On subtropical remote sensing in China: Research status, key tasks and innovative development approaches

  • role: First author第一作者
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

  • Email:wulx66@csu.edu.cn
  • Introduction:E-mailwulx66@csu.edu.cn 2019-04-232019-08-25沿2021-10-19便
WU Lixin1,  
  • Affiliation:

    College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

SUN Genyun2,  
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

MIAO Zelang1,  
  • Affiliation:

    College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

ZHANG Aizhu2,  
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

FENG Huihui1,  
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

HU Jun1,  
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

YANG Zefa1,  
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

WANG Wei1,  
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

CHEN Biyan1,  
  • Affiliation:

    School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

TANG Yuqi1

résumé

The subtropical region of China covers a vast area with special and unique geographical characteristics. Typical geographical characteristics include complex natural landscape with a mass of mountains and forests, cloudy and rainy climate, and rich biodiversity. And the subtropical region is the major producing areas of rice in China. Moreover, the subtropical region has abundant rivers, lakes, and mineral resources, which induce the sewage from the mining area spread widely along the river basins. All these geographical characteristics lead to high ecological environment sensitivity and frequent natural disasters of subtropical region in China. The characteristics of remote sensing for large range of rapid observation make it essential for precise monitoring of natural resources and environmental disasters in the wide subtropical region. Furthermore, it is urgent to develop special remote sensing theory and technology for subtropical region in China, so as to support the sustainable development and ecological civilization construction. In recent years, many researchers have gradually paid their attention to the subtropical satellite remote sensing. Internationally, the research topics mainly include land use cover change, urban environmental monitoring, wetland mapping, earthquake damage and its secondary disaster monitoring, water quality monitoring, vegetation biomass inversion, and aerosol parameter inversion and so on. In China, scholars mainly focus on some specific applications, such as flood disaster monitoring, mangrove monitoring, and forest degradation etc. These works provide abundant cases and raw materials for the formation and development of the theoretical system of subtropical remote sensing. However, most of the current studies just concentrated on some particular objects, local areas, or specific problems. So far, the theory and technology system of subtropical remote sensing is still in its infancy, and lacks in systematic analysis on characteristics of research status, fundamental problems and future development. In this paper, we first described the basic characteristics of subtropical region of China and the related practice researches in remote sensing. In this part, we analyzed the practice of remote sensing in subtropical region from the perspective of remote sensing data sources, including optical, hyperspectral, microwave, and multi-source data. Furthermore, we discussed the common problems of the practical researches in subtropical remote sensing. In this part, we analyzed and pointed out the two fundamental problems of subtropical remote sensing, i.e. the problems of geographical objects and remote sensing information. Accordingly, we elucidated the key tasks and scientific problems derived from the two fundamental problems. Then, we analyzed the historical opportunity of subtropical remote sensing development, and put forward the core scientific problems and development approaches of subtropical remote sensing that should be focused on in the future. In conclusion, there are some basic features and common problems in subtropical remote sensing. The innovation and development of subtropical remote sensing, including the collaborative observations of multiple sensors and various platforms, are inevitable trend. This paper aims to explore the development ideas for theories and methods of subtropical remote sensing. Meanwhile, we commit to clarify the innovation direction of subtropical remote sensing technology and application. Most important of all, we hope to promote the application of remote sensing in resource and environment monitoring, disaster prevention and reduction, and ecological civilization construction in the subtropical region of China.

mots-clés

subtropical remote sensing;geographical objects;remote sensing information;resources and environment;natural disasters

