基于ArcEngine的我国海岸带地质灾害信息管理系统的设计与研究

以VB 6.0为开发平台,利用ArcEngine 9.3组件库进行GIS的二次开发,通过分析海岸带地质灾害信息管理的实际求,建立了我国海岸带地质灾害信息管理系统。该系统具有空间数据和属性数据的查询与管理、空间统计分析、专题图制作及灾害危险性评价、数据转换与输出等功能,为海岸带地质灾害预测、预报和防治提供综合性服务。     

意大利西北皮埃蒙特（PIEMONTE）区地质灾害调查评价研究简介

本文介绍了在意大利西北部皮埃蒙特区阿尔卑斯山和亚平宁山开展的以滑坡灾害为主的地质灾害调查和评价工作。该项工作采用遥感技术与传统技术相结合的方式，将研究区分成六个环境地质分区。对每一个地质灾害点进行编码并评价其活动状态，划分四类地质灾害危害区。通过开发的多种地理信息软件工具，建立了集数据处理、错误纠正、及时更新和网络GIS服务为一体的3S地质灾害信息管理系统。     

基于AIS与GIS的船舶监控管理系统研究

通过AIS与GIS技术的集成,可以利用GIS强大的空间数据处理能力和空间分析功能促进AIS技术的应用。本研究将GIS的空间分析功能应用于AIS基站选址分析,对珠江口区域的AIS基站布局进行规划;应用Winsock网络通信接口进行开发,实现了多个AIS基站与一个或多个监控中心的点对点通信;基于组件式GIS进行系统设计与开发,实现了对近海船舶的自动识别和监控管理。     

开放式地质灾害实时监测及发布系统研究

采用嵌入式技术构建智能无人值守地质灾害监测终端,提供了开放的多灾种地质灾害数据集成接口。监测终端通过无线通信的方式组成分布式网络并将监测数据传输到监测中心,对地质灾害进行实时监测。监测中心将实时地质灾害数据和地质灾害预报预测结果通过Web GIS以B/S结构在因特网在线发布,提供了开放、分布式的地质灾害远程发布。     

地理信息系统的开发工具及其在地质灾害研究中的应用进展

地理信息系统技术（GIS）已经广泛渗透到了多种学科领域，从比较简单的，单一功能的、分散的系统发展到多功能的、共享的综合性信息系统，并向多媒体GIS、智能化、三维、虚拟现实及网络方向发展，新兴的地理信息系统将运用专家系统知识，进行分析、预报和辅助决策。本文介绍了地理信息系统的开发工具，从专业开发工具的组成结构上，可以归纳为集成式GIS、模块化GIS、组件式GIS和网络GIS等几个主要类别；总结了地理信息系统在地质灾害研究中的应用及其在地质灾害评价和管理、地质灾害的危险度区划评价和GIS与专家系统的集成应用等方面的进展态势。     

三峡库区遥感三维立体仿真系统及其应用

基于长江中上游(江津—宜昌段)地质调查项目完成的遥感影像、DEM、地质专题数据,构建了三维仿真系统。本系统通过对库区高精度三维地形地貌仿真和三维环境下的遥感影像、专题数据的一体化管理,实现了三维环境下的地质灾害人机交互遥感解译,以及在三维环境下的GIS空间分析和专业分析功能。通过两期三维场景对比、加载不同时期的影像数据、实时联动显示,直观清晰地开展研究区地质灾害、地形地貌、库岸线变化分析对比。系统大幅度提高了地质灾害遥感解译精度和工作效率,具有一定实际应用价值。     

