青藏高原河流网络高分CubeSat遥感监测
Tracking dynamic river networks in the Tibetan Plateau with high-resolution CubeSat imagery
- 2021年25卷第10期 页码:2142-2152
纸质出版日期: 2021-10-07
DOI: 10.11834/jrs.20219268
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纸质出版日期: 2021-10-07 ,
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章斯腾,陆欣,陆瑶,程亮,李满春,杨康.2021.青藏高原河流网络高分CubeSat遥感监测.遥感学报,25(10): 2142-2152
Zhang S T,Lu X,Lu Y,Cheng L,Li M C and Yang K. 2021. Tracking dynamic river networks in the Tibetan Plateau with high-resolution CubeSat imagery. National Remote Sensing Bulletin, 25(10):2142-2152
河流网络是地表水循环的重要组成部分,如何实现河流网络动态监测已成为河流遥感研究的热点。近年来,以PlanetScope为代表的CubeSat小卫星已具备了米级空间分辨率、1 d重访周期的优势,这为河流网络高时空分辨率动态监测提供了可能。本文以青藏高原长江源区的通天河流域(227 km
2
)为研究区,选取2017-05—2017-10 5期3 m空间分辨率CubeSat遥感影像,增强河流横纵剖面特征自动化提取了河流网络,研究了通天河流域河流网络动态变化,对比分析了3 m CubeSat与30 m Landsat 8、10 m Sentinel-2所提取的河流网络,以及5种现有水体数据集(GRWL,GSW,FROM-GLC 2017,OpenStreetMap,HydroSHEDS)。研究结果表明:(1)研究区内河流网络5月水系密度较低(0.38 km
-1
),7—8月河流网络进入丰水期,水系密度显著增加至0.61 km
-1
,9月河流网络进入平水期,水系密度趋于平稳(0.53 km
-1
),随后迅速退化并于10月开始冻结,水系密度迅速降低至0.37 km
-1
;(2)采用高空间分辨率CubeSat所提取的河流网络能够识别更多细小河流(河宽3—30 m),CubeSat所提取的河流总长分别为Landsat 8、Sentinel-2所提取河流总长的1.6倍和1.3倍;(3)CubeSat所提取的河流网络水系密度高于现有水体数据集(2.9—12.4倍),弥补了现有水体数据集无法反映细小河流的不足。
River networks play an important role in the terrestrial water. They have become a hotspot in river remotely sensed studies on using remotely sensed imagery to monitor river dynamic changes. Recent development of CubeSat satellite
such as PlanetScope
allows monitoring of river networks at high spatial and high temporal resolution by providing near-daily revisit time imagery at 3 m spatial resolution. We selected the Yangtze headwaters (Tongtian river basin
~227 km²) located in Tibetan Plateau as the study area. Five CubeSat images from May to October in 2017 were selected to extract river networks at 3 m resolution by enhancing the river cross sectional and longitudinal features
in order to monitor dynamic changes of in river networks at high-spatial resolution. In addition
we compared the 3 m CubeSat river networks with 30 m Landsat 8 and 10 m Sentinel-2 river networks
and the five existing hydrography data products including GRWL
GSW
FROM-GLC
OpenStreetMap
and HydroSHEDS. We concluded that: (1) Rivers in the study area begin to develop in May with drainage density of 0.38 km
-1
. July and August are the wet seasons
and the drainage density reaches the peak (0.61 km
-1
). In September
rivers reach the mean discharge with drainage density of 0.53 km
-1
and then the rivers degrade gradually with drainage density of 0.37 km
-1
and begin to freeze in October. (2) The high spatial resolution CubeSat river networks include more small rivers (3—30 m wide)
and the CubeSat river length is 1.6 and 1.3 times larger than Landsat 8 and Sentinel-2 river networks
respectively. (3) The drainage density of CubeSat river networks is 2.9 to 12.4 times larger than existing hydrography data products
thereby compensating for any lack in the spatial and temporal resolution of the existing river network products.