References

  1. 1.
    Ashton P and Zhu H. 2020. The tropical-subtropical evergreen forest transition in East Asia: an exploration. Plant Diversity, 42(4): 255-280
  2. 2.
    Berlanga-Robles C A and Ruiz-Luna A. 2011. Integrating remote sensing techniques, geographical information systems (GIS), and stochastic models for monitoring land use and land cover (LULC) changes in the northern coastal region of Nayarit, Mexico. GIScience and Remote Sensing, 48(2): 245-263
  3. 3.
    Brakenridge G R, Syvitski J P M, Overeem I, Higgins S A, Kettner A J, Stewart-Moore J A and Westerhoff R. 2013. Global mapping of storm surges and the assessment of coastal vulnerability. Natural Hazards, 66(3): 1295-1312
  4. 4.
    Buchroithner M. 1990. High-mountain remote sensing cartography//1st Internat. Symp. on High-Moutain Remote Sensing Cartography. At: Schladming, Germany
  5. 5.
    Carvalho F and Henriques D. 2000. Use of brewer ozone spectrophotometer for aerosol optical depth measurements on ultraviolet region. Advances in Space Research, 25(5): 997-1006
  6. 6.
    Chadwick J. 2011. Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties. International Journal of Remote Sensing, 32(21): 6765-6781
  7. 7.
    Chang Y, Wang S X, Zhou Y, Wang L T and Wang F T. 2019. A novel method of evaluating highway traffic prosperity based on nighttime light remote sensing. Remote Sensing, 12(1): 102
  8. 8.
    Chen B, Zhao G S, Han S and Lu L. 2019. Approach for annual deforestation extraction in subtropical zone combing GF-1 with LiDAR. Geomatics and Spatial Information Technology, 42(3): 79-83, 86
  9. 9.
    Chen B Q, Xiao X M, Li X P, Pan L H, Doughty R, Ma J, Dong J W, Qin Y W, Zhao B, Wu Z X, Sun R, Lan G Y, Xie G S, Clinton N and Giri C. 2017. A mangrove forest map of China in 2015: analysis of time series Landsat 7/8 and Sentinel-1A imagery in google earth engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 104-120
  10. 10.
    Chen B Y, Dai W J, Liu Z Z, Wu L X, Kuang C L and Ao M S. 2018. Constructing a precipitable water vapor map from regional GNSS network observations without collocated meteorological data for weather forecasting. Atmospheric Measurement Techniques, 11(9): 5153-5166
  11. 11.
    Chen J Q, Wei H, Li N, Chen S Q, Qu W Q and Zhang Y. 2020a. Exploring the spatial-temporal dynamics of the Yangtze River Delta urban agglomeration based on night-time light remote sensing technology. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5369-5383
  12. 12.
    Chen L F, Tao J H, Wang Z F, Li S S, Zhang Y, Fan M, Li X Y, Yu C, Zou M M, Su L and Tao M H. 2015. Review of satellite remote sensing of air quality. Journal of Atmospheric and Environmental Optics, 10(2): 117-125
  13. 13.
    Chen Y L, Feng L P, Mo J F, Mo W H, Ding M H and Liu Z P. 2020b. Identification of sugarcane with NDVI time series based on HJ-1 CCD and MODIS fusion. Journal of the Indian Society of Remote Sensing, 48(2): 249-262
  14. 14.
    Chen Z Y, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M and Hasiyuya. 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748-767
  15. 15.
    Cui L, Du H Q, Zhou G M, Li X J, Mao F J, Xu X J, Fan W L, Li Y G, Zhu D E, Liu T Y and Xing L Q. 2019. Combination of decision tree and mixed pixel decomposition for extracting bamboo forest information in China. Journal of Remote Sensing, 23(1): 166-176
  16. 16.
    Da Costa S M F and Cintra J P. 1999. Environmental analysis of metropolitan areas in Brazil. ISPRS Journal of Photogrammetry and Remote Sensing, 54(1): 41-49
  17. 17.
    Deng X L, Zeng G L, Zhu Z H, Huang Z X, Yang J C, Tong Z J, Yin X B, Wang T W and Lan Y B. 2020. Classification and feature band extraction of diseased citrus plants based on UAV hyperspectral remote sensing. Journal of South China Agricultural University, 41(6): 100-108
  18. 18.
    Dong L F, Du H Q, Mao F J, Han N, Li X J, Zhou G M, Zhu D E, Zheng J L, Zhang M, Xing L Q and Liu T Y. 2020a. Very high resolution remote sensing imagery classification using a fusion of random forest and deep learning technique—subtropical area for example. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 113-128
  19. 19.
    Dong Y Y, Xu F, Liu L Y, Du X P, Ren B Y, Guo A T, Geng Y, Ruan C, Ye H C, Huang W J and Zhu Y N. 2020b. Automatic system for crop pest and disease dynamic monitoring and early forecasting. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 4410-4418
  20. 20.
    Du P J. 2020. Progress of high resolution remotely sensed image progressing and urban application examples. Modern Surveying and Mapping, 43(1): 1-9
  21. 21.
    Du P J, Bai X Y, Luo J Q, Li E Z and Lin C. 2018. Advances of urban remote sensing. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 10(1): 16-29
  22. 22.
    Du W H, Qin Z H, Fan J L, Gao M F, Wang F and Abbasi B. 2019. An efficient approach to remove thick cloud in VNIR bands of multi-temporal remote sensing images. Remote Sensing, 11(11): 1284
  23. 23.
