Shifeng Fang

An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things

Climate change and environmental monitoring and management have received much attention recently, and an integrated information system (IIS) is considered highly valuable. This paper introduces a novel IIS that combines Internet of Things (IoT), Cloud Computing, Geoinformatics [remote sensing (RS), geographical information system (GIS), and global positioning system (GPS)], and e-Science for environmental monitoring and management, with a case study on regional climate change and its ecological effects. Multi-sensors and Web services were used to collect data and other information for the perception layer; both public networks and private networks were used to access and transport mass data and other information in the network layer. The key technologies and tools include real-time operational database (RODB); extraction-transformation-loading (ETL); on-line analytical processing (OLAP) and relational OLAP (ROLAP); naming, addressing, and profile server (NAPS); application gateway (AG); application software for different platforms and tasks (APPs); IoT application infrastructure (IoT-AI); GIS and e-Science platforms; and representational state transfer/Java database connectivity (RESTful/JDBC). Application Program Interfaces (APIs) were implemented in the middleware layer of the IIS. The application layer provides the functions of storing, organizing, processing, and sharing of data and other information, as well as the functions of applications in environmental monitoring and management. The results from the case study show that there is a visible increasing trend of the air temperature in Xinjiang over the last 50 years (1962-2011) and an apparent increasing trend of the precipitation since the early 1980s. Furthermore, from the correlation between ecological indicators [gross primary production (GPP), net primary production (NPP), and leaf area index (LAI)] and meteorological elements (air temperature and precipitation), water resource availability is the decisive factor with regard to the terrestrial ecosystem in the area. The study shows that the research work is greatly benefited from such an IIS, not only in data collection supported by IoT, but also in Web services and applications based on cloud computing and e-Science platforms, and the effectiveness of monitoring processes and decision-making can be obviously improved. This paper provides a prototype IIS for environmental monitoring and management, and it also provides a new paradigm for the future research and practice; especially in the era of big data and IoT.

An Integrated Approach to Snowmelt Flood Forecasting in Water Resource Management

Water scarcity and floods are the major challenges for human society both present and future. Effective and scientific management of water resources requires a good understanding of water cycles, and a systematic integration of observations can lead to better prediction results. This paper presents an integrated approach to water resource management based on geoinformatics including technologies such as Remote Sensing (RS), Geographical Information Systems (GIS), Global Positioning Systems (GPS), Enterprise Information Systems (EIS), and cloud services. The paper introduces a prototype IIS called Water Resource Management Enterprise Information System (WRMEIS) that integrates functions such as data acquisition, data management and sharing, modeling, and knowledge management. A system called SFFEIS (Snowmelt Flood Forecasting Enterprise Information System) based on the WRMEIS structure has been implemented. It includes operational database, Extraction-Transformation-Loading (ETL), information warehouse, temporal and spatial analysis, simulation/prediction models, knowledge management, and other functions. In this study, a prototype water resource management IIS is developed which integrates geoinformatics, EIS, and cloud service. It also proposes a novel approach to information management that allows any participant play the role as a sensor as well as a contributor to the information warehouse. Both users and public play the role for providing data and knowledge. This study highlights the crucial importance of a systematic approach toward IISs for effective resource and environment management.

An integrated information system for snowmelt flood early-warning based on internet of things

Floods and water resource management are major challenges for human in present and the near future, and snowmelt floods which usually break out in arid or semi-arid regions often cause tremendous social and economic losses, and integrated information system (IIS) is valuable to scientific and public decision-making. This paper presents an integrated approach to snowmelt floods early-warning based on geoinformatics (i.e. remote sensing (RS), geographical information systems (GIS) and global positioning systems (GPS)), Internet of Things (IoT) and cloud services. It consists of main components such as infrastructure and devices in IoT, cloud information warehouse, management tools, applications and services, the results from a case study shows that the effectiveness of flood prediction and decision-making can be improved by using the IIS. The prototype system implemented in this paper is valuable to the acquisition, management and sharing of multi-source information in snowmelt flood early-warning even in other tasks of water resource management. The contribution of this work includes developing a prototype IIS for snowmelt flood early-warning in water resource management with the combination of IoT, Geoinformatics and Cloud Service, with the IIS, everyone could be a sensor of IoT and a contributor of the information warehouse, professional users and public are both servers and clients for information management and services. Furthermore, the IIS provides a preliminary framework of e-Science in resources management and environment science. This study highlights the crucial significance of a systematic approach toward IISs for effective resource and environment management.

