环境评价中的综合制图及其自动分析与成图系统

本文分三个部分：第一部分主要阐明环境评价中综合制图的必要性、环境制图的主要种类和形式。第二部分主要说明环境评价综合制图的基本保证，即统一协调的目的和要求，介绍了环境评价综合制图的基本方法和过程。第三部分介绍了环境评价综合制图中使用自动分析和制图系统的过程和方法。该系统具有X、Y坐标数据（多边形）和网格数据相互转换、利用数字信息派生多种分析地图、进行多变量统计分析等多种功能的软件。最后介绍了环境质量电子计算机自动分析与评价及其综合制图的具体过程和方法。     

摄影测量数据库的设计与建立

摄影测量数据库主要存储和管理数字地图成果数据，包括数字高程模型数据（DEM）、数字矢量地图数据（DLG）、数字影像地图数据（DOM）和数字栅格地图数据（DRG）。本文主要介绍了摄影测量数据库的设计、建立以及应用分析等内容。     

通用机助市图系统的地图语言工具

通过摄影测量方式生产数字地形图，现已得到了制图界的广泛接受。作为生产地形图的第三代数字制图系统（DMS）投放市场已经两年多了，其中之一就是通用计算机辅助制图系统（UCAMS）。UCAMS的理论基础是地图语言。象任何其他语言一样，地图语言有两项基本内容：字母系统和语法，本文讨论的是生产数字地形图开发的地图，并介绍地图字母和地图语法，以及地图语言工具的实例。     

机助制图学——原理与前景

<正> 本书全面介绍了现代计算机制图中的情况,其中包括计算机制图的方法和计算机如何改变制图性质和地图形态的问题。此外,本书也简单提及.对制图员较为重要的实际问题。作者对将地图信息输入数据库,然后利用数据来生产地图的光栅法和失量法的优缺点做了全面考虑。在对CMAP和SYMAP程序及专题制图输出做全面介绍后指出:这些基于光栅原理的系统已经落后于时代,而采用失量方式的设备则是制作用户     

不同界线符号实部相交的自动化处理方法

制图规范中要求不同界线实部相交,然而当前制图软件多未考虑界线相交处虚线的处理,而且国内外文献针对实部相交的研究较少,尚未形成完整的解决方案,基于此,本文根据单元循环配置法绘制地图线状符号的基本思想,首先拆分原始单元块,然后分解原始的数据图层,最后将新单元块和新图层对应配置,并据此设计了自动化处理方法。通过实际数据验证了算法的有效性和合理性,该方法顾及了不同等级、不同类别界线的差异,并通过自动化的处理方法提高了地图编辑的效率。     

硬多边形名称注记定位线确定的拓扑图形化简模型研究

本文根据地图上的硬多边图形特点,结合名称注记定位要求,将其分为凸硬多边形与凹硬多边形两种形式.建立在拓扑特征分析基础上,提出了对称凹顶点与非对称凹顶点等概念、理论与基础模型体系,最后提出了硬多边形名称注记定位线确定模型.理论与实验分析表明所提出的概念与模型逻辑严密、结论正确,对硬多边形地图要素名称注记自动定位具有基础支持作用,也对数字环境下硬多边形地图要素自动制图综合中的图形简化具有一定的借鉴意义.     

数字地图的实用化

一数字地图在我国已进入实用阶段。由机助制图或自动制图系统输出的产品之一是数字地图。在《遥感辞典》中是这样定义数字地图的：“以数字形式存储在磁带上的地图，需要时可由电子计算机的图形输出设备（绘图仪或视屏）恢复为地图图形。”具体一点说，空间数据包括遥感数据、纸质地图资料、统计值，通过图／数转换装置的数字化仪或直接键人数据，经过制图软件驱动的计算机处理，获得磁盘形式的制图文件，这时便产生了数字地图的半成品。人们可以将这个磁盘，从能够驱动这种文件格式的计算机和视屏中回放出数字地图的图形，它不受比例尺和投…     

国际测量工作者联合会(FIG)简介

除国际大地测量与地球物理联合会（IUGG）、国际摄影测量与遥感协会（ISPRS）和国际地图制图协会（ICA）以外，国际测量工作者联合会（Federation Internationale des Geometres，简称FIG）是又一个较大的国际性测量组织。     

地图科学技术:由数字化到智能化

首先论述了地图学家和地图制图工程师们是怎样通过数据、大数据、时空大数据来改变自己的思维方式和工作方式, 并由此推动地图科学进入以“数据密集型计算”为特征的“第四科学范式”新时代;接着, 通过国家基础测绘地图产品（4D）数字化转型成果的功能和作用及其特性（一览性、解析性、知识性、基础性、表示内容的广泛性、表示对象的外拓性和时序性等）分析、非国家基础测绘地图产品的多样化和个性化进展、地图集“设计→编绘→出版”一体化数字化转型及2021年出版的有代表性地图集的介绍和特色分析, 认为地图（集）“设计→编绘→出版”全过程数字化转型取得了标志性成果;第三, 以地图制图综合“百年国际难题”的智能化突破、地图（集）“设计→编绘→出版”全过程由数字化到智能化的优化升级的深化研究为例, 总结了人工智能时代地图制图综合研究四个演进过程、难点和关键技术的突破, 深入分析了地图（集）“设计→编绘→生产”全过程由数字化到智能化的研究现状和需要进一步研究的问题。     

