Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing
Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liao, Weihua [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
remote sensing eco-environment remote sensing information granules |
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| Anmerkung: |
© Science in China Press 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of geographical sciences - Beijing : Science Press, 2001, 32(2022), 9 vom: 26. Aug., Seite 1813-1830 |
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| Übergeordnetes Werk: |
volume:32 ; year:2022 ; number:9 ; day:26 ; month:08 ; pages:1813-1830 |
| Links: |
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| DOI / URN: |
10.1007/s11442-022-2024-3 |
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| Katalog-ID: |
SPR050946684 |
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| 245 | 1 | 0 | |a Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing |
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| 520 | |a Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. | ||
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| 650 | 4 | |a remote sensing information granulation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Guangxi Beibu Gulf Economic Zone |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Jiang, Weiguo |4 aut | |
| 700 | 1 | |a Huang, Ziqian |4 aut | |
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10.1007/s11442-022-2024-3 doi (DE-627)SPR050946684 (SPR)s11442-022-2024-3-e DE-627 ger DE-627 rakwb eng Liao, Weihua verfasserin aut Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science in China Press 2022 Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. remote sensing eco-environment (dpeaa)DE-He213 spatiotemporal change (dpeaa)DE-He213 remote sensing information granules (dpeaa)DE-He213 remote sensing information granulation (dpeaa)DE-He213 Guangxi Beibu Gulf Economic Zone (dpeaa)DE-He213 Jiang, Weiguo aut Huang, Ziqian aut Enthalten in Journal of geographical sciences Beijing : Science Press, 2001 32(2022), 9 vom: 26. Aug., Seite 1813-1830 (DE-627)509402275 (DE-600)2227441-8 1861-9568 nnns volume:32 year:2022 number:9 day:26 month:08 pages:1813-1830 https://dx.doi.org/10.1007/s11442-022-2024-3 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_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_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_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_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_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_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_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 32 2022 9 26 08 1813-1830 |
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10.1007/s11442-022-2024-3 doi (DE-627)SPR050946684 (SPR)s11442-022-2024-3-e DE-627 ger DE-627 rakwb eng Liao, Weihua verfasserin aut Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science in China Press 2022 Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. remote sensing eco-environment (dpeaa)DE-He213 spatiotemporal change (dpeaa)DE-He213 remote sensing information granules (dpeaa)DE-He213 remote sensing information granulation (dpeaa)DE-He213 Guangxi Beibu Gulf Economic Zone (dpeaa)DE-He213 Jiang, Weiguo aut Huang, Ziqian aut Enthalten in Journal of geographical sciences Beijing : Science Press, 2001 32(2022), 9 vom: 26. Aug., Seite 1813-1830 (DE-627)509402275 (DE-600)2227441-8 1861-9568 nnns volume:32 year:2022 number:9 day:26 month:08 pages:1813-1830 https://dx.doi.org/10.1007/s11442-022-2024-3 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_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_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_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_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_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_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_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 32 2022 9 26 08 1813-1830 |
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10.1007/s11442-022-2024-3 doi (DE-627)SPR050946684 (SPR)s11442-022-2024-3-e DE-627 ger DE-627 rakwb eng Liao, Weihua verfasserin aut Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science in China Press 2022 Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. remote sensing eco-environment (dpeaa)DE-He213 spatiotemporal change (dpeaa)DE-He213 remote sensing information granules (dpeaa)DE-He213 remote sensing information granulation (dpeaa)DE-He213 Guangxi Beibu Gulf Economic Zone (dpeaa)DE-He213 Jiang, Weiguo aut Huang, Ziqian aut Enthalten in Journal of geographical sciences Beijing : Science Press, 2001 32(2022), 9 vom: 26. Aug., Seite 1813-1830 (DE-627)509402275 (DE-600)2227441-8 1861-9568 nnns volume:32 year:2022 number:9 day:26 month:08 pages:1813-1830 https://dx.doi.org/10.1007/s11442-022-2024-3 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_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_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_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_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_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_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_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 32 2022 9 26 08 1813-1830 |
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10.1007/s11442-022-2024-3 doi (DE-627)SPR050946684 (SPR)s11442-022-2024-3-e DE-627 ger DE-627 rakwb eng Liao, Weihua verfasserin aut Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science in China Press 2022 Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. remote sensing eco-environment (dpeaa)DE-He213 spatiotemporal change (dpeaa)DE-He213 remote sensing information granules (dpeaa)DE-He213 remote sensing information granulation (dpeaa)DE-He213 Guangxi Beibu Gulf Economic Zone (dpeaa)DE-He213 Jiang, Weiguo aut Huang, Ziqian aut Enthalten in Journal of geographical sciences Beijing : Science Press, 2001 32(2022), 9 vom: 26. Aug., Seite 1813-1830 (DE-627)509402275 (DE-600)2227441-8 1861-9568 nnns volume:32 year:2022 number:9 day:26 month:08 pages:1813-1830 https://dx.doi.org/10.1007/s11442-022-2024-3 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_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_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_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_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_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_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_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 32 2022 9 26 08 1813-1830 |
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10.1007/s11442-022-2024-3 doi (DE-627)SPR050946684 (SPR)s11442-022-2024-3-e DE-627 ger DE-627 rakwb eng Liao, Weihua verfasserin aut Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science in China Press 2022 Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. remote sensing eco-environment (dpeaa)DE-He213 spatiotemporal change (dpeaa)DE-He213 remote sensing information granules (dpeaa)DE-He213 remote sensing information granulation (dpeaa)DE-He213 Guangxi Beibu Gulf Economic Zone (dpeaa)DE-He213 Jiang, Weiguo aut Huang, Ziqian aut Enthalten in Journal of geographical sciences Beijing : Science Press, 2001 32(2022), 9 vom: 26. Aug., Seite 1813-1830 (DE-627)509402275 (DE-600)2227441-8 1861-9568 nnns volume:32 year:2022 number:9 day:26 month:08 pages:1813-1830 https://dx.doi.org/10.1007/s11442-022-2024-3 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_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_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_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_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_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_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_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 32 2022 9 26 08 1813-1830 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR050946684</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509110446.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230508s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11442-022-2024-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR050946684</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11442-022-2024-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liao, Weihua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Science in China Press 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. 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Liao, Weihua |
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Liao, Weihua misc remote sensing eco-environment misc spatiotemporal change misc remote sensing information granules misc remote sensing information granulation misc Guangxi Beibu Gulf Economic Zone Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing |
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Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing remote sensing eco-environment (dpeaa)DE-He213 spatiotemporal change (dpeaa)DE-He213 remote sensing information granules (dpeaa)DE-He213 remote sensing information granulation (dpeaa)DE-He213 Guangxi Beibu Gulf Economic Zone (dpeaa)DE-He213 |
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misc remote sensing eco-environment misc spatiotemporal change misc remote sensing information granules misc remote sensing information granulation misc Guangxi Beibu Gulf Economic Zone |
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spatiotemporal variations of eco-environment in the guangxi beibu gulf economic zone based on remote sensing ecological index and granular computing |
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Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing |
| abstract |
Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. © Science in China Press 2022 |
| abstractGer |
Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. © Science in China Press 2022 |
| abstract_unstemmed |
Abstract Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001–2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. © Science in China Press 2022 |
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| title_short |
Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing |
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https://dx.doi.org/10.1007/s11442-022-2024-3 |
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Jiang, Weiguo Huang, Ziqian |
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Jiang, Weiguo Huang, Ziqian |
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10.1007/s11442-022-2024-3 |
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2024-07-03T18:48:27.211Z |
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|
| score |
7.39894 |