{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:34:17Z","timestamp":1783524857629,"version":"3.55.0"},"reference-count":63,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51639005"],"award-info":[{"award-number":["51639005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52009061"],"award-info":[{"award-number":["52009061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The extraction of high-resolution geomorphic information from remote sensing images is a key technology for supporting mountain river research. Extracting small rivers (width &lt; 90 m) from complex backgrounds based on satellite images remains a challenging issue. In this research, we propose an improved random forest (RF) algorithm, RF-ANN (artificial neural network), by using neural networks and thermal infrared data for the extraction of river surfaces. We also develop an automated river width extraction (ARWE) method based on the central axis transformation algorithm and centerline automatic correction algorithm for the automatic extraction of the river widths across the whole basin. We chose the Huangfuchuan River Basin on the Loess Plateau, China, as a case study area. Chinese GF-1 and ZY-3 satellite images were implemented as the primary data source. We extracted the bankfull river surface and river widths of the Huangfuchuan River by using these two improved methods. The results show that the RF-ANN method has a total river surface extraction accuracy of 94.7%, and the extracted river surfaces cover more than 85% of the order 3 DEM river network. By implementing high-resolution DEM and thermal infrared data, RF-ANN effectively eliminates the disturbance of shadows of mountains and other features, which ensures the high accuracy of the extracted widths. It was verified that the maximum and minimum river widths that can be extracted in the Huangfuchuan River Basin are 297.4 m and 6.1 m, respectively. The overall error of river width extraction is 0.97 m, which is less than half of the pixel length of remote sensing images. The R2 and root mean square error (RMSE) of the estimated river width values are 0.99 and 1.49, respectively. For tiny rivers with widths narrower than 10 m, the error of river width extraction is 10.9%. The error of thin rivers whose widths range from 10 to 30 m is 4.9%. For small rivers ranging from 30 to 90 and rivers wider than 90 m, the error is 1.1% and 0.6%, respectively. The new approach provides an effective method for extracting the surface and width of mountain rivers in topographically complex regions by using high-resolution satellite images, which may provide a database for estimating river carbon emissions and related research in fluvial morphology and water resource management.<\/jats:p>","DOI":"10.3390\/rs14102370","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"2370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Automatic Extraction of Mountain River Surface and Width Based on Multisource High-Resolution Satellite Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuan","family":"Xue","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6474-2826","authenticated-orcid":false,"given":"Chao","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7149-0276","authenticated-orcid":false,"given":"Baosheng","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5444-4366","authenticated-orcid":false,"given":"Dan","family":"Li","sequence":"additional","affiliation":[{"name":"Emergency Science Research Academy, China Coal Research Institute, China Coal Technology and Engineering Group Co., Ltd., Beijing 100013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"147","article-title":"Review of water body information extraction based on satellite remote sensing","volume":"60","author":"Li","year":"2020","journal-title":"J. Tsinghua Univ. (Sci. Technol.)"},{"key":"ref_4","first-page":"1461","article-title":"Water body detection and delineation with Landsat TM data","volume":"66","author":"Frazier","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, D., Wang, G., Qin, C., and Wu, B. (2021). River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data. Remote Sens., 13.","DOI":"10.3390\/rs13142650"},{"key":"ref_6","first-page":"1649","article-title":"Researches of Soil Normalized Difference Water Index (NDWI) of Yongding River Based on Multispectral Remote Sensing Technology Combined with Genetic Algorithm","volume":"34","author":"Mao","year":"2014","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jhydrol.2009.12.016","article-title":"Water volume variations in Lake Izabal (Guatemala) from in situ measurements and ENVISAT Radar Altimeter (RA-2) and Advanced Synthetic Aperture Radar (ASAR) data products","volume":"382","author":"Medina","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_8","unstructured":"Sharma, O., Mioc, D., and Anton, F. (February, January 29). Feature Extraction and Simplification from Colour Images Based on Colour Image Segmentation and Skeletonization using the Quad-Edge data structure. Proceedings of the 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2007 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic. WSCG 2007, Short Communications."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.apsusc.2018.05.147","article-title":"Summary of the Research on Water Body Extraction and Application from Remote Sensing Image","volume":"43","author":"Wang","year":"2018","journal-title":"Sci. Surv. Mapp."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, D., Wu, B., Chen, B., Qin, C., Wang, Y., Zhang, Y., and Xue, Y. (2020). Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River. Remote Sens., 12.","DOI":"10.3390\/rs12172737"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the normalized difference water index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","first-page":"99","article-title":"A fast water information extraction method based on GF-2 remote sensing image","volume":"40","author":"Zou","year":"2019","journal-title":"J. Graph."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/JSTARS.2014.2387196","article-title":"A Simple Enhanced Water Index (EWI) for Percent Surface Water Estimation Using Landsat Data","volume":"8","author":"Wang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Acharya, T., Lee, D., Yang, I., and Lee, J. (2016). Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree. Sensors, 16.","DOI":"10.3390\/s16071075"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A Survey of Decision Tree Classifier Methodology","volume":"21","author":"Safavian","year":"1991","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_16","first-page":"45","article-title":"A study of extraction method of mountain surface water based on OLI data and decision tree method","volume":"26","author":"Zhang","year":"2017","journal-title":"Eng. Surv. Mapp."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1126\/science.aat0636","article-title":"Global extent of rivers and streams","volume":"361","author":"Allen","year":"2018","journal-title":"Science"},{"key":"ref_19","first-page":"144","article-title":"Selection of the best segmentation scale in high-resolution image segmentation","volume":"50","author":"Liu","year":"2014","journal-title":"Comput. Eng. Appl."},{"key":"ref_20","first-page":"1303","article-title":"Comparison and Analysis of Information Extraction Methods of Semiarid Land uti-lization Based on GF1 Image: Taking Jianping as an Example","volume":"36","author":"Ge","year":"2017","journal-title":"Glob. Geol."},{"key":"ref_21","first-page":"51","article-title":"Hierarchical Multi-scale Segmentation of Riverine Wetland Remote Sensing Image","volume":"5","author":"Liu","year":"2016","journal-title":"J. Netw. New Media"},{"key":"ref_22","first-page":"12","article-title":"Automatic extraction of small mountain river information and width based on China-made GF-1 satellites remote sense images","volume":"3","author":"Xue","year":"2020","journal-title":"Bull. Surv. Mapp."},{"key":"ref_23","first-page":"1526","article-title":"Application of red edge band in remote sensing extraction of surface water body: A case study based on GF-6 WFV data in arid area","volume":"52","author":"Lu","year":"2021","journal-title":"Hydrol. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Subedi, A., and Lee, D.H. (2019). Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal. Sensors, 19.","DOI":"10.3390\/s19122769"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_27","unstructured":"Eung, E.M.M., and Tint, T. (2018, January 28\u201330). Ayeyarwady River Regions Detection and Extraction System from Google Earth Imagery. Proceedings of the 2018 IEEE International Conference on Information Communication and Signal Processing (ICICSP) IEEE, Singapore."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Wei, H., Bi, F., Liu, F., Liu, W., Chen, H., and Yu, Y. (2015, January 14\u201316). Water body extraction based on the LBV transformation analysis for China GF-1 multi-spectral images. Proceedings of the IET International Radar Conference 2015, Hangzhou, China."},{"key":"ref_30","first-page":"991","article-title":"A method for continuous extraction of multispectrally classified urban rivers","volume":"66","author":"Zhang","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","unstructured":"Li, L. (2009). Experimental Study of the Temperature Variation Characteristic of Some Typical Ground Objects. [Master\u2019s Thesis, Northeastern University]. (In Chinese)."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.11834\/jrs.20211181","article-title":"Research on land surface temperature downscaling method based on diurnal temperature cycle model deviation coefficient calculation","volume":"25","author":"Wang","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_33","first-page":"102545","article-title":"Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data","volume":"104","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1038\/nature12760","article-title":"Global carbon dioxide emissions from inland waters","volume":"503","author":"Raymond","year":"2014","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1002\/rra.828","article-title":"The effect of altered flow regime on the frequency and duration of bankfull discharge: Murrumbidgee River, Australia","volume":"21","author":"Page","year":"2005","journal-title":"River Res. Appl."},{"key":"ref_36","unstructured":"Qian, N., Zhang, R., and Zhou, Z.D. (1987). Fluvial Processes, China Science Publishing & Media Ltd."},{"key":"ref_37","first-page":"1","article-title":"Watershed Sediment Dynamics and Modeling: A Watershed Modeling System for Yellow River","volume":"Volume 14","author":"Yang","year":"2015","journal-title":"Handbook of Environmental Engineering"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/02626660209492907","article-title":"A hillslope-based hydrological model using catchment area and width functions","volume":"47","author":"Yang","year":"2002","journal-title":"Hydrolog. Sci. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"333","DOI":"10.5194\/hess-15-333-2011","article-title":"Quantifying uncertainty in the impacts of climate change on river discharge in sub-catchments of the Yangtze and Yellow River Basins, China","volume":"15","author":"Xu","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_40","first-page":"642","article-title":"Intra-and Inter-annual Variations in the Relationship Between Suspended Sediment Concentra-tion and Discharge of the Huangfuchuan Watershed","volume":"28","author":"Wang","year":"2020","journal-title":"J. Basic Sci. Eng."},{"key":"ref_41","first-page":"176","article-title":"Generalized Hydraulic Geometry and Multi-frequency Down-stream Hydraulic Geometry of Mountain Rivers Originated from the Qinghai-Tibet Plateau","volume":"53","author":"Qin","year":"2022","journal-title":"J. Hydraul. Eng."},{"key":"ref_42","first-page":"26","article-title":"Development Status and Trend of Satellite Mapping","volume":"39","author":"Tang","year":"2018","journal-title":"Spacecr. Recovery Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0034-4257(98)00010-8","article-title":"Design and analysis for thematic map accuracy assessment: Fundamental principles","volume":"64","author":"Stehman","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"205","article-title":"Evaluation of vertical accuracy of open source Digital Elevation Model (DEM)","volume":"21","author":"Mukherjee","year":"2013","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.geomorph.2019.04.022","article-title":"High-efficient extraction of drainage networks from digital elevation models constrained by enhanced flow enforcement from known river maps","volume":"340","author":"Wu","year":"2019","journal-title":"Geomorphology"},{"key":"ref_46","first-page":"72","article-title":"Evaluation on Elevation Accuracy of Commonly Used DEM in Five Typical Areas of China","volume":"27","author":"Jiang","year":"2020","journal-title":"Res. Soil Water Conserv."},{"key":"ref_47","first-page":"1482","article-title":"Technology and Applications of Surverying and Mapping for ZY-3 Satellites","volume":"46","author":"Tang","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_48","unstructured":"Kohavi, R. (1995, January 20\u201325). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2015.12.055","article-title":"Comparing Landsat water index methods for automated water classification in eastern Australia","volume":"175","author":"Fisher","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/LGRS.2007.908305","article-title":"RivWidth: A software tool for the calculation of river widths from remotely sensed imagery","volume":"5","author":"Pavelsky","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/LGRS.2019.2920225","article-title":"RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction from Remotely Sensed Imagery","volume":"17","author":"Yang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.rse.2017.03.044","article-title":"RivaMap: An automated river analysis and mapping engine","volume":"202","author":"Isikdogan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_53","first-page":"1394","article-title":"Computing Medial Axis Transformations of the Geometric Model","volume":"30","author":"Zhong","year":"2018","journal-title":"J. Comput.-Aided Des. Comput. Graph."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.gmod.2014.03.007","article-title":"Computing a compact spline representation of the medial axis transform of a 2D shape","volume":"76","author":"Zhu","year":"2014","journal-title":"Graph. Models"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.gmod.2005.01.002","article-title":"The \u201c\u03bb-medial axis\u201d","volume":"67","author":"Chazal","year":"2005","journal-title":"Graph. Models"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0022-5193(73)90175-6","article-title":"Biological Shape and Visual Science (Part 1)","volume":"38","author":"Blum","year":"1973","journal-title":"J. Theor. Biol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liao, Z., Wang, Z., and Hu, S. (2008, January 12\u201315). Skeletonize Multi Width Ribbon-like Shapes Based on Difference Images and Frenet Frame. Proceedings of the 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC), Singapore.","DOI":"10.1109\/ICSMC.2008.4811384"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/TIP.2007.891800","article-title":"Wavelet-based approach to character skeleton","volume":"16","author":"You","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/S0304-4076(96)01818-0","article-title":"An R-squared measure of goodness of fit for some common nonlinear regression models","volume":"77","author":"Cameron","year":"1997","journal-title":"J. Econom."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1111\/j.1752-1688.2005.tb03740.x","article-title":"Hydrological modeling of the iroquois river watershed using HSPF and SWAT","volume":"41","author":"Singh","year":"2005","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.agrformet.2006.03.009","article-title":"Bias correction of daily GCM rainfall for crop simulation studies","volume":"138","author":"Ines","year":"2006","journal-title":"Agric. Forest Meteorol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.11834\/jrs.20219268","article-title":"Tracking dynamic river networks in the Tibetan Plateau with high-resolution CubeSat imagery","volume":"25","author":"Zhang","year":"2021","journal-title":"Natl. Remote Sens. Bull."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:43Z","timestamp":1760137843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,14]]},"references-count":63,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102370"],"URL":"https:\/\/doi.org\/10.3390\/rs14102370","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,14]]}}}