Changes in the Land Surface Energy Budget in Eastern China over the Past Three Decades: Contributions of Land-Cover Change and Climate Change

AbstractSensible heat flux (H), latent heat flux (LE), and net radiation (NR) are important surface energy components that directly influence climate systems. In this study, the changes in the surface energy and their contributions from global climate change and/or land-cover change over eastern China during the past nearly 30 years were investigated and assessed using a process-based land surface model [the Ecosystem–Atmosphere Simulation Scheme (EASS)]. The modeled results show that climate change contributed more to the changes of land surface energy fluxes than land-cover change, with their contribution ratio reaching 4:1 or even higher. Annual average temperature increased before 2000 and reversed thereafter; annual total precipitation continually decreased, and incident solar radiation continually increased over the past nearly 30 years. These climatic changes could lead to increased NR, H, and LE, assuming land cover remained unchanged during the past nearly 30 years. Among these meteorological var...

A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively.

An integrated system for land resources supervision based on the IoT and cloud computing

ABSTRACT Integrated information systems are important safeguards for the utilisation and development of land resources. Information technologies, including the Internet of Things (IoT) and cloud computing, are inevitable requirements for the quality and efficiency of land resources supervision tasks. In this study, an economical and highly efficient supervision system for land resources has been established based on IoT and cloud computing technologies; a novel online and offline integrated system with synchronised internal and field data that includes the entire process of ‘discovering breaches, analysing problems, verifying fieldwork and investigating cases’ was constructed. The system integrates key technologies, such as the automatic extraction of high-precision information based on remote sensing, semantic ontology-based technology to excavate and discriminate public sentiment on the Internet that is related to illegal incidents, high-performance parallel computing based on MapReduce, uniform storing and compressing (bitwise) technology, global positioning system data communication and data synchronisation mode, intelligent recognition and four-level (‘device, transfer, system and data’) safety control technology. The integrated system based on a ‘One Map’ platform has been officially implemented by the Department of Land and Resources of Guizhou Province, China, and was found to significantly increase the efficiency and level of land resources supervision. The system promoted the overall development of informatisation in fields related to land resource management.

The development and application of e-Geoscience in China

In the era of big data, scientific research is entering a key stage of scientific development under the guidance of a new paradigm, “e-Science”, and the core characteristics of which are collaboration and sharing. In the past decade, e-Science has rapidly developed around the world. There are now e-Science strategic plans, projects and extensive research activities on the national and international scales that encompass particle physics, astronomy, earth science, ecology, marine science, medicine, life sciences and other disciplines. However, there is no uniform and clear understanding of the essence, characteristics, infrastructure and application of e-Geoscience. This paper first discusses and analyzes the development of e-Science in a global context and then explores its development in China. Next, the development of e-Geoscience is discussed, particularly regarding the details of its design and implementation in China, including a conceptual model, a mode of application, a logical hierarchy, and functional and technical systems. Finally, the paper introduces a typical application, called the Northeast Asia Joint Scientific Exploration and Cooperative Research Platform (NAJSECRP), which is operating in research institutions in China, Russia and Mongolia. This platform can not only provide geodata and bibliographies and promote resource sharing but also provides a collaborative research platform for scientific exploration. In practice, this platform has been shown to save costs and improve the efficiency of transnational, interdisciplinary scientific exploration and cooperative research.

