Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index
Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can n...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Karimi, Mahshid [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 36(2022), 11 vom: 18. Apr., Seite 3707-3724 |
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| Übergeordnetes Werk: |
volume:36 ; year:2022 ; number:11 ; day:18 ; month:04 ; pages:3707-3724 |
| Links: |
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| DOI / URN: |
10.1007/s00477-022-02220-3 |
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SPR048396168 |
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| 520 | |a Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. | ||
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10.1007/s00477-022-02220-3 doi (DE-627)SPR048396168 (SPR)s00477-022-02220-3-e DE-627 ger DE-627 rakwb eng Karimi, Mahshid verfasserin aut Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. Principal component analysis (dpeaa)DE-He213 TRMM (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Combined drought index (dpeaa)DE-He213 Karkheh (dpeaa)DE-He213 Shahedi, Kaka aut Raziei, Tayeb aut Miryaghoubzadeh, Mirhassan aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2022), 11 vom: 18. Apr., Seite 3707-3724 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2022 number:11 day:18 month:04 pages:3707-3724 https://dx.doi.org/10.1007/s00477-022-02220-3 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_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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 11 18 04 3707-3724 |
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10.1007/s00477-022-02220-3 doi (DE-627)SPR048396168 (SPR)s00477-022-02220-3-e DE-627 ger DE-627 rakwb eng Karimi, Mahshid verfasserin aut Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. Principal component analysis (dpeaa)DE-He213 TRMM (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Combined drought index (dpeaa)DE-He213 Karkheh (dpeaa)DE-He213 Shahedi, Kaka aut Raziei, Tayeb aut Miryaghoubzadeh, Mirhassan aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2022), 11 vom: 18. Apr., Seite 3707-3724 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2022 number:11 day:18 month:04 pages:3707-3724 https://dx.doi.org/10.1007/s00477-022-02220-3 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_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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 11 18 04 3707-3724 |
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10.1007/s00477-022-02220-3 doi (DE-627)SPR048396168 (SPR)s00477-022-02220-3-e DE-627 ger DE-627 rakwb eng Karimi, Mahshid verfasserin aut Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. Principal component analysis (dpeaa)DE-He213 TRMM (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Combined drought index (dpeaa)DE-He213 Karkheh (dpeaa)DE-He213 Shahedi, Kaka aut Raziei, Tayeb aut Miryaghoubzadeh, Mirhassan aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2022), 11 vom: 18. Apr., Seite 3707-3724 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2022 number:11 day:18 month:04 pages:3707-3724 https://dx.doi.org/10.1007/s00477-022-02220-3 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_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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 11 18 04 3707-3724 |
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10.1007/s00477-022-02220-3 doi (DE-627)SPR048396168 (SPR)s00477-022-02220-3-e DE-627 ger DE-627 rakwb eng Karimi, Mahshid verfasserin aut Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. Principal component analysis (dpeaa)DE-He213 TRMM (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Combined drought index (dpeaa)DE-He213 Karkheh (dpeaa)DE-He213 Shahedi, Kaka aut Raziei, Tayeb aut Miryaghoubzadeh, Mirhassan aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2022), 11 vom: 18. Apr., Seite 3707-3724 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2022 number:11 day:18 month:04 pages:3707-3724 https://dx.doi.org/10.1007/s00477-022-02220-3 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_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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 11 18 04 3707-3724 |
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10.1007/s00477-022-02220-3 doi (DE-627)SPR048396168 (SPR)s00477-022-02220-3-e DE-627 ger DE-627 rakwb eng Karimi, Mahshid verfasserin aut Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. Principal component analysis (dpeaa)DE-He213 TRMM (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Combined drought index (dpeaa)DE-He213 Karkheh (dpeaa)DE-He213 Shahedi, Kaka aut Raziei, Tayeb aut Miryaghoubzadeh, Mirhassan aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2022), 11 vom: 18. Apr., Seite 3707-3724 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2022 number:11 day:18 month:04 pages:3707-3724 https://dx.doi.org/10.1007/s00477-022-02220-3 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_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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2022 11 18 04 3707-3724 |
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Enthalten in Stochastic environmental research and risk assessment 36(2022), 11 vom: 18. Apr., Seite 3707-3724 volume:36 year:2022 number:11 day:18 month:04 pages:3707-3724 |
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Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. 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meteorological and agricultural drought monitoring in southwest of iran using a remote sensing-based combined drought index |
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Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index |
| abstract |
Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index |
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https://dx.doi.org/10.1007/s00477-022-02220-3 |
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Shahedi, Kaka Raziei, Tayeb Miryaghoubzadeh, Mirhassan |
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Shahedi, Kaka Raziei, Tayeb Miryaghoubzadeh, Mirhassan |
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10.1007/s00477-022-02220-3 |
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2024-07-03T18:56:20.826Z |
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| score |
7.399047 |