Application of topographic elevation data generated by remote sensing approaches to flood inundation analysis model

Paddy and Water Environment - Trang 1-15 - 2024
Maulana Ibrahim Rau1, Atriyon Julzarika2, Natsuki Yoshikawa3, Takanori Nagano4, Masaomi Kimura5, Budi Indra Setiawan6, Lan Thanh Ha7
1Graduate School of Science and Technology, Niigata University, Niigata, Japan
2Research Organization for Earth and Maritime, National Research and Innovation Agency (BRIN), Cibinong, Indonesia
3Institute of Science and Technology, Niigata University, Niigata, Japan
4Graduate School of Agricultural Science, Kobe University, Kobe, Japan
5Faculty of Agriculture, Kindai University, Nakamachi, Nara, Japan
6Department of Civil and Environmental Engineering, Faculty of Agricultural Technology, IPB University, Bogor, Indonesia
7Institute of Water Resources Planning, Ministry of Agriculture and Rural Development, Hanoi, Vietnam

Tóm tắt

High-resolution topographic data are crucial for delta water management, such as hydrological modeling, inland flood routing, etc. Nevertheless, the availability of high-resolution topographic data is often lacking, particularly in low-lying regions in developing countries. This data scarcity poses a significant obstacle to inland flood modeling. However, collecting detailed topographic data is demanding, time-consuming, and costly, making remote sensing techniques a promising solution for developing flood inundation analysis models worldwide. This study presents a novel understanding for utilizing topographical elevations obtained using remote sensing techniques to create a flood inundation analysis model. In a study of three watersheds, Kameda, Niitsu, and Shirone (Japan), the assessment of digital terrain models (DTMs) showed that remote sensing-based DTMs (RS-DTMs) exhibited high reliability of coefficient of determination (R2) and root-mean-square errors, compared with the airborne LiDAR-based topography from the Geospatial Information Authority of Japan. Comparing the flood modeling results from LiDAR data and RS-DTM, with Kameda and Niitsu performing favorable outcomes, Shirone exhibited less accurate results. We hypothesized that this was caused by the topographic distortions due to lack of evenly distributed reference points. Hence, we revised the topography by adjusting both the slope and intercept from the regression equation. This verification successfully showed that the flood inundation volume correlation improved, achieving R2 results for the three watersheds ranging from 0.975 to 0.997 and Nash–Sutcliffe Efficiencies ranging from 0.938 to 0.986 between the resulting flood models based on the LiDAR data and RS-DTM. Based on these findings, we recognized the significance of uniformly distributed geodetic height points. In areas lacking height references, high-precision survey instruments can be employed for achieving uniform distribution.

