基于GIS城镇扩展的空间格局分析

以城镇扩展为核心内容的城市土地利用/土地覆盖变化已经成为目前国内土地利用/土地覆盖变化(LUCC)研究的热点.基于某地2002年至2007年土地利用数据,提取城镇用地信息,利用全局和局部空间关联模型,分析了区域城镇扩展的空间分布特征,得出了相关研究成果.     

基于遥感的长沙市城市热岛与土地利用/覆盖变化研究

基于多时相Landsat TM/ETM+影像,首先计算长沙市地表亮度温度,然后利用NDVI(归一化植被指数)、MNDWI(改进 的归一化水体指数)、NDBI(归一化建筑指数)和NDBaI(归一化裸土指数)4个指数,采用决策树分类方法对长沙市影像进行 土地利用/覆盖分类。在此基础上,对长沙市城市热岛的空间分布特征、时空演变特征以及城市热岛与土地利用/覆盖变化和各种影 响因子之间的关系进行研究。结果表明,随着长沙市城区范围的不断扩张,城市热岛范围也不断增大; 土地利用/覆盖类型的变化 会改变地表温度的空间分布,城市用地和裸地是城市热岛强度的主要贡献因素,水体和林地具有较好的降温作用。地表温度与4种 归一化指数的回归分析表明,它们之间存在明显的相关性,不同土地利用/覆盖类型的地表温度存在较大差异。     

基于ULAT的珠海市海岸带土地利用变化分析

以珠海市2006年、2010年和2016年的Landsat TM/ETM+卫星影像为基础,在GIS技术的支持下,首先利用遥感监督分类方法获取土地利用覆盖图;再采用单一土地利用动态度、综合土地利用动态度和强度等指标进行定量描述;然后结合GIS图形空间分析功能绘制景观足迹图,进而从定性和定量两个方面分析了珠海市土地利用的时间演化趋势和空间格局;最后依据城市景观分析技术(ULAT),对城市景观发展进行了总体分析。     

基于人口热力图和土地利用分类实现人流量空间分布的精确提取

为了实现区域人流量精确性、有效性提取,提出一种基于人口热力图提供的动态聚合人流数据获得更精确人流量分布的方法。该方法充分利用人口热力图数据的覆盖范围广、即时高效的特点,将研究区域按土地利用分类划分为含有不同人流权重值的地块,结合叠置分析等方法完成聚合人流量的再分配,进而实现区域人流量空间分布的精确提取。以上海国际旅游度假区的部分区域为例,进行人流量空间分布的研究,结果证明了方法的有效性。     

基于蚁群算法的土地利用(图)图斑合并方法

很多地图综合问题可以应用优化算法解决,土地利用(图)图斑合并也可以看作是一个优化问题。本文提出了基于蚁群算法的土地利用(图)图斑合并方法。实验结果表明,基于蚁群算法的图斑合并方法优于普通的迭代方法,该方法使综合后各地类面积变化较小且图斑紧凑性较好,是解决土地利用(图)图斑合并问题的一条可行之路。     

基于遥感的武汉地区LUCC对热岛效应的影响研究

利用1988年、2002年、2005年的武汉地区TM遥感影像,选取通用性较强的普适单通道法进行地温反演,得到3个时期的地表温度图、地表均温和标准差的变化情况。利用最大似然法进行监督分类,得到不同时期武汉地区的土地利用图。围绕这3年的土地利用/覆盖变化以及地表温度反演结果,探索不同地表覆盖类型与温度分布的关系,并对武汉地区热岛状况进行定性分析。     

MapGIS在土地利用现状制图中的应用

土地利用现状图作为土地资源调查工作的主要成果,反映了土地类型的数量、空间分布及土地利用的特点,在土地利用的规划、水资源的评价及空间数据库的建设方面具有重要的作用。以IRS-1C遥感卫片为数据源,讲述MapGIS在土地利用现状遥感解译和专题制图中的应用     

顾及人造地表空间聚集性的人口空间化探索——以江苏省为例

土地利用数据承载着人口分布的重要信息,基于土地利用/土地覆盖的人口数据空间化方法最为常见,但该方法未考虑地表覆盖的空间聚集性.本文以江苏省为例,以县级为研究单元,基于2010年全球地表覆盖数据(GlobeLand 30)中的人造地表数据,构建江苏省各县人口统计数据与各县人造地表面积之间的人口空间化模型.在此基础上,选用...     

