Grey analytic hierarchy process applied to effectiveness evaluation for groundwater potential zone delineation

An attempt has been made to identify plausible groundwater potential zones (GWPZ) based on Grey Analytic Hierarchy Process Method (Grey-AHP) using integrated remote sensing and geographic information system. Grey-AHP combines the advantages of classical analytic hierarchy process and grey clustering method for accurate estimation of weight coefficients. The method also examines the effectiveness of GWPZ identification process. The proposed methodology has been applied to the Hirakud canal command area, Odisha (India). Feature layers [e.g. soil type, geology] are utilized for groundwater potential index (GWPI) calculation. The resulting GWPI map has been classified into three GWPZ namely: good, moderate and poor. Effectiveness based on grey clustering method is found to be in between ‘better’ and ‘common’ classes. Value of coefficient of determination (R² = 0.865) supports the obtained effectiveness evaluation result. This analysis demonstrates the potential applicability of the methodology for a general aquifer system.     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

Integrating remote sensing and GIS techniques for monitoring and modeling shoreline evolution to support coastal risk management

The precise delineation of coastal areas subject to past, present, and future erosive processes plays a fundamental role in coastal risk management. Within this framework, satellite data represent a valuable synoptic and multi-temporal information source. Therefore, this research integrated remote sensing and GIS techniques for mapping and modeling shoreline evolution through time. Long-term shoreline’s proxy rates of advance and retreat were determined using Landsat data from the mid-1980s to 2011 and subsequently, a short-term scenario (3 years) was predicted and validated. Two different coastal environments, Oceanic and Mediterranean, were investigated. In the first, different proxies were analyzed, thereby enabling a multi-proxy analysis. Findings showed that the method provided more accurate results in higher energy environments (Oceanic) and where the coastline is not urbanized. Results also highlighted the importance of performing multi-proxy analyses in given study areas, to more reliably define shoreline modeling. Importantly, during the analyses, particular attention was given to assessing uncertainty, which is crucial when outcomes of scientific research are considered for management.     

Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models

The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m³s^?1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose.     

Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area,China

Urban sprawl has led to environmental problems and large losses of arable land in China. In this study, we monitor and model urban sprawl by means of a combination of remote sensing, geographical information system and spatial statistics. We use time-series data to explore the potential socio-economic driving forces behind urban sprawl, and spatial models in different scenarios to explore the spatio-temporal interactions. The methodology is applied to the city of Wuhan, China, for the period from 1990 to 2013. The results reveal that the built-up land has expanded and has dispersed in urban clusters. Population growth, and economic and transportation development are still the main causes of urban sprawl; however, when they have developed to certain levels, the area affected by construction in urban areas (Jian Cheng Qu (JCQ)) and the area of cultivated land (ACL) tend to be stable. Spatial regression models are shown to be superior to the traditional models. The interaction among districts with the same administrative status is stronger than if one of those neighbors is in the city center and the other in the suburban area. The expansion of urban built-up land is driven by the socio-economic development at the same period, and greatly influenced by its spatio-temporal neighbors. We conclude that the integration of remote sensing, a geographical information system, and spatial statistics offers an excellent opportunity to explore the spatio-temporal variation and interactions among the districts in the sprawling metropolitan areas. Relevant regulations to control the urban sprawl process are suggested accordingly.     

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。     

Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach

The study aims at delineating groundwater potential zones using geospatial technology and analytical hierarchy process (AHP) techniques in mining impacted hard rock terrain of Ramgarh and part of Hazaribagh districts, Jharkhand, India. Relevant thematic layers were prepared and assigned weight based on Saaty’s 9-point scale and normalized by eigenvector technique of AHP to identify groundwater prospect in the study area. The weighted linear combination method was applied to prepare the groundwater potential index in geographic information system. Final groundwater prospects were classified as excellent, very good, good, moderate, poor and very poor groundwater potential zones. Study thus revealed that the excellent, very good and good groundwater potential zones, respectively, cover 148.3, 373.66 and 438.86 km² of the study area, whereas the poor groundwater potential zone covers 180.05 km². Validation was done through a receiver operating characteristic curve, which indicated that AHP had good prediction accuracy (AUC = 75.45%).     