    Fang C Y, Wu H, Tao Z H, Gao D and Zhou H. 2016. The wetland information extraction research of Nanji wetland in Poyang Lake based on high resolution remote sensing image. Journal of Geo-Information Science, 18(6): 847-856
  24. 24.
    Fang S S, Lu P C, Liao K W, Wu F, Hu D P and Zhang W G. 2020. Analysis of typical winter thick fog events in Wuhan based on microwave radiometer data. Meteorological and Environmental Sciences, 43(4): 81-87
  25. 25.
    Feng H H and Liu Y B. 2014. Trajectory based detection of forest-change impacts on surface soil moisture at a basin scale [Poyang Lake Basin, China]. Journal of Hydrology, 514: 337-346
  26. 26.
    Feng H H and Liu Y B. 2015. Combined effects of precipitation and air temperature on soil moisture in different land covers in a humid basin. Journal of Hydrology, 531: 1129-1140
  27. 27.
    Feng H H, Zhao X F, Chen F and Wu L C. 2014. Using land use change trajectories to quantify the effects of urbanization on urban heat island. Advances in Space Research, 53(3): 463-473
  28. 28.
    Fraser A D, Massom R A and Michael K J. 2010. Generation of high-resolution east Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery. Remote Sensing of Environment, 114(12): 2888-2896
  29. 29.
    Fu Y C, Lu X Y, Zhao Y L, Zeng X T and Xia L L. 2013. Assessment impacts of weather and land use/land cover (LULC) change on urban vegetation net primary productivity (NPP): a case study in Guangzhou, China. Remote Sensing, 5(8): 4125-4144
  30. 30.
    Gong J Y, Zhang X, Xiang L G and Chen N C. 2019. Progress and applications for integrated sensing and intelligent decision in smart city. Acta Geodaetica et Cartographica Sinica, 48(12): 1482-1497
  31. 31.
    Gong P, Li X C, Wang J, Bai Y Q, Chen B, Hu T Y, Liu X P, Xu B, Yang J, Zhang W and Zhou Y Y. 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236: 111510
  32. 32.
    Guo S C, Du P J, Meng Y P, Wang X, Tang P F, Lin C and Xia J S. 2021. Dynamic monitoring on flooding situation in the middle and lower reaches of the Yangtze River region using Sentinel-1A time series. Journal of Remote Sensing, 25(10): 2127-2141
  33. 33.
    Guo S Q, Zhou J J and Nong Z X. 2019. Hydrogeological investigation and heavy metal migration regularity study of a Pb-Zn deposit in Guangxi. Mineral Resources and Geology, 33(3): 567-572
  34. 34.
    He J L. 2020. Study on the influence of cloud adjacency effects on the aerosol optical depth retrieval and its reducing method. Acta Geodaetica et Cartographica Sinica, 49(1): 132
  35. 35.
    He Q S, Li C C, Tang X, Li H L, Geng F H and Wu Y L. 2010. Validation of MODIS derived aerosol optical depth over the Yangtze River Delta in China. Remote Sensing of Environment, 114(8): 1649-1661
  36. 36.
    He Y, Huang C, Li H, Liu C S, Liu G H, Zhou Z C and Zhang C C. 2019. Land-cover classification of random forest based on Sentinel-2A image feature optimization. Resources Science, 41(5): 992-1001
  37. 37.
    Hu B, Chen J Y and Zhang X F. 2019. Monitoring the land subsidence area in a coastal urban area with InSAR and GNSS. Sensors, 19(14): 3181
  38. 38.
    Hu B, Yu M, Li Z Y, Wang D Y and Chen X D. 2021. Study of ice condition on the northeast route during window period based on onboard observation. Journal of Ship Mechanics, 25(8): 1001-1009
  39. 39.
    Hua Y. 2020. Ecological Risk Assessment of Heavy Metals in Sulfide Mining Area of Shuikoushan, Hunan. Beijing: China University of Geosciences (Beijing)
  40. 40.
    Huang S F, Ma J W and Sun Y Y. 2021. Situation and prospect of flood disaster monitoring by remote sensing in China. China Water Resources, (15): 15-17
  41. 41.
    Jendryke M, McClure S C, Balz T and Liao M S. 2017. Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information. International Journal of Digital Earth, 10(7): 675-686
  42. 42.
    Ji J Y, Li X J, Du H Q, Mao F J, Fan W L, Xu Y X, Huang Z H, Wang J Y and Kang F F. 2021. Multiscale leaf area index assimilation for moso bamboo forest based on Sentinel-2 and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 104: 102519
  43. 43.
    Jia M M, Wang Z M, Zhang Y Z, Ren C Y and Song K S. 2015. Landsat-based estimation of mangrove forest loss and restoration in Guangxi Province, China, influenced by human and natural factors. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1): 311-323
  44. 44.
    Jiang G J, Loiselle S A, Yang D T, Ma R H, Su W and Gao C J. 2020. Remote estimation of chlorophyll a concentrations over a wide range of optical conditions based on water classification from VIIRS observations. Remote Sensing of Environment, 241: 111735
  45. 45.
    Jiang R, Sanchez-Azofeifa A, Laakso K, Xu Y, Zhou Z Y, Luo X W, Huang J H, Chen X and Zang Y. 2021. Cloud cover throughout all the paddy rice fields in Guangdong, China: impacts on Sentinel 2 MSI and Landsat 8 OLI optical observations. Remote Sensing, 13(15): 2961
  46. 46.
    Jiang Y N, Liao M S, Wang H M, Zhang L and Balz T. 2016. Deformation monitoring and analysis of the geological environment of Pudong international airport with persistent scatterer SAR interferometry. Remote Sensing, 8(12): 1021
  47. 47.
