A population density grid for Spain

This article describes a high-resolution land cover data set for Spain and its application to dasymetric population mapping (at census tract level). Eventually, this vector layer is transformed into a grid format. The work parallels the effort of the Joint Research Centre (JRC) of the European Commission, in collaboration with Eurostat and the European Environment Agency (EEA), in building a population density grid for the whole of Europe, combining CORINE Land Cover with population data per commune. We solve many of the problems due to the low resolution of CORINE Land Cover, which are especially visible with Spanish data. An accuracy assessment is carried out from a simple aggregation of georeferenced point population data for the region of Madrid. The bottom-up grid constructed in this way is compared to our top-down grid. We show a great improvement over what has been reported from commune data and CORINE Land Cover, but the improvements seem to come entirely from the higher resolution data sets and not from the statistical modeling in the downscaling exercise. This highlights the importance of providing the research community with more detailed land cover data sets, as well as more detailed population data. The dasymetric grid is available free of charge from the authors upon request.     

Small-area estimation of land cover statistics by post-stratification of a national area frame survey

The objective of this paper is to examine a method for estimation of land cover statistics for local environments from available area frame surveys of larger, surrounding areas. The method is a simple version of the small-area estimation methodology. The starting point is a national area frame survey of land cover. This survey is post-stratified using a coarse land cover map based on topographic maps and segmentation of satellite images. The approach is to describe the land cover composition of each stratum and subsequently use the results to calculate land cover statistics for a smaller area where the relative distribution of the strata is known. The method was applied to a mountain environment in Gausdal in Eastern Norway and the result was compared to reference data from a complete in situ land cover map of the study area. The overall correlation (Pearson’s rho) between the observed and the estimated land cover figures was r = 0.95. The method does not produce a map of the target area and the estimation error was large for a few of the land cover classes. The overall conclusion is, however, that the method is applicable when the objective is to produce land cover statistics and the interest is the general composition of land cover classes - not the precise estimate of each class. The method will be applied in outfield pasture management in Norway, where it offers a cost-efficient way to screen the management units and identify local areas with a land cover composition suitable for grazing. The limited resources available for in situ land cover mapping can then be allocated efficiently to in-depth studies of the areas with the highest grazing potential. It is also expected that the method can be used to compile land cover statistics for other purposes as well, provided that the motivation is to describe the overall land cover composition and not to provide exact estimates for the individual land cover classes.     

Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.     

Quantitative estimation and spatial reconstruction of urban and rural construction land in Jiangsu Province, 1820–1985

Land cover is the most evident landscape signal to characterize the influence of human activities on terrestrial ecosystems. Since the industrial revolution, the expansion of construction land has profoundly changed the status of land use coverage and changes. This study is proposed to reconstruct the spatial pattern of construction land (urban construction land and rural settlement land) for five historical periods over the past 200 years in Jiangsu Province with 200 m × 200 m grids on the basis of quantitative estimation. Urban construction land is estimated based on data about city walls, four gates along walls, and other socio-economic factors. Rural settlement land is calculated based on the rural population and per capita housing allowance. The spatial pattern of historical construction land is simulated based on the distribution of modern construction land in 1985 with a quantitative-boundarysuitability control method and thorough consideration over connectivity of different land use types. The study concludes that: (1) the amount of construction land in Jiangsu Province is estimated at 963.46 km² in 1820, 1043.46 km² in 1911, 1672.40 km² in 1936, 1980.34 km² in 1952 and 10,687.20 km² in 1985; and (2) the spatial distribution of construction land features the great proclivity to water bodies and main roads and the strong polarization of existent residence. The results are verified directly and indirectly by applying the trend verification of construction land changes and patterns, the correlation analysis between rural settlement land and local arable land, and quantitative accuracy test of the reconstructed construction land to actual historical survey maps covering four sample regions in 1936.     

