城乡用地遥感识别与时空变化研究进展
Progress in Remote Sensing Recognition and Spatio-temporal Changes Study of Urban and Rural Land Use
查看参考文献112篇
|
文摘
|
土地利用/覆被变化在全球环境变化和可持续发展研究中具有极其重要的意义。论文在国内外土地利用/覆被变化研究梳理的基础上,从城乡用地遥感识别和时空变化两个方面归纳和总结了国内外土地利用/覆被变化的研究前沿和进展。研究发现在遥感和GIS技术支撑下土地利用/覆被变化研究形成了独特的研究范式,多分析方法、多数据源和多时段融合成为当前研究的重要趋势。目前在城乡生活-生产-生态空间识别研究领域取得了较大进展,但仍然存在 “重城市和大城市、轻中小城市和城镇”的问题。时空变化研究重点从城市和乡村两个地域单元展开。城市扩张和城市蔓延的原因、后果、特征、过程、格局、模式和测度方法成为城市土地利用研究重点。耕地保护和乡村居民点变化研究成为乡村土地利用研究的两大核心。在研究取向上总体呈现出重城市轻乡村的趋势。当前研究应该以问题为导向、注重应用出口的探寻,构建适合中国国情的研究框架,鼓励多学科、多领域的综合交叉借鉴,创新理论和方法,打造本土性和原创性的创新成果。 |
|
其他语种文摘
|
Land use and cover changes play an important role in the study of global environmental change and sustainable development. This paper summarizes the related research progresses in two fields, land use and cover information recognition based on remote sensing images and their spatial and temporal changes. The study found that land use and cover studies have already formed a particular research paradigm under the support of RS and GIS technique. There is an important research trend of combination of multi analysis methods, multi data sources and multiple periods. Current studies have made significant progress in the recognition of ecological-production-living space. However, much attention has been paid on the studies in cities especially big cities, while the studies in small and medium-sized cities and towns do not obtain enough attention. The studies in spatiotemporal changes of land use and cover focus on urban and rural units. The causes, consequences, characteristics, processes, patterns and measuring methods of urban expansion and urban sprawl are the main aspects of urban land use and cover studies. Cultivated land protection and rural residential land changes are the focuses of rural land use and cover studies. There is an undesirable tendency that urban land use and cover received more attentions than rural ones. Future studies should pay more attention to improving problem-orientated research and application of research findings, so as to build an innovative research framework suitable for China. Integrated studies of multidisciplinary and multi-field should be encouraged to create native and original achievements. |
|
来源
|
自然资源学报
,2016,31(4):703-718 【核心库】
|
|
DOI
|
10.11849/zrzyxb.20150324
|
|
关键词
|
土地利用/覆被变化
;
遥感
;
GIS
;
三生空间识别
;
城市扩张
;
综述
|
|
地址
|
1.
中国科学院地理科学与资源研究所, 中国科学院区域可持续发展分析与模拟重点实验室, 北京, 100101
2.
中山大学地理科学与规划学院, 广东省城市化与地理环境空间模拟重点实验室, 广州, 510275
|
|
语种
|
中文 |
|
文献类型
|
综述型 |
|
ISSN
|
1000-3037 |
|
学科
|
社会科学总论;自动化技术、计算机技术 |
|
基金
|
国家自然科学基金
|
|
文献收藏号
|
CSCD:5692076
|
参考文献 共
112
共6页
|
|
1.
Forkuor G. Dynamics of land-use and land-cover change in Freetown, Sierra Leone and its effects on urban and peri-urban agriculture-a remote sensing approach.
International Journal of Remote Sensing,2011,32(4):1017-1037
|
CSCD被引
4
次
|
|
|
|
|
2.
Wang Y C. Patterns and trends in land-use land-cover change research explored using self-organizing map.
International Journal of Remote Sensing,2011,32(13):3765-3790
|
CSCD被引
2
次
|
|
|
|
|
3.
Aplin P. On scales and dynamics in observing the environment.
International Journal of Remote Sensing,2006,27(11):2123-2140
|
CSCD被引
3
次
|
|
|
|
|
4.
Kasetkasem T. Super-resolution land cover mapping using a Markov random field based approach.
Remote Sensing of Environment,2005,96(3/4):302-314
|
CSCD被引
33
次
|
|
|
|
|
5.
Verbeke L P C. Reusing back-propagation artificial neural networks for land cover classification in tropical savannahs.
International Journal of Remote Sensing,2004,25(14):2747-2771
|
CSCD被引
3
次
|
|
|
|
|
6.
Stehman S V. Sampling designs for accuracy assessment of land cover.
International Journal of Remote Sensing,2009,30(20):5243-5272
|
CSCD被引
18
次
|
|
|
|
|
7.
Kiage L M. Recent land-cover/use change associated with land degradation in the Lake Baringo Catchment, Kenya, East Africa: Evidence from Landsat TM and ETM.
International Journal of Remote Sensing,2007,28(19):4285-4309
|
CSCD被引
2
次
|
|
|
|
|
8.
Friedl M A. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets.
Remote Sensing of Environment,2010,114(1):168-182
|
CSCD被引
183
次
|
|
|
|
|
9.
Lu D. A survey of image classification methods and techniques for improving classification performance.
International Journal of Remote Sensing,2007,28(5):823-870
|
CSCD被引
124
次
|
|
|
|
|
10.
Kavzoglu T. The use of backpropagating artificial neural networks in land cover classification.
International Journal of Remote Sensing,2003,24(23):4907-4938
|
CSCD被引
17
次
|
|
|
|
|
11.
Shalan M A. An evaluation of fuzzy classifications from IRS 1C LISS III imagery: A case study.
International Journal of Remote Sensing,2003,24(15):3179-3186
|
CSCD被引
3
次
|
|
|
|
|
12.
Schmidt K S. Mapping coastal vegetation using an expert system and hyperspectral imagery.
Photogrammetric Engineering and Remote Sensing,2004,70(6):703-715
|
CSCD被引
4
次
|
|
|
|
|
13.
Chen G. Object-based change detection.
International Journal of Remote Sensing,2012,33(14):4434-4457
|
CSCD被引
46
次
|
|
|
|
|
14.
Schneider A. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on'urban ecoregions.
Remote Sensing of Environment,2010,114(8):1733-1746
|
CSCD被引
45
次
|
|
|
|
|
15.
Zhang Q. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data.
Remote Sensing of Environment,2011,115(9):2320-2329
|
CSCD被引
70
次
|
|
|
|
|
16.
Small C. A global analysis of human settlement in coastal zones.
Journal of Coastal Research,2003,19(3):584-599
|
CSCD被引
16
次
|
|
|
|
|
17.
Liu Z F. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008.
Landscape and Urban Planning,2012,106(1):62-72
|
CSCD被引
138
次
|
|
|
|
|
18.
Lu D S. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data.
Remote Sensing of Environment,2008,112(9):3668-3679
|
CSCD被引
87
次
|
|
|
|
|
19.
何春阳. 基于夜间灯光数据的环渤海地区城市化过程.
地理学报,2005,60(3):409-417
|
CSCD被引
51
次
|
|
|
|
|
20.
李景刚. 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.
遥感学报,2007,11(1):115-126
|
CSCD被引
29
次
|
|
|
|
|
了解气象卫星更多信息，请访问