New automated method for extracting river information using optimized spectral threshold water index
Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and bui...
Ausführliche Beschreibung
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| Autor*in: |
Li, Chaojun [verfasserIn] Wang, Shijie [verfasserIn] Bai, Xiaoyong [verfasserIn] Tan, Qiu [verfasserIn] Yang, Yujie [verfasserIn] Li, Qin [verfasserIn] Wu, Luhua [verfasserIn] Xiao, Jianyong [verfasserIn] Qian, Qinghuan [verfasserIn] Chen, Fei [verfasserIn] Li, Huiwen [verfasserIn] Cao, Yue [verfasserIn] Wang, Mingming [verfasserIn] Wang, Jinfeng [verfasserIn] Tian, Shiqi [verfasserIn] Lu, Qian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 12(2018), 1 vom: 31. Dez. |
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| Übergeordnetes Werk: |
volume:12 ; year:2018 ; number:1 ; day:31 ; month:12 |
| Links: |
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| DOI / URN: |
10.1007/s12517-018-4124-z |
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| Katalog-ID: |
SPR025974378 |
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| 520 | |a Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. | ||
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| 650 | 4 | |a River buffer zone |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Building removal |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wang, Shijie |e verfasserin |4 aut | |
| 700 | 1 | |a Bai, Xiaoyong |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Qiu |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yujie |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Qin |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Luhua |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Jianyong |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Qinghuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Huiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Yue |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Mingming |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jinfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Shiqi |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Qian |e verfasserin |4 aut | |
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10.1007/s12517-018-4124-z doi (DE-627)SPR025974378 (SPR)s12517-018-4124-z-e DE-627 ger DE-627 rakwb eng 550 ASE Li, Chaojun verfasserin aut New automated method for extracting river information using optimized spectral threshold water index 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. Landsat8 (dpeaa)DE-He213 Digital elevation model (dpeaa)DE-He213 Mountain shadow (dpeaa)DE-He213 River buffer zone (dpeaa)DE-He213 Building removal (dpeaa)DE-He213 Wang, Shijie verfasserin aut Bai, Xiaoyong verfasserin aut Tan, Qiu verfasserin aut Yang, Yujie verfasserin aut Li, Qin verfasserin aut Wu, Luhua verfasserin aut Xiao, Jianyong verfasserin aut Qian, Qinghuan verfasserin aut Chen, Fei verfasserin aut Li, Huiwen verfasserin aut Cao, Yue verfasserin aut Wang, Mingming verfasserin aut Wang, Jinfeng verfasserin aut Tian, Shiqi verfasserin aut Lu, Qian verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 12(2018), 1 vom: 31. Dez. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:12 year:2018 number:1 day:31 month:12 https://dx.doi.org/10.1007/s12517-018-4124-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2018 1 31 12 |
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10.1007/s12517-018-4124-z doi (DE-627)SPR025974378 (SPR)s12517-018-4124-z-e DE-627 ger DE-627 rakwb eng 550 ASE Li, Chaojun verfasserin aut New automated method for extracting river information using optimized spectral threshold water index 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. Landsat8 (dpeaa)DE-He213 Digital elevation model (dpeaa)DE-He213 Mountain shadow (dpeaa)DE-He213 River buffer zone (dpeaa)DE-He213 Building removal (dpeaa)DE-He213 Wang, Shijie verfasserin aut Bai, Xiaoyong verfasserin aut Tan, Qiu verfasserin aut Yang, Yujie verfasserin aut Li, Qin verfasserin aut Wu, Luhua verfasserin aut Xiao, Jianyong verfasserin aut Qian, Qinghuan verfasserin aut Chen, Fei verfasserin aut Li, Huiwen verfasserin aut Cao, Yue verfasserin aut Wang, Mingming verfasserin aut Wang, Jinfeng verfasserin aut Tian, Shiqi verfasserin aut Lu, Qian verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 12(2018), 1 vom: 31. Dez. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:12 year:2018 number:1 day:31 month:12 https://dx.doi.org/10.1007/s12517-018-4124-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2018 1 31 12 |
