A hierarchical model for estimating methane emission from wetlands using MODIS data and ARIMA modeling

A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April 2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software.     

Climate applications of a global, 2-hourly atmospheric precipitable water dataset derived from IGS tropospheric products

A global, 2-hourly atmospheric precipitable water (PW) dataset is produced from ground-based GPS measurements of zenith tropospheric delay (ZTD) using the International Global Navigation Satellite Systems (GNSS) Service (IGS) tropospheric products (~80–370 stations, 1997–2006) and US SuomiNet product (169 stations, 2003–2006). The climate applications of the GPS PW dataset are highlighted in this study. Firstly, the GPS PW dataset is used as a reference to validate radiosonde and atmospheric reanalysis data. Three types of systematic errors in global radiosonde PW data are quantified based on comparisons with the GPS PW data, including measurement biases for each of the fourteen radiosonde types along with their characteristics, long-term temporal inhomogeneity and diurnal sampling errors of once and twice daily radiosonde data. The comparisons between the GPS PW data and three reanalysis products, namely the NCEP-NCAR (NNR), ECMWF 40-year (ERA-40) and Japanese reanalyses (JRA), show that the elevation difference between the reanalysis grid box and the GPS station is the primary cause of the PW difference. Secondly, the PW diurnal variations are documented using the 2-hourly GPS PW dataset. The PW diurnal cycle has an annual-mean, peak-to-peak amplitude of 0.66, 0.53 and 1.11 mm for the globe, Northern Hemisphere, and Southern Hemisphere, respectively, with the time of the peak ranging from noon to late evening depending on the season and region. Preliminary analyses suggest that the PW diurnal cycle in Europe is poorly represented in the NNR and JRA products. Several recommendations are made for future improvements of IGS products for climate applications.     

《昆明市城市排水系统分布专题地图》的编制研究

本文就利用ARCMAP软件编制<昆明城市排水系统分布专题地图>的实践过程,尝试将地图编制与排水管线地理信息系统建设实现很好的结合,实现图-库一体化联接,并最终实现以地下管线探测数据的地图可视化过程,从而完成专业管线专题地图的编制,为城市排水规划、建设与决策提供直观依据.     

HPABM: A Hierarchical Parallel Simulation Framework for Spatially-explicit Agent-based Models

A Hierarchical Parallel simulation framework for spatially-explicit Agent-Based Models (HPABM) is developed to enable computationally intensive agent-based models for the investigation of large-scale geospatial problems. HPABM allows for the utilization of high-performance and parallel computing resources to address computational challenges in agent-based models. Within HPABM, an agent-based model is decomposed into a set of sub-models that function as computational units for parallel computing. Each sub-model is comprised of a sub-set of agents and their spatially-explicit environments. Sub-models are aggregated into a group of super-models that represent computing tasks. HPABM based on the design of super- and sub-models leads to the loose coupling of agent-based models and underlying parallel computing architectures. The utility of HPABM in enabling the development of parallel agent-based models was examined in a case study. Results of computational experiments indicate that HPABM is scalable for developing large-scale agent-based models and, thus, demonstrates efficient support for enhancing the capability of agent-based modeling for large-scale geospatial simulation.     

利用MATLAB实现图像处理与分析

本文通过对图像的均衡操作、二值图像的处理、小波去噪等实例，介绍了MATLAB进行图像处理和分析的实用方法与技巧，它对各种软钉包、进行图像处理和分析等，提供了高效便捷的方法。     

Long-term polar motion prediction using normal time–frequency transform

This paper presents normal time–frequency transform (NTFT) application in harmonic/quasi-harmonic signal prediction. Particularly, we use the normal wavelet transform (a special NTFT) to make long-term polar motion prediction. Instantaneous frequency, phase and amplitude of Chandler wobble, prograde and retrograde annual wobbles of Earth’s polar motion are analyzed via the NTFT. Results show that the three main wobbles can be treated as quasi-harmonic processes. Current instantaneous harmonic information of the three wobbles can be acquired by the NTFT that has a kernel function constructed with a normal half-window function. Based on this information, we make the polar motion predictions with lead times of 1 year and 5 years. Results show that our prediction skills are very good with long lead time. An abnormality in the predictions occurs during the second half of 2005 and first half of 2006. Finally, we provide the future (starting from 2013) polar motion predictions with 1- and 5-year leads. These predictions will be used to verify the effectiveness of the method proposed in this paper.     

Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain,Northeast China

Increasing interest in wetlands for environmental management requires an understanding of the location, spatial extent, and configuration of the resource. The National Wetlands Inventory is the most commonly used data source for this information. However, its accuracy is limited in some contexts, such as agricultural and forested wetlands. An large number of studies have mapped wetlands worldwide from the perspective of land use and land cover change. However, information on the actual wetland planting areas annually is limited, which greatly impacts ongoing research. In this case study of the West Songnen Plain, we developed a simple algorithm for the quick mapping of wetlands by utilizing their unique physical features, such as annual display of phenological land-cover change of exposed soils, shallow flooding water, and plants from multi-temporal Landsat images. Temporal variations of the Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) derived from Landsat images in 2010 for wetlands at different growth stages were analyzed. Results show that during the ante-tillering phase, the NDVI value (above zero) is lower than the LSWI value of paddies because of flooding of shallow water; during the reproductive and ripening phases, the NDVI value is higher than the LSWI value (above zero); and during the post-harvest wetland planting phase, the NDVI value is still higher than the LSWI value, but the LSWI value is negative. Wetland areas can be detected using one or two images in the optimum time window. The algorithm based on the difference of NDVI and LSWI values derived from Landsat images was used to extract the actual wetland planting area. Validated alongside statistical data, the algorithm showed high accuracy. Therefore, this algorithm highlights the unique features of wetlands and can help in mapping the actual wetland area annually on a regional scale. Results further indicate that the new method has a classification accuracy of 92 %. In comparison, two traditional methods based on Landsat-7/ETM registered accuracy rates of only 83 % and 87 % respectively.     

Multi-resolution seamless image database

This paper presents the basic concepts and principles, data structure and high efficient spatial index for multi-resolution image database. The database is characterized by arrangement of multi-resource image data and seamless mosaic, distribution-based storage and management, integration with other spatial database software such as GeoStar and GeoGrid developed by Wuhan Technical University of Surveying and Mapping.     

Development of a data mining method for land control

Land resources are facing crises of being misused, especially for an intersection area between town and country, and land control has to be enforced. This paper presents a development of data mining method for land control. A vector-match method for the prerequisite of data mining i. e., data cleaning is proposed, which deals with both character and numeric data via vectorizing character-string and matching number. A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse. In order to monitor land use dynamically and accurately, it is suggested to set up a real-time land control system based on GPS, digital photogrammetry and online data mining. Finally, the means is applied in the intersection area between town and country of Wuhan city, and a set of knowledge about land control is discovered.