Free software: A review,in the context of disaster management

This article examines the nature of freely available geospatial software and information systems in the context of disaster management. The use of geospatial data is crucial to effective disaster management, from preparedness to response and recovery. However, to make efficient use of available data and information – before, during and after a disaster – reliable software is required. The software applications examined in this paper range from Geographical Information Systems, to the processing of remotely sensed images, crowd-source mapping, web applications and content management systems. Trends and challenges are considered, and guidelines are given, to foster and encourage the provision of information by Freeware and Open Source Software. Free geoinformatics can help to optimize the limited financial, technological and manpower resources that many organisations face, providing a sustainable input to analytical activities.     

Application of machine learning techniques in groundwater potential mapping along the west coast of India

ABSTRACT

Groundwater potential mapping (GWPM) in the coastal zone is crucial for the planning and development of society and the environment. The current study is aimed to map the groundwater potential zones of Sindhudurg coastal stretch on the west coast of India, using three machine learning models: random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine (SVM). In order to achieve the objective, 15 groundwater influencing factors including elevation, slope, aspect, slope length (LS), profile curvature, plan curvature, topographical wetness index (TWI), distance from streams, distance from lineaments, lithology, geomorphology, soil, land use, normalized difference vegetation index (NDVI), and rainfall were considered for inter-thematic correlations and overlaid with spring and well occurrences in a spatial database. A total of 165 spring and well locations were identified, which had been divided into two classes: training and validation, at the ratio of 70:30, respectively. The RF, BRT, and RF-SVM ensemble models have been applied to delineate the groundwater potential zones and categorized into five classes, namely very high, high, moderate, low, and very low. RF, BRT, and ensemble model results showed that 33.3%, 35.6%, and 36.8% of the research area had a very high groundwater potential zone. These models were validated with area under the receiver operating characteristics (AUROC) curve. The accuracy of RF (94%) and hybrid model (93.4%) was more efficient than BRT (89.8%) model. In order to further evaluate and validate, four different sites were subsequently chosen, and we obtained similar results, ensuring the validity of the applied models. Additionally, ground-penetrating radar (GPR) technique was applied to predict the groundwater table and validated by measured wells. The mean difference between measured and GPR predicted groundwater table was 14 cm, which reflected the importance of GPR to guide the location of new wells in the study region. The outcomes of the study will help the decision-makers, government agencies, and private sectors for sustainable planning of groundwater in the area. Overall, the present study provides a comprehensive high-precision machine learning and GPR-based groundwater potential mapping.     

Delineation of groundwater potential zone in hard rock terrain of India using remote sensing,geographical information system (GIS) and analytic hierarchy process (AHP) techniques

The present study has been undertaken to delineate the groundwater potential zones in the hard rock terrain of Palamu district, Jharkhand using the advanced applications of remote sensing, geographical information systems and analytic hierarchy process techniques. The integration and analyses of various thematic databases viz., geomorphology, lithology, soil, slope, lineament density, weathered zone thickness, drainage density and rainfall proved useful in the delineation of GWP zones. The study indicates that only 136?km² of the study area exhibit excellent groundwater potential, 248?km² has very good groundwater potential, whereas 36.89 and 38.23% are under poor and very poor groundwater potential zones, respectively. Hence, only a total of 11.6% of the area (490?km²) is classified as high to excellent groundwater potential. The final groundwater prospect map obtained was classified as excellent potential, very good potential, good potential, moderate potential, poor potential and very poor potential zone.     

An integrated methodology for soil moisture analysis using multispectral data in Mongolia

Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.     