Broad scale forest cover reconstruction from historical topographic maps

Land cover change is one of the major contributors to global change, but long-term, broad-scale, detailed and spatially explicit assessments of land cover change are largely missing, although the availability of historical maps in digital formats is increasing. The problem often lies in efficiency of analyses of historical maps for large areas. Our goal was to assess different methods to reconstruct land cover and land use from historical maps to identify a time-efficient and reliable method for broad-scale land cover change analysis. We compared two independent forest cover reconstruction methods: first, regular point sampling, and second, wall-to-wall mapping, and tested both methods for the Polish Carpathians (20,000 km²) for the 1860s, 1930s and 1970s. We compared the two methods in terms of their reliability for forest change analysis, relative to sampling error, point location and landscape context including local forest cover, area of the spatial reference unit and forest edge-to-core ratio. Our results showed that the point-based analysis overestimated forest cover in comparison to wall-to-wall mapping by 1–3%, depending on the mapping period. The reasons for the differences were mainly the backdating approach and map generalisation rather than the point grid position or sampling error. When we compared forest cover trajectories over time, we found that the point-based reconstruction captured forest cover dynamics with a comparable accuracy to the wall-to-wall mapping. More broadly, our assessment showed that historical maps can provide valuable data on long-term land cover trends, and that point-based sampling can be an efficient and accurate way to assess forest area and change trends. We suggest that our point-based approach could allow land cover mapping across much of Europe starting in the 1800s. Our findings are important because they suggest that land cover change, a key component of global change, can be assessed over large areas much further back in time than it is commonly done. This would allow to truly understand path dependencies, land use legacies, and historical drivers of land cover change.     

Comparison of land cover maps using fuzzy agreement

A generic problem associated with different land cover maps that cover the same geographical area is the use of different legend categories. There may be disagreement in many areas when comparing different land cover products even though the legend shows the same or very similar land cover class. To capture the uncertainty associated with both differences in the legend and the difficulty in classification when comparing two land cover maps, expert knowledge and a fuzzy logic framework are used to map the fuzzy agreement. The methodology is illustrated by comparing the Global Land Cover 2000 data set and the MODIS global land cover product. Overall accuracy measures are calculated, and the spatial fuzzy agreement between the two land cover products is provided. This approach can be used to improve the overall confidence in a land cover product, since areas of severe disagreement can be highlighted, and areas can be identified that require further attention and possible re-mapping.     

Urban Expansion Pattern and Land Use Dynamics in Dhaka, 1989–2014

Rapid change in land use and land cover (LULC) and unplanned urban expansion in Dhaka City, Bangladesh, receives continuous attention from local policymakers and the international community. This study employed a supervised classification procedure and postclassification change detection technique to estimate major changes between different LULC classes. The study revealed that built-up area increased significantly from 1989 to 2014. The total urban growth of 81.54 percent resulted in a substantial decrease in natural vegetation cover and agricultural land. In addition, water bodies have declined consistently over the last twenty-five years. The overall accuracy of LULC change maps produced from Landsat data ranged from 89.72 percent to 92.97 percent. The results should contribute to ongoing LULC information updates while forecasting possible future LULC change and sustainable development under greater population density.     

Integration of multi-source remote sensing data for land cover change detection

The objective of this study is to develop a methodology to integrate multi-source remote sensing data into a homogeneous time series of land cover maps in order to carry out change detection. We developed a method to increase the comparability between land cover maps coming from panchromatic aerial photographs and SPOT XS (multi-spectral) data by equalizing their levels of thematic content and spatial details. The methodology was based on the hypotheses that: (1) map generalization can improve the integration of data for change detection purpose, and (2) the spatial structure of a land cover map, as measured by a set of landscape metrics, is an indicator of the level of generalization of that map. Firstly, the methodology for data integration was developed by using land cover maps generated from near-synchronous data. Results revealed that, by controlling successively the parameters that influence the level of map generalization, the percentage of agreement between the near-synchronous land cover maps can be increased from 42% to 93%. The computation of five landscape metrics for a set of generalized land cover maps and for the target map allowed us to optimize the level of generalization by measuring the similarity in landscape pattern of the maps. The optimum level of generalization of the land cover map obtained from the aerial photographs for comparison with a land cover map derived from SPOT XS data was found at a resolution of 41m for two generalization levels of the thematic content. The spatial structure of a land cover map, as measured by a set of landscape metrics, is thus a good indicator of the level of generalization of this map. Secondly, the method was applied by integrating a land cover map obtained from aerial photographs of 1954 with a land cover map obtained from a SPOT XS image of 1992.     