    Jin H A, Li A N, Bian J H, Zhao W, Zhang Z J and Nan X. 2016. Leaf area index (LAI) estimationfrom remotely sensed observations in different topographic gradients over southwestern China. Remote Sensing Technology and Application, 31(1): 42-50
  48. 48.
    Jing C B, Zhou W Q, Qian Y G, Yu W J and Zheng Z. 2021. A novel approach for quantifying high-frequency urban land cover changes at the block level with scarce clear-sky Landsat observations. Remote Sensing of Environment, 255: 112293
  49. 49.
    Kaya Ş, Curran P J and Llewellyn G. 2005. Post‐earthquake building collapse: a comparison of government statistics and estimates derived from SPOT HRVIR data. International Journal of Remote Sensing, 26(13): 2731-2740
  50. 50.
    Kim G, Baek I, Stocker M D, Smith J E, Van Tassell A L, Qin J W, Chan D E, Pachepsky Y and Kim M S. 2020. Hyperspectral imaging from a multipurpose floating platform to estimate Chlorophyll-a concentrations in irrigation pond water. Remote Sensing, 12(13): 2070
  51. 51.
    Kou Z X, Yao Y H and Hu Y F. 2020. Delimitation of the northern boundary of the subtropical zone in China by geodetector. Geographical Research, 39(12): 2821-2832
  52. 52.
    Lacroix P, Berthier E and Maquerhua E T. 2015. Earthquake-driven acceleration of slow-moving landslides in the Colca Valley, Peru, detected from Pléiades images. Remote Sensing of Environment, 165: 148-158
  53. 53.
    Lee D, Son S, Joo H, Kim K, Kim M J, Jang H K, Yun M S, Kang C K and Lee S H. 2020. Estimation of the particulate organic carbon to chlorophyll-a ratio using MODIS-aqua in the East/Japan Sea, South Korea. Remote Sensing, 12(5): 840
  54. 54.
    Lei F G, Wang J H and Chen S W. 1984. Preparation of vegetation map of liangshuangbanna nature reserve by using aerial photos. Forest Inventory and Planning, (3): 24-26
  55. 55.
    Li A N, Bian J H, Zhang Z J, Zhao W and Yin G F. 2016a. Progresses, opportunities, and challenges of mountain remote sensing research. Journal of Remote Sensing, 20(5): 1199-1215
  56. 56.
    Li A N, Yin G F, Jin H A, Bian J H and Zhao W. 2016b. Principles and methods for the retrieval of biophysical variables in mountainous areas. Remote Sensing Technology and Application, 31(1): 1-11
  57. 57.
    Li D, Qin K, Wu L X, Xu J, Letu H, Zou B, He Q and Li Y F. 2019a. Evaluation of JAXA himawari-8-AHI level-3 aerosol products over eastern China. Atmosphere, 10(4): 215
  58. 58.
    Li D R, Luo H and Shao Z F. 2016. Review of impervious surface mapping using remote sensing technology and its application. Geomatics and Information Science of Wuhan University, 41(5): 569-577, 703
  59. 59.
    Li H K, Wu L X and Liu X S. 2014. Change detection of ground-surface environment in rare earth mining area based on multi-temporal remote sensing: a case in Lingbei rare earth mining area. Journal of China University of Mining and Technology, 43(6): 1077-1094
  60. 60.
    Li L W, Li N, Lu D S and Chen Y Y. 2019b. Mapping Moso bamboo forest and its on-year and off-year distribution in a subtropical region using time-series Sentinel-2 and Landsat 8 data. Remote Sensing of Environment, 231: 111265
  61. 61.
    Li Q Q, Lu Y, Hu S B, Hu Z W, Li H Z, Liu P, Shi T Z, Wang C S, Wang J J and Wu G F. 2016. Review of remotely sensed geo-environmental monitoring of coastal zones. Journal of Remote Sensing, 20(5): 1216-1229
  62. 62.
    Li T W and Cheng X. 2021. Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach. International Journal of Applied Earth Observation and Geoinformation, 101: 102356
  63. 63.
    Li W T. 2018. Distribution Characteristics and Sources of Cd-Contamination in Shizhuyuan Mine and its Surrounding Soils, Hunan Province. Beijing: China University of Geosciences (Beijing)
  64. 64.
    Li X S, Li H, Chen D H, Liu Y F, Liu S S, Liu C F and Hu G Q. 2020. Multiple classifiers combination method for tree species identification based on GF-5 and GF-6. Scientia Silvae Sinicae, 56(10): 93-104
  65. 65.
    Li Y Q, Liang W and Cao W. 2018. Urban heat island pattern analysis of typical city in the subtropical region: a case study of Nanning. Urban Development Studies, 25(7): C14-C18
  66. 66.
    Li Z, Hu H P, Yang M H and Chen M. 2018. Forest classification based on Landsat-8 multi-temporal remote sensing image. Geomatics and Spatial Information Technology, 41(9): 147-149
  67. 67.
    Li Z Q, Chen X F, Ma Y, Qie L L, Hou W Z and Qiao Y L. 2018. An overview of atmospheric correction for optical remote sensing satellites. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 10(1): 6-15
  68. 68.
    Li Z W, Xu W B, Feng G C, Hu J, Wang C C, Ding X L and Zhu J J. 2012. Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation-dependent interpolation model. Geophysical Journal International,189(2): 898-910
  69. 69.
    Liang H, He J and Lei J J. 2020. Monitoring of corn canopy blight disease based on UAV hyperspectral method. Spectroscopy and Spectral Analysis, 40(6): 1965-1972
  70. 70.
    Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H, Yao Y J, Yuan W P, Zhang X T and Zhao X. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875-898