Integration of SAR-derived river inundation areas,high-precision topographic data and a river flow model toward near real-time flood management

Since several space-borne synthetic aperture radar (SAR) instruments providing high spatial resolutions and multi-polarisation capabilities will be mounted on satellites to be launched from 2006 onwards, radar imagery promises to become an indispensable asset for many environmental monitoring applications. Due to its all weather, day and night capabilities, SAR imagery presents obvious advantages over optical instruments, especially in flood management applications. To date, however, the coarse spatial resolution of available SAR datasets restricts the information that can be reliably extracted and processing techniques tend to be limited to binary floodplain segmentation into ‘flooded’ and ‘non flooded’ areas. It is the purpose of this paper to further improve the exploitation of SAR images in hydraulic modelling and near real-time crisis management by means of developing image processing methodologies that allow for the extraction of water levels at any point of the floodplain. As high-precision digital elevation models (DEM) produced, for instance, from airborne laser scanning become more readily available, methods can be exploited that combine SAR-derived flood extent maps and precise topographic data for retrieving water depth maps. In a case study of a well-documented flood event in January 2003 on the River Alzette, Grand Duchy of Luxembourg, a root mean squared error (R.M.S.E.) of 41 cm was obtained by comparing the SAR-derived water heights with surveyed high water marks that were collected during image acquisition. Water levels that were computed by a previously calibrated hydraulic model also suggest that the water surface profiles provided by the combined use of topographic data and SAR accurately reflect the true water line. The extraction of flooded areas within vegetated areas further demonstrates the usefulness of the proposed methodology.     

浙江省海宁市TM图像土地利用自动分类精度评价方法的试验研究

按照国家土管局土地利用现状分类标准,利用叠合的海宁市长安镇１∶１万同时期土地详查电子地图的全域地面实况数据,在遥感图像处理常规分类方法的范畴内,采取最大程度利于提高分类精度的措施,所得的分类精度上限为６５％。研究表明,通常在试验区的小块局部中选择训练样本和选择评价分类精度的参考像元,对精度评价有显著影响,参考像元质量和数量的限制很可能是这些年来一些同类研究报道了较高分类精度结果的主要原因     

GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea

Groundwater productivity-potential (GPP) was analysed using the data mining models of an artificial neural network (ANN) and a support vector machine (SVM) in Boryeong city, Korea. The groundwater-productivity data with specific capacity (SPC) is strongly related to hydrogeological factors, and hence the relation may allow for groundwater potential mapping from hydrogeological factors through the ANN and SVM models. A back-propagation algorithm was used for the ANN model while a polynomial kernel was adopted for the SVM model. For the validation of the GPP maps generated from the ANN and SVM models, the area-under-the-curve analysis was performed using the SPC values of well data. The accuracies achieved from the ANN and SVM models are about 83.57 and 80.83%, respectively. It proves that the ANN and SVM models will be highly conducive to developing useful groundwater resources.     

GIS数据库几何精度评价及验证方法初探

通过用误差传播理论及 GIS数据库与遥感资料的叠合两种方法 ,对海宁市土地详查 GIS数据库进行几何精度分析 ,结果说明这两种方法是可行的。其中第二种方法会随着甚高分辨率遥感资料的普及而发挥越来越重要的作用。     

Development of groundwater favourability map using GIS-based driven data mining models: an approach for effective groundwater resource management

The development of groundwater favourability map is an effective tool for the sustainability management of groundwater resources in typical agricultural regions, such as southern Perak Province, Malaysia. Assessing the potentiality and pollution vulnerability of groundwater is a fundamental phase of favourability mapping. A geographic information system (GIS)-based Boolean operator of a spatial analyst module was applied to combine a groundwater potentiality map (GPM) model and a groundwater vulnerability to pollution index (GVPI) map, thereby establishing the favourable zones for drinking water exploration in the investigated area. The area GPM model was evaluated by applying a GIS-based Dempster–Shafer–evidential belief function model. In the evaluation, six geoelectrically determined groundwater potential conditioning factors (i.e. overburden resistivity, overburden thickness, aquifer resistivity, aquifer thickness, aquifer transmissivity and hydraulic conductivity) were synthesized by employing the probability-based algorithms of the model. The generated thematic maps of the seven hydrogeological parameters of the DRASTIC model were considered as pollution potential conditioning factors and were analysed with the developed ordered weighted average–DRASTIC index model algorithms to construct the GVPI map. Approximately 88.8 and 85.71% prediction accuracies for the Groundwater Potentiality and GVPI maps were established using the reacting operating characteristic curve method and water quality status–vulnerability zone relationship scheme, respectively. Finally, the area groundwater favourability map (GFM) model was produced by applying a GIS-based Boolean operator on the Groundwater Potentiality and GVPI maps. The GFM model reveals three distinct zones: ‘not suitable’, ‘less suitable’ and ‘very suitable’ zones. The area analysis of the GFM model indicates that more than 50% of the study area is covered by the ‘very suitable’ zones. Results produce a suitability map that can be used by local authorities for the exploitation and management of drinking water in the area. The study findings can also be applied as a tool to help increase public awareness of groundwater issues in developing countries.     