《中国人口地图集》的编制特点

本文叙述了《中国人口地图集》的编制特点。图集主要使用了准确的详细的1982年第三次人口普查资料,并用计算机制图技术完成了人口数据的提取、处理、人口指标值计算、统计分析、制图分级和图形输出。在文中同时对图集的自动编制过程作了简要说明。     

The potential of old maps and encyclopaedias for reconstructing historic European land cover/use change

Continental to global reconstructions of historic land cover/use are important inputs for many environmental, ecological and biogeochemical studies. While local to regional reconstructions frequently make use of old topographic maps and land use statistics, continental to global reconstructions are mostly model-based reconstructions. As a result they are subject to large uncertainties. A wealth of historic land cover/use maps and statistics have been produced and these are now more accessible due to the ending of copyrights and secrecy statuses, enthusiastic hobby communities and national cartographic institutes or cadastres that have a strategy towards data sharing with society. In this paper we made use of historic statistics and old topographic maps to demonstrate the added value for model-based reconstructions of historic land cover/use for Central Europe back to 1900. We harmonized these diverse data types and different types of historic land data were incorporated into the land use reconstructions. The added value of using these data was evaluated using historical maps by performing a reconstruction with and without the historic information. The accuracy of the land allocation in the historic reconstruction was improved by 16.5% using historic maps. Additionally, historic maps improved the representation of the spatial structure of landscapes. The historic land cover/use statistics used showed a strong agreement with independent estimates, like historic maps.     

全国1:25万土地覆被遥感制图精度在内蒙古东部的实地验证

土地覆被是全球变化和区域可持续发展研究的重要内容,土地覆被遥感制图是支持全球变化和区域可持续发展研究的基础数据,制图质量是相关研究成果科学性的重要保证.本文针对全国1∶25万土地覆被遥感制图的精度评价,用大类分错扣1分,同一大类中的小类分错扣0.5分的计分评价方法,共选取190个样点,在内蒙古东部进行了实地验证.验证结...     

Land cover in Norway based on an area frame survey of vegetation types

The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km² at 18?km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric–vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.     

中国乡镇人口分布时空变化及其影响因素

精细尺度的人口变化研究对于理解、模拟和调控人地系统具有重要参考价值,乡镇级的人口数据可以精细化揭示中国人口时空分布的演变特征。本文建立了2010年和2015年中国乡镇人口空间数据库,借助GIS空间分析方法和地理探测器模型揭示了乡镇人口时空变化特征及其影响因素。研究表明：2015年乡镇人口数量均值为2.95万、密度均值为374人/km²,乡镇人口数量和密度分布的区域差异较大;乡镇人口与土地面积分布相较县域呈现更加不均衡特征,人口密度大于150人/km²的19403个乡镇（60.81%）,人口总量占比为81.76%、国土面积占比为20.27%,近似符合“二八定律”;2010—2015年,超过70.94%的乡镇人口数量有所增加（平均增幅19.33%）,9273个乡镇人口减少（平均减幅13.33%）,其中1684个乡镇（5.28%）的减幅超过20%,主要分布在东北、内蒙古和云南的边境地区;人口密度分布受地形起伏等自然地理条件,以及夜间灯光、路网密度所反映的社会经济因素等的综合影响,其作用机制存在明显的空间异质性。研究认为,乡镇人口的大幅增减变化及其显著差异格局对人地系统的优化调控,特别是编制国土空间规划、实施乡村振兴战略和保障国防安全等带来一定挑战,应给予足够重视。     

Space–time multiple regression model for grid-based population estimation in urban areas