基于ETM＋制作土地利用覆盖图——以制作北京1：5万土地利用覆盖图为例

简要介绍了基于LANDSAT7 ETM＋影像,采用计算机非监督分类、监督分类与人工解译相结合的方法制作土地利用覆盖图的过程和所采用的关键技术,给出了适用于规模化生产土地利用覆盖数据的工艺流程图。使用该方法制作的十一种分类要素的北京地区1：5万土地利用覆盖图,平均分类精度为84．85％,可以满足一般用户对土地利用覆盖图的要求。     

土地利用图综合流程研究

土地利用图作为一种典型的专题地图,其综合是目前我国正在进行的第二轮土地利用总体规划工作的一部分,所以研究土地利用图综合具有重要的意义。土地利用图空间上全覆盖、无重叠以及语义上层次化的特点决定了土地利用图的综合是一个复杂的过程。土地利用图的综合过程大致可以分为预处理、图形简化、狭长图斑线化、小图斑综合（聚合和融合）以及综合质量评价等几个步骤,结合实际工作对各个步骤进行详细的阐述,结果证明文中采用的方法基本能够实现土地利用图的自动综合,并且综合结果能够满足用户需求。     

用ANUDEM建立水文地貌关系正确DEM的方法研究

针对区域尺度的径流、水土流失定量评价和植被适宜性评价等研究工作需要,利用1∶25万数字地形图和ANUDEM软件,对黄土丘陵区中等分辨率水文地貌关系正确DEM建立方法进行了研究。结果表明该方法所建立的DEM,可以正确反映地貌梁、沟结构及其与流水线网络的关系,对地形描述的能力优于TIN方法建立的DEM;利用ANUDEM和1∶25万地形图插值建立黄土丘陵区DEM的三个主要参数分别为分辨率50或100,计算迭代次数40,第二糙率系数0.8。     

基于多源高分辨率遥感影像的2 m不透水面一张图提取

不透水率是衡量城市生态环境状况的一个重要指标。当前全球范围内仅有1 km和30 m分辨率尺度的不透水面专题信息,无法满足城市尺度水文模型建模、海绵城市规划和建设需求。提出了图谱信息融合的不透水面提取模型,实现了基于深度学习的不透水面提取新方法,研制了不透水面遥感全流程提取和监测软件。基于多源高分辨率遥感影像首次完成了中国31个省（直辖市、自治区）的2 m不透水面专题信息提取,形成全国不透水面一张图,为海绵城市和生态城市的建设提供了基础数据支撑和技术监测手段。     

基于LUR的武汉市PM2.5浓度空间分布模拟

基于稀疏监测点的监测数据无法直接获取城市内部空气污染的高分辨率空间分布。以武汉市为例,研究了基于土地利用回归(landuseregression,LUR)模型的大气PM2.5浓度高分辨率空间分布模拟。采用双变量相关分析识别出与PM2.5浓度相关性最高的4个影响因子,分别是1000m缓冲区内道路长度,500m缓冲区内水域面积,500m缓冲区内建设用地面积以及工业污染影响。采用PM2.5月平均浓度和识别出的影响因子连同气象条件(月平均温度和月降水量)进行多元线性回归分析,相关系数R2达到0.905,调整后的R2为0.885。在研究区建立均匀格网(2km×2km),利用得到的LUR方程计算格点PM2.5浓度值,应用空间插值制成武汉市主城区夏季PM2.5浓度空间分布模拟图。模拟结果显示,主城区有三个PM2.5浓度高值中心,分别为青山工业区、江北工业区和汉口汉西建材市场区域。汉阳南部、武昌南部的大型湖泊和水域面积比例较大的区域表现为两个PM2.5浓度低值中心。     

Investigating relationships between Landsat-7 ETM data and spatial segregation of LULC types under shifting agriculture in southern Cameroon

This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM⁺ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM⁺ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6 km) spatial dependence as compared to those varying only within 1 km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM⁺ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape.     

