Application appraisal in catchment hydrological analysis based on SRTM 1 Arc-Second DEM

doi:10.6046/gtzyyg.2017.02.20

Abstract
References
Related Articles
Metrics

Download: PDF(787 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

High-precision DEM data constitute the basis of watershed hydrology analysis. SRTM 1 Arc-Second Global elevation data, released by US Geological Survey, offer worldwide coverage data at a resolution of 1″ (30 m). In order to evaluate and analyze the potential watershed hydrologic applications of SRTM, the authors used Tanghe watershed in Hebi as the experimental area and airborne LiDAR DEM data as a reference to assess vertical accuracy of SRTM (1″) data and the impact of slope, aspect, land cover on errors of SRTM (1″). Hydrologic indexes based on the terrain, such as Topographic Wetness Index (TWI), Length Slope Factor (LSF) and Stream Power Index (SPI),were computed for analysis. Finally the basin’s characteristic parameters, such as catchment basin area, longest path length, shape factor, curvature coefficient, were extracted from the two DEM data and the results were compared. Studies show that SRTM (1″) DEM data have high precision, the RMSE of the original data is 5.98 m, and the RMSE of the data with the elimination of the plane displacement is reduced to 4.32 m. Hydrological analysis shows that SRTM DEM and LiDAR DEM produce some different results: the average of TWI of SRTM is slightly higher, the average of SLF and SPI is lower and the dispersion degree is smaller. This is associated with the terrain distortion of SRTM DEM in micro-topography and high slope area. The basin parameters extracted from both of the DEM data have smaller differences, which shows that SRTM DEM (1″) has wide application prospects in hydrologic analysis.

Keywords domestic satellite ZY-1 02C GF-1 remote sensing image background data updating

Issue Date: 03 May 2017

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	ZHANG Youying
	YU Jiangkuan
	ZHANG Dandan
	LIN Huanuan

Cite this article:

ZHANG Youying,YU Jiangkuan,ZHANG Dandan, et al. Application appraisal in catchment hydrological analysis based on SRTM 1 Arc-Second DEM[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 138-143.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.02.20 OR https://www.gtzyyg.com/EN/Y2017/V29/I2/138

[1] Schellekens J,Brolsma R J,Dahm R J,et al.Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model[J].Environmental Modelling & Software,2014,61:98-105.
[2] Taufik M,Putra Y S,Hayati N.The utilization of global digital elevation model for watershed management a case study:Bungbuntu sub watershed,pamekasan[J].Procedia Environmental Sciences,2015,24:297-302.
[3] 张会平,刘少峰,孙亚平,等.基于SRTM-DEM区域地形起伏的获取及应用[J].国土资源遥感,2006(1):31-35.doi:10.6046/gtzyyg.2006.01.07.
Zhang H P,Liu S F,Sun Y P,et al.The acquisition of local topographic relief and its application:An SRTM-DEM analysis[J].Remote Sensing for Land and Resources,2006(1):31-35.doi:10.6046/gtzyyg.2006.01.07.
[4] 万杰,廖静娟,许涛,等.基于ICESat/GLAS高度计数据的SRTM数据精度评估——以青藏高原地区为例[J].国土资源遥感,2015,27(1):100-105.doi:10.6046/gtzyyg.2015.01.16.
Wan J,Liao J J,Xu T,et al.Accuracy evaluation of SRTM data based on ICESat/GLAS altimeter data:A case study in the Tibetan plateau[J].Remote Sensing for Land and Resources,2015,27(1):100-105.doi:10.6046/gtzyyg.2015.01.16.
[5] 包黎莉,秦承志,朱阿兴.地形湿度指数算法误差的定量评价[J].地理科学进展,2011,30(1):57-64.
Bao L L,Qin C Z,Zhu A X.Quantitative error assessment of topographic wetness index algorithms[J].Progress in Geography,2011,30(1):57-64.
[6] Moore I D,Wilson J P.Length-slope factors for the revised universal soil loss equation:Simplied method of estimation[J].Journal of Soil and Water Conservation,1992,47(5):423-428.
[7] Moore I D,Grayson R B,Ladson A R.Digital terrain modelling:A review of hydrological,geomorphological,and biological applications[J].Hydrological Processes,1991,5(1):3-30.
[8] 于海洋,卢小平,程钢,等.基于LiDAR数据的流域水系网络提取方法研究[J].地理与地理信息科学,2013,29(1):17-21,27.
Yu H Y,Lu X P,Cheng G,et al.Watershed channel network extraction from LiDAR data[J].Geography and Geo-Information Science,2013,29(1):17-21,27.