三峡库区巴东段地质灾害因子关联规则挖掘

选择地质灾害较为发育的巴东县为研究区,并以该区灾害点为数据样本,利用GIS将灾害点与地层岩性、高程、坡度、坡向、水系组合、遥感影像土地利用分类结果等6个影响因子进行叠加分析,选取灾害点的灾害类型、灾害规模、灾害体的物质类型、高程差、水系岸别等5个属性与叠加分析结果利用Apriori算法进行关联规则挖掘,最后挖掘出灾害规模与水系组合间关系等单因素间关联以及不同灾害属性与各因子间的关系等多因素间关联。通过与前人的相关研究成果对比分析,证明得出的规则具有合理性并符合实际情况,可为地质灾害分析决策提供先验知识。     

面向城市固体废物管理辅助决策的GIS解决方案

把城市固体废物产生预测、固体废物的运输、固体废物的处理的优化决策、固体废物的处理的定量经济评价等作为一个整体考虑，提出了一体化的GIS解决方案。利用灰色预测模型进行固体废物产生量及成分预测，并借助于组件GIS的开发技术增强其空间表达和分析能力，为城市固体废物产生预测、评价、处理提供决策支持。     

GIS三维可视化技术在天山公路地质灾害评价与决策信息系统中的应用研究

从GIS的三维可视化技术在天山公路地质灾害评价与决策信息系统中的应用研究入手,分析探讨了利用ArcEngine和C#集成方法制作三维可视化界面的关键技术,同时研究了运用ArcEngine集成技术在地质灾害属性查询、地形剖面图分析等一系列过程。分析研究了将二维数据转换为三维数据的技术流程,为实现三维可视化分析和构建三维虚拟现实系统提供了解决方案。研究表明,GIS三维可视化技术与公路地质灾害评价决策结合在一起,能更直观、生动、形象地反映出公路沿线地质灾害情况,为正确的决策支持方案提供科学的依据。     

MATLAB辅助GIS空间分析建模实验设计与应用

根据地理信息系统GIS的特点和空间分析实验的特点,提出利用MATLAB软件进行GIS空间分析实验设计,并结合一个实例,给出了MATLAB空间分析建模实验应用的主要内容和方法。此实验设计应用有助于学生较快地理解GIS空间分析实验原理,学会利用MATLAB软件分析解决GIS地学实际问题。     

Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

Population density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km² with a root mean squared error (RMSE) of 71.56 persons/km², and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km² cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.     

Quantifying the spatial distribution of soil mass wasting processes after the 2008 earthquake in Wenchuan, China: A case study of the Longmenshan area

The beautiful Longmenshan area is one of the main tourist attractions in Sichuan Province, China. The epicenter of a catastrophic earthquake measured at 8.0 Ms (China Seismological Bureau), occurred within this area at Wenchuan (31°01′16″N, 103°22′01″E) at 14:28 May 12, 2008 (Beijing time). The earthquake triggered numerous types of landslide transport and hazards, including soil and debris avalanches, rockfalls, slumps, debris flows, creation of barrier lakes and slope flattenings. This paper examines the landslide hazards in the Longmenshan area caused by the earthquake using remotely sensed images, mainly Beijing-1 Microsatellite data before and after the earthquake, compared to digital elevation maps and slope gradient maps, land use and vegetation cover maps. Areas of erosion and loss of vegetation were compared from pre- and post-earthquake data, from which were calculated changes in vegetated areas, bare slopes, and mass movement during the earthquake. These events occurred over altitudes from 1000 to 4000 m and on slope angles between 25 and 55°. The results show that the total area of erosion and land movement due to the earthquake increased by 86.3 km² (19.2% of the study area). Compared with pre-earthquake, the areas of very low intensity soil erosion and moderate intensity soil erosion were respectively reduced by 3.6 km², 24.3 km² and 30.9 km². On the other hand, the areas of severe and very severe intensity soil erosion were substantially increased by 45.8 km² and 99.2 km². In the post-earthquake stage, the bare areas (vegetation cover < 15%) have increased by 65.8 km². Without vegetation, the denuded earthquake damaged slopes and other high risk sites have become severe erosion problems. Thus, it is essential to continue long-term monitoring of mass wasting in the denuded areas and evaluate potential risk sites for future landslides and debris flows. We anticipate that these results will be helpful in decision making and policy planning for recovery and reconstruction in the earthquake-affected area.     