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10.1007/s12517-018-4124-z doi (DE-627)SPR025974378 (SPR)s12517-018-4124-z-e DE-627 ger DE-627 rakwb eng 550 ASE Li, Chaojun verfasserin aut New automated method for extracting river information using optimized spectral threshold water index 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. Landsat8 (dpeaa)DE-He213 Digital elevation model (dpeaa)DE-He213 Mountain shadow (dpeaa)DE-He213 River buffer zone (dpeaa)DE-He213 Building removal (dpeaa)DE-He213 Wang, Shijie verfasserin aut Bai, Xiaoyong verfasserin aut Tan, Qiu verfasserin aut Yang, Yujie verfasserin aut Li, Qin verfasserin aut Wu, Luhua verfasserin aut Xiao, Jianyong verfasserin aut Qian, Qinghuan verfasserin aut Chen, Fei verfasserin aut Li, Huiwen verfasserin aut Cao, Yue verfasserin aut Wang, Mingming verfasserin aut Wang, Jinfeng verfasserin aut Tian, Shiqi verfasserin aut Lu, Qian verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 12(2018), 1 vom: 31. Dez. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:12 year:2018 number:1 day:31 month:12 https://dx.doi.org/10.1007/s12517-018-4124-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2018 1 31 12 |
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10.1007/s12517-018-4124-z doi (DE-627)SPR025974378 (SPR)s12517-018-4124-z-e DE-627 ger DE-627 rakwb eng 550 ASE Li, Chaojun verfasserin aut New automated method for extracting river information using optimized spectral threshold water index 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. Landsat8 (dpeaa)DE-He213 Digital elevation model (dpeaa)DE-He213 Mountain shadow (dpeaa)DE-He213 River buffer zone (dpeaa)DE-He213 Building removal (dpeaa)DE-He213 Wang, Shijie verfasserin aut Bai, Xiaoyong verfasserin aut Tan, Qiu verfasserin aut Yang, Yujie verfasserin aut Li, Qin verfasserin aut Wu, Luhua verfasserin aut Xiao, Jianyong verfasserin aut Qian, Qinghuan verfasserin aut Chen, Fei verfasserin aut Li, Huiwen verfasserin aut Cao, Yue verfasserin aut Wang, Mingming verfasserin aut Wang, Jinfeng verfasserin aut Tian, Shiqi verfasserin aut Lu, Qian verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 12(2018), 1 vom: 31. Dez. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:12 year:2018 number:1 day:31 month:12 https://dx.doi.org/10.1007/s12517-018-4124-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2018 1 31 12 |
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10.1007/s12517-018-4124-z doi (DE-627)SPR025974378 (SPR)s12517-018-4124-z-e DE-627 ger DE-627 rakwb eng 550 ASE Li, Chaojun verfasserin aut New automated method for extracting river information using optimized spectral threshold water index 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. Landsat8 (dpeaa)DE-He213 Digital elevation model (dpeaa)DE-He213 Mountain shadow (dpeaa)DE-He213 River buffer zone (dpeaa)DE-He213 Building removal (dpeaa)DE-He213 Wang, Shijie verfasserin aut Bai, Xiaoyong verfasserin aut Tan, Qiu verfasserin aut Yang, Yujie verfasserin aut Li, Qin verfasserin aut Wu, Luhua verfasserin aut Xiao, Jianyong verfasserin aut Qian, Qinghuan verfasserin aut Chen, Fei verfasserin aut Li, Huiwen verfasserin aut Cao, Yue verfasserin aut Wang, Mingming verfasserin aut Wang, Jinfeng verfasserin aut Tian, Shiqi verfasserin aut Lu, Qian verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 12(2018), 1 vom: 31. Dez. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:12 year:2018 number:1 day:31 month:12 https://dx.doi.org/10.1007/s12517-018-4124-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2018 1 31 12 |
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English |
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Enthalten in Arabian journal of geosciences 12(2018), 1 vom: 31. Dez. volume:12 year:2018 number:1 day:31 month:12 |
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Enthalten in Arabian journal of geosciences 12(2018), 1 vom: 31. Dez. volume:12 year:2018 number:1 day:31 month:12 |
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Article |
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Landsat8 Digital elevation model Mountain shadow River buffer zone Building removal |
