地球系统空间观测:从科学卫星到月基平台

五十多年来,全球性对地观测已形成强大的技术能力和系统体系,在不同应用领域发挥了重要作用。随着对陆地、大气、海洋研究的深入,地球系统科学和全球变化研究正在向空间对地观测技术提出新的重大战略性需求。本文描述了面向全球变化应对、发展全球变化系列科学卫星的方案;提出面向宏观地球科学现象探测、构建月基对地观测系统的设想;同时,作为宏观地球科学现象研究的一个方向,论述了利用地球科学卫星和月基对地观测技术开展全球变化"三极"对比研究的思路。     

Assessing environmental impacts of urban growth using remote sensing

ABSTRACT

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.     

基于DMSP/OLS夜光遥感影像的城市扩张政策驱动因素分析

城市扩张的速度和方向已成为社会关注的焦点,利用夜光遥感进行城市扩张驱动力成因分析是近年来的研究热点。本文采用DMSP卫星所获取的年平均中国区域夜光影像数据,首先对1992—2012年共21年的时序夜光影像进行相对辐射定标;其次通过经验阈值法进行城市建成区面积提取,并通过Landsat影像进行精度验证;最后引入计量经济模型,以地级市主政官员政治周期为解释变量,对全国1992—2012年地级市扩张的政策驱动力因素进行了归因分析。通过本文的分析可知,地级市主政官员政治周期的更替对城市扩张方向存在显著影响。     

基于DMSP/OLS灯光数据的快速城市化过程的生态效应评价研究——以环渤海城市群地区为例

快速有效地评估城市化过程带来的生态环境后果,对于优化城市土地利用格局、降低和防范城市生态环境风险,非常必要。因此本文综合利用DMSP/OLS夜间灯光数据和SPOT/VGT时间序列数据等多源遥感信息,以NDVI与时间的积分值来表征一定时间段内的植被初级生产力,探讨了环渤海城市群地区城市化过程对植被初级生产力的季节性变化影响。发现：（1）研究区全年总的平均初级生产力总体表现为城市地区低于非城市地区的特征。（2）研究区平均初级生产力一般是在8月份最高,而在1月份最低;同时,一个生长季内,平均初级生产力总体呈现为4-11月城市地区低于非城市地区,而12月到次年3月则是城市地区要高于非城市地区的趋势,但这种趋势在各土地覆盖类型间也存在很大的不同。（3）研究区全年总的平均初级生产力,城市地区NDVI为110.23d/km^2,而非城市地区为123.94d/km^2,两者相差13.71d/km^2,即城市化过程已经在一定程度上减弱了研究区的植被初级生产力。     

基于遥感信息和DSM的城市土地利用变化监测研究

建立了一套基于遥感信息和DSM的变化检测信息管理系统,该系统主要包括DSM数据管理模块、DSM变化检测模块和变化结果表达模块。系统利用多时相的数字表面模型(DSM),通过变化检测模块检测出城市地区人工建筑物、植被等的变化情况,检测结果可以应用在基本农田保护、土地利用变化监测以及违法用地查处等方面,为地理国(市)情监测提供技术支持。检测过程自动化程度高、快速、准确。     

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R² value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.     

Next generation of global land cover characterization,mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).     

Change detection of buildings using an airborne laser scanner

This study employed an airborne laser scanner to detect changes of buildings by acquiring a digital surface model (DSM) data of urban areas. Simple comparison between DSM data sets acquired at different occasions successfully detected building changes without omission errors. A CCD array image simultaneously acquired with the DSM data was also automatically orthorectified with the DSM data and indicated to help revise the building database efficiently.     

Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data

India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.     

Estimation of Light Pollution Using Satellite Remote Sensing and Geographic Information System Techniques

The primary focus of this research is to estimate light pollution in the urban and suburban regions of Pakistan with the help of satellite remote sensing (SRS) and geographic information system (GIS) techniques. Analog maps and multi-temporal nighttime images of the Defense Meteorological Satellite Program (DMSP) onboard Operational Linescan System (OLS) sensor were used in this study. A series of direct and indirect light pollution maps of the study area were generated and analyzed. The results of the study show that in the urban environment, light pollution is mainly due to artificial nightlight sources.     