[1]	ZHENG Xiongwei, PENG Bei, SHANG Kun. Assessment of the interpretation ability of domestic satellites in geological remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 1-10.
[2]	WANG Rong, ZHAO Hongli, JIANG Yunzhong, HE Yi, DUAN Hao. Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops[J]. Remote Sensing for Natural Resources, 2021, 33(3): 72-79.
[3]	JIANG Xiao, LU Yunge, SUN Ang, LI Yongzhi, LIAN Zheng. The application of domestic GF-1 satellite data to geological and mineral resources survey abroad: A case study of Faryab area, Iran[J]. Remote Sensing for Land & Resources, 2021, 33(1): 199-204.
[4]	LI Xusheng, LIU Yufeng, CHEN Donghua, LIU Saisai, LI Hu. Cloud detection based on support vector machine with image features for GF-1 data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 55-62.
[5]	Yizhe WANG, Guo LIU, Li GUO, Shihu ZHAO, Xueli ZHANG. Research on ortho-rectification and true color synthesis technique of GF-1 WFV data in China-Pakistan Economic Corridor[J]. Remote Sensing for Land & Resources, 2020, 32(2): 213-218.
[6]	Zhuhong ZHANG, Baoyun WANG, Yumei SUN, Caidong LI, Xianchen SUN, Lingli ZHANG. River extraction from GF-1 satellite images combining stroke width transform and a geometric feature set[J]. Remote Sensing for Land & Resources, 2020, 32(2): 54-62.
[7]	Ning WANG, Jiahua CHENG, Hanye ZHANG, Hongjie CAO, Jun LIU. Application of U-net model to water extraction with high resolution remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(1): 35-42.
[8]	Hui YUAN, Qiming QIN, Yuanheng SUN. Validation of LAI retrieval results of winter wheat in Yancheng, Luohe area of Henan Province[J]. Remote Sensing for Land & Resources, 2020, 32(1): 162-168.
[9]	Jida PENG, Chungui ZHANG. Remote sensing monitoring of vegetation coverage by GF-1 satellite: A case study in Xiamen City[J]. Remote Sensing for Land & Resources, 2019, 31(4): 137-142.
[10]	Xiaotong LI, Xianlin QIN, Shuchao LIU, Guifen SUN, Qian LIU. Estimation of forest leaf area index based on GF-1 WFV data[J]. Remote Sensing for Land & Resources, 2019, 31(3): 80-86.
[11]	Chen GAO, Jian XU, Dan GAO, Lili WANG, Yeqiao WANG. Retrieval of concentration of total suspended matter from GF-1 satellite and field measured spectral data during flood period in Poyang Lake[J]. Remote Sensing for Land & Resources, 2019, 31(1): 101-109.
[12]	Yilin JIA, Wen ZHANG, Lingkui MENG. A study of selection method of NDWI segmentation threshold for GF-1 image[J]. Remote Sensing for Land & Resources, 2019, 31(1): 95-100.
[13]	Guifen SUN, Xianlin QIN, Shuchao LIU, Xiaotong LI, Xiaozhong CHEN, Xiangqing ZHONG. Potential analysis of typical vegetation index for identifying burned area[J]. Remote Sensing for Land & Resources, 2019, 31(1): 204-211.
[14]	Jian LIAO, Xingfa GU, Yulin ZHAN, Yazhou ZHANG, Xinyu REN, Shuaiyi SHI. A method based on harmonic model for generating synthetic GF-1 images[J]. Remote Sensing for Land & Resources, 2018, 30(3): 106-112.
[15]	Ruijun WANG, Bokun YAN, Mingsong LI, Shuangfa DONG, Yongbin SUN, Bing WANG. Remote sensing interpretation of important ore-controlling geological units in Hongshan Region of Gansu Province using GF-1 image and its application[J]. Remote Sensing for Land & Resources, 2018, 30(2): 162-170.

Viewed

Full text

Abstract

Cited

Shared

Discussed