We can collect, store, and analyze a huge amount of information about human mobility and social interaction activities due to the emergence of information and communication technologies and location-enabled mobile devices under cyber physical system frameworks. The high spatial resolution of population data on a multi-temporal scale is required by transport planners, human geographers, social scientists, and emergency management teams. In this study, we build a space-time multiple regression model to estimate grid-based (500 m × 500 m) spatial resolution at multi-temporal scale (30-min intervals) population data based on the space-time relationship among geospatially enabled person trip (PT) survey data and incorporate both mobile call (MC) and geotagged Twitter (GT) data. Since using geospatially enabled PT survey data as dependent variables enables us to acquire actual population amounts, which strongly depend on MCs and social interaction activities. Although many grids have a strong correlation between PT and MC/GT, some show fewer correlation results, especially where the grids have factories, schools, and workshops in which fewer MCs are found but a large population is presented. Although GT data are sparser than MCs, people from amusement and tourist areas can be detected by GT data. The space-time multiple regression model can also estimate the different amounts of populations based on human travel behavior that changes over space and time. According to accuracy assessments, the night-time estimated results, especially between 00:00 and 06:30, strongly correlate with national census data except in places where the grids have railway and subway stations.     

Classification of densities and characteristics of curve of population centers in China by GIS

In this paper, with the spatial analysis functions in ArcGIS and the county-level census data of 2000 in China, the population density map was divided and shown by classes, meanwhile, the map system of population distribution and a curve of population centers were formed; in accordance with the geographical proximity principle, the classes of population densities were reclassified and a population density map was obtained which had the spatial clustering characteristic. The multi-layer superposition based on the population density classification shows that the population densities become denser from the Northwest to the Southeast; the multi-layer clustering phenomenon of the Chinese population distribution is obvious, the populations have a water-based characteristic gathering towards the rivers and coastlines. The curve of population centers shows the population densities transit from the high density region to the low one on the whole, while in low-density areas there are relatively dense areas, and in high-density areas there are relatively sparse areas. The reclassification research on the population density map based on the curve of population centers shows that the Chinese population densities can be divided into 9 classes, hereby, the geographical distribution of Chinese population can be divided into 9 type regions: the concentration core zone, high concentration zone, moderate concentration zone, low concentration zone, general transitional zone, relatively sparse area, absolute sparse area, extreme sparse area, and basic no-man's land. More than 3/4 of the population of China is concentrated in less than 1/5 of the land area, and more than half of the land area is inhabited by less than 2% of the population, the result reveals a better space law of China’s population distribution.     

基于GIS的中国人口重心的密度分级与曲线特征

In this paper, with the spatial analysis functions in ArcGIS and the county-level census data of 2000 in China, the population density map was divided and shown by classes, meanwhile, the map system of population distribution and a curve of population centers were formed; in accordance with the geographical proximity principle, the classes of population densities were reclassified and a population density map was obtained which had the spatial clustering characteristic. The multi-layer superposition based on the population density classification shows that the population densities become denser from the Northwest to the Southeast; the multi-layer clustering phenomenon of the Chinese population distribution is obvious, the populations have a water-based characteristic gathering towards the rivers and coastlines. The curve of population centers shows the population densities transit from the high density region to the low one on the whole, while in low-density areas there are relatively dense areas, and in high-density areas there are relatively sparse areas. The reclassification research on the population density map based on the curve of population centers shows that the Chinese population densities can be divided into 9 classes, hereby, the geographical distribution of Chinese population can be divided into 9 type regions: the concentration core zone, high concentration zone, moderate concentration zone, low concentration zone, general transitional zone, relatively sparse area, absolute sparse area, extreme sparse area, and basic no-man's land. More than 3/4 of the population of China is concentrated in less than 1/5 of the land area, and more than half of the land area is inhabited by less than 2% of the population, the result reveals a better space law of China’s population distribution.     

