Anterior|Siguiente

Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements

  • role： First author第一作者
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

  • Email：wanchj15@lzu.edu.cn
  • Introduction：万昌君，1997年生，男，研究方向为遥感数据产品的分析和验证。E-mail: wanchj15@lzu.edu.cn
WAN Changjun1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

  • Email：wuxd@lzu.edu.cn
  • Introduction：吴小丹，1987年生，女，副教授，研究方向为定量遥感数据产品的反演和真实性检验。E-mail: wuxd@lzu.edu.cn
WU Xiaodan1*,  
  • Affiliation：

    College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China

LIN Xingwen2

resumen

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.

palabra clave

spatial and temporal scale;spatial and temporal variation;geographical factors;surface heterogeneity;remote sensing;multi-source data

References

  1. 1.
    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
  2. 2.
    Asner G P. 2001. Cloud cover in Landsat observations of the Brazilian Amazon. International Journal of Remote Sensing, 22(18): 3855–3862
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    Chen X. 2008. MSSA method of vegetation remote sensing monitoring on the mainstream of the Tarim River. Arid Land Geography, 31(1): 1–9
  7. 7.
    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
  8. 8.
    Chen Y Q and He Y B. 2005. Scale issues in the analysis of land use/cover change. Economic Geography, 25(2): 152–155
  9. 9.
    Cohen W B and Goward S N. 2004. Landsat’s role in ecological applications of remote sensing. BioScience, 54(6): 535–545
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    Donnay J P, Barnsley M J and Longley P A. 2014. Remote Sensing and Urban Analysis: GISDATA 9. London: CRC Press
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    Gao S. 2017. Summary of unmanned aerial vehicle platform and measurement technology. Henan Science and Technology(3): 55–56
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    Jarvis P G. 1995. Scaling processes and problems. Plant, Cell and Environment, 18(10): 1079–1089
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    Li S C and Cai Y L. 2005. Some scaling issues of geography. Geographic Research, 24(1): 11–18
  41. 41.
    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
  42. 42.
    Li X W. 2005. Retrospect, prospect and innovation in quantitative remote sensing. Journal of Henan University (Natural Science), 35(4): 49–56
  43. 43.
    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
  44. 44.
    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
  45. 45.
    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
  46. 46.
    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
  47. 47.
    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
  48. 48.
    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
  49. 49.
    Lu D S. 2006. The potential and challenge of remote sensing‐based biomass estimation. International Journal of Remote Sensing, 27(7): 1297–1328
  50. 50.
    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
  51. 51.
    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
  52. 52.
    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
  53. 53.
    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
  54. 54.
    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
  55. 55.
    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
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    Parajka J and Blöschl G. 2008. Spatio‐temporal combination of MODIS images–potential for snow cover mapping. Water Resources Research, 44(3): W03406
  60. 60.
    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
  61. 61.
    Price J C. 1994. How unique are spectral signatures?. Remote Sensing of Environment, 49(3): 181–186
  62. 62.
    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
  63. 63.
    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
  64. 64.
    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
  65. 65.
    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
  66. 66.
    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
  67. 67.
    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
  68. 68.
    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
  69. 69.
    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
  70. 70.
    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
  71. 71.
    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
  72. 72.
    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
  73. 73.
    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
  74. 74.
    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
  75. 75.
    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
  76. 76.
    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
  77. 77.
    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
  78. 78.
    Wu J G, Jones K B, Li H and Loucks O L. 2006. Scaling and Uncertainty Analysis in Ecology. Dordrecht: Springer
  79. 79.
    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
  80. 80.
    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
  81. 81.
    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
  82. 82.
    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
  83. 83.
    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
  84. 84.
    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
  85. 85.
    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
  86. 86.
    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
  87. 87.
    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
  88. 88.
    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
  89. 89.
    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
  90. 90.
    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
  91. 91.
    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
  92. 92.
    Yu Z R. 2008. Landscape Ecology. Beijing: Chemical Industry Press.
  93. 93.
    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
  94. 94.
    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
  95. 95.
    Zhang J. 2010. Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1): 5–24
  96. 96.
    Zhang L. 2004. Change Detection in Remotely Sensed Imagery Using Multivariate Statistical Analysis. Wuhan: Wuhan University.
  97. 97.
    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
  98. 98.
    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
  99. 99.
    Zhang N. 2006. Scale issues in ecology: concepts of scale and scale analysis. Acta Ecologica Sinica, 26(7): 2340–2355
  100. 100.
    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
  101. 101.
    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
  102. 102.
    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
  103. 103.
    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
  104. 104.
    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
  105. 105.
    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
  106. 106.
    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
  107. 107.
    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

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website