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Arabian journal of geosciences |
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Li, Chaojun @@aut@@ Wang, Shijie @@aut@@ Bai, Xiaoyong @@aut@@ Tan, Qiu @@aut@@ Yang, Yujie @@aut@@ Li, Qin @@aut@@ Wu, Luhua @@aut@@ Xiao, Jianyong @@aut@@ Qian, Qinghuan @@aut@@ Chen, Fei @@aut@@ Li, Huiwen @@aut@@ Cao, Yue @@aut@@ Wang, Mingming @@aut@@ Wang, Jinfeng @@aut@@ Tian, Shiqi @@aut@@ Lu, Qian @@aut@@ |
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2018-12-31T00:00:00Z |
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However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. 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| author |
Li, Chaojun |
| spellingShingle |
Li, Chaojun ddc 550 misc Landsat8 misc Digital elevation model misc Mountain shadow misc River buffer zone misc Building removal New automated method for extracting river information using optimized spectral threshold water index |
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Li, Chaojun |
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| topic_title |
550 ASE New automated method for extracting river information using optimized spectral threshold water index Landsat8 (dpeaa)DE-He213 Digital elevation model (dpeaa)DE-He213 Mountain shadow (dpeaa)DE-He213 River buffer zone (dpeaa)DE-He213 Building removal (dpeaa)DE-He213 |
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ddc 550 misc Landsat8 misc Digital elevation model misc Mountain shadow misc River buffer zone misc Building removal |
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ddc 550 misc Landsat8 misc Digital elevation model misc Mountain shadow misc River buffer zone misc Building removal |
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ddc 550 misc Landsat8 misc Digital elevation model misc Mountain shadow misc River buffer zone misc Building removal |
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New automated method for extracting river information using optimized spectral threshold water index |
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| title_full |
New automated method for extracting river information using optimized spectral threshold water index |
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Li, Chaojun |
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Arabian journal of geosciences |
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Arabian journal of geosciences |
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Li, Chaojun Wang, Shijie Bai, Xiaoyong Tan, Qiu Yang, Yujie Li, Qin Wu, Luhua Xiao, Jianyong Qian, Qinghuan Chen, Fei Li, Huiwen Cao, Yue Wang, Mingming Wang, Jinfeng Tian, Shiqi Lu, Qian |
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Li, Chaojun |
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verfasserin |
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new automated method for extracting river information using optimized spectral threshold water index |
| title_auth |
New automated method for extracting river information using optimized spectral threshold water index |
| abstract |
Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. |
| abstractGer |
Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. |
| abstract_unstemmed |
Abstract The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings. |
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| container_issue |
1 |
| title_short |
New automated method for extracting river information using optimized spectral threshold water index |
| url |
https://dx.doi.org/10.1007/s12517-018-4124-z |
| remote_bool |
true |
| author2 |
Wang, Shijie Bai, Xiaoyong Tan, Qiu Yang, Yujie Li, Qin Wu, Luhua Xiao, Jianyong Qian, Qinghuan Chen, Fei Li, Huiwen Cao, Yue Wang, Mingming Wang, Jinfeng Tian, Shiqi Lu, Qian |
| author2Str |
Wang, Shijie Bai, Xiaoyong Tan, Qiu Yang, Yujie Li, Qin Wu, Luhua Xiao, Jianyong Qian, Qinghuan Chen, Fei Li, Huiwen Cao, Yue Wang, Mingming Wang, Jinfeng Tian, Shiqi Lu, Qian |
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572421877 |
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c |
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| doi_str |
10.1007/s12517-018-4124-z |
| up_date |
2024-07-03T18:06:06.233Z |
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1803582141896851456 |
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| score |
7.4006395 |