Huan Pei

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.

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.

Snow information abstraction based on remote sensing data: Taking the north of Xinjiang for example

This paper proposes an applicable approach for snow information abstraction in northern Xinjiang Basin using MODIS data. Linear spectral mixture analysis (LSMA) was used to calculate snow cover fractions (SF) within a pixel, which was used to establish a regression function with NDSI. In addition, 80 snow depths samples were collected in the study region. The correlation between image spectra reflectance and snow depth as well as the comparison between measured snow spectra and image spectra was analyzed. An algorithm was developed for snow depth inversion on the basis of the correlation between snow depth and snow spectra in the region. The results indicated that the model of SF had a high accuracy with the mean absolute error 0.06 tested by 26 true measured values and the validation for snow depth model using another dataset with 50 sampling sites showed an RMSE of 1.63. Our study showed that MODIS data provide an alternative method for snow information abstraction through development of algorithms suitable for local application.

Mapping drought status of winter wheat from MODIS data in North China Plain

Drought is very severe in North China Plain, where winter wheat is one of the most important cropping systems. In this paper, we present an approach to map drought status of winter wheat in the plain for better farming management. The approach is based on the temperature-vegetation dryness index (TVDI) computed from the wet and dry edges of Ts-NDVI space. Using the MODIS data, we applied the approach to map drought status in North China Plain for the winter wheat growing period from March to May in 2006. Our results show that spatial variation of agricultural drought is very obvious in the region. Severe drought was observed in eastern Hebei, western Shandong, and northwestern Henan province respectively. The weather reports from China Meteorological Administration were used to validate our mapping results of the drought status. The highly accordance of our drought mapping results with the reported drought distribution from CMA confirms the applicability of TVDI approach in drought mapping in North China Plain.

Study of land use/cover classification based on decision rules and multi-features

An approach based on decision rules algorithm and multi-features was proposed for desertification monitoring in Turpan Oasis using SPOT images. At first, the common methods such as principal component transformation, tasseled cap transformation and minimum noise fraction transformation were used to extract spectral feature, and vegetation index as well as wetness index were also calculated. Elevation and slope were generated from DEM. Then inter-class separability method was applied to choose the optimum features. Based on supervised classification results and each feature, a decision tree model was built to extract the land use/cover information especially desertification distribution. The results showed that the accuracy of decision tree classification is 88% and the kappa coefficient is 0.76, which had increased by 7%, 10% and 11% than maximum likelihood method, markov distance method and the minimum distance method. The whole process may provide reference for land use/cover real-time monitoring in oasis of arid areas.

Study on the temporal and spatial distribution and changes of land surface temperature in winter in Urumqi, Xinjiang

The Urumqi City which located at the foot of the northern slope of the Tianshan Mountains in Xinjiang, has a special basin-type topographic feature, with the long, cold and serious air pollution winter. With the rapid process of urbanization and development in the recent years, whether has the effect of Urban Heat Island(UHI) has been widespread concerned these years. Land Surface Temperature (LST) in urban regions is an important influencing factor and indicator of the Urban Heat Island (UHI). Urumqi City was taken to be the typical study area in this paper, MODIS data in December of every year from 2000 to 2007 were chosen to take the LST retriving with the Split Window Algorithm (Qin, Mao, etc.), which is a mature algorithm, with higher precision and simple method, and the average absolute error of LST retriving products were ± 1.2 °C, according to the analysis of the temporal and spatial distribution and changes of LST, the following conclusions had been made: From the Monthly Compositon LST of Urumqi in December from 2000 to 2007, Urban Heat Island (UHI) effect of Urumqi City in winter is not obvious and there is no disciplinary changes of LST and no visible changes in temporal and spatial distribution of LST, in despite of eruptible development of urbanization.

MODIS-based analysis of snow distribution and change in Emin River basin, Xinjiang, China

Serious snowmelt flood happened in the north of Xinjiang in March each year and it brought serious effect to local peoples life and national production. Researching on snow distribution and change can provide decision support for preventing flood and alleviating disaster, Meanwhile, accurate snow mapping on a drainage area and snow cover depletion curve can provide parameters for simulating snowmelt runoff. This paper introduced unmixed pixel method and extracting snow cover according to the elevation zone, analyzing snow change in each elevation zone from March 4th to 12th 2005 in Emin river basin, Xinjiang, China integrating MODIS data and GIS. In addition, this paper analyzed the reason of flood formation and making regression analysis of snow change with weather factor by stepwise regression. The result shows that the correlation between snow change and temperature, as well as precipitation is high in 400-900m elevation zone (r = 0.9).