Multiple band digital orthophoto quadrangle data: A source for generating land use maps

Abstract

The purpose of this study was to investigate the use of color infrared‐digital orthophoto quadrangle (CIR‐DOQ) data to generate land use/land cover (LULC) maps and to incorporate them as data layers in geographic information systems (GIS) involving various resource management scenarios. The Danville 7.5‐minute quadrangle located in the southern part of Limestone and Morgan counties, Alabama, was used as the study site. Data for the special CIR‐DOQ were generated by scanning four 9x9 inch CIR aerial photographs at a uniform pixel sample grid of 25 microns resulting in 2 meters ground sample resolution. One‐half of the quadrangle was used to identify training sites for performing a supervised classification of the data and the other half to verify the accuracy of the classification. The CIR‐DOQ data were found to be adequate for using a supervised classification algorithm to differentiate major LULC classes, resulting in a classification accuracy of 93 percent. The superior spatial quality of the data over commençai satellite data affords resource managers an opportunity to more effectively study land cover and surface hydrological properties of an area, soil moisture and surface soil textures, as well as differentiate among vegetation species, using remote sensing techniques. However, caution must be exercised when using multispectral classification techniques to classify mosaicked CIRDOQ data because of the image enhancements used to generate the final product. In its present form, there are some limitations to the use of the data for performing spectral classifications. Hozvever, the high spatial resolution of the data enables even the novice resource planner to effectively use the data in visual interpretations of major LULC classes.     

Estimation of Sediment Yield and Selection of Suitable Sites for Soil Conservation Measures in Ahar River Basin of Udaipur,Rajasthan using RS and GIS Techniques

The present study aimed at quantification of sediment yield for Ahar River basin of Udaipur district in Rajasthan, India by a regional empirical model using RS and GIS techniques. The land use/land cover (LULC) map of the study area was prepared by supervised classification using satellite imagery of IRS-P6 LISS III. Overall accuracy of the prepared LULC map was 90.78%. The major portion of the study area (49%) is covered with rangeland. Slope map for the study area was developed using digital elevation model. The slope in most of the study area (40% of the total area) ranges from 1% to 4%. In addition, drainage density map of the study area was generated on micro-watershed basis. The study area is covered by a dendritic pattern of drainage which shows that rocks in the area are homogeneous and uniformly resistant to water flow. The drainage density in the study area is 1.11 km km⁻². Annual sediment yield of the study area was quantified by Garde model. The mean annual runoff and sediment yield for the area was 37.58 million m³ and 8,760 m³/year, respectively. Finally, appropriate sites for construction of soil conservation measures are suggested using Boolean logic analysis method on combined slope and drainage maps.     

基于纸质地图的单像地形图快速数字修测系统

提出了一种基于纸质地形图的单像地形图数字修测方法,它能在不具有矢量地形图的地区,根据新摄取的航摄像片直接对原有的地形图分版图的扫描影像进行修测,然后用绘图输出修测后的像素地图,或用于ＧＩＳ中相应数据的更新。     

遥感和GIS技术应用于伊拉克南部Basrah省土地利用/覆盖变化及城市扩张研究(英文)

In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map, and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250 000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km² in 1990 to 3 794.9 km² in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time. Supported by the Al-Basrah University, Iraq, the Geo-information Science and Technology Program (No. IRT 0438)China).     

利用DPGrid系统制作1∶500正射影像图

利用了DPGrid系统,实现了新疆奎屯市城区1∶500数字正射影像图生产,试验表明其可以实现城市大比例尺数字正射影像图的高效率生产。     

Mapping impervious surface distribution in China using multi-source remotely sensed data

ABSTRACT

Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling.