土地利用数据尺度转换的精度损失分析

由于栅格数据便于空间分析,因而通常将矢量数据转化成栅格数据来进行空间分析.在转化过程中,选用不同的栅格大小,其面积和精度损失是不同的.针对这个问题,以重庆市110万的土地利用矢量数据为例,探讨了不同栅格大小下,各种土地利用类型在转化过程的面积和精度损失.研究表明(1)栅格大小＜100m时,其精度损失均＜3.3%.(2)在1000m时,其精度损失达到50%以上的地类有湖泊、水库坑塘、滩地、农村居民点用地、工交建设用地、戈壁、裸岩石地、平原区旱地和坡度＞25°的旱地,这些地类的平均图斑大小均在＜51hm2.(3)在30m～1000m间,平均图斑大小＜82hm2的地类随着栅格的由小变大,其面积变得比实际面积小;平均图斑大小＞101hm2的地类中除高盖度的草地和河渠之外,却与此相反.     

矢量和栅格数据土地利用结构分维值比较——以苏州为例

分形理论对处理土地利用结构之类的非线性对象具有较强实用价值, 分维值作为该理论的重要测度指标, 其计算常基于栅格数据, 由此导致了土地结构边界信息的损失, 也减弱了分维值对土地利用空间结构特性的揭示作用。本文以苏州市1:10 万土地利用数据为例, 对研究区的土地利用空间格局进行分形分析, 通过分析不同栅格大小的土地利用分维值, 建立分维值与栅格粒度之间的定量关系, 并通过栅格数据推算矢量分维值, 分析推算误差及其原因。结果表明, 苏州市土地利用格局符合分形理论的一般规律;苏州市整体土地利用程度较高, 耕地、建设用地结构较为简单, 而未利用地、林草地结构较复杂, 水域斑块结构复杂性较低;随着栅格粒度的增加, 土地利用分维值呈现增加趋势, 且粒度与分维值之间存在二次函数的定量关系, 可以通过此关系式在一定误差范围内推求矢量数据的土地结构分维值, 斑块数量与整体规模是影响推求精度的重要原因。     

栅格化属性精度损失的评估方法及其尺度效应分析——以四川省1:25 万土地覆被数据为例

选择在600 m~30 km 16 个尺度上,在ArcGIS 中利用常用的面积最大值法(Rule ofMaximum Area,RMA) 对2005 年四川省1:25 万土地覆被矢量数据进行栅格化,并采用两种属性精度损失评估方法：传统的常规分析方法和一种新的基于栅格单元分析方法,来对比分析在这两种评估方法下RMA栅格化的属性(这里是指面积) 精度损失随尺度的变化特征。结果表明：(1) 在同一尺度下采用基于栅格单元方法分析所得的研究区平均属性精度损失大于常规分析方法分析得到的平均属性精度损失,且二者之间的差异在1~10 km内很明显,当栅格单元大于10km时,两种方法得到的平均属性精度损失的差值稳定,且其随尺度的变化曲线趋于平行;(2) 基于栅格单元分析方法不仅能够准确地定量估计RMA栅格化的属性精度损失,而且能客观地反映属性精度损失的空间分布规律;(3) 对四川省1:25 万土地覆被数据进行面积最大值法(RMA)栅格化的适宜尺度域最好不要超过800 m,在该尺度域内数据工作量适宜,且RMA栅格化属性精度损失小于2.5%。     

山区县域土地利用/覆被变化多尺度多模型模拟对比——以云南省勐腊县为例

土地利用/覆被变化(LUCC)模拟及其尺度效应一直是土地变化科学的研究内容与难点。本文选择西南沿边山区县域——云南省勐腊县为试验区,利用LCM、FLUS、CA-Markov和CLUE-S探究该县域LUCC模拟的最适模型和栅格尺度。结合1994年、2004年两期Landsat TM影像分类生成的土地利用/覆被数据,以及自然与社会经济数据,在60、90、120、150、180、210和240 m七个栅格尺度下模拟该县域2014年土地利用/覆被空间格局,并以同期土地利用/覆被遥感分类数据为参考来评价模型模拟精度,筛选出最适模型和栅格尺度,进而模拟该县域2024年土地利用/覆被格局。研究表明:四种模型均能较好地模拟该县域土地利用/覆被变化(K_(standard)=0.67～0.84),其中以LCM模型模拟精度最高(K_(standard)=0.77～0.84);各模型对不同土地利用/覆被类型的适用性不同,LCM模型模拟林地、园地和耕地效果最好,而FLUS模型模拟建设用地更佳;四种模型模拟LUCC的最适栅格尺度均为120 m,其K_(standard)0.70。整合不同地类的最适模型和栅格尺度模拟结果有利于提高山区县域LUCC模拟精度。     