基于地图的地理信息可视化现状与发展

地理信息可视化是在地理数据库驱动下,以地图形式表达地理信息的过程。地图可视化和GIS可视化是地学可视化研究的两个重要部分,地图可视化通常在制图系统中完成,脱离了地理数据库,不便于地图更新;GIS可视化又缺乏足够的地图制图工具。文中探讨了地理数据库驱动下的地图表达机制,融入了地图制图机制与GIS符号化,形成GIS数据管理、数据综合、符号化、图形综合一体化的地图表达模型。     

A comparison of alternative criteria for defining fuzzy boundaries on fuzzy categorical maps

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Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

An Automated Approach for Land Cover Classification Based on a Fuzzy Supervised Learning Framework

This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data. The land cover classification is treated as an automatically supervised learning procedure, which can be divided into automatic sample selection and fuzzy supervised classification. Once a series of features were extracted from multi-source datasets, spectral matching method is used to determine the degrees of membership of auto-selected pixels, which indicates the probability of the pixel to be distinguished as a specific land cover type. In order to make full use of this probability, a fuzzy support vector machine (SVM) classification method is used to handle samples with membership degrees. This method is applied to Landsat Thematic Mapper (TM) data of two areas located in Northern China. The automatic classification results are compared with visual interpretation. Experimental results show that the proposed method classifies the remote sensing data with a competitive and stable accuracy, and demonstrate that an objective land cover classification result is achievable by combining several advanced machine learning methods.     

Fuzzy mapping of tropical land cover along an environmental gradient from remotely sensed data with an artificial neural network

Remote sensing is the only feasible means of mapping and monitoring land cover at regional to global scales. Unfortunately the maps are generally derived through the use of a conventional 'hard' classification algorithm and depict classes separated by sharp boundaries. Such approaches and representations are often inappropriate particularly when the land cover being represented may be considered to be fuzzy. The definition of boundaries between classes can therefore be difficult from remotely sensed data, particularly for continuous land cover classes which are separated by a fuzzy boundary which may also vary spatially in time. In this paper a neural network was used to derive fuzzy classifications of land cover along a transect crossing the transition from moist semi-deciduous forest to savanna in West Africa in February and December 1990. The fuzzy classifications revealed both sharp and gradual boundaries between classes located along the transect. In particular, the fuzzy classifications enabled the definition of important boundary properties, such as width and temporal displacement.     

SSM／I微波辐射计数据中国陆地覆盖特征季节变化分析

利用星载微波辐射计SSM／I多通道、多时相亮温数据开展了中国陆地覆盖特征的季节变化研究。通过对国内外星载微波辐射应用研究分析，提出了归一化极化指数(NDPI)的概念。处理了1997年4月、7月、10月和1998年1月的(每月的20、24日各两天)多通道SSM／I数据，在此基础上计算形成了第一幅中国陆地区域的归一化微波极化指数图，开展了中国陆地区域覆盖特征随季节变化的研究。研究结果表明，不同的陆地覆盖特征有特征的NDPI值，NDPI随季节而变化，植被、水分是引起NDPI变化的主要因子。     

Estimating the uncertainty of land-cover extrapolations while constructing a raster map from tabular data

This paper presents novel techniques to estimate the uncertainty in extrapolations of spatially-explicit land-change simulation models. We illustrate the concept by mapping a historic landscape based on: 1) tabular data concerning the quantity in each land cover category at a distant point in time at the stratum level, 2) empirical maps from more recent points in time at the grid cell level, and 3) a simulation model that extrapolates land-cover change at the grid cell level. This paper focuses on the method to show uncertainty explicitly in the map of the simulated landscape at the distant point in time. The method requires that validation of the land-cover change model be quantified at the grid-cell level by Kappa for location (Klocation). The validation statistic is used to estimate the certainty in the extrapolation to a point in time where an empirical map does not exist. As an example, we reconstruct the 1951 landscape of the Ipswich River Watershed in Massachusetts, USA. The technique creates a map of 1951 simulated forest with an overall estimated accuracy of 0.91, with an estimated users accuracy ranging from 0.95 to 0.84. We anticipate that this method will become popular, because tabular information concerning land cover at coarse stratum-level scales is abundant, while digital maps of the specific location of land cover are needed at a finer spatial resolution. The method is a key to link non-spatial models with spatially-explicit models.     

Production of global land cover data – GLCNMO

Abstract

Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and expert's comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.     

Disaggregating and mapping crop statistics using hypertemporal remote sensing

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.     

Multi-source change detection with PALSAR data in the Southern of Pará state in the Brazilian Amazon

Optical data is broadly used for change detection studies, despite being hindered by atmospheric conditions. Synthetic Aperture Radar (SAR) data can be useful for change detection in areas with frequent cloud coverage as SAR systems are capable of obtaining images almost independently from atmospheric conditions. This study aims to verify the difference in results of using SAR data instead of optical data for change detection purposes. Different levels of one hierarchical legend and both pixel and region-based classifiers were used. Change results were evaluated considering the use of rectangular matrices to incorporate the occurrence of impossible changes and relative comparison between change maps. Although the change maps obtained using only optical data were more accurate than those using either one or two land cover classifications based on L-band SAR data, the difference in the accuracy of change maps decreases with the use of less detailed legends. Additionally, results indicate that L-band SAR and multi-sensor approaches are adequate for deforestation identification even if post-classification results did not achieve global accuracy values superior to 0.86. The most accurate change detection results obtained in this work were not associated with the overall accuracy of land cover classifications, but with the distribution and accuracy of specific land cover classes.