Groundwater vulnerability mapping: A GIS and fuzzy rule based integrated tool

Contamination of groundwater has become a major concern in recent years. Since testing of water quality of all domestic and irrigation wells within large watersheds is not economically feasible, one frequently used monitoring strategy is to develop contamination potential maps of groundwater, and then prioritize those wells located in the potentially highly contaminated areas for testing of contaminants. However, generation of contamination potential maps based on groundwater sensitivity and vulnerability is not an easy task due inherent uncertainty. Therefore, the overall goal of this research is to improve the methodology for the generation of contamination potential maps by using detailed landuse/pesticide and soil structure information in conjunction with selected parameters from the DRASTIC model. The specific objectives of this study are (i) to incorporate GIS, GPS, remote sensing and the fuzzy rule-based model to generate groundwater sensitivity maps, and (ii) compare the results of our new methodologies with the modified DRASTIC Index (DI) and field water quality data. In this study, three different models were developed (viz. DI_fuzz, VI_fuzz and VI_{fuzz_ped}) and were compared to the DI. Once the preliminary fuzzy logic-based (DI_fuzz) was generated using selected parameters from DI, the methodology was further refined through VI_fuzz and VI_{fuzz_ped} models that incorporated landuse/pesticide application and soil structure information, respectively. This study was conducted in Woodruff County of the Mississippi Delta region of Arkansas. Water quality data for 55 wells were used to evaluate the contamination potential maps. The sensitivity map generated by VI_{fuzz_ped} with soil structure showed significantly better coincidence results when compared with the field data.     

基于决策树与监督、非监督分类方法相结合模型的遥感应用研究

遥感影像解译中的决策树分类法一般是引入NDVI植被指数、亮度阈值法、DTM、空间结构、纹理、和其它一些地貌特征来实现地物类别的分离;而传统的监督分类、非监督分类是直接基于像元的亮度值而进行的分类,两者各有优缺点。将两者在遥感影像解译中结合使用,建立统一的分类模型,并以皖东地区TM影像为例进行了分类实验,结果证明,采用该模型分类比单一的最大似然法分类精度提高了4.45%,Kappa指数提高了0.107,该模型能有效地提高影像分类的精度。     

Using remote sensing and GIS to investigate the impacts of tourism on forest cover in the Annapurna Conservation Area,Nepal

Tourism is Nepal's largest industry, giving people in rural areas an alternative to subsistence farming. Tourism can have an impact on the forest cover of a region as trees provide firewood for cooking and heating and timber for building accommodation. In 1986 the Annapurna Conservation Area Project was established to ensure that tourism was managed more sustainably, which includes minimising its impact on the forest cover. This study assesses the impacts of tourism on the forest cover in the Annapurna region by comparing Landsat images from 1999 to 2011. This was achieved through spectral classification of different land cover and by assessing the change in forest cover in relation to increasing distances from tourism villages. A major problem with remote sensing in mountainous regions such as Nepal is shadow caused by the relief. This issue was addressed by only assessing areas which were free from shadow, which in effect meant a sample was used rather than the whole study region. The results indicate that there has been an 8 percent reduction in overall forest extent, but this change varies by region. In the northern, drier regions there has been a net increase in forest cover, while in the southern regions there has been a net reduction in forests. The influence of tourism facilities on forest is also variable. Around each of the tourism villages sampled there was a general trend of forest removal decreasing as the distance from each village increased, which indicates tourism does have a negative impact on forests. However, there was an opposite trend in the northern villages that were well inside the conservation area.     

遥感与GIS支持下的榆林地区土地利用动态变化研究

利用1986、1996和2000年人机交互解译的遥感影像资料,采用GIS空间分析技术,通过叠加3个不同时期的土地利用图,分析了榆林地区1986～2000年期间土地利用时空变化特征,同时还分析了榆林地区14年间沙地逆转恢复的情况。结果表明：1986～1996年期间土地利用变化主要表现为：沙地减少了22．25％,草地、林地和城乡居住建设用地分别增加了6．51％、4．63％和3．55％。1996～2000期间,沼泽地、盐碱地分别减少了26．11％、24．39％,沙地仅减少0．79％,草地减少了2．86％,林地仅增加了0．17％,而旱地增加了3．28％。城乡居住建设用地增加了14．13％。14年间沙地面积减少了128684．17hm^2,沙地净逆转为草地118872．99h^2、旱地3605．07hm^2、林地4049．52hm^2。     

The impact of armed conflict and forced migration on urban expansion in Goma: Introduction to a simple method of satellite-imagery analysis as a complement to field research

The effects of armed conflict on cities constitute a large field of research in both conflict studies and urban studies. The topic of urban expansion is also explored by the domain of remote sensing, a sub-branch of geoinformation science. Remote sensing researchers analyze the spatial development of cities in conflict and non-conflict environments using satellite imagery.However, a dialogue or collaboration between these disciplines is virtually non-existent in the scientific discourse, mainly due to stark differences in their methodologies – namely, intensive on-the-ground field research in the case of conflict and urban studies and highly elaborate computer-based analysis of remotely-gathered data in the case of remote sensing.We aim to demonstrate a simple and thus feasible approach for the use of satellite imagery by non-experts of remote sensing, to add a spatio-temporal dimension to the results of in-depth field studies. We apply our approach to the city of Goma, in the eastern region of the Democratic Republic of the Congo, which is located at the center of protracted armed conflict that has raged for decades. With the support of local knowledge acquired during field visits, we visually analyze a time series of Landsat data and add our own results to those of existing research. Contextualizing the mapped results of Goma's urban expansion between 1986 and 2015, we show how urban growth is linked to particular waves of forced displacement caused by different stages of armed conflict and one particular natural disaster.     

An SVG-based method to support spatial analysis in XML/GML/SVG-based WebGIS

XML/GML/SVG-based approaches are promising for building Web-based geographic information systems (WebGIS). However, current XML/GML/SVG-based WebGISs are lacking in spatial analysis. Some of them are designed for web mapping only. Others adopt a server-side solution for spatial analysis, which suffers from the ‘bottleneck’ problem and results in a high network transmission load. Load-balancing spatial analysis between server side and browser side can be used to solve the above problems. This article focuses on one of the key building blocks of load-balancing spatial analysis, that is, SVG-based spatial analysis which enables spatial querying and analysis directly on SVG (on the browser side). After analyzing the workflow of spatial analysis, we identify and focus on two key issues in providing spatial analysis on SVG: SVG-based spatial information representation and SVG-based spatial extended structured query language (SSESQL). For the first issue, a theoretical foundation is set up to develop an SVG-based spatial information representation model. Some spatial operators are designed and integrated into an SSESQL to support spatial querying on SVG. Finally, we design and implement two case studies. The results of these case studies show that the proposed method is feasible and operable in supporting spatial analysis directly on SVG on the browser side. The proposed method can be easily incorporated with some existing methods (e.g., GML-based spatial analysis on the server side) to provide load-balancing spatial analysis (load balancing between server side and browser side) in XML/GML/SVG-based WebGIS. As a result, users can access high-performance spatial analysis simply via a web browser (such as Internet Explorer and Firefox).     

Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.