Tài liệu tham khảo

Bruinsma SL, Sánchez-Ortiz N, Olmedo E, Guijarro N (2012) Evaluation of the DTM-2009 thermosphere model for benchmarking purposes. J Space Weather Space Clim 2:A04. https://doi.org/10.1051/swsc/2012005 Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press Chen H, Liang Q, Liu Y, Xie S (2018) Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling. J Hydrol 559:56–70. https://doi.org/10.1016/j.jhydrol.2018.01.056 Escobar-Silva EV, Almeida CMd, Silva GBLd, Bursteinas I, Rocha FKLd, de Oliveira CG, Fagundes MR, Paiva RCDd (2023) Assessing the extent of flood-prone areas in a south-American megacity using different high-resolution DTMs. Water 15(6):1127. https://doi.org/10.3390/w15061127 Ettritch G, Hardy A, Bojang L, Cross D, Bunting P, Brewer P (2018) Enhancing digital elevation models for hydraulic modelling using flood frequency detection. Remote Sens Environ 217:506–522. https://doi.org/10.1016/j.rse.2018.08.029 Gamba P, Dell Acqua F, Houshmand B (2002) SRTM data Characterization in urban areas. Int Arch Photogram Remote Sens Spatial Inf Sci 34:55–58 Geospatial Information Authority of Japan (GSI) (2020) The latest DEM data (5-meter mesh) [Data set of Kameda, Niitsu, and Shirone]. Ministry of Land, Infrastructure, Transport and Tourism. https://fgd.gsi.go.jp/download/menu.php. Geospatial Information Authority of Japan (GSI) (2023) Reference Point Results Browsing Service [Reference points set of Kameda, Niitsu, and Shirone]. Ministry of Land, Infrastructure, Transport and Tourism. https://sokuseikagis1.gsi.go.jp. Guth P (2003) Geomorphology of DEMs: quality assessment and scale effects. Paper No. 175–2. In Proceedings of GSA, Seattle Annual Meeting, November 2–5, 2003. Harel O (2009) The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation. J Appl Stat 36(10):1109–1118. https://doi.org/10.1080/02664760802553000 Hengl T, Heuvelink GBM, Rossiter DG (2007) About regression-kriging: from equations to case studies. Comput Geosci 33(10):1301–1315. https://doi.org/10.1016/j.cageo.2007.05.001 Jiang W, Yu J, Wang Q, Yue Q (2022) Understanding the effects of digital elevation model resolution and building treatment for urban flood modelling. J Hydrol: Reg Studies 42:101122. https://doi.org/10.1016/j.ejrh.2022.101122 Julzarika A, Aditya T, Subaryono S, Harintaka H (2021a) The latest DTM using InSAR for dynamics detection of Semangko fault-Indonesia. Geodesy Cartography (vilnius) 47(3):118–130. https://doi.org/10.3846/gac.2021.12621 Julzarika A, Aditya T, Subaryono HH, Dewi RD, Subehi L (2021b) Integration of the latest digital terrain model (DTM) with Synthetic aperture radar (SAR) bathymetry. J Degrad Min Lands Manag 8(3):2759–2768. https://doi.org/10.15243/jdmlm.2021.083.2759 Julzarika A, Harintaka H (2019) Utilization of Sentinel Satellite for Vertical Deformation Monitoring in Semangko Fault-Indonesia. In The 40th Asian Conference on Remote Sensing (ACRS 2019), 1–7. https://a-a-r-s.org/proceeding/ACRS2019/WeA2-3.pdf. Julzarika A (2021) The Updated DTM Model using ALOS PALSAR/PALSAR-2 and Sentinel-1 Imageries for Dynamic Topography. Dissertation. Universitas Gadjah Mada. Jurjević L, Gašparović M, Liang X, Balenović I (2021) Assessment of close-range remote sensing methods for dtm estimation in a lowland deciduous forest. Remote Sens 13(11):2063. https://doi.org/10.3390/rs13112063 Kimura N, Kiri H, Kanada S, Kitagawa I, Yoshinaga I, Aiki H (2019) Flood simulations in mid-latitude agricultural land using regional current and future extreme weathers. Water 11(11):2421. https://doi.org/10.3390/w11112421 Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) Lidar remote sensing for ecosystem studies: lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. J BioSci 52(1):19–30. https://doi.org/10.1641/0006-3568(2002)052[0019:lrsfes]2.0.co;2 Magruder L, Neuenschwande A, Klotz B (2021) Digital terrain model elevation corrections using space-based imagery and ICESat-2 laser altimetry. Remote Sens Environ 264:112621. https://doi.org/10.1016/j.rse.2021.112621 Meesuk V, Vojinovic Z, Mynett AE, Abdullah AF (2015) Urban flood modelling combining top-view LiDAR data with ground-view SfM observations. Adv Water Resour 75:105–117. https://doi.org/10.1016/j.advwatres.2014.11.008 Merkuryeva G, Merkuryev Y, Sokolov BV, Potryasaev S, Zelentsov VA, Lektauers A (2015) Advanced river flood monitoring, modelling and forecasting. J Comput Sci 10:77–85. https://doi.org/10.1016/j.jocs.2014.10.004 Mesa-Mingorance JL, Ariza-López FJ (2020) Accuracy assessment of digital elevation models (DEMs): a critical review of practices of the past three decades. Remote Sens 12(16):2630. https://doi.org/10.3390/rs12162630 Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part 1. A discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6 Néelz S, Pender G, Villanueva I, Wilson M, Wright NG, Bates P, Mason D, Whitlow C (2006) Using remotely sensed data to support flood modelling. In: Proceedings of the Institution of Civil Engineers: Water Management. https://doi.org/10.1680/wama.2006.159.1.35. Nguyen NB, Nguyen NH, Tran DT, Tran PT, Pham TG, Nguyen TM (2020) Assessing damages of agricultural land due to flooding in a lagoon region based on remote sensing and GIS: case study of the Quang Dien district, Thua Thien Hue province, central Vietnam. J Vietnam Environ 12(2):100–107. https://doi.org/10.13141/jve.vol12.no2.pp100-107 Pavlova AI, Pavlov AV (2018) Analysis of correction methods for digital terrain models based on satellite data. Optoelectron Instr Proc 54:445–450. https://doi.org/10.3103/S8756699018050035 Rau MI, Hidayatulloh MH, Suharnoto Y, Arif C (2021) Evaluation of flood modelling using online visual media: case study of Ciliwung River at Situ Duit Bridge, Bogor City, Indonesia. In IOP Conf Series: Earth Environ Sci 622(1):012041. https://doi.org/10.1088/1755-1315/622/1/012041 Rucci A, Ferretti A, Monti Guarnieri A, Rocca F (2012) Sentinel 1 SAR interferometry applications: the outlook for sub millimeter measurements. Remote Sens Environ 120:156–163. https://doi.org/10.1016/j.rse.2011.09.030 Stock JD, Bellugi D, Dietrich WE, Allen D (2002) Comparison of SRTM topography to USGS and high-resolution laser altimetry topography in steep landscapes: case studies from Oregon and California. In AGU Fall Meeting Abstracts 2002:H21G – H29 Suhadha AG, Julzarika A (2022) Dynamic displacement using DInSAR of Sentinel-1 in Sunda Strait. Trends Sci 19(13):4623. https://doi.org/10.48048/tis.2022.4623 Van Liew MW, Arnold JG, Garbrecht JD (2003) Hydrologic simulation on agricultural watersheds: choosing between two models. Transact ASAE 46(6):1539–1551. https://doi.org/10.13031/2013.15643 Wedajo GK, LiDAR DEM (2017) Data for flood mapping and assessment; opportunities and challenges: a review. J Remote Sens Gis 6:2015–2018. https://doi.org/10.4172/2469-4134.1000211 Wilson JP (2012) Digital terrain modeling. Geomorphology 137(1):107–121. https://doi.org/10.1016/j.geomorph.2011.03.012 Xu K, Fang J, Fang Y, Sun Q, Wu C, Liu M (2021) The importance of digital elevation model selection in flood simulation and a proposed method to reduce DEM errors: a case study in Shanghai. Int J Disaster Risk Sci 12:890–902. https://doi.org/10.1007/s13753-021-00377-z Yasuda H, Shirato M, Goto C, Yamada T (2003) Development of rapid numerical inundation model for the levee protection activity. Doboku Gakkai Ronbunshu 740:1–17. https://doi.org/10.2208/jscej.2003.740_1 Yoshikawa N, Miyazu S, Yasuda H, Misawa S (2011) Development of inundation analysis model for low-lying agricultural reservoir. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 67(4), I_991–I_996. https://doi.org/10.2208/jscejhe.67.I_991.