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Received：14 August 2018，
Accepted：19 December 2018，
Published：2019-11
移动端阅览
地理要素的时空变化分析对于了解和掌握地表的规律性有着重要的作用。利用地面站点观测、实地调查等传统方式获取数据，对地理要素进行时空变化分析是最常用的方法。但该类方法往往表现的是“点尺度”观测，不能在大尺度情况下准确地反映地表的时空变化信息。遥感卫星能以一定的时间间隔获得空间连续的对地“面尺度”观测数据，然而其特定的空间和时间分辨率使其获取的地表信息仍十分有限。同时地表的空间异质性和个别地表短时间内的快速变化，使得利用遥感数据对地理要素进行时空变化分析时，时空变化分析结果会随观测尺度而发生改变。本文从空间尺度和时间尺度两方面综述遥感数据时空尺度对地理要素的时空变化分析产生的影响和原因，并针对这些问题总结了减小时空尺度对结果不确定性影响的现行方法。可以通过多源遥感协同观测和反演、尺度转换、空间建模等方法减小空间尺度引起的不确定性；通过联合多时相遥感数据的方法减小时间尺度引起的不确定性。在实际应用中，应根据所观测地理要素的实际情况，综合分析选择合适的方法。
The detection of the changes in surface parameters is important to understand the regularity of the Earth’s surface. Traditional data sources
such as in situ stations and field observations
have been extensively used to analyze the spatiotemporal changes in geographic parameters. However
this kind of data only provides information at " point scales
” which cannot comprehensively reflect the spatiotemporal change information of land surfaces. Remote sensing provides a practical means of spatially and continuously obtaining the land surface information at regular intervals. However
the achievable information remains limited by specific spatial and temporal resolutions. Furthermore
results of change detection are typically inconsistent when different remote sensing data are used because of the heterogeneities and seasonal change in land surfaces. This study reviewed the causes and effects of spatial and temporal scales on the change detection of land surface parameters. The methods for reducing the uncertainty of change detection results are summarized. Spatial scale involves the size of the spatial extent and spatial resolution. Similarly
temporal scale partially refers to the length of time range and temporal resolution. The features of the surface landscape are complexity
heterogeneity
and fragmentation. Consequently
the geographic entity exists at a specific spatial and temporal frame. Thus
the completeness of the acquired surface information depends on the spatial and temporal scales of remote sensing data. The geographic elements
which are homogeneous on a scale
may be heterogeneous on another scale. Generally
the remote sensing data at large pixel scales frequently combine the detailed information contained in small pixel scales. Low temporal resolution can result in the inability to capture the rapid changes in land surfaces. For the uncertainty caused by spatial scale
the methods for reducing such kind of effects include multisource remote sensing collaborative observation and inversion
scale conversion
and spatial modeling. For the temporal scale
the methods for reducing such kind of effect are mainly focused on combining multitemporal remote sensing data. The advantages and disadvantages of these methods in practical applications were analyzed in this study. Although many scholars have developed methods for reducing the influence of spatiotemporal scale on the change detection of geographic elements
a universal method is difficult to establish given the contradiction between the spatially and temporally varying characteristics of geographic elements and the nature of remote sensing data. However
these impacts can be minimized by selecting appropriate observation scales and improving the algorithms based on the characteristics of surface parameters. With the diversity of high-quality satellite data and the improvement of the algorithms
the uncertainties of change detection results can be reduced.