基于随机森林模型的珠江三角洲30 m格网人口空间化

人口空间化是实现人口统计数据与其他环境资源空间数据融合分析的有效途径。本文选取夜间灯光数据、道路网数据、水域分布数据、建成区数据、数字高程模型和地形坡度数据作为影响珠江三角洲人口分布的变量因子,利用随机森林模型对珠江三角洲2010年人口数据进行了30 m格网空间化,并将模拟结果与三个公开数据集作精度对比,最后基于随机森林模型的变量因子重要性分析珠江三角洲人口空间分布的影响因素。结果表明：本文模拟整体精度达到82.32%,均优于WorldPop数据集以及中国公里网格人口数据集,接近GPW数据集,而且在人口密度中等区域模拟精度最高;通过对变量因子重要性进行度量,发现夜间灯光强度是珠江三角洲人口分布的最重要指示性指标,到水域的距离、到建成区的距离和路网密度对珠江三角洲人口分布均具有重要作用。利用随机森林模型结合多源信息能够实现高空间分辨率的人口空间化,可为精细化城市管理提供重要数据源,也可为相关政策决策制定提供支持。     

Satellite image based vegetation classification of a large area using limited ground reference data: a case study in the Usa Basin, north-east European Russia

Predicted global changes can be studied effectively by combining spatially explicit data sets on vegetation and other landscape properties with process models. However, detailed knowledge of the vegetation distribution of remote Arctic areas is relatively scarce. This paper shows how a mesoscale vegetation and land cover classification of a large, remote Arctic area can be conducted at a fine spatial resolution (30 m cell size) using a limited ground reference data set. The study area is the catchment of the River Usa (93 500 km²) in north-eastern European Russia. Vegetation zones in the Usa Basin range from taiga in the south to forest-tundra and tundra in the north, and to alpine in the Ural mountains in the east. Classification was done using a mosaic of spectrally adjusted Landsat TM5 images from five different dates and a semi-supervised method. Ground reference data were collected during the summers of 1998, 1999 and 2000. Accuracy of the 21-class vegetation type/land cover classification produced was tested against test points interpreted from oblique aerial photographs taken from a helicopter (logistic limitations prohibited the collection of representative ground reference data). The main vegetation types (forests, willow dominated stands and meadows, peatlands, tundra heaths, mainly unvegetated areas, and water bodies) were distinguished with relatively high accuracy: 84% of the test points were classified correctly. Spatially detailed land cover data sets like the one described here allow detailed landscape-level analysis and process modelling on many different subjects.     

Urban mapping,accuracy, & image classification: A comparison of multiple approaches in Tsukuba City,Japan

The rapid growth of urban space and its environmental challenges require precise mapping techniques to represent complex earth surface features more accurately. In this study, we examined four mapping approaches (unsupervised, supervised, fuzzy supervised and GIS post-processing) using Advanced Land Observing Satellite images to predict urban land use and land cover of Tsukuba city in Japan. Intensive fieldwork was conducted to collect ground truth data. A random stratified sampling method was chosen to generate geographic reference data for each map to assess the accuracy. The accuracies of the maps were measured, producing error matrices and Kappa indices. The GIS post-processing approach proposed in this research improved the mapping results, showing the highest overall accuracy of 89.33% as compared to other approaches. The fuzzy supervised approach yielded a better accuracy (87.67%) than the supervised and unsupervised approaches. The fuzzy supervised approach effectively dealt with the heterogeneous surface features in residential areas. This paper presents the strengths of the mapping approaches and the potentials of the sensor for mapping urban areas, which may help urban planners monitor and interpret complex urban characteristics.     

Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization

This study evaluates land use/cover changes and urban expansion in Greater Dhaka, Bangladesh, between 1975 and 2003 using satellite images and socio-economic data. Spatial and temporal dynamics of land use/cover changes were quantified using three Landsat images, a supervised classification algorithm and the post-classification change detection technique in GIS. Accuracy of the Landsat-derived land use/cover maps ranged from 85 to 90%. The analysis revealed that substantial growth of built-up areas in Greater Dhaka over the study period resulted significant decrease in the area of water bodies, cultivated land, vegetation and wetlands. Urban land expansion has been largely driven by elevation, population growth and economic development. Rapid urban expansion through infilling of low-lying areas and clearing of vegetation resulted in a wide range of environmental impacts, including habitat quality. As reliable and current data are lacking for Bangladesh, the land use maps produced in this study will contribute to both the development of sustainable urban land use planning decisions and also for forecasting possible future changes in growth patterns.