Urban land use mapping and zoning of Bombay Metropolitan Region using remote sensing data

Bombay Metropolitan Region covering an area of about 4,360 sq. km. was selected for urban land use studies and for urban land use zoning. Urban land use mapping was carried out using SPOT multispectral linear array imagery on 1∶25,000 scale employing visual analysis tehcniques. Fifteen maps were prepared depicting the spatial distribution of various urban classes in the Greater Bombay and New Bombay regions. Sixteen urban land use maps were also prepared using Landsat TM data showing the distribution of land use pattern on 1∶50,000 scale for the entire metropolitan region. Urban land use zoning was carried out based upon suitability index on 1∶250,000 scale. This map provides information on the areas to be used for construction and areas to be kept under green belt in the metropolitan region. This study is a joint venture of Space Applications Centre with Bombay Metropolitan Development authority.     

基于Landsat ETM+数据的银川城市热岛研究

利用1999年8月12日的Landsat-7 ETM+数据,经辐射定标、NDVI及地表比辐射率计算,通过简化大气辐射传输模型,对银川市的地表温度进行了反演。在此基础上,对银川市的城市热岛分布特征和热场变异指数进行了研究。结果表明,银川市具有典型城市热岛效应,城区的地表温度比市郊引黄灌区高出10℃左右;银川市生态环境质量出现了两个极端,即市区生态评价指标为极差,而市郊引黄灌区的生态评价指标为优。     

Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas

Shadows commonly exist in high resolution satellite imagery, particularly in urban areas, which is a combined effect of low sun elevation, off-nadir viewing angle, and high-rise buildings. The presence of shadows can negatively affect image processing, including land cover classification, mapping, and object recognition due to the reduction or even total loss of spectral information in shadows. The compensation of spectral information in shadows is thus one of the most important preprocessing steps for the interpretation and exploitation of high resolution satellite imagery in urban areas. In this study, we propose a new approach for global shadow compensation through the utilization of fully constrained linear spectral unmixing. The basic assumption of the proposed method is that the construction of the spectral scatter plot in shadows is analogues to that in non-shadow areas within a two-dimension spectral mixing space. In order to ensure the continuity of land covers, a smooth operator is further used to refine the restored shadow pixels on the edge of non-shadow and shadow areas. The proposed method is validated using the WorldView-2 multispectral imagery collected from downtown Toronto, Ontario, Canada. In comparison with the existing linear-correlation correction method, the proposed method produced the compensated shadows with higher quality.     

Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people's interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.     

志愿者民航客机夜光遥感方法与数据验证

夜光遥感是遥感技术一个活跃的分支,能够反映人类社会活动的痕迹。在城市监测、经济参数评估、重大事件评估等方面有着独特的优势。近年来夜光数据的应用不断拓展,这对传统的卫星夜光遥感数据提出了更高的要求。本文基于大众遥感、志愿者遥感理念,提出了一种夜光遥感数据获取的新方法——民航客机遥感（PARS）。以长沙市为研究区域获取PARS夜光遥感影像,并将所得数据与传统卫星遥感数据作对比。结果表明,PARS捕获的夜光遥感数据在分辨率、波段及时效性上比传统方式表现得更为优秀,是一种低成本、灵活及多样化的遥感数据获取方式,具有很大的发展潜力。     