  71. 71.
    Lin H and Zhang H S. 2021. Tropical and subtropical remote sensing: needs, challenges, and opportunities. Journal of Remote Sensing, 25(1): 276-291
  72. 72.
    Lin H, Zhang H S, Lin Y Y, Wei S and Wu Z F. 2018. Spatiotemporal changes of gridded urban population in the Guangdong-Hong Kong-Macao greater bay area based on impervious surface-population correlation. Progress in Geography, 37(12): 1644-1652
  73. 73.
    Lin Y, Li L, Yu J, Hu Y, Zhang T H, Ye Z L, Syed A and Li J. 2021. An optimized machine learning approach to water pollution variation monitoring with time-series Landsat images. International Journal of Applied Earth Observation and Geoinformation, 102: 102370
  74. 74.
    Liu J, Wang S Y and Li D M. 2014. The analysis of the impact of land-use changes on flood exposure of Wuhan in Yangtze River Basin, China. Water Resources Management, 28(9): 2507-2522
  75. 75.
    Liu L X, Pang Y, Ren H B and Li Z Y. 2019. Predict tree species diversity from GF-2 satellite data in a subtropical forest of China. Scientia Silvae Sinicae, 55(2): 61-74
  76. 76.
    Liu M K, Guan L, Chen G and Zhao W. 2020. Retrieval of sea surface temperature from HY-2A scanning microwave radiometer. IEEE Transactions on Geoscience and Remote Sensing, 58(10): 7216-7231
  77. 77.
    Liu M L, Skidmore A K, Wang T J, Liu X N, Wu L and Tian L W. 2019. An approach for heavy metal pollution detected from spatio-temporal stability of stress in rice using satellite images. International Journal of Applied Earth Observation and Geoinformation, 80: 230-239
  78. 78.
    Liu Q, Yue G S, Ding X B, Yang K, Feng G C and Xiong Z Q. 2019. Temporal and spatial characteristics analysis of deformation along foshan subway using time series InSAR. Geomatics and Information Science of Wuhan University, 44(7): 1099-1106
  79. 79.
    Liu Q H, Zhong B, Tang P, Zhang H H, Li H Y, Wu S L, Xin X Z, Li J, Jia L, Shan X J, Zhang Z, Wen J G, Du Y M, Li L, Yang A X, Li H, Hu G C, Zhao J, Zhang H L, Yu S S, Dou B C and Wu J J. 2018. Remote sensing data products oriented quantita-tive computing system——the GSC best practice data computing environment. Journal of Global Change Data and Discovery, 2(3): 271-278
  80. 80.
    Liu Q H, Zhong B, Wu J T, Xiao Z Q and Wang Q. 2011. Quantitative Inversion and Assimilation for Remote Sensing of Environment. Beijing: Science Press
  81. 81.
    Liu W B, Tao J B, Chen H P and Chen R Q. 2018. Moritoring of urbanization process by VPAUI index in central China from 2001 to 2013-take Wuhan, Changsha and Nanchang as examples. Journal of Huazhong Normal University (Natural Sciences), 52(4): 557-564
  82. 82.
    Liu W X, Liu X L, Weng F H and Zhou C P. 2011. Monitoring of soil moisture variation based on AMSR-E passive microwave remote sensing. Tropical Geography, 31(3): 272-277
  83. 83.
    Liu X L and Miao C. 2018. Large-scale assessment of landslide hazard, vulnerability and risk in China. Geomatics, Natural Hazards and Risk, 9(1): 1037-1052
  84. 84.
    Liu X L, Ou S L, Lu S F and Yue C R. 2020. Estimation of forest volume based on Sentinel-1 a microwave remote sensing data. Journal of West China Forestry Science, 49(6): 128-136
  85. 85.
    Liu X N, Feng Z M, Jiang L G and Zhang J H. 2012. Rubber plantations in xishuangbanna: remote sensing identification and digital mapping. Resources Science, 34(9): 1769-1780
  86. 86.
    Liu X S, Dong X J, Qian J R, Guo C, Zhao J C and Zhan J Q. 2021. Airborne LiDAR-based debris flow source area recognition in lush mountainous area. Geomatics and Information Science of Wuhan University, 45(11): 1-16
  87. 87.
    Lu N, Hernandez A J and Ramsey R D. 2015. Land cover dynamics monitoring with Landsat data in Kunming, China: a cost-effective sampling and modelling scheme using Google Earth imagery and random forests. Geocarto International, 30(2): 186-201
  88. 88.
    Luo Y, Luo M, Fan J Z, Luo L Y and Duan S R. 2020. Precision evaluation of FY-3B MWRI based soil moisture product in Hunan Province. Guizhou Agricultural Sciences, 48(7): 113-118
  89. 89.
    Madugundu R, Nizalapur V and Jha C S. 2008. Estimation of LAI and above-ground biomass in deciduous forests: western ghats of Karnataka, India. International Journal of Applied Earth Observation and Geoinformation, 10(2): 211-219
  90. 90.
    Malahlela O, Cho M A and Mutanga O. 2014. Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data. International Journal of Remote Sensing, 35(17): 6397-6417
  91. 91.
    Mao F J, Li X J, Du H Q, Zhou G M, Han N, Xu X J, Liu Y L, Chen L and Cui L. 2017. Comparison of two data assimilation methods for improving MODIS LAI time series for bamboo forests. Remote Sensing, 9(5): 401
  92. 92.
    Masemola C, Cho M A and Ramoelo A. 2016. Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga, South Africa. International Journal of Remote Sensing, 37(18): 4401-4419
  93. 93.
