Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors
Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed tha...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shifaw, Eshetu [verfasserIn] Sha, Jinming [verfasserIn] Li, Xiaomei [verfasserIn] Bao, Zhongcong [verfasserIn] Legass, Asmamaw [verfasserIn] Belete, Marye [verfasserIn] Ji, Jianwan [verfasserIn] Su, Yung-Chih [verfasserIn] Addis, Amsalu K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Environmental earth sciences - Berlin : Springer, 2009, 78(2019), 17 vom: 19. Aug. |
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| Übergeordnetes Werk: |
volume:78 ; year:2019 ; number:17 ; day:19 ; month:08 |
| Links: |
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| DOI / URN: |
10.1007/s12665-019-8537-0 |
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| Katalog-ID: |
SPR026753243 |
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| 520 | |a Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. | ||
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| 700 | 1 | |a Su, Yung-Chih |e verfasserin |4 aut | |
| 700 | 1 | |a Addis, Amsalu K. |e verfasserin |4 aut | |
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10.1007/s12665-019-8537-0 doi (DE-627)SPR026753243 (SPR)s12665-019-8537-0-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Shifaw, Eshetu verfasserin aut Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. Farmland (dpeaa)DE-He213 Hotspot (dpeaa)DE-He213 Transitions (dpeaa)DE-He213 Fragmentation metrics (dpeaa)DE-He213 PLSR model (dpeaa)DE-He213 Pingtan Island (dpeaa)DE-He213 Sha, Jinming verfasserin aut Li, Xiaomei verfasserin aut Bao, Zhongcong verfasserin aut Legass, Asmamaw verfasserin aut Belete, Marye verfasserin aut Ji, Jianwan verfasserin aut Su, Yung-Chih verfasserin aut Addis, Amsalu K. verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 78(2019), 17 vom: 19. Aug. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:78 year:2019 number:17 day:19 month:08 https://dx.doi.org/10.1007/s12665-019-8537-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 ASE AR 78 2019 17 19 08 |
| spelling |
10.1007/s12665-019-8537-0 doi (DE-627)SPR026753243 (SPR)s12665-019-8537-0-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Shifaw, Eshetu verfasserin aut Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. Farmland (dpeaa)DE-He213 Hotspot (dpeaa)DE-He213 Transitions (dpeaa)DE-He213 Fragmentation metrics (dpeaa)DE-He213 PLSR model (dpeaa)DE-He213 Pingtan Island (dpeaa)DE-He213 Sha, Jinming verfasserin aut Li, Xiaomei verfasserin aut Bao, Zhongcong verfasserin aut Legass, Asmamaw verfasserin aut Belete, Marye verfasserin aut Ji, Jianwan verfasserin aut Su, Yung-Chih verfasserin aut Addis, Amsalu K. verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 78(2019), 17 vom: 19. Aug. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:78 year:2019 number:17 day:19 month:08 https://dx.doi.org/10.1007/s12665-019-8537-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 ASE AR 78 2019 17 19 08 |
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10.1007/s12665-019-8537-0 doi (DE-627)SPR026753243 (SPR)s12665-019-8537-0-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Shifaw, Eshetu verfasserin aut Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. Farmland (dpeaa)DE-He213 Hotspot (dpeaa)DE-He213 Transitions (dpeaa)DE-He213 Fragmentation metrics (dpeaa)DE-He213 PLSR model (dpeaa)DE-He213 Pingtan Island (dpeaa)DE-He213 Sha, Jinming verfasserin aut Li, Xiaomei verfasserin aut Bao, Zhongcong verfasserin aut Legass, Asmamaw verfasserin aut Belete, Marye verfasserin aut Ji, Jianwan verfasserin aut Su, Yung-Chih verfasserin aut Addis, Amsalu K. verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 78(2019), 17 vom: 19. Aug. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:78 year:2019 number:17 day:19 month:08 https://dx.doi.org/10.1007/s12665-019-8537-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 ASE AR 78 2019 17 19 08 |
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10.1007/s12665-019-8537-0 doi (DE-627)SPR026753243 (SPR)s12665-019-8537-0-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Shifaw, Eshetu verfasserin aut Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. Farmland (dpeaa)DE-He213 Hotspot (dpeaa)DE-He213 Transitions (dpeaa)DE-He213 Fragmentation metrics (dpeaa)DE-He213 PLSR model (dpeaa)DE-He213 Pingtan Island (dpeaa)DE-He213 Sha, Jinming verfasserin aut Li, Xiaomei verfasserin aut Bao, Zhongcong verfasserin aut Legass, Asmamaw verfasserin aut Belete, Marye verfasserin aut Ji, Jianwan verfasserin aut Su, Yung-Chih verfasserin aut Addis, Amsalu K. verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 78(2019), 17 vom: 19. Aug. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:78 year:2019 number:17 day:19 month:08 https://dx.doi.org/10.1007/s12665-019-8537-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 ASE AR 78 2019 17 19 08 |
