An Automated GIS Procedure for Delineating River and Lake Boundaries

Digitization of river and lake boundaries is an essential task in several hydrologic studies, which, in most cases, is performed manually by using aerial photographs. This paper presents an automatic geographic information system (GIS) procedure for delineating river and lake boundaries using aerial photographs. The GIS procedure is based on supervised classification of aerial photographs by using the National Hydrography Dataset (NHD) for creating training areas. Four study areas – Lake Palestine in Texas, Brazos River in Texas, Neuse River in North Carolina, and Snake River in Idaho – are used to demonstrate the approach. The results from the automatic boundary delineation process are evaluated by comparing them to manually digitized boundaries. The boundaries delineated by the automatic procedure matched the manually digitized boundaries with an overall difference of less than five percent in terms of area measurement. Besides saving about 85 percent of manual labor, the GIS procedure provides a consistent way of delineating water bodies, and provides estimation of river channel widths, which, in conjunction with existing NHD data, can be used to estimate average flow depths. Supplementing the regional scale NHD data with local scale attributes such as channel width and depth may lead to increased use of NHD in local scale studies.     

Evaluating the Quality of the National Hydrography Dataset for Watershed Assessments in Metropolitan Regions

The purpose of this paper is to evaluate the "fitness for use" of the high-resolution National Hydrography Dataset (NHD) for regional watershed assessments. The Nature Conservancy's GIS tools were combined with various data sources and techniques to identify and fix four types of problems in the high resolution NHD—dangling streams, attribute errors, flow divergences, and duplicate streams. This effort generated a single-line natural flow network with correct arcnode topology, no dangling streams (with the exception of streams terminating in low relief basins), and few, if any, attribute designation problems. The revised NHD network improved on the original representation of the stream network and accompanying sub-watersheds in several ways.     

基于特征点修正的SRTM数据在风能资源微观评估中的应用

在风能资源微观评估中,高精度地形数据通常较难获取,寻找一种可替代数据尤为重要。本文以全球免费共享的SRTM数据(90m水平分辨率)为地形数据源,利用风图谱分析及应用程序(WAsP)作为风资源评估工具,对广东遮浪岛区域进行了定点风能资源评估。通过将评估结果与1:5千地形数据条件下的结果进行对比,分析了SRTM数据用于风能资源微观评估的精度。接着,本文提出了地形数据的特征点修正方法,利用该方法处理后的SRTM数据,平均风速在两种不同地形数据源下的相对误差由10%降为5%,使得修正后的SRTM数据可适用于风能资源微观评估。     

Automated Extraction of Natural Drainage Density Patterns for the Conterminous United States through High-Performance Computing

Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrological and geomorphological analysis by indicating areas where too few headwater channels are represented. Natural drainage density patterns are not consistently available in existing hydrographical data bases for the United States because compilation and capture criteria historically varied, along with climate, during the period of data collection over the various terrain types throughout the country. This paper demonstrates an automated workflow that is being tested in a high-performance computing environment by the U.S. Geological Survey (USGS) to map natural drainage density patterns at the 1:24,000-scale (24K) for the conterminous United States. Hydrographic network drainage patterns may be extracted from elevation data to guide corrections for existing hydrographic network data. The paper describes three stages in this workflow including data pre-processing, natural channel extraction, and generation of drainage density patterns from extracted channels. The workflow is implemented in parallel fashion by simultaneously executing procedures on multiple subbasin watersheds within the U.S. National Hydrography Dataset (NHD). Pre-processing defines parameters needed for the extraction process. Extraction proceeds in standard fashion: filling sinks, developing flow direction and weighted flow accumulation rasters. Drainage channels with assigned Strahler stream order are extracted within a subbasin and simplified. Drainage density patterns are then estimated with 100-m resolution and subsequently smoothed with a low-pass filter. The extraction process is found to be of better quality in higher slope terrains. Concurrent processing through the high-performance computing environment is shown to facilitate and refine the choice of drainage density extraction parameters and more readily improve extraction procedures than conventional processing.     

An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.     

Partial polygon pruning of hydrographic features in automated generalization

This article demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi‐scale hydrographic database. Water features are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features – such as long, sinuous reservoir arms, stream channels too narrow at the target scale, and islands that begin to coalesce – entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the US Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This article describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the US.     