河流网络遥感信息提取动态监测CubeSat青藏高原
river networkremote sensing information extractiondynamic monitoringCubeSatTibetan Plateau
Allen G H and Pavelsky T M. 2015. Patterns of river width and surface area revealed by the satellite-derived North American River Width data set. Geophysical Research Letters, 42(2): 395-402 [DOI: 10.1002/2014gl062764http://dx.doi.org/10.1002/2014gl062764]
Allen G H and Pavelsky T M. 2018. Global extent of rivers and streams. Science, 361(6402): 585-588 [DOI: 10.1126/science.aat0636http://dx.doi.org/10.1126/science.aat0636]
Allen G H, Pavelsky T M, Barefoot E A, Lamb M P, Butman D, Tashie A and Gleason C J. 2018. Similarity of stream width distributions across headwater systems. Nature Communications, 9(1): 610 [DOI: 10.1038/s41467-018-02991-whttp://dx.doi.org/10.1038/s41467-018-02991-w]
Alsdorf D E, Rodríguez E and Lettenmaier D P. 2007. Measuring surface water from space. Reviews of Geophysics, 45(2):
RG2002 [DOI: 10.1029/2006RG000197http://dx.doi.org/10.1029/2006RG000197]
Benstead J P and Leigh D S. 2012. An expanded role for river networks. Nature Geoscience, 5(10): 678-679 [DOI: 10.1038/ngeo1593http://dx.doi.org/10.1038/ngeo1593]
Butman D, Stackpoole S, Stets E, McDonald C P, Clow D W and Striegl R G. 2016. Aquatic carbon cycling in the conterminous United States and implications for terrestrial carbon accounting. Proceedings of the National Academy of Sciences of the United States of America, 113(1): 58-63 [DOI: 10.1073/pnas.1512651112http://dx.doi.org/10.1073/pnas.1512651112]
Cooley S W, Smith L C, Ryan J C, Pitcher L H and Pavelsky T M. 2019. Arctic‐boreal lake dynamics revealed using CubeSat imagery. Geophysical Research Letters, 46(4): 2111-2120 [DOI: 10.1029/2018gl081584http://dx.doi.org/10.1029/2018gl081584]
Cooley S W, Smith L C, Stepan L and Mascaro J. 2017. Tracking dynamic northern surface water changes with high-frequency planet CubeSat imagery. Remote Sensing, 9(12): 1306 [DOI: 10.3390/rs9121306http://dx.doi.org/10.3390/rs9121306]
Feng D M, Gleason C J, Yang X and Pavelsky T M. 2019. Comparing discharge estimates made via the BAM algorithm in high-order Arctic rivers derived solely from optical CubeSat, Landsat, and Sentinel-2 data. Water Resources Research, 55(9): 7753-7771 [DOI: 10.1029/2019WR025599http://dx.doi.org/10.1029/2019WR025599].
Gleason C J andSmith L C. 2014. Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proceedings of the National Academy of Sciences of the United States of America, 111(13): 4788-4791 [DOI: 10.1073/pnas.1317606111http://dx.doi.org/10.1073/pnas.1317606111]
Gong P, Wang J, Yu L, Zhao Y C, Zhao Y Y, Liang L, Niu Z G, Huang X M, Fu H H, Liu S, Li C C, Li X Y, Fu W, Liu C X, Xu Y, Wang X Y, Cheng Q, Hu L Y, Yao W B, Zhang H, Zhu P, Zhao Z Y, Zhang H Y, Zheng Y M, Ji L Y, Zhang Y W, Chen H, Yan A, Guo J H, Yu L, Wang L, Liu X J, Shi T T, Zhu M H, Chen Y L, Yang G W, Tang P, Xu B, Giri C, Clinton N, Zhu Z L, Chen J and Chen J. 2013. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7): 2607-2654 [DOI: 10.1080/01431161.2012.748992http://dx.doi.org/10.1080/01431161.2012.748992]
Haklay M and Weber P. 2008. OpenStreetMap: user-generated street maps. IEEE Pervasive Computing, 7(4): 12-18 [DOI: 10.1109/MPRV.2008.80http://dx.doi.org/10.1109/MPRV.2008.80]
Jung M, Reichstein M, Ciais P, Seneviratne S I, Sheffield J, Goulden M L, Bonan G, Cescatti A, Chen J Q, De Jeu R, Dolman A J, Eugster W, Gerten D, Gianelle D, Gobron N, Heinke J, Kimball J, Law B E, Montagnani L, Mu Q Z, Mueller B, Oleson K, Papale D, Richardson A D, Roupsard O, Running S, Tomelleri E, Viovy N, Weber U, Williams C, Wood E, Zaehle S and Zhang K. 2010. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318): 951-954 [DOI: 10.1038/nature09396http://dx.doi.org/10.1038/nature09396]
Lehner B, Verdin K and Jarvis A. 2006. HydroSHEDS Technical Documentation, Version 1.0. Washington, DC: World Wildlife Fund US: 1-27