基于GlobeLand30的全球城乡建设用地空间分布与变化统计分析

城乡建设用地分布与变化是人类活动的直观标志和生态足迹,在环境变化研究、地理国(世)情监测和可持续发展研究等方面发挥着重要作用。以往人们对一些城市、区域或国家的城乡建设用地分布与变化进行过较为深入系统的研究,但在全球尺度上,这方面研究尚为空白。本文是利用我国自主研制的世界上首套30m空间分辨率全球地表覆盖数据集GlobeLand30的人造地表数据层,首次开展了全球城乡建设用地的空间分布及变化的统计分析。它采用用地面积、构成占比和增量占比等主要指标,统计全球范围内城乡建设用地的空间分布及2000年至2010年10年间的变化,重点分析了2010年全球、各大洲及主要国家的城乡建设用地分布现状与地域差异,2000年至2010年全球、主要国家的建设用地变化以及其主要土地来源。研究结果表明,2010年全球城乡建设用地总面积为118.75×104km2,占全球陆表面积的0.88%;2000年至2010年全球城乡建设用地面积增加了5.74×104 km2,变化率为5.08%,其中,中国和美国新增城乡建设用地约占全球的一半;新增城乡建设用地占用最多的是耕地,占总量的50.26%。这些为研究全球陆表人类活动的空间分布特征与变化趋势提供了翔实的信息和知识。     

Fluidity of land use patterns over the last century in and around the Baroda Urban Complex,Gujarat state

The fluidity of land-use patterns over the last century in and around the Baroda Urban Complex has been worked out using Survey of India topographic maps (1876–78, 1959–60) and SPOT satellite imagery (1988). The most striking feature of this study was the alarming loss of non-built up areas comprising agricultural land to urban sprawl. In 1876–78, non-built up land constituted 701.30 sq. km out of a total of 714 sq. km whereas in 1988, it was reduced to 625.27 sq. km. This urban growth pattern would not be conducive for sustainable development.     

建国以来无锡市区土地利用变化及存在问题的遥感分析与研究

本文通过对建国后三个时期遥感资料的解译,结合城市发展的社会、经济等信息资料,对无锡市区土地利用变化作了分析,论述了城市用地规模、结构、布局的发展演变特征和郊区土地使用变化特点,并就合理利用土地资源的若干问题作了探讨。     

Issues in the application of Digital Surface Model data to correct the terrain illumination effects in Landsat images

The accuracy of topographic correction of Landsat data based on a Digital Surface Model (DSM) depends on the quality, scale and spatial resolution of the DSM data used and the co-registration between the DSM and the satellite image. A physics-based bidirectional reflectance distribution function (BRDF) and atmospheric correction model in conjunction with a 1-second DSM was used to conduct the analysis in this paper. The results show that for the examples used from Australia, the 1-second DSM, can provide an effective product for this task. However, it was found that some remaining artefacts in the DSM data, originally due to radar shadow, can still cause significant local errors in the correction. Where they occur, false shadows and over-corrected surface reflectance factors can be observed. More generally, accurate co-registration between satellite images and DSM data was found to be critical for effective correction. Mis-registration by one or two pixels could lead to large errors of retrieved surface reflectance factors in gully and ridge areas. Using low-resolution DSM data in conjunction with high-resolution satellite images will also fail to correct significant terrain components where they occur at the finer scales of the satellite images. DSM resolution appropriate to the resolution of satellite image and the roughness of the terrain is needed for effective results, and the rougher the terrain, the more critical will be the accurate registration.     

Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data

Very high spatial and temporal resolution remote sensing data facilitate mapping highly complex and diverse urban environments. This study analyzed and demonstrated the usefulness of combined high-resolution aerial digital images and elevation data, and its processing using object-based image analysis for mapping urban land covers and quantifying buildings. It is observed that mapping heterogeneous features across large urban areas is time consuming and challenging. This study presents and demonstrates an approach for formulating an optimal land cover classification rule set over small representative training urban area image, and its subsequent transfer to the multisensor, multitemporal images. The classification results over the training area showed an overall accuracy of 96%, and the application of rule set to different sensor images of other test areas resulted in reduced accuracies of 91% for the same sensor, 90% and 86% for the different sensors temporal data. The comparison of reference and classified buildings showed ±4% detection errors. Classification through a transferred rule set reduced the classification accuracy by about 5%–10%. However, the trade-off for this accuracy drop was about a 75% reduction in processing time for performing classification in the training area. The factors influencing the classification accuracies were mainly the shadow and temporal changes in the class characteristics.