开源软件VTP中的大规模实时地形绘制算法实验研究

本文对开源3DGIS软件VTP中的一种视点相关多分辨率地形模型生成算法——McNally算法进行了实验研究,结果表明:三角形数量大于5000时,用该算法绘制的地形弹跳现象消失;地形赋予纹理后,分辨率变化对显示的效果的影响很小,因此,在满足显示效果的情况下,没有必要追求过高的分辨率,即三角形目标个数。     

A method to generalize stream flowlines in small-scale maps by a variable flow-based pruning threshold

The aim of this paper is to explore and describe a method of automated generalization designed to produce a map which strikes a balance between cartographic and hydrologic representations. Following a discussion of scholarly literature on generalization, we describe a novel method for automated generalization of hydrographic stream data, using the National Hydrography Data Set (NHDPlus) as an example.

Traditional hydrography shows a fairly uniform density of stream flowlines over space. While this is pleasing to the eye, traditional methods tend to under-represent rivers in humid areas and over-represent them in arid areas. We address this problem through a method in automated generalization to produce a high-quality presentation of hydrographic data, suitable for display as a wall map or in an atlas. Streams are pruned based on a variable flow threshold, derived from the local mean annual precipitation by a regression equation.

After running the model using different parameters, we produce a more satisfactory portrayal of stream networks in the United States that communicates the flow of water through rivers and reflects the regional climate. Specific advantages in generalizing with variable flow threshold include (1) the method allows for fine gradations in output scale; (2) the output maps tend to minimize density variations in the raw data; (3) the subjective criteria are easily derived; and (4) the method can be performed rapidly on large data sets, as long as the stream data has been enriched with reliable flow rates.     

Semi-automatic Process for Hybrid DTM Generalization based on Structural Elements Multi-analysis

Abstract

The generalization of digital terrain models (DTMs) is a tool of great potential for simultaneous cartographic and photogrammetry generation processes at different scales, the main object of which is to feed different geographic information systems (GIS). These GIS enable multi-scale analysis and visualization through different data bases. This research proposes a semi-automatic DTM generalization process conditioned by a series of predefined parameters resulting in the generation of hybrid DTMs at different scales starting from a single cloud of points obtained through large-scale massive data acquisition processes. The generalization results obtained, applied on different areas of different relief, offer specific application ranks for each parameter with great precision, in contrast with DTMs obtained directly in each scale.     

A Comparison Between Contour Elevation Data Sources for DEM Creation and Soil Carbon Prediction, Coshocton, Ohio

A raster and vector GIS was created for the North Appalachian Experimental Watershed (NAEW) from legacy (1960) 1:2,400‐scale contour maps. The intent of the study was to use terrain data for the spatial modeling of soil organic carbon. It was hypothesized that DEMs derived from these data would be more accurate and therefore more useful for terrain‐based soil modeling than those from USGS 1:24,000‐scale contour data. Central tasks for this study were to digitally capture the 1:2,400‐scale maps, convert digital contour data sources to raster DEMs at multiple resolutions, and derive terrain attributes. A flexible approach was adopted, using software outside of mainstream GIS sources where scientifically or practically advantageous. Elevation contours and streamlines were converted to raster DEMs using ANUDEM. DEMs ranging in resolution from 0.5–30 m were tested for accuracy against precision carrier‐phase GPS data. The residual standard deviation was 1.68 meters for the USGS DEM and 0.36 meters for the NAEW DEM. The optimal horizontal resolution for the NAEW DEM was 5 m and for the USGS 10 m. Five and 10 m resolution DEMs from both data sources were tested for carbon prediction. Multiple terrain parameters were derived as proxies for surficial processes. Soil samples (n = 184) were collected on four zero‐order watersheds (conventional tillage, no‐till, hay and pasture). Multiple least squares regressions (m.l.s.) were used to predict mass C (kg m^?2, 30 cm depth) from topographic information. Model residuals were not spatially autocorrelated. Statistically significant topographic parameters were attained most consistently from the 5 m NAEW DEM. However, topography was not a strong predictor of carbon for these watersheds, with r² ranging from 0.23 to 0.58.     