Li Z W, Yu G A, Xu M Z, Hu X Y, Yang H M and Hu S X. 2016. Progress in studies on river morphodynamics in Qinghai-Tibet Plateau. Advances in Water Science, 27(4): 617-628
李志威, 余国安, 徐梦珍, 胡旭跃, 杨洪明, 胡世雄. 2016. 青藏高原河流演变研究进展. 水科学进展, 27(4): 617-628 [DOI: 10.14042/j.cnki.32.1309.2016.04.017http://dx.doi.org/10.14042/j.cnki.32.1309.2016.04.017]
Liu K, Song C Q, Ke L H, Jiang L, Pan Y Y and Ma R H. 2019. Global open-access DEM performances in Earth's most rugged region High Mountain Asia: a multi-level assessment. Geomorphology, 338: 16-26 [DOI: 10.1016/j.geomorph.2019.04.012http://dx.doi.org/10.1016/j.geomorph.2019.04.012]
McCabe M F, Rodell M, Alsdorf D E, Miralles D G, Uijlenhoet R, Wagner W, Lucieer A, Houborg R, Verhoest N E C, Franz T E, Shi J C, Gao H L and Wood E F. 2017. The future of Earth observation in hydrology. Hydrology and Earth System Sciences, 21(7): 3879-3914 [DOI: 10.5194/hess-21-3879-2017http://dx.doi.org/10.5194/hess-21-3879-2017]
McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Meyer J L, Strayer D L, Wallace J B, Eggert S L, Helfman G S and Leonard N E. 2007. The contribution of headwater streams to biodiversity in river networks. JAWRA Journal of the American Water Resources Association, 43(1): 86-103 [DOI: 10.1111/j.1752-1688.2007.00008.xhttp://dx.doi.org/10.1111/j.1752-1688.2007.00008.x]
Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422 [DOI: 10.1038/nature20584http://dx.doi.org/10.1038/nature20584]
Poursanidis D, Traganos D, Chrysoulakis N and Reinartz P. 2019. Cubesats allow high spatiotemporal estimates of satellite-derived bathymetry. Remote Sensing, 11(11): 1299 [DOI: 10.3390/rs11111299http://dx.doi.org/10.3390/rs11111299]
Raymond P A, Hartmann J, Lauerwald R, Sobek S, McDonald C, Hoover M, Butman D, Striegl R, Mayorga E, Humborg C, Kortelainen P, Dürr H, Meybeck M, Ciais P and Guth P. 2013. Global carbon dioxide emissions from inland waters. Nature, 503(7476): 355-359 [DOI: 10.1038/nature12760http://dx.doi.org/10.1038/nature12760]
Sjögersten S, Black C R, Evers S, Hoyos‐Santillan J, Wright E L and Turner B L. 2014. Tropical wetlands: a missing link in the global carbon cycle? Global Biogeochemical Cycles, 28(12): 1371-1386 [DOI: 10.1002/2014GB004844http://dx.doi.org/10.1002/2014GB004844]
Tadono T, Ishida H, Oda F, Naito S, Minakawa K and Iwamoto H. 2014. Precise global DEM generation by ALOS PRISM. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-4: 71-76 [DOI: 10.5194/isprsannals-II-4-71-2014http://dx.doi.org/10.5194/isprsannals-II-4-71-2014]
Takaku J, Tadono T, Tsutsui K and Ichikawa M. 2016. Validation of “AW3D” global DSM generated from Alos Prism. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III-43: 25-31 [DOI: 10.5194/isprs-annals-III-4-25-2016http://dx.doi.org/10.5194/isprs-annals-III-4-25-2016]
Yamazaki D, Trigg M A and Ikeshima D. 2015. Development of a global ~90m water body map using multi-temporal Landsat images. Remote Sensing of Environment, 171: 337-351 [DOI: 10.1016/j.rse.2015.10.014http://dx.doi.org/10.1016/j.rse.2015.10.014]
Yang K, Li M C, Liu Y X, Cheng L, Huang Q H and Chen Y M. 2015. River detection in remotely sensed imagery using gabor filtering and path opening. Remote Sensing, 7(7): 8779-8802 [DOI: 10.3390/rs70708779http://dx.doi.org/10.3390/rs70708779]
Yuan X Q, Li G Y, Gao X M, Zhang W J and Lu J. 2018. Evaluation of AW3D 30m DSM Data elevation quality and precision validation of typical region. Geomatics and Spatial Information Technology, 41(4): 98-101, 105
袁小棋, 李国元, 高小明, 张文君, 禄兢. 2018. AW3D 30mDSM数据质量分析及部分典型区域精度验证. 测绘与空间地理信息, 41(4): 98-101, 105
Editorial board of Chinese Academy of Sciences for “Physical Geography of China” book series. 1981. Physical Geography of China (Surface Water). Beijing: Science Press, 8-64
中国科学院《中国自然地理》编辑委员会. 1981. 中国自然地理(地表水). 北京: 科学出版社, 8-64
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