevation.

Urbanization has created a significant gap between the Three Northeast Provinces (TNP) and the Yangtze River Delta (YRD) since China’s reform and opening-up. In this study, we explain this problem ...

Land-use change is a typical geographic evolutionary process characterized by spatial heterogeneity. As such, the driving factors, conversion rules, and rate of change vary for different regions around the world. However, most cellular automata (CA) models use the same transition rules for all cells in the model space when simulating land-use change. Thus, spatial heterogeneity change is ignored in the model, which means that these models are prone to over- or under simulation, resulting in a large deviation from reality. An effective means of accounting for the influence of spatial heterogeneity on the quality of the CA model is to establish a partitioned model based on cellular space partitioning. This study established a partitioned, dual-constrained CA model using the area-weighted frequency of land-use change (AWFLUC) to capture its spatial heterogeneity. This model was used to simulate the land-use evolution of the Dianchi Lake watershed. First, the CA space was divided into subzones using a dual-constrained spatial clustering method. Second, an artificial neural network (ANN) was used to automatically acquire conversion rules to construct an ANN-CA model of land-use change. Finally, land-use changes were simulated using the ANN-CA model based on data from 2006 to 2016, and model reliability was validated. The experimental results showed that compared with the non-partitioned CA model, the partitioned counterpart was able to improve the accuracy of land-use change simulation significantly. Furthermore, AWFLUC is an important indicator of the spatial heterogeneity of land-use change. The shapes of the division spaces were more similar to reality and the simulation accuracy was higher when AWFLUC was considered as a land-use change characteristic.

This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p &lt, 0.001), migration route (OR = 0.993, p &lt, 0.01), river (OR = 0.861, p &lt, 0.001), lake(OR = 0.992, p &lt, 0.001), road (OR = 0.906, p &lt, 0.001), railway (OR = 0.980, p &lt, 0.001), temperature (OR = 1.170, p &lt, 0.01), precipitation (OR = 0.615, p &lt, 0.001) and relative humidity (OR = 1.337, p &lt, 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk &gt, 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk &gt, 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections.

With the development of economy, growth of population and urbanization, urban land is increasing and the speed of urban expansion is speeding up. Mastering the spatial-temporal process of urban expansion is of great significance for natural environment and human development. In this paper, taking the Dianchi Lake Basin as an example, based on the TM and OLI image data from 1990 to 2015, every five years as a time node, the decision tree classification method is used to extract and analyze the urban land for each period of image data. The results showed that the area of urban land in Dianchi Lake Basin increased from 1990 to 2015 with 462km2, with an average annual growth rate of 5.9%, of which the fastest expansion rate has occurred from 2010 to 2015. The impervious surface expansion is radiation-like growth centered around the urban area of Kunming, mainly in the eastern and northern parts of Dianchi.

Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

The impervious surface has become an important index to evaluate the urban environmental quality and measure the development level of urbanization. At present, the use of remote sensing technology to extract impervious surface has become the main way. In this paper, a method to extract impervious surface based on rule algorithm is proposed. The main ideas of the method is to use the rule-based algorithm to extract impermeable surface based on the characteristics and the difference which is between the impervious surface and the other three types of objects (water, soil and vegetation) in the seven original bands, NDWI and NDVI. The steps can be divided into three steps: 1) Firstly, the vegetation is extracted according to the principle that the vegetation is higher in the near-infrared band than the other bands; 2) Then, the water is extracted according to the characteristic of the water with the highest NDWI and the lowest NDVI; 3) Finally, the impermeable surface is extracted based on the fact that the impervious surface has a higher NDWI value and the lowest NDVI value than the soil.In order to test the accuracy of the rule algorithm, this paper uses the linear spectral mixed decomposition algorithm, the CART algorithm, the NDII index algorithm for extracting the impervious surface based on six remote sensing image of the Dianchi Lake Basin from 1999 to 2014. Then, the accuracy of the above three methods is compared with the accuracy of the rule algorithm by using the overall classification accuracy method. It is found that the extraction method based on the rule algorithm is obviously higher than the above three methods.

