Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake
Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Qian [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of mountain science - Beijing : Science Press, 2004, 19(2022), 11 vom: Nov., Seite 3131-3147 |
|---|---|
| Übergeordnetes Werk: |
volume:19 ; year:2022 ; number:11 ; month:11 ; pages:3131-3147 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11629-022-7553-9 |
|---|
| Katalog-ID: |
SPR051148382 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR051148382 | ||
| 003 | DE-627 | ||
| 005 | 20230509120555.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230508s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11629-022-7553-9 |2 doi | |
| 035 | |a (DE-627)SPR051148382 | ||
| 035 | |a (SPR)s11629-022-7553-9-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wang, Qian |e verfasserin |0 (orcid)0000-0001-9518-7047 |4 aut | |
| 245 | 1 | 0 | |a Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 | ||
| 520 | |a Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. | ||
| 650 | 4 | |a Vegetation recovery |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Enhanced Vegetation Index |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ensemble empirical mode decomposition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Geographically and Temporally Weighted Regression |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wang, Ze-gen |0 (orcid)0000-0002-2831-6176 |4 aut | |
| 700 | 1 | |a Yong, Zhi-wei |0 (orcid)0000-0003-0000-6659 |4 aut | |
| 700 | 1 | |a Zhao, Kai |0 (orcid)0000-0003-0386-5344 |4 aut | |
| 700 | 1 | |a Xiong, Jun-nan |0 (orcid)0000-0001-6997-838X |4 aut | |
| 700 | 1 | |a Du, Xue-mei |0 (orcid)0000-0003-3740-3914 |4 aut | |
| 700 | 1 | |a Zhao, Yang |0 (orcid)0000-0002-7672-8407 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of mountain science |d Beijing : Science Press, 2004 |g 19(2022), 11 vom: Nov., Seite 3131-3147 |w (DE-627)494836954 |w (DE-600)2197632-6 |x 1993-0321 |7 nnns |
| 773 | 1 | 8 | |g volume:19 |g year:2022 |g number:11 |g month:11 |g pages:3131-3147 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s11629-022-7553-9 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_138 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_152 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_187 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_250 | ||
| 912 | |a GBV_ILN_281 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_374 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_636 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2001 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2008 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2010 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2025 | ||
| 912 | |a GBV_ILN_2026 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2031 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2037 | ||
| 912 | |a GBV_ILN_2038 | ||
| 912 | |a GBV_ILN_2039 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2093 | ||
| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2107 | ||
| 912 | |a GBV_ILN_2108 | ||
| 912 | |a GBV_ILN_2110 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2144 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2188 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_2232 | ||
| 912 | |a GBV_ILN_2336 | ||
| 912 | |a GBV_ILN_2446 | ||
| 912 | |a GBV_ILN_2470 | ||
| 912 | |a GBV_ILN_2472 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2522 | ||
| 912 | |a GBV_ILN_2548 | ||
| 912 | |a GBV_ILN_2700 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4246 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4328 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4336 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 19 |j 2022 |e 11 |c 11 |h 3131-3147 | ||
| author_variant |
q w qw z g w zgw z w y zwy k z kz j n x jnx x m d xmd y z yz |
|---|---|
| matchkey_str |
article:19930321:2022----::eeaineoeyrnsnedadmnneflmtcagadnhooeifcosnhsvrldm |
| hierarchy_sort_str |
2022 |
| publishDate |
2022 |
| allfields |
10.1007/s11629-022-7553-9 doi (DE-627)SPR051148382 (SPR)s11629-022-7553-9-e DE-627 ger DE-627 rakwb eng Wang, Qian verfasserin (orcid)0000-0001-9518-7047 aut Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. Vegetation recovery (dpeaa)DE-He213 Enhanced Vegetation Index (dpeaa)DE-He213 Ensemble empirical mode decomposition (dpeaa)DE-He213 Geographically and Temporally Weighted Regression (dpeaa)DE-He213 Wang, Ze-gen (orcid)0000-0002-2831-6176 aut Yong, Zhi-wei (orcid)0000-0003-0000-6659 aut Zhao, Kai (orcid)0000-0003-0386-5344 aut Xiong, Jun-nan (orcid)0000-0001-6997-838X aut Du, Xue-mei (orcid)0000-0003-3740-3914 aut Zhao, Yang (orcid)0000-0002-7672-8407 aut Enthalten in Journal of mountain science Beijing : Science Press, 2004 19(2022), 11 vom: Nov., Seite 3131-3147 (DE-627)494836954 (DE-600)2197632-6 1993-0321 nnns volume:19 year:2022 number:11 month:11 pages:3131-3147 https://dx.doi.org/10.1007/s11629-022-7553-9 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_374 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_2700 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 19 2022 11 11 3131-3147 |