    Mhawish A, Sorek-Hamer M, Chatfield R, Banerjee T, Bilal M, Kumar M, Sarangi C, Franklin M, Chau K, Garay M and Kalashnikova O. 2021. Aerosol characteristics from earth observation systems: a comprehensive investigation over South Asia (2000—2019). Remote Sensing of Environment, 259: 112410
  94. 94.
    Miao Z L, Xiao Y L, Shi W Z, He Y G, Gamba P, Li Z B, Samat A, Wu L X, Li J and Wu H. 2019. Integration of satellite images and open data for impervious surface classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4): 1120-1133
  95. 95.
    Monteiro A T, Carvalho-Santos C, Lucas R, Rocha J, Costa N, Giamberini M, da Costa E M and Fava F. 2021. Progress in grassland cover conservation in southern European mountains by 2020: a transboundary assessment in the iberian peninsula with satellite observations (2002—2019). Remote Sensing, 13(15): 3019
  96. 96.
    Morley P J, Donoghue D N M, Chen J C and Jump A S. 2019. Quantifying structural diversity to better estimate change at mountain forest margins. Remote Sensing of Environment, 223: 291-306
  97. 97.
    Morris M, Chew C, Reager J T, Shah R and Zuffada C. 2019. A novel approach to monitoring wetland dynamics using CYGNSS: everglades case study. Remote Sensing of Environment, 233: 111417
  98. 98.
    Mu Y X, Wu M Q, Niu Z, Huang W J and Yang J. 2020. Method of remote sensing extraction of cultivated land area under complex conditions in southern region. Remote Sensing Technology and Application, 35(5): 1127-1135
  99. 99.
    Nichol J, Wong M S, Fung C and Leung K K M. 2006. Assessment of urban environmental quality in a subtropical city using multispectral satellite images. Environment and Planning B: Planning and Design, 33(1): 39-58
  100. 100.
    Pan J H and Hu Y X. 2018. Spatial Identification of multi-dimensional poverty in rural China: a perspective of nighttime-light remote sensing data. Journal of the Indian Society of Remote Sensing, 46(7): 1093-1111
  101. 101.
    Peel M C, Finlayson B L and McMahon T A. 2007. Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11(5): 1633-1644
  102. 102.
    Prasad C R S, Thayalan S, Reddy R S and Reddy P S A. 1990. Use of Landsat imagery for mapping soil and land resources for development planning in parts of northern Karnataka, India. International Journal of Remote Sensing, 11(10): 1889-1900
  103. 103.
    Qi Y, Wu L X, Ding Y F, He M, Mao W F, Xie B S and Zhou S Y. 2020. Mining seismic thermal anomalies from massive satellite passive microwave images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020: 281-286
  104. 104.
    Qi Y, Wu L X, Mao W F, Ding Y F and He M. 2021b. Discriminating possible causes of microwave brightness temperature positive anomalies related with May 2008 Wenchuan earthquake sequence. IEEE Transactions on Geoscience and Remote Sensing, 59(3): 1903-1916
  105. 105.
    Qi Y, Wu L X, Mao W F, Ding Y F and Liu Y J. 2021a. Satellite passive microwave remote sensing for seismic thermal anomaly: phenomena and mechanisms//Proceedings of 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels: IEEE: 8696-8699
  106. 106.
    Rajapakse R M S S, Tripathi N K and Honda K. 2002. Spectral characterization and LAI modelling for the tea (Camellia sinensis (L.) O. Kuntze) canopy. International Journal of Remote Sensing, 23(18): 3569-3577
  107. 107.
    Rajesh A, Jiji G W and Raj J D. 2020. Estimating the pollution level based on heavy metal concentration in water bodies of tiruppur district. Journal of the Indian Society of Remote Sensing, 48(1): 47-57
  108. 108.
    Rao B W. 2014. The Research of Heavy Metals Migration Simulation Under The Tailing Geotechnical Seepage and Pollution Control. Changsha: Central South University
  109. 109.
    Rio J N R and Lozano-Garcı́a D F. 2000. Spatial filtering of radar data (RADARSAT) for wetlands (brackish marshes) classification. Remote Sensing of Environment, 73(2): 143-151
  110. 110.
    Shan N, Zhen T Y and Wang Z S. 2009. High-speed and high-accuracy algorithm for cloud detection and its application. Journal of Remote Sensing, 13(6): 1147-1162
  111. 111.
    Shen X, Cao L, Xu T and She G H. 2015. Classification of Pinus massoniana and secondary deciduous tree species in northern subtropical region based on high resolution and hyperspectral remotely sensed data. Chinese Journal of Plant Ecology, 39(12): 1125-1135
  112. 112.
    Shi X G, Liao M S, Li M H, Zhang L and Cunningham C. 2016. Wide-area landslide deformation mapping with multi-path ALOS PALSAR data stacks: a case study of Three Gorges Area, China. Remote Sensing, 8(2): 136
  113. 113.
    Shi X G, Zhang L, Balz T and Liao M S. 2015. Landslide deformation monitoring using point-like target offset tracking with multi-mode high-resolution TerraSAR-X data. ISPRS Journal of Photogrammetry and Remote Sensing, 105: 128-140
  114. 114.
    Sui H G, Zhao B F, Xu C, Zhou M T, Du Z T and Liu J Y. 2021. Rapid extraction of flood disaster emergency information with multi-modal sequence remote sensing images. Geomatics and Information Science of Wuhan University, 46(10): 1441-1449
  115. 115.