Alves D B and Pérez-Cabello F. 2017. Multiple remote sensing data sources to assess spatio-temporal patterns of fire incidence over Campos Amazônicos Savanna Vegetation Enclave (Brazilian Amazon). Science of the Total Environment, 601-602: 142–158
Asner G P. 2001. Cloud cover in Landsat observations of the Brazilian Amazon. International Journal of Remote Sensing, 22(18): 3855–3862
巴桑, 杨秀海, 拉珍, 郑照军, 旷达, 拉巴. 2012. 基于多源数据的西藏地区积雪变化趋势分析. 冰川冻土, 34(5): 1023–1030
Ba S, Yang X H, La Z, Zheng Z J, Kuang D and La B. 2012. Variation of snow cover over Tibet autonomous region based on multi-source data. Journal of Glaciology and Geocryology, 34(5): 1023–1030
Bierman P, Lewis M, Ostendorf B and Tanner J. 2011. A review of methods for analysing spatial and temporal patterns in coastal water quality. Ecological Indicators, 11(1): 103–114
陈怀亮, 刘玉洁, 杜子璇, 邹春辉. 2010. 基于卫星遥感数据的黄淮海地区植被覆盖时空变化特征. 生态学杂志, 29(5): 991–999
Chen H L, Liu Y J, Du Z X and Zou C H. 2010. Spatiotemporal variations of vegetation cover in Huang-Huai-Hai area based on remote sensing data. Chinese Journal of Ecology, 29(5): 991–999
陈曦. 2008. 塔里木河干流植被遥感监测时空多尺度协同分析方法. 干旱区地理, 31(1): 1–9
Chen X. 2008. MSSA method of vegetation remote sensing monitoring on the mainstream of the Tarim River. Arid Land Geography, 31(1): 1–9
陈先刚, 张一平, 张小全, 郭颖. 2008. 过去50年中国竹林碳储量变化. 生态学报, 28(11): 5218–5227
Chen X G, Zhang Y P, Zhang X Q and Guo Y. 2008. Carbon stock changes in bamboo stands in China over the last 50 years. Acta Ecologica Sinica, 28(11): 5218–5227
陈佑启, 何英彬. 2005. 论土地利用/覆盖变化研究中的尺度问题. 经济地理, 25(2): 152–155
Chen Y Q and He Y B. 2005. Scale issues in the analysis of land use/cover change. Economic Geography, 25(2): 152–155
Cohen W B and Goward S N. 2004. Landsat’s role in ecological applications of remote sensing. BioScience, 54(6): 535–545
Comber A, Fisher P, Brunsdon C and Khmag A. 2012. Spatial analysis of remote sensing image classification accuracy. Remote Sensing of Environment, 127: 237–246
Coppin P R, Bauer M E. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3/4): 207–234
de Jong R, Verbesselt J, Schaepman M E and de Bruin S. 2012. Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change. Global Change Biology, 18(2): 642–655
Deng J S, Wang K, Hong Y and Qi J G. 2009. Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape and Urban Planning, 92(3/4): 187–198
Donnay J P, Barnsley M J and Longley P A. 2014. Remote Sensing and Urban Analysis: GISDATA 9. London: CRC Press
范闻捷, 盖颖颖, 徐希孺, 闫彬彦. 2013. 遥感反演离散植被有效叶面积指数的空间尺度效应. 中国科学: 地球科学, 43(2): 280–286
Fan W J, Gai Y Y, Xu X R and Yan B Y. 2013. The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing. Science China Earth Sciences, 43(2): 280–286
冯桂香, 明冬萍. 2015. 分形定量选择遥感影像最佳空间分辨率的方法与实验. 地球信息科学学报, 17(4): 478–485
Feng G X and Ming D P. 2015. Fractal based method on selecting the optimal spatial resolution for remote sensing image. Journal of Geo-Information Science, 17(4): 478–485
Gandhi G M, Parthiban S, Thummalu N and Christy A. 2015. NDVI: vegetation change detection using remote sensing and GIS–a case study of Vellore District. Procedia Computer Science, 57: 1199–1210
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207–2218
高尚. 2017. 无人机平台及测量技术综述. 河南科技(3): 55–56