Propagating effects of database generalization on the viewshed

Few studies have systematically examined the effects of different possible generalization methods on the products of GIS operations. In this paper the effects of generalizing a Digital Elevation Model (DEM) on the area which is determined to be visible (the viewshed) is examined. Among the many different methods of generalization which are possible, a limited set of operators is examined here. First, they all fit geometrically from one resolution to another, and they can be grouped into two types: regular sampling and statistical summaries. In the latter class four different operations are used: for any cell at the target resolution, elevations are determined, from the arithmetic mean, the maximum, the minimum, and the maximum difference from the mean of the cells within the kernel at the original scale. Changes in resolution of 0.5, 0.33, 0.25, and 0.2 of the original study area are studied at 100 viewing points in each of two study areas. At the original resolution a 120 × 120 pixel area was examined, giving generalizations to 60 × 60, 40 × 40, 30 × 30, and 24 × 24 pixels. The viewsheds determined over these different resolution DEMs are compared with a number of possible viewsheds derived by generalization of the viewshed over the original DEM. Of those tested, the maximum deviation from the mean within kernel emerges as the most reliable estimate of the pattern and area of the viewshed at all resolutions. The importance of this conclusion is that different generalization operators yield more or less faithful versions of the ungeneralized product (the viewshed), and it seems indicative that a similar variety in stability of the product with generalization operator will result in many, if not all, complex products of spatial analysis.     

基于小波多尺度分析的DEM数据综合研究

在同一地区,随着数字高程模型(DEM)分辨率的降低,或者地形图比例尺的缩小,DEM或地形图所描述的地形表面的细节部分不断舍弃而表现出宏观的骨架特征。本文将小波多尺度分析方法和方根模型结合模拟这一过程,由基于1:1万比例尺地形图建立的DEM生成了两种新的较小比例尺DEM,对比不同比例尺等高线可知,较小比例尺保持了较大比例尺的山体轮廓、山脊、谷地走向等地貌形态的塑造。采用坡度和剖面曲率两个参数及信息论的方法,分别对原始DEM和新生成的DEM进行分析和验证,结果表明,DEM经过数据综合,不但地形表面的轮廓越来越平缓,而且地形表面的细节也越来越平滑。利用方根模型作为小波高频系数阈值选取的依据更贴近于传统制图综合方法,能够将比例尺的改变与综合程度结合起来,实现任意比例尺DEM的自动综合。     

海底地貌数据综合研究进展

海底地貌数据与人类认知海洋、探索海洋、利用海洋息息相关,海底地貌数据综合是海图编制、海洋空间数据处理的重点研究内容之一。海底地貌数据综合属于地图综合的研究范畴,但受历史渊源、学科体系、应用范畴等诸多因素影响,常被认为是地图综合中相对独立、特殊的研究内容。本文在明确海底地貌数据综合特殊性的基础上,从数据增强、综合方法、质量评价3个方面梳理海底地貌数据综合研究进展,归纳了几种常见的海底地貌数据综合应用案例,并从研究内容、技术方法、实践应用3个方面探讨了海底地貌数据综合的发展趋势。     

地形分析中栅格中间层构建的尺度适应性

栅格中间层是指在地形分析中由DEM、DSM、矢量数据等原始数据,经过一系列栅格运算获取且可以重用的基础栅格层。本文系统研究了栅格中间层构建的尺度适应性,定义了范围、比例尺、分辨率和分析4种尺度,并确定了范围、比例尺和分辨率尺度的确定原则;特别对于分析尺度,通过地貌因子计算进行了切线、剖面曲率栅格层分析尺度适宜性试验,分析了曲率计算中适宜的算法和分析尺度,保证了地形基础因子栅格中间层具有满足分析要求的数值精度和匹配地形起伏的形态精度。     

基于GIS的城市道路网无级比例尺信息提取

阐述了无级比例尺矢量数据信息处理技术的基本原理、流程与方法,研究了城市道路网信息提取的实现方法,概括了无级比例尺信息提取过程中要素选取的"取舍"原则,提出了面向对象的城市道路网分类体系,并运用层次分析法对同级别城市道路进行重要性排序,从而实现了将各级别城市道路的重要性进行总体排序,为城市道路网无级比例尺信息的内容选取提供了前提.     