    Sun T, Xu M Y, Dong X J and Pan X. 2021. Application of airborne LiDAR technology in geological hazard investigation in mountainous area with dense vegetation. Bulletin of Surveying and Mapping, (4): 90-97
  116. 116.
    Tang H J and Li Z L. 2014. Land surface temperature retrieval from thermal infrared data//Tang H J and Li Z L, eds. Quantitative Remote Sensing in Thermal Infrared. Berlin: Springer: 93-143
  117. 117.
    Tang Y Q, Feng L P, Li M, Nie P and Lan C Y. 2016. Land use change analysis of Changzhutan urban area from 1978 to 2015. Geomatics and Spatial Information Technology, 39(10): 5-10
  118. 118.
    Thenkabail P S, Schull M and Turral H. 2005. Ganges and Indus River basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sensing of Environment, 95(3): 317-341
  119. 119.
    Wang H, Li J W, Gao Z Q, Yim S H L, Shen H F, Ho H C, Li Z Y, Zeng Z L, Liu C, Li Y B, Ning G C and Yang Y J. 2019a. High-spatial-resolution population exposure to PM2.5 pollution based on multi-satellite retrievals: a case study of seasonal variation in the Yangtze River Delta, China in 2013. Remote Sensing, 11(23): 2724
  120. 120.
    Wang H H, Wang J and Cui Y H. 2020. Remote sensing monitoring on spatial differentiation of suspended sediment concentration in a river-lake system based on Sentinel-2 MSI imaging:a case for Shengjin Lake and connected Yangtze River section in Anhui Province. Environmental Science, 41(3): 1207-1216
  121. 121.
    Wang H Q, Feng G C, Xu B, Yu Y P, Li Z W, Du Y N and Zhu J J. 2017. Deriving spatio-temporal development of ground subsidence due to subway construction and operation in delta regions with ps-insar data: a case study in Guangzhou, China. Remote Sensing, 9(10): 1004
  122. 122.
    Wang J, Han P and Li G X. 2021. The extraction and analysis of deformation features of hualien earthquake based on Sentinel-1A/B SAR data. Journal of the Indian Society of Remote Sensing, 49(9): 2069-2077
  123. 123.
    Wang Q, Yang Y P, Zhao S H and Liu S H. 2018. Application of HJ-1 in China’s ecological environment. Space International, (9): 16-19.
  124. 124.
    Wang Q. 2021. Progress of environmental remote sensing monitoring technology in China and some related frontier issues. Journal of Remote Sensing, 25(1): 25-36
  125. 125.
    Wang W, Mao F Y, Pan Z X, Gong W, Yoshida M, Zou B and Ma H Y. 2019c. Evaluating aerosol optical depth from Himawari-8 with sun photometer network. Journal of Geophysical Research: Atmospheres, 124(10): 5516-5538
  126. 126.
    Wang W, Mao F Y, Zou B, Guo J P, Wu L X, Pan Z X and Zang L. 2019b. Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China. Science of the Total Environment, 675: 658-666
  127. 127.
    Wang X Q, Dou A X, Ding X and Li Y W. 2015. Advance on the RS-based emergency seismic intensity assessment. Journal of Geo-Information Science, 17(12): 1536-1544
  128. 128.
    Wu B F. 2000. Operational remote sensing methods for agricultural statistics. Acta Geographica Sinica, 55(1): 25-35
  129. 129.
    Wu B F, Zhang M, Zeng H W, Yan N N, Zhang X, Xing Q and Chang S. 2019. Twenty years of cropwatch: progress and prospect. Journal of Remote Sensing, 23(6): 1053-1063
  130. 130.
    Wu L X, Li J, Miao Z L, Wang W, Chen B Y, Li Z W, Dai W J and Xu W B. 2021. Pattern and directions of spaceborne-airborne-ground collaborated intelligent monitoring on the geo-hazards developing environment and disasters in glacial basin. Acta Geodaetica et Cartographica Sinica, 50(8): 1109-1121
  131. 131.
    Wu L X, Qin K and Liu S J. 2012. GEOSS-based thermal parameters analysis for earthquake anomaly recognition. Proceedings of the IEEE, 100(10): 2891-2907
  132. 132.
    Wu R, Wang J Y, Zhang D C and Wang S J. 2021. Identifying different types of urban land use dynamics using point-of-interest (POI) and random forest algorithm: the case of Huizhou, China. Cities, 114: 103202
  133. 133.
    Wu W, Miao Z L, Xiao Y L, Li Z B, Zhang A S, Samat A, Du N C, Xu Z K and Gamba P. 2020. New scheme for impervious surface area mapping from SAR images with auxiliary user-generated content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5954-5970
  134. 134.
    Wu X W, Yin F Z and Sun J Q. 2011. The key technical analysis on remote sensing dynamic monitoring in antarctic based on image processing. Geomatics World, 9(2): 74-79
  135. 135.
    Xie B S, Zhou S Y, Wu L X, Mao W F, Wang W. 2022. RockSL: an integrated rock spectral library for better global shared services. Big Earth Data
  136. 136.
    Xing Q G, Zheng X Y, Shi P, Hao J J, Yu D F, Liang S Z, Liu D Y and Zhang Y Z. 2011. Monitoring “green tide” in the Yellow Sea and the East China Sea using multi-temporal and multi-source remote sensing images. Spectroscopy and Spectral Analysis, 31(6): 1644-1647
  137. 137.
    Xu H, Li M L, Liang H B, Li Z S and Wu X. 2018. Review of the evaluation index system for degraded forest ecosystems. Acta Ecologica Sinica, 38(24): 9034-9042
  138. 138.
    Xu H Q. 2013. A remote sensing urban ecological index and its application. Acta Ecologica Sinica, 33(24): 7853-7862
  139. 139.