Gao S. 2017. Summary of unmanned aerial vehicle platform and measurement technology. Henan Science and Technology(3): 55–56
Garrigues S, Allard D, Baret F and Weiss M. 2006. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment, 105(4): 286–298
Giri C, Pengra B, Long J and Loveland T R. 2013. Next generation of global land cover characterization, mapping, and monitoring. International Journal of Applied Earth Observation and Geoinformation, 25: 30–37
官宝红, 李君, 曾爱斌, 邓劲松, 张军. 2008. 杭州市城市土地利用对河流水质的影响. 资源科学, 30(6): 857–863
Guan B Y, Li J, Zeng A B, Deng J S and Zhang J. 2008. Impacts of urban land use on water quality in Hangzhou. Resources Science, 30(6): 857–863
韩刚, 李瑞平, 王思楠, 田鑫, 胡勇平, 樊爱霞, 孙燕. 2017. 基于多尺度遥感数据的荒漠化草原旱情监测及时空特征. 江苏农业学报, 33(6): 1301–1308
Han G, Li R P, Wang S N, Tian X, Hu Y P, Fan A X and Sun Y. 2017. Study on drought monitoring and spatial and temporal characteristics of desertification grassland based on multi-scale remote sensing data. Jiangsu Journal of Agricultural Sciences, 33(6): 1301–1308
韩震, 金亚秋, 恽才兴. 2006. 神舟三号CMODIS数据获取长江口悬浮泥沙含量的时空分布. 遥感学报, 10(3): 381–386
Han Z, Jin Y Q and Yun C X. 2006. Spatial and temporal distributions of suspended sediment contents in the Yangtze River Estuary using the CMODIS image data from China’s SZ-3 spacecraft. Journal of Remote Sensing, 10(3): 381–386
郝大磊, 肖青, 闻建光, 游冬琴, 吴小丹, 林兴稳, 吴胜标. 2018. 定量遥感升尺度转换方法研究进展. 遥感学报, 22(3): 408–423
Hao D L, Xiao Q, Wen J G, You D Q, Wu X D, Lin X W and Wu S B. 2018. Advances in upscaling methods of quantitative remote sensing. Journal of Remote Sensing, 22(3): 408–423
Healey S P, Cohen W B, Yang Z Q and Krankina O N. 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97(3): 301–310
Herold M, Couclelis H and Clarke K C. 2005. The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems, 29(4): 369–399
黄波, 赵涌泉. 2017. 多源卫星遥感影像时空融合研究的现状及展望. 测绘学报, 46(10): 1492–1499
Huang B and Zhao Y Q. 2017. Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1492–1499
Immerzeel W W, Droogers P, De Jong S M and Bierkens M F P. 2009. Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing. Remote sensing of Environment, 113(1): 40–49
Jarvis P G. 1995. Scaling processes and problems. Plant, Cell and Environment, 18(10): 1079–1089
Jensen J R and Cowen D C. 1999. Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogrammetric Engineering and Remote Sensing, 65: 611–622
嵇涛, 刘睿, 杨华, 何太蓉, 吴建峰. 2015. 多源遥感数据的降水空间降尺度研究——以川渝地区为例. 地球信息科学学报, 17(1): 108–117
Ji T, Liu R, Yang H, He T R and Wu J F. 2015. Spatial downscaling of precipitation using multi-source remote sensing data: a case study of Sichuan-Chongqing Region. Journal of Geo-Information Science, 17(1): 108–117
Jorgensen P V. 2000. Determination of cloud coverage over Denmark using Landsat MSS/TM and NOAA-AVHRR. International Journal of Remote Sensing, 21(17): 3363–3368
Ju J C and Roy D P. 2008. The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sensing of Environment, 112(3): 1196–1211
康悦, 李振朝, 田辉, 刘蓉, 史小康, 张静辉, 文军. 2011. 黄河源区植被变化趋势及其对气候变化的影响过程研究. 气候与环境研究, 16(4): 505–512
Kang Y, Li Z C, Tian H, Liu R, Shi X K, Zhang J H and Wen J. 2011. Trend of vegetation evaluation and its responses to climate change over the source region of the Yellow River. Climatic and Environmental Research, 16(4): 505–512