Remote sensing and GIS analysis for mapping spatio-temporal changes of erosion and deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas rivers,Greece

Wetlands are among Earth's most dynamic, diverse and varied habitats as the balance between land and water surfaces provide shelter to a unique mixture of plant and animal species. This study explores the changes in two Mediterranean wetland delta environments formed by the Axios and Aliakmonas rivers located in Greece, over a 25-year period (1984–2009). Direct photo-interpretation of four Landsat TM images acquired during the study period was performed. Furthermore, a sophisticated, semi-automatic image classification method based on support vector machines (SVMs) was developed to streamline the mapping process. Deposition and erosion magnitudes at different temporal scales during the study period were quantified using both approaches based on coastline surface area changes. Analysis using both methods was conducted in a geographical information systems (GIS) environment.Direct photo-interpretation, which formed our reference dataset, showed noticeable changes in the coastline deltas of both study areas, with erosion occurring mostly in the earlier periods (1990–2003) in both river deltas followed by deposition in more recent years (2003–2009), but at different magnitudes. Spatial patterns of coastline changes predicted from the SVMs showed similar trends. In absolute terms SVMs predictions of sediment erosion and deposition in the studied area were different in the order of 5–20% in comparison to photo-interpretation, evidencing the potential capability of this method in coastline changes monitoring. One of the main contributions of our work lies to the use of the SVMs classifier in coastal mapping of changes, since to our knowledge use of this technique has been under-explored in this application domain. Furthermore, this study provides important contribution to the understanding of Mediterranean river delta dynamics and their behaviours, and corroborates the usefulness of EO technology and GIS as an effective tool in policy decision making and successful landscape management. The latter is of considerable scientific and practical value to the wider community of interested users, given the continued open access to observations from this satellite radiometer globally.     

The use of local indicators of spatial association to improve LiDAR-derived predictions of potential amphibian breeding ponds

We examined whether spatially explicit information improved models that use LiDAR return signal intensity to discriminate in-pond habitat from terrestrial habitat at 24 amphibian breeding ponds. The addition of Local Indicators of Spatial Association (LISA) to LiDAR return intensity data significantly improved predictive models at all ponds, reduced residual error by as much as 74%, and appeared to improve models by reducing classification errors associated with types of in-pond vegetation. We conclude that LISA statistics can help maximize the information content that can be extracted from time resolved LiDAR return data in models that predict the occurrence of small, seasonal ponds.      

GIS技能竞赛辅助“空间分析与建模”教学的探索与实践

本文在简介“空间分析与建模”教学概况的基础上,分析了高校进行“空间分析与建模”教学的教学目标,通过指导学生参加高校GIS技能竞赛,结合竞赛依托平台进行技能与技巧的培训和指导,并将参赛实践引入到“空间分析与建模”的课程教学中,使得教学与竞赛相结合,促进了“空间分析与建模”教学的改革,使该课程教学始终与全国性学生竞赛实践紧...     

基于GIS的某省土地整理绩效评价系统设计

采用GIS技术、数据库技术和网络技术,集成土地整治项目专项工程专题信息,研究开发某省土地整治项目绩效评价系统,实现某省土地整治项目的地域管理、部门管理、联络员管理及专家系统查询等系统管理功能,项目基本信息管理、成效分析指标管理、年度绩效考核指标管理、单个子项目绩效考核指标管理等绩效评价指标管理两大功能.为土地整治综合项目的建设布局、规划设计、实施监督、检查验收和成效评估提供信息化技术平台.