    Xu N X, Tian Q J, Shen H F and Xu K J. 2018. Classification of Pinus massoniana and Cunninghamia lanceolata using hyperspectral image based on differential transformation. Remote Sensing for Land and Resources, 30(4): 28-32
  140. 140.
    Xu T, Cao L, Shen X and She G H. 2015. Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data. Chinese Journal of Plant Ecology, 39(4): 309-321
  141. 141.
    Xu X H, Huang P, Huang S F, Sun Y Y and Zhang X P. 2021. Application of satellite radar remote sensing for emergency monitoring of flood disasters in Poyang Lake. China Flood and Drought Management, 31(4): 10-14
  142. 142.
    Xu Z Y, Li Y C, Li M Y, Li C and Wang L. 2020. Forest biomass retrieval based on Sentinel-1A and Landsat 8 image. Journal of Central South University of Forestry and Technology, 40(11): 147-155
  143. 143.
    Yan M C. 2007. Research and contrast on several vegetation-classification methods of high-resolution satellite image data. Journal of Remote Sensing, 11(2): 235-240
  144. 144.
    Yang Q Y, Zheng D and Wu S H. 2006. On subtropical zone of China. Journal of Subtropical Resources and Environment, 1(3): 1-10
  145. 145.
    Yang Z, Lu X P, Wu Y B, Miao P J and Zhou J L. 2020. Retrieval and model construction of water quality parameters for UAV hyperspectral remote sensing. Science of Surveying and Mapping, 45(9): 60-64, 95
  146. 146.
    Yin H J, Zhu J J, Li Z W, Ding X L and Wang C C. 2011. Ground subsidence monitoring in mining area using DInSAR SBAS algorithm. Acta Geodaetica et Cartographica Sinica, 40(1): 52-58
  147. 147.
    Zeng C, Liu Y L, Stein A and Jiao L M. 2015. Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China. International Journal of Applied Earth Observation and Geoinformation, 34: 10-24
  148. 148.
    Zhang J L, Pjam T T H, Kalacska M and Turner S. 2014. Using Landsat thematic mapper records to map land cover change and the impacts of reforestation programmes in the borderlands of southeast Yunnan, China: 1990—2010. International Journal of Applied Earth Observation and Geoinformation, 31: 25-36
  149. 149.
    Zhang M N, Huang H B, Li Z C, Hackman K O, Liu C, Andriamiarisoa R L, Raherivelo T N A N, Li Y X and Gong P. 2020. Automatic high-resolution land cover production in madagascar using Sentinel-2 time series, tile-based image classification and google earth engine. Remote Sensing, 12(21): 3663
  150. 150.
    Zhang X, Liu L Y, Chen X D, Gao Y, Xie S and Mi J. 2021. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data, 13(6): 2753-2776
  151. 151.
    Zhang X H. 2016. Decision tree algorithm of automatically extracting mangrove forests information from Landsat 8 OLI imagery. Remote Sensing for Land and Resources, 28(2): 182-187
  152. 152.
    Zhang Y Z, Huang Z J, Fu D Y, Tsou J Y, Jiang T C, Liang X S and Lu X. 2018. Monitoring of chlorophyll-a and sea surface silicate concentrations in the south part of Cheju Island in the East China Sea using MODIS data. International Journal of Applied Earth Observation and Geoinformation, 67: 173-178
  153. 153.
    Zhang Y Z and Zhang F X. 1994. Application of Landsat thematic mapper data to landuse classification and thematic information extraction in subtropical economic forest zone. Remote Sensing for Land and Resources, 6(2): 28-33
  154. 154.
    Zhao D Z, Qu C Y, Shan X J, Gong W Y, Zhang Y F and Zhang G H. 2018. InSAR and GPS derived coseismic deformation and fault model of the 2017 Ms7.0 Jiuzhaigou earthquake in the Northeast Bayanhar block. Tectonophysics, 726: 86-99
  155. 155.
    Zhao X, Song Y Q, Liu Y L, Chen F X and Hu Y M. 2020. Population spatialization based on satellite remote sensing and POI data: Guangzhou as an example. Tropical Geography, 40(1): 101-109
  156. 156.
    Zhao X Y, Zhang J, Zhang D Y, Zhou X G, Liu X H and Xie J. 2019. Comparison between the effects of visible light and multispectral sensor based on low-altitude remote sensing platform in the evaluation of rice sheath blight. Spectroscopy and Spectral Analysis, 39(4): 1192-1198
  157. 157.
    Zheng G, Yang J S, Li X F, Zhou L Z, Ren L, Chen P, Zhang H G and Lou X L. 2019. Using artificial neural network ensembles with crogging resampling technique to retrieve sea surface temperature from HY-2A scanning microwave radiometer data. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 985-1000
  158. 158.
    Zhou F, Xu Y P and Lv H H. 2012. Analysis of the vegetation change in the Yangtze River delta based on MODIS-EVI time series data. Resources and Environment in the Yangtze Basin, 21(11): 1363-1369
  159. 159.
    Zhou T Y, Wang Z H, Qin H Y and Zeng Y Q. 2020. Remote sensing extraction of geothermal anomaly based on terrain effect correction. Journal of Remote Sensing, 24(3): 265-276
  160. 160.
    Zhu J S. 2015. Research on aerosol inversion method of high resolution satellite. Global City Geography, 5(10): 251-252
  161. 161.
    Zhu K Z. 1958. Subtropical zone in China. Chinese Science Bulletin, 9(17): 524-528

Lire l'article complet

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