基于GIS的城市道路网无级比例尺信息提取

阐述了无级比例尺矢量数据信息处理技术的基本原理、流程与方法,研究了城市道路网信息提取的实现方法,概括了无级比例尺信息提取过程中要素选取的“取舍”原则,提出了面向对象的城市道路网分类体系,并运用层次分析法对同级别城市道路进行重要性排序,从而实现了将各级别城市道路的重要性进行总体排序,为城市道路网无级比例尺信息的内容选取提供了前提。     

黄土丘陵区数字地貌模型与遥感影象分类

黄土丘陵区地形破碎、沟壑纵横，为遥感影象的土地利用自动分类带来了很大困难。仅依靠传统的光谱统计分类方法，难以达到较高的分类精度。本文拟探讨在黄土丘陵区运用大比例尺地形图数据，建立区域数字地貌模型；并在模糊数学理论的指导下，运用数字地貌模型对影象的分层分类结果进行修正和细化。研究表明，此方法能够有效地提高土地利用分类精度和机助制图比例尺。     

基于Arcobjects的约束Delaunay三角网支持下的土地利用图斑综合

关于以约束Delaunay三角网剖分模型为基础的地图综合的研究,已经有了很多成熟的算法理论,但在应用中这些模型算法建立的约束性Delaunay三角网都比较复杂,并且在动态更新上,这些模型在实际应用中存在一定的困难。基于此问题,本文提出了以地类图斑多边形为数据源,利用Arcob jects建立TIN模型,从中获取内部约束性Delaunay三角网并提取骨架线,利用骨架线进行剖分多边形,达到图斑多边形融合的目的,来解决大比例尺到小比例尺数据转换中地类图斑的制图综合问题。     

Assessment of Shuttle Radar Topography Mission Elevation Data Based on Topographic Maps in Turkey

Depending on scale, topographic maps depicting the shape of the land surfaces of the Earth are produced from different data sources. National topographic maps at a scale of 1:25 000 (25K maps) produced by General Command of Mapping are used as the base map set in Turkey. This map set, which consists of approximately 5500 sheets, covers the whole country and is produced using photogrammetric methods. Digital Elevation Models (DEMs) created from these maps are also available. Recently, another data source, Synthetic Aperture Radar (SAR) interferometric data, has become more important than those produced by conventional methods. The Shuttle Radar Topography Mission (SRTM) contains elevation data with 3 arc-second resolution and 16 m absolute height error (90 percent confidence level). These data are freely available via the Internet for approximately 80 percent of the Earth's land mass. In this study, SRTM DEM was compared with DEM derived from 25K topographic maps for different parts of Turkey. The study areas, each covering four neighboring 25K maps, and having an area of approximately 600 km², were chosen to represent various terrain characteristics. For the comparison, DEMs created from the 25K maps were obtained and organized as files for each map sheet in vector format, containing the digitized contour lines. From these data, DEMs in the resolution of 3 arc-second were created (25K-DEM), in the same structure as the SRTM DEM, allowing the 25K-DEMs and the SRTM DEM to be compared directly. The results show that the agreement of SRTM DEM to the 25K-DEM is within about 13 m, which is less than the SRTM's targeted error of 16 m. The spatial distribution of the height differences between SRTM-DEM and the 25K-DEM and correlation analysis show that the differences were mainly related to the topography of the test areas. In some areas, local height shifts were determined.     

星地多源数据的区域土壤有机质数字制图

土壤有机质(SOM)是全球碳循环、土壤养分的重要组成部分,精确估算土壤有机质含量具有重要意义。本文以中国东北—华北平原为研究区,收集了1078个土壤样本,以遥感数据(MODIS,TRMM和STRM数据)与土壤地面光谱数据为预测因子,运用基于树形结构的数据挖掘技术构建土壤有机质-环境预测因子模型进行数字土壤制图。通过不同建模样本数建模精度比较,选择300个样本数时的模型为最优模型。建模结果表明土壤光谱和气候因子是研究区SOM变异的主控因子,生物因子次之,而地形因子影响最小。预测结果经检验,RMSE为7.25,R2为0.69,RPD为1.53制图结果与基于第二次全国土壤普查数据的土壤有机质地图具有相似的分布规律,呈现SOM自东北向西南递减的趋势。通过比较分析发现,经过20年左右的土地开发与利用,研究区低SOM和高SOM含量土壤面积减少,而中等SOM含量土壤面积增加。