柯长青, 彭海涛, 孙波, 谢红接. 2013. 2002年—2011年北极海冰时空变化分析. 遥感学报, 17(2): 459–466
Ke C Q, Peng H T, Sun B and Xie H J. 2013. Spatio-temporal variability of Arctic sea ice from 2002 to 2011. Journal of Remote Sensing, 17(2): 459–466
Kennedy R E, Yang Z Q and Cohen W B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment, 114(12): 2897–2910
Kim H C, Son S, Kim Y H, Khim J S, Nam J, Chang W K, Lee J H, Lee C H and Ryu J. 2017. Remote sensing and water quality indicators in the Korean West coast: spatio-temporal structures of MODIS-derived chlorophyll-a and total suspended solids. Marine Pollution Bulletin, 121(1/2): 425–434
李冰, 刘镕源, 刘素红, 刘强, 刘峰, 周公器. 2012. 基于低空无人机遥感的冬小麦覆盖度变化监测. 农业工程学报, 28(13): 160–165
Li B, Liu R Y, Liu S H, Liu Q, Liu F and Zhou G Q. 2012. Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system. Transactions of the Chinese Society of Agricultural Engineering, 28(13): 160–165
李双成, 蔡运龙. 2005. 地理尺度转换若干问题的初步探讨. 地理研究, 24(1): 11–18
Li S C and Cai Y L. 2005. Some scaling issues of geography. Geographic Research, 24(1): 11–18
Li X D, Ling F, Foody G M, Ge Y, Zhang Y H and Du Y. 2017. Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment, 196: 293–311
李小文. 2005. 定量遥感的发展与创新. 河南大学学报(自然科学版), 35(4): 49–56
Li X W. 2005. Retrospect, prospect and innovation in quantitative remote sensing. Journal of Henan University (Natural Science), 35(4): 49–56
梁雷, 郭华东, 李新武. 2013. 基于微波辐射计的南极冰盖冻融时空变化分析. 遥感学报, 17(2): 430–438
Liang L, Guo H D and Li X W. 2013. Analysis of spatio-temporal variation of snowmelt in Antarctica derived from microwave radiometer data. Journal of Remote Sensing, 17(2): 430–438
Liu J Y, Liu M L, Tian H Q, Zhuang D F, Zhang Z X, Zhang W, Tang X M and Deng X Z. 2005. Spatial and temporal patterns of China’s cropland during 1990-2000: an analysis based on Landsat TM data. Remote Sensing of Environment, 98(4): 442–456
刘凯, 毋河海, 艾廷华, 秦耀辰. 2008. 地理信息尺度的三重概念及其变换. 武汉大学学报(信息科学版), 33(11): 1178–1181
Liu K, Wu H H, Ai T H and Qin Y C. 2008. Three-tiered concepts of scale of geographical information and its transformation. Journal of Wuhan University (Information Science Edition), 33(11): 1178–1181
柳树福, 熊隽, 吴炳方. 2011. ETWatch中不同尺度蒸散融合方法. 遥感学报, 15(2): 255–269
Liu S F, Xiong J and Wu B F. 2011. ETWatch: a method of multi-resolution ET data fusion. Journal of Remote Sensing, 15(2): 255–269
Liu Y, Dong C M, Guan Y P, Chen D K, McWilliams J and Nencioli F. 2012. Eddy analysis in the subtropical zonal band of the North Pacific Ocean. Deep Sea Research Part I: Oceanographic Research Papers, 68: 54–67
刘雅妮, 辛晓洲, 柳钦火, 周春艳. 2010. 基于多尺度遥感数据估算地表通量的方法及其验证分析. 地球科学进展, 25(11): 1261–1272
Liu Y N, Xin X Z, Liu Q H and Zhou C Y. 2010. Method and validation for surface fluxes estimation based on multi-scale remotely sensed data. Advances in Earth Science, 25(11): 1261–1272
Lu D S. 2006. The potential and challenge of remote sensing‐based biomass estimation. International Journal of Remote Sensing, 27(7): 1297–1328
罗芳琼, 吴春梅. 2009. 时间序列分析的理论与应用综述. 柳州师专学报, 24(3): 113–117
Luo F Q and Wu C M. 2009. Study of theory and application of time series analysis. Journal of Liuzhou Teachers College, 24(3): 113–117