基于矢量的城市扩张多智能体模拟——以广州市番禺区为例

运用自下而上的多智能体建模方法构建城市扩张模型,研究城市扩张的基本特征和规律,对新型城镇化建设具有重要的理论和现实意义。但传统的多智能体模拟大多是基于栅格数据构建,不同的格网大小、邻域形状及邻域大小将产生不同的模拟结果。为克服传统栅格数据受模拟尺度的影响,采用城市土地利用现状图,构建矢量多智能体城市扩张动态模型。智能体依据“宜居性”评价指标,并遵从个人偏好,选择合适区位,模拟城市扩张的时空动态过程。将模型应用于广州市番禺区,模拟了其2003-2008年城市扩张情况。最后将模拟结果与实际土地利用现状图进行对比,结果表明,地块的转化精度为63.09%,非转化精度为90.74%,总精度为85.83%,具有较好的模拟精度,可以为新型城镇化建设提供有效的决策支持。     

基于矢量和栅格数据的土地利用稳定性研究——以山东省临沂市为例

数据格式(矢量和栅格)和栅格粒度的选择对准确分析土地利用稳定性、正确指导土地利用规划至关重要。该文以临沂市2010年1∶5 000比例尺土地利用现状图为基础数据源,运用分形理论和GIS技术研究不同数据源下临沂市区土地利用类型的分维值和稳定度。结果表明:临沂市土地利用总体稳定性弱,建设用地、园地稳定性较强,水体、未利用地、林地稳定性较弱,耕地、交通用地、草地属于受数据格式影响大的敏感地类;敏感地类突出反映了矢量和栅格研究结果的偏差,最佳适宜粒度下的栅格数据产生的是平稳偏差,通过采用第一尺度域内的粒度作为最佳适宜粒度降低了与矢量结果的偏差。该研究对土地利用景观格局分析中数据源的选择具有参考意义。     

基于栅格数据的土地利用格局解释模型及应用

在一定空间分辨率的栅格上用二值型变量表征土地利用格局,是一种常见的土地利用格局表征形式。解释基于这一表征形式的土地利用格局与自然环境条件、社会经济因素之间的关系多需引入非线性模型。本文基于非线性统计方法构建了基于栅格数据的土地利用格局解释模型,不仅刻画了土地利用格局与影响因子之间的非线性统计关系,解决了分类变量引入统计模型的难题,还消除了土地利用格局解释模型中概率估计值常常超过概率值域的问题。太仆寺旗的应用研究表明,栅格尺度土地利用格局解释模型能准确表达土地利用分布格局与自然环境条件、社会经济因素间的定量关系,所得结论为太仆寺旗土地利用规划、土地资源优化利用提供了决策参考信息。     

地理元胞自动机模型的尺度敏感性及原因

地理元胞自动机模型的模拟精度会受到元胞尺度的影响。以杭州市土地利用变化模拟为例,分析了元胞尺度分别为50m×50m、100m×100m、150m×150m和200m×200m时地理元胞自动机模型的模拟精度,对地理元胞自动机模型的尺度敏感性进行了分析;并从元胞转换规则入手,研究了元胞自动机模型尺度敏感性产生的原因:(1)元胞尺度会对地理元胞自动机模型的模拟精度产生影响,元胞尺度越精细模拟精度越高;(2)元胞自动机模型的尺度敏感性与元胞尺度相关,在有些尺度区间上表现得明显,而在有些尺度区间上表现并不明显;(3)孤立元胞是元胞自动机模型尺度敏感性产生的主要原因。研究表明,随着元胞尺度的增大,元胞空间的孤立元胞增多,这些孤立元胞本身及其周围元胞具有较低的邻域函数值和较小的转换概率值,并影响了地理元胞自动机模型的模拟精度。     