Background A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014. Methods Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans. Results The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p

Land Use and Land Cover Change (LUCC) is the essential content of the study of the current global environmental change and sustainable development, and has a profound influence on regional water cycle, atmospheric circulation, environmental quality, climate change, and terrestrial ecosystem productivity. It is also the most important reason causing biodiversity attenuation. The MAS/LUCC model of this paper is designed to explore the mechanism of the spatial and temporal evolution of the land use change, which results from the interaction among government, residents, farmers, and other agents, the interaction between multi-agent and the environment where the agents live. The model also introduces the ant colony optimization to improve the simulation accuracy. Taking Erhai Lake Basin as a case, MAS/LUCC used the watershed land use classification map in 2000 as the initial data, predicted and simulated the land use types and structure of the watershed in 2010, using the land use classification map of 2010 as validation data. The accuracy of the model is 79.71% or so by the point-to-point verification method, and the ant colony optimization improves the precision of 8.94% compared with the model without it. Thus, it can be able to provide auxiliary decision support for the government and land-use planners to develop a land policy.

This paper combines geographic information technology and casualties assessment model, research on how quickly to get the results of casualties assessment calculation. In this paper, we introduced GIS-based seismic casualties assessment system's principle and design, Five-story structure based system architecture, functional realize and other content. Use the 6.4 earthquake occurred in Ning'er County, Pu'er City, Yunnan Province as an example to make once casualties assessment, and made a brief analysis of the results.

In order to guide the city's waterlogging control, it's very necessary to research the urban waterlogging occurrence rules and how urbanization affects the urban waterlogging. In this paper, we select the catchment area located in Kunming Dongfeng East Road as study area to construct the urban waterlogging simulation model based on GIS and SWMM model. The procedure of model building contains sub-basin division, drainage system generalization, and parameter settings. Then different urbanization degrees and different rainfall return period settings will be tested on the constructed model. The results show that the model can be used for dynamically simulating urban waterlogging. Both runoff volume and peak flow increased significantly with increasing return period. In addition, with the development of urbanization, urban impervious area increased rapidly, but decreased in rainfall infiltration, while the risk of floods increased. This impact was more obvious in the short return period rainfall conditions.

This paper discusses how to use the ArcEngine components and Orcale database to quickly draw the Yunnan Province digital isoseisms. Besides the current general ellipse model, the paper was the first time to complete the line source model of isoseisms drawing. At the same time, it improved the Binary search algorithm in order to quickly find closest active fault. Then the existing isoseisms file provided three correction methods: parameter correction, aftershocks correction and manual correction, which were respectively applied for the different data and purpose of the correction. Finally, taking an example of the simulation for Ning'er 6.4 and Lijiang 7.0 earthquake to demonstrate the method of fast drawing Yunnan digital isoseisms, and the method turned out to be feasible and practicable.

This paper discusses the issues about the cyanobacteria bloom identification and monitoring methods based on MODIS satellite images of Dianchi Lake. Through a comparative analysis of the spectral characteristics on typical surface features (clean water, muddy water, vegetation and cyanobacteria) and different concentration of cyanobacteria bloom covering over the water surface of Dianchi Lake, we can get the response sensitive bands information of cyanobacteria bloom. We can also extract the spatial distribution and divide the concentration classification threshold of cyanobacteria bloom in Dianchi Lake by using false color synthesis (MODIS:6-2-1) method, single-band method, ratio vegetation index (RVI) method, normalized difference vegetation index (NDVI) method, enhanced vegetation index (EVI) method. At the same time, we can analyse the dynamic process about cyanobacteria bloom through multitemporal remote sensing images and difference operation.

Since AIDS was first discovered, mathematical models of the transmission dynamics of this disease have been developed to determine the drivers of the epidemic. It has become apparent that the most important factor in the propagation of the disease is the aggregate effects of individual behavior and perception of risk in a geographic environment. Agent-based models (ABM) allow one to simulate the social and sexual interactions which may describe the societal context in which HIV may spread easily. Using this framework, we develop an agent-based modeling approach to simulate sexual transmission of HIV/AIDS in an urban environment, as a result of individuals' interactions in a grid context. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, allowing them to move between places within the urban environment for representing the spatial heterogeneity and the complexity involved in HIV diffusion. In order to better describe agent's conduct, each agent has its only gender, age, sex partner, life expectancy, condom use and other attributes. In each time step, every agent may move, find sex partner, separate from his sex partner or die. The model is implemented on the Repast Simphony 1.2. The simulation results show the factors such as density of population, condom use and vision of people play significant roles in the spread of HIV/AIDS. So related departments should vigorously promote the use of condoms, pay more attention to key areas and strengthen moral education to slow down the speed of sexual transmission of HIV/AIDS when establishing measures in prevention and control the spreading of AIDS.