毛学刚, 王静文, 范文义. 2016. 基于遥感与地统计的森林生物量时空变异分析. 北京林业大学学报, 38(2): 10–19
Mao X G, Wang J W and Fan W Y. 2016. Spatial and temporal variation of forest biomass based on remote sensing and geostatistics. Journal of Beijing Forestry University, 38(2): 10–19
Masek J G and Collatz G J. 2006. Estimating forest carbon fluxes in a disturbed southeastern landscape: integration of remote sensing, forest inventory, and biogeochemical modeling. Journal of Geophysical Research: Biogeosciences, 111(G1): G01006
Masek J G, Huang C Q, Wolfe R, Cohen W, Hall F, Kutler J and Nelson P. 2008. North American forest disturbance mapped from a decadal Landsat record. Remote Sensing of Environment, 112(6): 2914–2926
Mathur A and Foody G M. 2008. Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29(8): 2227–2240
孟宝平, 陈思宇, 崔霞, 冯琦胜, 梁天刚. 2015. 基于多源遥感数据的高寒草地生物量反演模型精度——以夏河县桑科草原试验区为例. 草业科学, 32(11): 1730–1739
Meng B P, Chen S Y, Cui X, Feng Q S and Liang T G. 2015. The accuracy of grassland vegetation biomass estimated model based on multi-source remote sensing data——As a case of experimental area in Sangke grassland in Xiahe County. Pratacultural Science, 32(11): 1730–1739
明冬萍. 2006. 高分辨率遥感特征基元提取与格局判别方法研究. 北京: 中国科学院地理科学与资源研究所
Ming D P. 2006. Study of Parcel Unit Extraction Based-on Features and Pattern Distinguishment from High Spatial Resolution Remote Sensing Images. Beijing: Institute of Geographical Sciences and Natural Resources Research, CAS
Ozdogan M. 2010. The spatial distribution of crop types from MODIS data: temporal unmixing using independent component analysis. Remote Sensing of Environment, 114(6): 1190–1204
潘东华, 贾慧聪, 贺原惠子, 尹圆圆, 张万昌. 2018. 不同时空尺度旱灾风险评价数字平台的建设. 灾害学, 33(1): 23–26
Pan D H, Jia H C, He Y H Z, Yin Y Y and Zhang W C. 2018. Construction of digital platform of agricultural drought risk assessment in different spatial and temporal scales of China. Journal of Catastrophology, 33(1): 23–26
Parajka J and Blöschl G. 2008. Spatio‐temporal combination of MODIS images–potential for snow cover mapping. Water Resources Research, 44(3): W03406
彭建, 吕慧玲, 马晶, 刘焱序. 2015. 基于阈值分割的京津唐城市群生态用地多源遥感识别. 生态学杂志, 34(1): 204–211
Peng J, Lyu H L, Ma J and Liu Y X. 2015. Multi-source remote sensing of ecologica land in Beijing-Tianjin-Tangshan Metropolitan based on threshold segmentation method. Chinese Journal of Ecology, 34(1): 204–211
Price J C. 1994. How unique are spectral signatures?. Remote Sensing of Environment, 49(3): 181–186
Qiao H B and Cheng D F. 2009. Application of EOS/MODIS-NDVI at different time sequences on monitoring winter wheat acreage in Henan Province//Proceedings of 2009 International Conference on Environmental Science and Information Application Technology. Wuhan: IEEE: 113-115 [DOI: 10.1109/ESIAT.2009.159 http://dx.doi.org/10.1109/ESIAT.2009.159 ]
Ranson K J, Kovacs K, Sun G and Kharuk V I. 2003. Disturbance recognition in the boreal forest using radar and Landsat-7. Canadian Journal of Remote Sensing, 29(2): 271–285
史磊刚, 于景鑫, 陶欢, 刘建刚, 淮贺举, 徐长春, 李奇峰. 2018. 基于RS和GIS的北京冬小麦面积多尺度时空变化研究. 麦类作物学报, 38(1): 41–49
Shi L G, Yu J X, Tao H, Liu J G, Huai H J, Xu C C and Li Q F. 2018. Multi-scale temporal and spatial variation of winter wheat in Beijing based on RS and GIS. Journal of Triticeae Crops, 38(1): 41–49