Research on Land Surface Thermal-Hydrologic Exchange in Southern China under Future Climate and Land Cover Scenarios

Climate change inevitably leads to changes in hydrothermal circulation. However, thermal-hydrologic exchanging caused by land cover change has also undergone ineligible changes. Therefore, studying the comprehensive effects of climate and land cover changes on land surface water and heat exchanges enables us to well understand the formation mechanism of regional climate and predict climate change with fewer uncertainties. This study investigated the land surface thermal-hydrologic exchange across southern China for the next 40 years using a land surface model (ecosystem-atmosphere simulation scheme (EASS)). Our findings are summarized as follows. (i) Spatiotemporal variation patterns of sensible heat flux (H) and evapotranspiration (ET) under the land cover scenarios (A2a or B2a) and climate change scenario (A1B) are unanimous. (ii) Both H and ET take on a single peak pattern, and the peak occurs in June or July. (iii) Based on the regional interannual variability analysis, H displays a downward trend (10%) and ET presents an increasing trend (15%). (iv) The annual average H and ET would, respectively, increase and decrease by about 10% when woodland converts to the cultivated land. Through this study, we recognize that land surface water and heat exchanges are affected greatly by the future climate change as well as land cover change.

Snow mapping for water resource management using MODIS satellite data in northern Xinjiang, China

Snow is the most important freshwater resource in northern Xinjiang, which is a typical inland arid ecosystem in western China. Snow mapping can provide useful information for water resource management in this arid ecosystem. An applicable approach for snow mapping in Northern Xinjiang Basin using MODIS data was proposed in this paper. The approach of linear spectral mixture analysis (LSMA) was used to calculate snow cover fractions within a pixel, which was used to establish a regression function with NDSI at a 250-meter grid resolution. Field campaigns were conducted to examine whether NDSI can be used to extend the utility of the snow mapping approach to obtain sub-pixel estimates of snow cover. In addition, snow depths at 80 sampling sites were collected in the study region. The correlation between image reflectivity and snow depth as well as the comparison between measured snow spectra and image spectra were analyzed. An algorithm was developed on the basis of the correlation for snow depth mapping in the region. Validation for another dataset with 50 sampling sites showed an RMSE of 1.63, indicating that the algorithm was able to provide an estimation of snow depth at an accuracy of 1.63cm. The results indicated that snow cover area can reach 81% and average snow depth was 13.8 cm in north Xinjiang in January 2005. Generally speaking, the snow cover and depth had a trend of gradually decreasing from north to south and from the surroundings to the center. Temporally, the cover reached a maximum in early January, and the depth reached a maximum was ten days later. Snow duration was so different in different regions with the Aletai region having the longest and the Bole having the shortest. In the period of snow melting, snow depth decreased earlier, afterward snow cover dwindled. Our study showed that the spatial and temporal variation of snow cover was very critical for water resource management in the arid inland region and MODIS satellite data provide an alternative for snow mapping through dedicated development of mapping algorithms suitable for local application.

Impacts of land use/cover change on spatial variation of land surface temperature in Urumqi, China

Land use/cover change (LUCC) has significant impacts on regional environment. Land surface temperature (LST) is an important indicator for assessment of regional environment especially in big cities where urban heat island is very obvious. In this study, remote sensing and geographic information systems (GIS) were used to detect LUCC for assessment of its impacts on spatial variation of LST in Urumqi, a big city in northwestern China. Two Landsat TM/ETM+ images respectively in 1987 and 2002 were examined for LUCC detection. LST and NDVI were computed from the images for different land use/cover types. Impacts of LUCC on regional environment can be assessment through LST difference during the period. Our results showed that land use/cover changes were very obvious in Urumqi between 1987 and 2002 due to rapid expansion of the city. Urban/built-up land increased by almost twice in the period, while the barren land, the forestland and water area declined. The increase of urban/built-up land was mainly from the barren land. Spatial distribution of LST in the city has been highly altered as a result of urban expansion. The urban/built-up area had LST increase by 4.48% during the period. The LST difference between built-up land and other land use/cover types also significantly increased between 1978 and 2002, with high LST increase area corresponding to the urban expansion regions. Moreover, changes of vegetation also had shaped many impacts on spatial variation of LST in the city. We found that NDVI has a negative correlation with LST among the land use/cover types. This probably is due to the ecological function of vegetation in cooling down the surface from high evapotranspiration. The study demonstrated that combination of remote sensing and GIS provided an efficient way to examine LUCC for assessment of its impacts on regional environment in big cities.