| spelling |
10.1007/s11629-022-7553-9 doi (DE-627)SPR051148382 (SPR)s11629-022-7553-9-e DE-627 ger DE-627 rakwb eng Wang, Qian verfasserin (orcid)0000-0001-9518-7047 aut Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. Vegetation recovery (dpeaa)DE-He213 Enhanced Vegetation Index (dpeaa)DE-He213 Ensemble empirical mode decomposition (dpeaa)DE-He213 Geographically and Temporally Weighted Regression (dpeaa)DE-He213 Wang, Ze-gen (orcid)0000-0002-2831-6176 aut Yong, Zhi-wei (orcid)0000-0003-0000-6659 aut Zhao, Kai (orcid)0000-0003-0386-5344 aut Xiong, Jun-nan (orcid)0000-0001-6997-838X aut Du, Xue-mei (orcid)0000-0003-3740-3914 aut Zhao, Yang (orcid)0000-0002-7672-8407 aut Enthalten in Journal of mountain science Beijing : Science Press, 2004 19(2022), 11 vom: Nov., Seite 3131-3147 (DE-627)494836954 (DE-600)2197632-6 1993-0321 nnns volume:19 year:2022 number:11 month:11 pages:3131-3147 https://dx.doi.org/10.1007/s11629-022-7553-9 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_374 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_2700 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 19 2022 11 11 3131-3147 |
| allfields_unstemmed |
10.1007/s11629-022-7553-9 doi (DE-627)SPR051148382 (SPR)s11629-022-7553-9-e DE-627 ger DE-627 rakwb eng Wang, Qian verfasserin (orcid)0000-0001-9518-7047 aut Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. Vegetation recovery (dpeaa)DE-He213 Enhanced Vegetation Index (dpeaa)DE-He213 Ensemble empirical mode decomposition (dpeaa)DE-He213 Geographically and Temporally Weighted Regression (dpeaa)DE-He213 Wang, Ze-gen (orcid)0000-0002-2831-6176 aut Yong, Zhi-wei (orcid)0000-0003-0000-6659 aut Zhao, Kai (orcid)0000-0003-0386-5344 aut Xiong, Jun-nan (orcid)0000-0001-6997-838X aut Du, Xue-mei (orcid)0000-0003-3740-3914 aut Zhao, Yang (orcid)0000-0002-7672-8407 aut Enthalten in Journal of mountain science Beijing : Science Press, 2004 19(2022), 11 vom: Nov., Seite 3131-3147 (DE-627)494836954 (DE-600)2197632-6 1993-0321 nnns volume:19 year:2022 number:11 month:11 pages:3131-3147 https://dx.doi.org/10.1007/s11629-022-7553-9 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_374 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_2700 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 19 2022 11 11 3131-3147 |
| allfieldsGer |
10.1007/s11629-022-7553-9 doi (DE-627)SPR051148382 (SPR)s11629-022-7553-9-e DE-627 ger DE-627 rakwb eng Wang, Qian verfasserin (orcid)0000-0001-9518-7047 aut Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. Vegetation recovery (dpeaa)DE-He213 Enhanced Vegetation Index (dpeaa)DE-He213 Ensemble empirical mode decomposition (dpeaa)DE-He213 Geographically and Temporally Weighted Regression (dpeaa)DE-He213 Wang, Ze-gen (orcid)0000-0002-2831-6176 aut Yong, Zhi-wei (orcid)0000-0003-0000-6659 aut Zhao, Kai (orcid)0000-0003-0386-5344 aut Xiong, Jun-nan (orcid)0000-0001-6997-838X aut Du, Xue-mei (orcid)0000-0003-3740-3914 aut Zhao, Yang (orcid)0000-0002-7672-8407 aut Enthalten in Journal of mountain science Beijing : Science Press, 2004 19(2022), 11 vom: Nov., Seite 3131-3147 (DE-627)494836954 (DE-600)2197632-6 1993-0321 nnns volume:19 year:2022 number:11 month:11 pages:3131-3147 https://dx.doi.org/10.1007/s11629-022-7553-9 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_374 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_2700 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 19 2022 11 11 3131-3147 |
| allfieldsSound |
10.1007/s11629-022-7553-9 doi (DE-627)SPR051148382 (SPR)s11629-022-7553-9-e DE-627 ger DE-627 rakwb eng Wang, Qian verfasserin (orcid)0000-0001-9518-7047 aut Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. Vegetation recovery (dpeaa)DE-He213 Enhanced Vegetation Index (dpeaa)DE-He213 Ensemble empirical mode decomposition (dpeaa)DE-He213 Geographically and Temporally Weighted Regression (dpeaa)DE-He213 Wang, Ze-gen (orcid)0000-0002-2831-6176 aut Yong, Zhi-wei (orcid)0000-0003-0000-6659 aut Zhao, Kai (orcid)0000-0003-0386-5344 aut Xiong, Jun-nan (orcid)0000-0001-6997-838X aut Du, Xue-mei (orcid)0000-0003-3740-3914 aut Zhao, Yang (orcid)0000-0002-7672-8407 aut Enthalten in Journal of mountain science Beijing : Science Press, 2004 19(2022), 11 vom: Nov., Seite 3131-3147 (DE-627)494836954 (DE-600)2197632-6 1993-0321 nnns volume:19 year:2022 number:11 month:11 pages:3131-3147 https://dx.doi.org/10.1007/s11629-022-7553-9 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_374 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_2700 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 19 2022 11 11 3131-3147 |