宋长青, 程昌秀, 史培军. 2018. 新时代地理复杂性的内涵. 地理学报, 73(7): 1204–1213
Song C Q, Cheng C X and Shi P J. 2018. Geography complexity: new connotations of geography in the new era. Acta Geographica Sinica, 73(7): 1204–1213
宋茜, 周清波, 吴文斌, 胡琼, 余强毅, 唐华俊. 2015. 农作物遥感识别中的多源数据融合研究进展. 中国农业科学, 48(6): 1122–1135
Song Q, Zhou Q B, Wu W B, Hu Q, Yu Q Y and Tang H J. 2015. Recent progresses in research of integrating multi-source remote sensing data for crop mapping. Scientia Agricultura Sinica, 48(6): 1122–1135
苏理宏, 李小文, 黄裕霞. 2001. 遥感尺度问题研究进展. 地球科学进展, 16(4): 544–548
Su L H, Li X W and Huang Y X. 2001. An review on scale in remote sensing. Advance in Earth Sciences, 16(4): 544–548
Sun B and Zhou Q M. 2016. Expressing the spatio-temporal pattern of farmland change in arid lands using landscape metrics. Journal of Arid Environments, 124: 118–127
孙雷刚, 刘剑锋, 徐全洪. 2014. 河北坝上地区植被覆盖变化遥感时空分析. 国土资源遥感, 26(1): 167–172
Sun L G, Liu J F and Xu Q H. 2014. Remote sensing based temporal and spatial analysis of vegetation cover changes in Bashang Area of Hebei Province. Remote Sensing for Land and Resources, 26(1): 167–172
唐华俊, 吴文斌, 杨鹏, 周清波, 陈仲新. 2010. 农作物空间格局遥感监测研究进展. 中国农业科学, 43(14): 2879–2888
Tang H J, Wu W B, Yang P, Zhou Q B and Chen Z X. 2010. Recent progresses in monitoring crop spatial patterns by using remote sensing technologies. Scientia Agricultura Sinica, 43(14): 2879–2888
田静, 苏红波, 陈少辉, 于静洁. 2011. 黑河中游绿洲化荒漠化的时空变化遥感分析. 资源科学, 33(2): 347–355
Tian J, Su H B, Chen S H and Yu J J. 2011. Spatial-temporal processes of desertification and oasification in the middle reaches of the Heihe River based on remote sensing. Resources Science, 33(2): 347–355
Turker M and Ozdarici A. 2011. Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study. International Journal of Remote Sensing, 32(24): 9735–9768
万玮, 肖鹏峰, 冯学智, 李晖, 马荣华, 段洪涛. 2010. 近30年来青藏高原羌塘地区东南部湖泊变化遥感分析. 湖泊科学, 22(6): 874–881
Wan W, Xiao P F, Feng X Z, Li H, Ma R H and Duan H T. 2010. Remote sensing analysis for changes of lakes in the southeast of Qiangtang area, Qinghai-Tibet Plateau in recent 30 years. Journal of Lake Science, 22(6): 874–881
王朗, 傅伯杰, 吕一河, 曾源. 2010. 生态恢复背景下陕北地区植被覆盖的时空变化. 应用生态学报, 21(8): 2109–2116
Wang L, Fu B J, Lyu Y H and Zeng Y. 2010. Spatio-temporal variations of vegetation cover in northern Shaanxi Province under the background of ecological restoration. Chinese Journal of Applied Ecology, 21(8): 2109–2116
王伟超, 邹维宝. 2013. 高分辨率遥感影像信息提取方法综述. 北京测绘(4): 1–5
Wang W C and Zou W B. 2013. Methods of extraction in high resolution remote sensing image information. Beijing Surveying and Mapping(4): 1–5
王祎婷, 谢东辉, 李小文. 2014. 构造地理要素趋势面的尺度转换普适性方法探讨. 遥感学报, 18(6): 1139–1146
Wang Y T, Xie D H and Li X W. 2014. Universal scaling methodology in remote sensing science by constructing geographic trend surface. Journal of Remote Sensing, 18(6): 1139–1146
Woodcock C E and Ozdogan M. 2012. Trends in land cover mapping and monitoring//Gutman G, Janetos A C, Justice C O, Moran E F, Mustard J F, Rindfuss R R, Skole D, Lee Turner II B and Cochrane M A, eds. Land Change Science. Dordrecht: Springer: 367-377
Wu J G, Jones K B, Li H and Loucks O L. 2006. Scaling and Uncertainty Analysis in Ecology. Dordrecht: Springer
