Integrating Landsat Imageries and Digital Elevation Models to Infer Water Level Change in Hoover Dam

	Integrating Landsat Imageries and Digital Elevation Models to Infer Water Level Change in Hoover Dam
	Tseng, Kuo-Hsin 1; Shum, C.K.1; Kim, Jin-Woo 1; Wang, Xianwei 1; Zhu, Kefeng 1; Cheng, Xiao 1
刊名	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
	2016
卷号	9 期号:4 页码:1696-1709
关键词	SPACEBORNE THERMAL EMISSION REFLECTION RADIOMETER ASTER TEMPORAL RESOLUTION SATELLITE IMAGERY BLENDING LANDSAT ALGORITHM PRODUCTS DISAGGREGATION REFINEMENTS VALIDATION
英文摘要	The Thematic Mapper onboard Landsat 4, 5, and Enhanced Thematic Mapper Plus (TM/ETM+) onboard Landsat 7 have frequency bands (green and SWIR) to effectively measure water body extents and their changes via the Modified Normalized Difference Water Index (MNDWI). Here, we developed a technique, called the thematic imagery-altimetry system (TIAS), to infer the vertical water changes from MNDWI horizontal water extent changes by integrating long-term TM/ETM+ imageries with available digital elevation models (DEMs). The result is a technique to quantify water level changes of natural or artificial water bodies over two decades. Several DEMs were used to compute intersects with TM/ETM+ water extent time series to evaluate the robustness of the technique. These DEMs include: the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map version 2 (ASTER-GDEM2, at 1 arcsec resolution), the Shuttle Radar Topography Mission version 2 (SRTM C-band at 1 arcsec), and the Global Multiresolution Terrain Elevation Data (GMTED2010 at 7.5 arcsec). We demonstrated our technique near Hoover Dam (HD) in Lake Mead to quantify its respective decadal water level changes. The dammed water had experienced extraordinary level variation in the past 20 years due to natural decline from intake or artificial impoundments. The discrepancy of the HD water level changes from an analysis of 32-year (1984-2015) time series, including 584 Landsat scenes, using the GMTED2010 DEM, has a RMSE reached 0.85 ± 0.63 m (91% of data) as compared with in situ stage record. © 2015 IEEE.
学科主题	Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20160401844923
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39555]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Center for Space and Remote Sensing Research, National Central University, Taoyuan, Taiwan 2. Institute of Hydrological and Oceanic Sciences, National Central University, Taoyuan, Taiwan 3. State Key Laboratory of Geodesy and Earth's Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China 4. Division of Geodetic Science, School of Earth Sciences, Ohio State University, Columbus 5.OH, United States 6. School of Earth Sciences, Southern Methodist University, Dallas 7.TX, United States 8. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Beijing Normal University, Beijing, China 9. Center for Global Sea Level Change, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates 10. College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
推荐引用方式 GB/T 7714	Tseng, Kuo-Hsin,Shum, C.K.,Kim, Jin-Woo,et al. Integrating Landsat Imageries and Digital Elevation Models to Infer Water Level Change in Hoover Dam[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(4):1696-1709.
APA	Tseng, Kuo-Hsin,Shum, C.K.,Kim, Jin-Woo,Wang, Xianwei,Zhu, Kefeng,&Cheng, Xiao.(2016).Integrating Landsat Imageries and Digital Elevation Models to Infer Water Level Change in Hoover Dam.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,9(4),1696-1709.
MLA	Tseng, Kuo-Hsin,et al."Integrating Landsat Imageries and Digital Elevation Models to Infer Water Level Change in Hoover Dam".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9.4(2016):1696-1709.