| language |
English |
| source |
Enthalten in Journal of mountain science 19(2022), 11 vom: Nov., Seite 3131-3147 volume:19 year:2022 number:11 month:11 pages:3131-3147 |
| sourceStr |
Enthalten in Journal of mountain science 19(2022), 11 vom: Nov., Seite 3131-3147 volume:19 year:2022 number:11 month:11 pages:3131-3147 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Vegetation recovery Enhanced Vegetation Index Ensemble empirical mode decomposition Geographically and Temporally Weighted Regression |
| isfreeaccess_bool |
false |
| container_title |
Journal of mountain science |
| authorswithroles_txt_mv |
Wang, Qian @@aut@@ Wang, Ze-gen @@aut@@ Yong, Zhi-wei @@aut@@ Zhao, Kai @@aut@@ Xiong, Jun-nan @@aut@@ Du, Xue-mei @@aut@@ Zhao, Yang @@aut@@ |
| publishDateDaySort_date |
2022-11-01T00:00:00Z |
| hierarchy_top_id |
494836954 |
| id |
SPR051148382 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR051148382</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509120555.0</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/s11629-022-7553-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051148382</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11629-022-7553-9-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">Wang, Qian</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-9518-7047</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake</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 Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vegetation recovery</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Enhanced Vegetation Index</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ensemble empirical mode decomposition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Geographically and Temporally Weighted Regression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Ze-gen</subfield><subfield code="0">(orcid)0000-0002-2831-6176</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yong, Zhi-wei</subfield><subfield code="0">(orcid)0000-0003-0000-6659</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Kai</subfield><subfield code="0">(orcid)0000-0003-0386-5344</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiong, Jun-nan</subfield><subfield code="0">(orcid)0000-0001-6997-838X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Xue-mei</subfield><subfield code="0">(orcid)0000-0003-3740-3914</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Yang</subfield><subfield code="0">(orcid)0000-0002-7672-8407</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of mountain science</subfield><subfield code="d">Beijing : Science Press, 2004</subfield><subfield code="g">19(2022), 11 vom: Nov., Seite 3131-3147</subfield><subfield code="w">(DE-627)494836954</subfield><subfield code="w">(DE-600)2197632-6</subfield><subfield code="x">1993-0321</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:19</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:11</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:3131-3147</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11629-022-7553-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">19</subfield><subfield code="j">2022</subfield><subfield code="e">11</subfield><subfield code="c">11</subfield><subfield code="h">3131-3147</subfield></datafield></record></collection>
|
| author |
Wang, Qian |
| spellingShingle |
Wang, Qian misc Vegetation recovery misc Enhanced Vegetation Index misc Ensemble empirical mode decomposition misc Geographically and Temporally Weighted Regression Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake |
| authorStr |
Wang, Qian |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)494836954 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1993-0321 |
| topic_title |
Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake Vegetation recovery (dpeaa)DE-He213 Enhanced Vegetation Index (dpeaa)DE-He213 Ensemble empirical mode decomposition (dpeaa)DE-He213 Geographically and Temporally Weighted Regression (dpeaa)DE-He213 |
| topic |
misc Vegetation recovery misc Enhanced Vegetation Index misc Ensemble empirical mode decomposition misc Geographically and Temporally Weighted Regression |
| topic_unstemmed |
misc Vegetation recovery misc Enhanced Vegetation Index misc Ensemble empirical mode decomposition misc Geographically and Temporally Weighted Regression |
| topic_browse |
misc Vegetation recovery misc Enhanced Vegetation Index misc Ensemble empirical mode decomposition misc Geographically and Temporally Weighted Regression |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Journal of mountain science |