吴小丹, 闻建光, 肖青, 李新, 刘强, 唐勇, 窦宝成, 彭菁菁, 游冬琴, 李小文. 2015. 关键陆表参数遥感产品真实性检验方法研究进展. 遥感学报, 19(1): 75–92
Wu X D, Wen J G, Xiao Q, Li X, Liu Q, Tang Y, Dou B C, Peng J J, You D Q and Li X W. 2015. Advances in validation methods for remote sensing products of land surface parameters. Journal of Remote Sensing, 19(1): 75–92
夏列钢, 王卫红, 杨海平. 2016. 多分辨率协同遥感地块利用分类方法研究. 地球信息科学学报, 18(5): 649–654
Xia L G, Wang W H and Yang H P. 2016. Remotely sensed land patch classification by collaborating with multi-resolution data. Journal of Geo-Information Science, 18(5): 649–654
夏天, 吴文斌, 余强毅, 杨鹏, 周清波, 唐华俊. 2014. 农作物空间格局动态变化模拟模型(CROPS)构建. 中国农业资源与区划, 35(1): 44–51
Xia T, Wu W B, Yu Q Y, Yang P, Zhou Q B and Tang H J. 2014. Simulating the spatial dynamics of cropping pattern: the crops model. Chinese Journal of Agricultural Resources and Regional Planning, 35(1): 44–51
Xian G and Crane M. 2005. Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment, 97(2): 203–215
谢登峰, 张锦水, 潘耀忠, 孙佩军, 袁周米琪. 2015. Landsat 8和MODIS融合构建高时空分辨率数据识别秋粮作物. 遥感学报, 19(5): 791–805
Xie D F, Zhang J S, Pan Y Z, Sun P J and Yuan Z M Q. 2015. Fusion of MODIS and Landsat 8 images to generate high spatial-temporal resolution data for mapping autumn crop distribution. Journal of Remote Sensing, 19(5): 791–805
徐希孺, 范闻捷, 陶欣. 2009. 遥感反演连续植被叶面积指数的空间尺度效应. 中国科学D辑: 地球科学, 39(1): 79–87
Xu X R, Fan W J and Tao X. 2009. The spatial scaling effect of continuous canopy Leaves Area Index retrieved by remote sensing. Science in China Series D: Earth Sciences, 39(1): 79–87
Yang C H, Everit J H and Murden D. 2011. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 75(2): 347–354
Yang L M, Xian G, Klaver J M and Deal B. 2003. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69(9): 1003–1010
杨清华, 齐建伟, 孙永军. 2001. 高分辨率卫星遥感数据在土地利用动态监测中的应用研究. 国土资源遥感(4): 20–27
Yang Q H, Qi J W and Sun Y J. 2001. The application of high resolution satellite remotely sensed data to land use dynamic monitoring. Remote Sensing for Land and Resources(4): 20–27
杨忍, 刘彦随, 陈玉福, 李婷婷. 2013. 环渤海地区耕地复种指数时空变化遥感反演及影响因素探测. 地理科学, 33(5): 588–593
Yang R, Liu Y S, Chen Y F and Li T T. 2013. The remote sensing inversion for spatial and temporal changes of multiple cropping index and detection for influencing factors around Bohai Rim in China. Scientia Geographica Sinica, 33(5): 588–593
姚鲁烽, 何书金, 赵歆. 2013. 地理学时空变化类论文的写作. 地理学报, 68(7): 1007–1011
Yao L F, He S J and Zhao X. 2013. Writing of papers on geography spatial-temporal variation. Acta Geographica Sinica, 68(7): 1007–1011
殷守敬, 吴传庆, 王桥, 马万栋, 朱利, 姚延娟, 王雪蕾, 吴迪. 2013. 多时相遥感影像变化检测方法研究进展综述. 光谱学与光谱分析, 33(12): 3339–3342
Yin S J, Wu C Q, Wang Q, Ma W D, Zhu L, Yao Y J, Wang X L and Wu D. 2013. Review of change detection methods using multi-temporal remotely sensed images. Spectroscopy and Spectral Analysis, 33(12): 3339–3342
于磊, 赵彦伟, 汪思慧, 田竹君. 2011. 不同时空尺度下流域景观格局与水质的相关性研究——以嫩江右岸典型子流域为例. 水土保持通报, 31(5): 254–258
Yu L, Zhao Y W, Wang S H and Tian Z J. 2011. Correlation between landscape pattern and river water quality at various temporal and spatial scales——a case study of typical sub-watersheds of Nenjiang River right bank. Bulletin of Soil and Water Conservation, 31(5): 254–258
宇振荣. 2008. 景观生态学. 北京: 化学工业出版社
Yu Z R. 2008. Landscape Ecology. Beijing: Chemical Industry Press.