土地利用分类对景观格局指数的影响

基于景观格局指数的空间格局分析是当前景观生态学研究的重要基础内容,不仅数据源准确度、尺度效应显著影响景观格局指数,土地利用类型划分也对景观格局指数具有显著影响,但我们对这种影响的总体理解尚很缺乏。本研究选取24种常用景观格局指数,以深圳市宝安区为试验区,探讨景观格局指数随土地利用分类系统变化的基本规律。研究结果表明,土地利用分类对景观格局指数的确具有显著影响。而根据景观格局指数对土地利用类型数目变化响应的可预测性,可将其分为三类：① 第一类指数随土地利用分类系统变化的可预测性强,能用简单函数关系 (对数函数关系、S形曲线、反比曲线关系) 来表达,包括斑块数目、斑块密度、边界密度、平均斑块面积、景观形状指数、平均斑块形状指数、周长面积比分维数、平均斑块分维数、聚合度、Shannon多样性指数、Simpson多样性指数和修改Simpson多样性指数;② 第二类指数随土地利用分类系统变化可预测性较差,表现为典型的分段 (或阶梯形) 变化,存在多种可能 (S形曲线、直线、反比曲线与复合曲线关系),包括斑块面积标准差、斑块面积变异系数、最大斑块指数、面积加权平均斑块形状指数、面积加权平均斑块分维数、分离度和斑块结合度;③ 第三类指数由于在度量相关空间格局特征时考虑了土地利用类型数多少的影响,随土地利用分类系统变化呈无规律变化,难以用一种简单函数或分段函数来预测其变化行为,包括蔓延度、Shannon均匀度指数、Simpson均匀度指数、修改Simpson均匀度指数和优势度指数。这些指数随土地利用分类系统变化的变化规律,使试验区不同时期、不同土地利用分类系统下的空间格局比较成为可能。     

云南哈尼梯田景观格局指数筛选研究（英文）

本文旨在筛选出一系列适合描述哈尼梯田景观格局的指数。我们从面积与边缘、形状、核心面积、隔离度/临近度、聚集度、多样性6个方面选取了47个景观指数,基于ArcGIS平台,将土地利用图矢量数据转化成栅格数据,栅格大小由30m×30m,以5m为间隔,增加至175m×175m,共计30幅土地利用图。运用SPSS软件,通过Pearson相关分析,47个景观格局指数分为21个小组,组内指数的两两相关性都在90%以上。每组选取代表性的指数,进行主成分分析,得出前5个主成分的方差变量能够占总方差变量的93%。选取旋转后各分量中具有最大相关性的5个景观结构指数来表达主成分的5个因子分别是分散指数(Splitting index[SPLIT]),斑块面积分布方差指数(Patch area distribution[AREA_CV]),香农多样性指数(Shannon’s diversity index[SHDI]),几何最近邻距离加权平均值指数(Euclidean nearest neighbor distance distribution[ENN_AM]),总核心面积指数(Total core area[TCA])。考虑到哈尼梯田的实际景观特征并结合前人的研究成果,我们增加平均分维数(Patch fractal dimension distribution[FRAC_MN])作为第6个指数。由于景观指数具有尺度效应,我们通过景观格局指数与尺度变化关系图,计算得出红河哈尼梯田景观格局指数的尺度域为40–45m之间。通过分维分析法,识别出哈尼梯田的特征格局在40–45m之间,即研究哈尼梯田的景观格局的最适宜栅格大小为40–45m之间。通过研究,我们认为以上6个景观指数能够反映哈尼梯田的景观格局特征,同时也可以运用这6个指数来描述类似哈尼梯田的景观格局。     

Determination of rasterizing error a case study with the soil map of The Netherlands

Abstract

The digital soil map of The Netherlands (scale 1: 50 000) in vector form was rasterized using three sizes of raster cell and two different rasterizing methods. The rasterizing errors were estimated for several map sheets of different complexity using the double-conversion method, the Switzer method and the Goodchild method. The relationship between the complexity of the maps, indicated by the boundary index, and the rasterizing error is presented as a regression equation. The double-conversion method provided a better estimation of the rasterizing error than the other two methods. Differences of less than 1 per cent were found between the rasterizing methods used.     

