Predicting salinity impacts of land-use change: Groundwater modelling with airborne electromagnetics and field data,SE Queensland,Australia

Throughout Australia, there is concern that land use change is mobilizing salt stored in the landscape, causing salinity in soil and water resources. Salt in the landscape becomes a salinity risk only if it is mobilized by groundwater movement. A combination of modelled groundwater behaviour under various land uses with three-dimensional salt-load maps developed from airborne electromagnetic survey (AEM) provides a practical tool to assess potential salt movement.AEM survey of the country around St. George, SE Queensland, revealed a potential salinity threat: significant salt stores in the uplands adjacent to flood plains which support important irrigation developments and which drain to the Darling River system. A conceptual model of the regional hydrogeology was built upon three-dimensional AEM data, an investigation-drilling program, and direct field measurement of hydraulic conductivities. This information was incorporated in a Flowtube groundwater model and groundwater responses to five different land management options were tested over a 100-year period. Surface water storage on relatively permeable soils and continuous irrigated cotton both resulted in water tables reaching the soil surface; rain-fed wheat and pasture both resulted in a raised watertable, but both established a new equilibrium without the water table reaching the ground surface.     

Environmental geophysics mapping salinity and water resources

Salinity and fresh water are two sides of the same coin, most conveniently measured by electrical conductivity; they can now be mapped rapidly in three dimensions using airborne electromagnetics (AEM). Recent developments in the calibration of airborne data against in-field measurements and additional information from radiometrics, magnetics and digital elevation models lend new insights into salinity, groundwater flow systems and water resources. Freshwater resources can be mapped, and salinity risk and the outcome of management interventions may be forecast, on the basis of the specific architecture of complete groundwater flow systems-enabling practical, cost-effective protection and development of water resources.     

Three-dimensional mapping of salt stores in the Murray-Darling Basin,Australia: 2. Calculating landscape salt loads from airborne electromagnetic and laboratory data

Salt is widespread in the Australian landscape – in soil, regolith and groundwater – leading to concerns that land management practices may be putting much agricultural land and important water resources at risk of salinisation. Defining the location and nature of salt stores is an important first step in understanding the processes leading to salinity of soils, streams and groundwater resources, and predicting areas that may be at risk. Airborne geophysics can define subsurface salt stores and mobilisation pathways. Airborne electromagnetics (AEM) can map the three-dimensional conductivity structure of the landscape but does not, intrinsically, quantify the amount of salt. Salinity, moisture content, porosity and mineralogy all contribute to the electromagnetic signal, and each can vary significantly throughout the landscape.In the Lower Balonne catchment, Queensland, the relationship between AEM and the amount of salt in the landscape was quantified using laboratory analyses of pore fluids from core samples. A general statistical relationship was established between AEM conductivity and salt load (defined as the product of pore fluid salinity, porosity and moisture content)—with a significant positive correlation although data were generally widely dispersed. Comparison of calculated salt load with borehole electromagnetic logs gives insight into the factors contributing to dispersion in the AEM data.The relationship transforms bulk conductivity to salt load in 5 m layers, allowing the generation of a three-dimensional map of the salt load. This is a powerful tool for identifying areas that may require monitoring and management interventions to reduce salinity risk. An example is given of salt loads beneath an established irrigation area in the Lower Balonne catchment, Queensland.     

空间辅助决策支持系统的应用模型及管理研究

应用建模是空间辅助决策支持系统有效管理和使用模型的基础。本文抽象了空间辅助决策应用模型的组成结构和建立过程,分析了空间辅助决策应用模型的特点;接着分基础空间模型库、基础非空间模型库、应用模型库、模型管理库四部分探讨了空间决策支持系统中应用模型的管理,最后给出了相应的建设性结果。     

Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields

This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R² of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R² and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.     

渭南市旅游资源管理评价信息系统的设计与实现

旅游资源单体作为旅游资源普查的重要成果,具有数量多、分布广、资料丰富等特点。利用计算机对旅游资源普查成果资料进行集中管理、分析、评价,对旅游资源的合理规划利用和旅游业的跨越式发展具有重要意义。本研究以组件式地理信息系统软件为基础平台,以GIS技术、数据库技术和模块设计技术为基本技术支持,设计开发了渭南市旅游资源管理评价信息系统,对其需求分析、体系结构、数据库建设及功能实现进行了详细探讨。     

多维动态GIS在海岛保护与利用管理中的应用

针对海岛空间信息的特点,分析了传统管理手段的缺陷,提出了利用多维动态GIS技术解决海岛及其周边海域三维特性和数据多源、多尺度、多时相以及海岛的历史变迁问题,并探讨了应用的关键技术,为海岛综合管理提供一个新的解决方案,提高了海岛管理的可视化、信息化、高效化和科学化.     

多源空间信息集成技术在铁路用地信息管理中的应用

铁路信息化和数据标准化对铁路用地信息管理提出了更高的要求,多源空间信息集成技术是集成各种铁路空间数据、实现铁路用地信息化管理的基础。本文根据铁路用地空间数据的来源和组成,针对数据的多源、多尺度、动态等特点,提出了基于元数据驱动的铁路用地空间数据集成技术框架,设计了三层分布式C/S铁路用地信息系统结构,所构建的"武汉铁路局铁路用地综合信息管理系统"已取得理想的实际应用效果。     

面向电子政务的广播电视场强覆盖计算方法研究

本文就地理信息系统(GIS)在广播电视政务中的应用进行论述,通过分析GIS的优势与特点,阐述了基于GIS技术的广播电视场强覆盖计算方法,具有一定的现实意义。     

Implications of management strategies and vegetation change in the Mount St. Helens blast zone

The 1980 eruptions of Mount St. Helens provided an excellent opportunity for scientists to investigate the recovery of vegetation communities following a major geologic disturbance. An important and often overlooked aspect in these studies is the human factor in recovery processes, and specifically, the different management approaches taken towards re-establishment of vegetation on lands under the control of various owners. This study examines vegetation changes throughout the 1980 blast zone using a time series of Landsat-derived Normalized Difference Vegetation Index (NDVI) images and change detection methods to assess the changes over 25 years, from 1980 to 2005, as a function of human management combined with ecological factors. This long-term tracking of change indicates that differences in the speed of vegetation re-establishment and consequent rates of change substantially reflect human involvement and varying management strategies.     

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m², R² = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m², R² = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.