曾红伟, 李丽娟. 2011. 澜沧江及周边流域TRMM 3B43数据精度检验. 地理学报, 66(7): 994–1004
Zeng H W and Li L J. 2011. Accuracy validation of TRMM 3B43 data in Lancang river basin. Acta Geographica Sinica, 66(7): 994–1004
Zhan X, Sohlberg R A, Townshend J R G, DiMiceli C, Carroll M L, Eastman J C, Hansen M C and DeFries R S. 2002. Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment, 83(1/2): 336–350
Zhang J. 2010. Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1): 5–24
张路. 2004. 基于多元统计分析的遥感影像变化检测方法研究. 武汉: 武汉大学
Zhang L. 2004. Change Detection in Remotely Sensed Imagery Using Multivariate Statistical Analysis. Wuhan: Wuhan University.
张莉, 吴文斌, 左丽君, 周清波. 2011. 基于EOS/MODIS数据的南方水稻面积提取技术. 中国农业资源与区划, 32(4): 39–44
Zhang L, Wu W B, Zou L J and Zhou Q B. 2011. Extracting planting area of paddy rice in southern China by using EOS/MODIS data. Chinese Journal of Agricultural Resources and Regional Planning, 32(4): 39–44
张明伟, 周清波, 陈仲新, 周勇, 刘佳, 宫攀. 2008. 基于MODIS时序数据分析的作物识别方法. 中国农业资源与区划, 29(1): 31–35
Zhang M W, Zhou Q B, Chen Z X, Zhou Y, Liu J and Gong P. 2008. Method for identifying plants analyzed by time order data based on MODIS. China Journal of Agricultural Resources and Regional Planning, 29(1): 31–35
张娜. 2006. 生态学中的尺度问题: 内涵与分析方法. 生态学报, 26(7): 2340–2355
Zhang N. 2006. Scale issues in ecology: concepts of scale and scale analysis. Acta Ecologica Sinica, 26(7): 2340–2355
张莹, 雷国平, 林佳, 张慧. 2012. 扎龙自然保护区不同空间尺度景观格局时空变化及其生态风险. 生态学杂志, 31(5): 1250–1256
Zhang Y, Lei G P, Lin J and Zhang H. 2012. Spatiotemporal change and its ecological risk of landscape pattern in different spatial scales in Zhalong Nature Reserve. Chinese Journal of Ecology, 31(5): 1250–1256
Zhang Y H, Foody G M, Ling F, Li X D, Ge Y, Du Y and Atkinson P M. 2018. Spatial-temporal fraction map fusion with multi-scale remotely sensed images. Remote Sensing of Environment, 213: 162–181
张友静, 王军战, 鲍艳松. 2010. 多源遥感数据反演土壤水分方法. 水科学进展, 21(2): 222–228
Zhang Y J, Wang J Z and Bao Y S. 2010. Soil moisture retrieval from multi-resource remotely sensed images over a wheat area. Advances in Water Science, 21(2): 222–228
赵忠明, 孟瑜, 岳安志, 黄青青, 孔赟珑, 袁媛, 刘晓奕, 林蕾, 张蒙蒙. 2016. 遥感时间序列影像变化检测研究进展. 遥感学报, 20(5): 1110–1125
Zhao Z M, Meng Y, Yue A Z, Huang Q Q, Kong Y L, Yuan Y, Liu X Y, Lin L and Zhang M M. 2016. Review of remotely sensed time series data for change detection. Journal of Remote Sensing, 20(5): 1110–1125
周国模, 姜培坤. 2004. 毛竹林的碳密度和碳贮量及其空间分布. 林业科学, 40(6): 20–24
Zhou G M and Jiang P K. 2004. Density, storage and spatial distribution of carbon in Phyllostachy pubescens forest. Scientia Silvae Sinicae, 40(6): 20–24
朱长明, 李均力, 常存, 张新, 骆剑承. 2014. 新疆干旱区湿地景观格局遥感动态监测与时空变异. 农业工程学报, 30(15): 229–238
Zhu C M, Li J L, Chang C, Zhang X and Luo J C. 2014. Remote sensing detection and spatio-temporal change analysis of wetlands in Xinjiang arid region. Transactions of the Chinese Society of Agricultural Engineering, 30(15): 229–238
Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610–2623
Zhu X L, Helmer E H, Gao F, Liu D S, Chen J and Lefsky M A. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172: 165–177
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