| hierarchy_parent_id |
494836954 |
| hierarchy_top_title |
Journal of mountain science |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)494836954 (DE-600)2197632-6 |
| title |
Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake |
| ctrlnum |
(DE-627)SPR051148382 (SPR)s11629-022-7553-9-e |
| title_full |
Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake |
| author_sort |
Wang, Qian |
| journal |
Journal of mountain science |
| journalStr |
Journal of mountain science |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
txt |
| container_start_page |
3131 |
| author_browse |
Wang, Qian Wang, Ze-gen Yong, Zhi-wei Zhao, Kai Xiong, Jun-nan Du, Xue-mei Zhao, Yang |
| container_volume |
19 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Wang, Qian |
| doi_str_mv |
10.1007/s11629-022-7553-9 |
| normlink |
(ORCID)0000-0001-9518-7047 (ORCID)0000-0002-2831-6176 (ORCID)0000-0003-0000-6659 (ORCID)0000-0003-0386-5344 (ORCID)0000-0001-6997-838X (ORCID)0000-0003-3740-3914 (ORCID)0000-0002-7672-8407 |
| normlink_prefix_str_mv |
(orcid)0000-0001-9518-7047 (orcid)0000-0002-2831-6176 (orcid)0000-0003-0000-6659 (orcid)0000-0003-0386-5344 (orcid)0000-0001-6997-838X (orcid)0000-0003-3740-3914 (orcid)0000-0002-7672-8407 |
| title_sort |
vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the wenchuan earthquake |
| title_auth |
Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake |
| abstract |
Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. © Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| collection_details |
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_374 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_2700 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 |
| container_issue |
11 |
| title_short |
Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake |
| url |
https://dx.doi.org/10.1007/s11629-022-7553-9 |
| remote_bool |
true |
| author2 |
Wang, Ze-gen Yong, Zhi-wei Zhao, Kai Xiong, Jun-nan Du, Xue-mei Zhao, Yang |
| author2Str |
Wang, Ze-gen Yong, Zhi-wei Zhao, Kai Xiong, Jun-nan Du, Xue-mei Zhao, Yang |
| ppnlink |
494836954 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11629-022-7553-9 |
| up_date |
2024-07-03T20:05:02.640Z |
| _version_ |
1803589624946229248 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR051148382</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509120555.0</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/s11629-022-7553-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051148382</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11629-022-7553-9-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">Wang, Qian</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-9518-7047</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake</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 Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vegetation recovery</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Enhanced Vegetation Index</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ensemble empirical mode decomposition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Geographically and Temporally Weighted Regression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Ze-gen</subfield><subfield code="0">(orcid)0000-0002-2831-6176</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yong, Zhi-wei</subfield><subfield code="0">(orcid)0000-0003-0000-6659</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Kai</subfield><subfield code="0">(orcid)0000-0003-0386-5344</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiong, Jun-nan</subfield><subfield code="0">(orcid)0000-0001-6997-838X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Xue-mei</subfield><subfield code="0">(orcid)0000-0003-3740-3914</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Yang</subfield><subfield code="0">(orcid)0000-0002-7672-8407</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of mountain science</subfield><subfield code="d">Beijing : Science Press, 2004</subfield><subfield code="g">19(2022), 11 vom: Nov., Seite 3131-3147</subfield><subfield code="w">(DE-627)494836954</subfield><subfield code="w">(DE-600)2197632-6</subfield><subfield code="x">1993-0321</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:19</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:11</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:3131-3147</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11629-022-7553-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">19</subfield><subfield code="j">2022</subfield><subfield code="e">11</subfield><subfield code="c">11</subfield><subfield code="h">3131-3147</subfield></datafield></record></collection>
|
| score |
7.4001703 |