栅格化属性精度损失的评估方法及其尺度效应(英文)

Rasterization is a conversion process accompanied with information loss, which includes the loss of features’ shape, structure, position, attribute and so on. Two chief factors that affect estimating attribute accuracy loss in rasterization are grid cell size and evaluating method. That is, attribute accuracy loss in rasterization has a close relationship with grid cell size; besides, it is also influenced by evaluating methods. Therefore, it is significant to analyze these two influencing factors comprehensively. Taking land cover data of Sichuan at the scale of 1:250,000 in 2005 as a case, in view of data volume and its processing time of the study region, this study selects 16 spatial scales from 600 m to 30 km, uses rasterizing method based on the Rule of Maximum Area (RMA) in ArcGIS and two evaluating methods of attribute accuracy loss, which are Normal Analysis Method (NAM) and a new Method Based on Grid Cell (MBGC), respectively, and analyzes the scale effect of attribute (it is area here) accuracy loss at 16 different scales by these two evaluating methods comparatively. The results show that: (1) At the same scale, average area accuracy loss of the entire study region evaluated by MBGC is significantly larger than the one estimated using NAM. Moreover, this discrepancy between the two is obvious in the range of 1 km to 10 km. When the grid cell is larger than 10 km, average area accuracy losses calculated by the two evaluating methods are stable, even tended to parallel. (2) MBGC can not only estimate RMA rasterization attribute accuracy loss accurately, but can express the spatial distribution of the loss objectively. (3) The suitable scale domain for RMA rasterization of land cover data of Sichuan at the scale of 1:250,000 in 2005 is better equal to or less than 800 m, in which the data volume is favorable and the processing time is not too long, as well as the area accuracy loss is less than 2.5%.     

积温数据栅格化方法的实验

根据中国 4 0 0多个气象站 1995年的积温数据分析 ,≥ 0℃积温和≥ 10℃积温与经纬度、海拔高度的相关性分别为r2 =0 96 5 6和r2 =0 94 0 2。以≥ 0℃积温数据为例 ,利用聚类分析法 ,将全国分为 7个积温计算区 ,每个区分别构建模型 ,通过“回归方程计算 +空间残差”的方法对全国积温数据进行栅格化。验证结果为 :计算值与实测值之间的线性相关系数r2 =0 9889,平均相对误差 3 5 6 % ,相对误差在 5 %以内的气象站个数占验证气象站总数的 86 %。     

基于GIS的新疆气温数据栅格化方法研究

以新疆99个气象台站1971-2010年年平均气温为数据源,采用多元回归结合空间插值的方法对新疆区域气温数据进行栅格化研究。建立了年平均气温与台站经纬度和海拔高度的多元回归模型,对于残差数据的插值采用了反距离权重法(IDW) 、普通克立格法 (Kriging)和样条函数法(Spline)3种目前应用广泛的空间插值方法,针对于这3种方法进行了基于MAE和RMSIE的交叉验证和对比分析,结果表明在新疆的年平均气温的GIS插值方案中,IDW方法精度总体要高于其他两种插值方法。     

DEM提取黄土高原地面坡度的不确定性

选择陕北黄土高原6个典型地貌类型区为试验样区,采用野外实测及高精度的1:1万比例尺DEM为基准数据,研究栅格分辨率及地形粗糙度对DEM所提取地面平均坡度精度的影响。结果显示,对于1:1万比例尺DEM,5 m是保证该地区地形描述精度的理想分辨率尺度;多要素逐步回归模拟的方法进一步揭示了DEM所提取的地面平均坡度误差E与栅格分辨率X以及地形起伏的代表性因子-沟壑密度S之间存在的量化关系为E = (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625,该结果也为确定适用的DEM分辨率提供了理论依据。     

不同分辨率DEM提取地面坡度的不确定性模拟:以在黄土高原的试验为例

Slope is one of the crucial terrain variables in spatial analysis and land use planning,especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas,but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.     

Simulation on slope uncertainty derived from DEMs at different resolution levels: a case study in the Loess Plateau

Slope is one of the crucial terrain variables in spatial analysis and land use planning, especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas, but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.     

Simulation on slope uncertainty derived from DEMs at different resolution levels： a case study in the Loess Plateau

Slope is one of the crucial terrain variables in spatial analysis and land use planning, especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas, but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as(0.0015S2 0.031S-0.0325)X-0.0045S2-0.155S 0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.