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10.1007/s12665-019-8537-0 doi (DE-627)SPR026753243 (SPR)s12665-019-8537-0-e DE-627 ger DE-627 rakwb eng 550 ASE 38.95 bkl Shifaw, Eshetu verfasserin aut Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. Farmland (dpeaa)DE-He213 Hotspot (dpeaa)DE-He213 Transitions (dpeaa)DE-He213 Fragmentation metrics (dpeaa)DE-He213 PLSR model (dpeaa)DE-He213 Pingtan Island (dpeaa)DE-He213 Sha, Jinming verfasserin aut Li, Xiaomei verfasserin aut Bao, Zhongcong verfasserin aut Legass, Asmamaw verfasserin aut Belete, Marye verfasserin aut Ji, Jianwan verfasserin aut Su, Yung-Chih verfasserin aut Addis, Amsalu K. verfasserin aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 78(2019), 17 vom: 19. Aug. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:78 year:2019 number:17 day:19 month:08 https://dx.doi.org/10.1007/s12665-019-8537-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 ASE AR 78 2019 17 19 08 |
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Enthalten in Environmental earth sciences 78(2019), 17 vom: 19. Aug. volume:78 year:2019 number:17 day:19 month:08 |
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Farmland Hotspot Transitions Fragmentation metrics PLSR model Pingtan Island |
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Shifaw, Eshetu @@aut@@ Sha, Jinming @@aut@@ Li, Xiaomei @@aut@@ Bao, Zhongcong @@aut@@ Legass, Asmamaw @@aut@@ Belete, Marye @@aut@@ Ji, Jianwan @@aut@@ Su, Yung-Chih @@aut@@ Addis, Amsalu K. @@aut@@ |
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Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. 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Shifaw, Eshetu |
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Shifaw, Eshetu Sha, Jinming Li, Xiaomei Bao, Zhongcong Legass, Asmamaw Belete, Marye Ji, Jianwan Su, Yung-Chih Addis, Amsalu K. |
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farmland dynamics in pingtan, china: understanding its transition, landscape structure and driving factors |
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Farmland dynamics in Pingtan, China: understanding its transition, landscape structure and driving factors |
| abstract |
Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. |
| abstractGer |
Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. |
| abstract_unstemmed |
Abstract The knowledge of farmland dynamics is pivotal to design its management sustainably and enhance food security. Using remote sensing and socioeconomic data, this paper analyses farmland transition, its landscape structure, and the associated drivers in Pingtan Island. The results revealed that the decline of farmland was much faster (317 ha/year) compared with its expansion (106.9 ha/year) during the study period. Across periods, farmland experienced a moderate-to-very rapid intensity of change. It tends to lose than to persist and its stable part decreased continuously. The changed part of farmland was largely attributable to the swap changes than that of the net change. Both its inward and outward conversions were generally limited to a few dominant cover types that include urban, shrub, forest and grassland. In response to farmland shrinkage and the increase of labour wages in other economic sectors, farmers have engaged in off-farm activities to support their economy, besides the compensation they owned from the government. The shape complexity and the variability of size among farmland patches showed a decrease. Except in the first period; however, the level of disintegration among patches increased successively. Both expanded and stable part of farmland exhibited more dispersed spatial configuration than that of the decreased part in the last two decades (1996–2017). Farmland change was significantly influenced by factors such as GDP, wage, per capita GDP, road expansion and the total population in the first component of a regression model (C1) that contains the highest proportion of variance in both dependent and explanatory variables (≥ 72%). Policies of ecological management and economic development also accelerated farmland decline, which calls for its stricter protection policies. The result of this study would serve as the baseline information for farmland management in the study area and as a reference for future studies. |
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| score |
7.4019957 |