A national-scale GIS-based system for modelling impacts of land use on water quality

Management of freshwater quality requires modelling tools for rapid evaluation of land use and management scenarios. This paper presents the CLUES (Catchment Land Use and Environmental Sustainability) model to address this need. CLUES provides steady state, spatially distributed, integrated catchment models tightly coupled to GIS software to predict mean annual loads of total nitrogen, total phosphorus, sediments and E. coli, and concentration of nutrients throughout New Zealand (268,000 km²) with a subcatchment resolution of 0.5 km². CLUES also estimates potential nutrient concentrations for estuaries and provides key farm socio-economic indicators. The model includes a user interface for study area selection, scenario creation, data geo-visualisation, and export of results. It is pre-populated with spatial data and parameter values for New Zealand. Evaluation of the model and a summary of applications demonstrate the tractability and utility of national-scale rapid scenario assessment tools within a GIS framework.     

Remote sensing and GIS applications of surface and near-surface hydromorphological features in Darfur region,Sudan

Groundwater modelling and mapping in a region partly inaccessible using traditional techniques is practically very difficult, and time and cost consuming. However, integration of remote sensing and GIS enables more reliable mapping and analysis of hydromorphological elements such as palaeolakes. In the present study, a set of automated morphometric techniques of fitting a bivariate quadratic surface in a moving window of size 3 × 3 was used and modified to map and analyse hydromorphological elements in northwestern Sudan. Geomorphometry results indicate that the palaeodrainage network incision and depressions may have received most of the rainwater during late Quaternary wet phases. The results revealed that of the total 2000 km² coverage of the unnamed depression, about 49.6% was classified as accumulation zone with a recharge area of about 992 km², while the Wadi Fesh Fesh and Oyo depressions were shown to have the smallest areas of accumulation zone, approximately 31.3% and 37.3%, respectively. Validation of the methods was made by comparing trends and textural features from a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) with 30 m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM and 0.6 m spatial resolution Quickbird images and geological maps of the study area. The maps obtained showed a strong correlation, indicating that the proposed methods are very efficient tools for regional mapping and analysing hydromorphological elements in arid regions and remote and inaccessible regions.     

Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota

Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196 km² Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30 m. Third, we used an additional procedure to model the small wetlands (less than 0.8 ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R² of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra- and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R² between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64 km² within the study area where the modeled wetland water areas ranged from about 2 to 14 km². These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.     

A predictive modelling technique for human population distribution and abundance estimation using remote-sensing and geospatial data in a rural mountainous area in Kenya

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km² (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km²; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D ², adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands.     

Technical Note: Mapping and forecasting of North Indian winter fog: an application of spatial technologies

In India, the Indo‐Gangetic plain (part of Northern India) is invariably affected by dense fog in the winter months every year due to typical meteorological, environmental and prevailing terrain conditions. Pollution also plays an important role in the formation of fog (smoke+fog = smog) in India. Using National Oceanic and Space Administration‐advanced very high resolution radiometer data the fog‐affected regions in Northern India were delineated and the spatial extent of fog for the winter months of the years 2002–03, 2003–04 and 2004–05 (December–February) were studied and mapped. Forecast for future fog based on the analysis of satellite and meteorological (air temperature, relative humidity and wind speed) data was also done. The fog‐affected areas were classified into maximum‐fog‐affected area, moderately fog‐affected area and least fog‐affected area. It has been found that in the winter months of the years 2002–03, 2003–04 and 2004–05, the fog‐affected area in Northern India was about 867 000 km², 625 000 km² and 706 800 km² respectively. The maximum fog‐affected area was found to be 606 400 km², the moderately fog‐affected area was found to be 230 400 km² and the least fog‐affected area was found to be 404 500 km². Further, based on meteorological parameters, such as temperature, humidity and wind speed along with elevation data was used to derive an approach for future fog prediction in this region.     

The use of remote-sensing techniques to monitor dense reservoir networks in the Brazilian semiarid region

Reservoirs are the main water source in the Brazilian semiarid region, especially in the crystalline-geology watersheds, forming high-density reservoir networks (HdRN). However, in most cases, the construction of these reservoirs has been done without technical supervision. The objective of this work was to map and evaluate the spatial distribution of the 25,000 km² Orós Reservoir Basin (ORB) HdRN, in semiarid Brazil, with the help of remote-sensing tools associated with geographic information systems (GIS). Using LANDSAT 5 images of the end of the 2011 rainy season of the ORB, the remote-sensing technique allowed the identification of 6002 polygons, which corresponded to only 4717 reservoirs (implying a misidentification of 21%). Between 2002 and 2011, a 17.5% increase (and 1.81% annual increase) in the number of reservoirs in the basin was observed, still lower than the annual increment from 1970 to 2002, when an average increase of 2.64% per year was observed, in other studies. The perimeter of the reservoirs ranged from 0.250 to 560 km and the individual water surface area ranged from 0.004 to 195.0 km², resulting in a total surface of 465.0 km². Analysing the surface area of the strategic reservoirs, results showed that the estimation of the surface area (from remote sensing with manual polygon adjustment) yielded values very close to those of the on-site monitored areas, with R² = 0.99 and normalized difference index ranging from ?0.02 to +0.09. The reservoir density in the ORB in 2011 was 0.19 reservoirs km^–2, higher than the recommended optimum density of 0.15 reservoirs km^–2 basin. Analysis of reservoir density by municipality recorded values ranging from 0.02 to 0.40 reservoirs km^–2. The sedimentary-geology municipalities presented a reservoir density on average 80% lower than the that of the crystalline-geology municipalities, indicating a strong relationship between geology and reservoir density. Neither population density nor rainfall explained the spatial distribution of reservoirs within the basin, both yielding R² lower than 0.1. This remote-sensing survey of reservoirs demonstrated two major flaws: the misidentification of shadows as reservoirs and the inability to identify the presence of macrophytes, which negatively affected the number and surface area of the target reservoirs. Despite these problems, remote sensing has been shown to be a technique of great potential in the planning and management of water resources in regions with dense reservoir networks.     

Modelling yearly flooding extent of the Waza-Logone floodplain in northern Cameroon based on MODIS and rainfall data

The Sahelian floodplains are of high ecological and economical importance, providing water and fresh pasture in the dry season. A spatial model is presented to predict the yearly flooding extent of the Waza-Logone floodplain based on cumulative runoff in the catchment area and estimations of the soil moisture prior to the flooding. Observations of flooding extent were based on thresholding 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) shortwave infrared (SWIR) images. The Soil Conservation Service Curve Number (SCS-CN) method was used to calculate cumulative runoff within the Logone catchment area based on rainfall estimates (RFEs) for Africa. MODIS SWIR images acquired prior to the flooding were used as indicators for soil moisture. The mean observed flooding extent of the Waza-Logone floodplain during the period 2000–2005 was 6747 km² with a standard deviation of 1838 km². Multiple regression analysis was performed to create a predictive model forecasting flooding extent 1.5 months in advance with a coefficient of determination (R ²) equal to 0.957. Multiple regression modelling was also performed for three subregions separately. The 1.5-month forecast model for the Waza subregion resulted in the highest accuracy (R ²?=?0.950). A floodwater distribution map was created for this subregion model, allowing determination where the flooding occurs for an estimated flood size. The average additional error caused by the mapping procedure was 138 km², which is relatively small compared to an average flooded area of 3211 km² for the Waza subregion. As the flooding extent in the Waza-Logone floodplain is highly correlated to the amount of natural resources available in the dry season, the model may be a valuable tool for sustainable management of these resources.     

Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.