Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China
Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Xiao Hong [verfasserIn] Huang, Qing Lin [verfasserIn] Zhang, Chao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: European journal of forest research - Berlin : Springer, 2004, 129(2010), 5 vom: 06. Jan., Seite 975-980 |
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| Übergeordnetes Werk: |
volume:129 ; year:2010 ; number:5 ; day:06 ; month:01 ; pages:975-980 |
| Links: |
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| DOI / URN: |
10.1007/s10342-009-0351-x |
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SPR009734104 |
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| 520 | |a Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. | ||
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| 700 | 1 | |a Zhang, Chao |e verfasserin |4 aut | |
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10.1007/s10342-009-0351-x doi (DE-627)SPR009734104 (SPR)s10342-009-0351-x-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Zhang, Xiao Hong verfasserin aut Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. Forest landscape dynamics (dpeaa)DE-He213 Markov model (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Forest landscape restoration (FLR) (dpeaa)DE-He213 Huang, Qing Lin verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of forest research Berlin : Springer, 2004 129(2010), 5 vom: 06. Jan., Seite 975-980 (DE-627)378132512 (DE-600)2134019-5 1612-4677 nnns volume:129 year:2010 number:5 day:06 month:01 pages:975-980 https://dx.doi.org/10.1007/s10342-009-0351-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.40 ASE AR 129 2010 5 06 01 975-980 |
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10.1007/s10342-009-0351-x doi (DE-627)SPR009734104 (SPR)s10342-009-0351-x-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Zhang, Xiao Hong verfasserin aut Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. Forest landscape dynamics (dpeaa)DE-He213 Markov model (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Forest landscape restoration (FLR) (dpeaa)DE-He213 Huang, Qing Lin verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of forest research Berlin : Springer, 2004 129(2010), 5 vom: 06. Jan., Seite 975-980 (DE-627)378132512 (DE-600)2134019-5 1612-4677 nnns volume:129 year:2010 number:5 day:06 month:01 pages:975-980 https://dx.doi.org/10.1007/s10342-009-0351-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.40 ASE AR 129 2010 5 06 01 975-980 |
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10.1007/s10342-009-0351-x doi (DE-627)SPR009734104 (SPR)s10342-009-0351-x-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Zhang, Xiao Hong verfasserin aut Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. Forest landscape dynamics (dpeaa)DE-He213 Markov model (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Forest landscape restoration (FLR) (dpeaa)DE-He213 Huang, Qing Lin verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of forest research Berlin : Springer, 2004 129(2010), 5 vom: 06. Jan., Seite 975-980 (DE-627)378132512 (DE-600)2134019-5 1612-4677 nnns volume:129 year:2010 number:5 day:06 month:01 pages:975-980 https://dx.doi.org/10.1007/s10342-009-0351-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.40 ASE AR 129 2010 5 06 01 975-980 |
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10.1007/s10342-009-0351-x doi (DE-627)SPR009734104 (SPR)s10342-009-0351-x-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Zhang, Xiao Hong verfasserin aut Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. Forest landscape dynamics (dpeaa)DE-He213 Markov model (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Forest landscape restoration (FLR) (dpeaa)DE-He213 Huang, Qing Lin verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of forest research Berlin : Springer, 2004 129(2010), 5 vom: 06. Jan., Seite 975-980 (DE-627)378132512 (DE-600)2134019-5 1612-4677 nnns volume:129 year:2010 number:5 day:06 month:01 pages:975-980 https://dx.doi.org/10.1007/s10342-009-0351-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.40 ASE AR 129 2010 5 06 01 975-980 |
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10.1007/s10342-009-0351-x doi (DE-627)SPR009734104 (SPR)s10342-009-0351-x-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Zhang, Xiao Hong verfasserin aut Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. Forest landscape dynamics (dpeaa)DE-He213 Markov model (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Forest landscape restoration (FLR) (dpeaa)DE-He213 Huang, Qing Lin verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of forest research Berlin : Springer, 2004 129(2010), 5 vom: 06. Jan., Seite 975-980 (DE-627)378132512 (DE-600)2134019-5 1612-4677 nnns volume:129 year:2010 number:5 day:06 month:01 pages:975-980 https://dx.doi.org/10.1007/s10342-009-0351-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.40 ASE AR 129 2010 5 06 01 975-980 |
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Enthalten in European journal of forest research 129(2010), 5 vom: 06. Jan., Seite 975-980 volume:129 year:2010 number:5 day:06 month:01 pages:975-980 |
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Forest landscape dynamics Markov model Prediction Forest landscape restoration (FLR) |
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European journal of forest research |
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Zhang, Xiao Hong @@aut@@ Huang, Qing Lin @@aut@@ Zhang, Chao @@aut@@ |
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To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. 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Zhang, Xiao Hong |
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Zhang, Xiao Hong ddc 630 bkl 48.40 misc Forest landscape dynamics misc Markov model misc Prediction misc Forest landscape restoration (FLR) Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China |
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630 ASE 630 640 ASE 48.40 bkl Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China Forest landscape dynamics (dpeaa)DE-He213 Markov model (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Forest landscape restoration (FLR) (dpeaa)DE-He213 |
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ddc 630 bkl 48.40 misc Forest landscape dynamics misc Markov model misc Prediction misc Forest landscape restoration (FLR) |
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ddc 630 bkl 48.40 misc Forest landscape dynamics misc Markov model misc Prediction misc Forest landscape restoration (FLR) |
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Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China |
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Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China |
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Zhang, Xiao Hong Huang, Qing Lin Zhang, Chao |
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analysis of forest landscape dynamics based on forest landscape restoration: a case study of yong’an city, fujian province, china |
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Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China |
| abstract |
Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. |
| abstractGer |
Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. |
| abstract_unstemmed |
Abstract Forest landscape restoration (FLR) is a process that aims to regain ecological integrity and enhance human well-being in deforested or degraded forest landscapes. To ensure that restoration efforts are successful, the first step is to understand the dynamics of the forest landscape and the dominant forces responsible for its change. Taking Yong’an city, Fujian province in China as a case for study, this paper constructed a Markov model to predict the dynamics of the forest landscape based on sample-plot data of Continuous Forest Inventory at a county level. The study area was divided into eight landscape element types based on FLR, including approximated primary forest, secondary broad-leaved forest, secondary forest of Pinus massoniana, natural bamboo forest, planted forest, non-timber product forest, degraded forest land and non-forestry land. The analysis showed the following: (1) the extent of reforestation of planted forest, non-timber product forest and secondary forest of Pinus massoniana would be greater than that of deforestation of approximated primary forest, broad-leaved secondary forest and natural bamboo forest. Therefore, the total area covered by forest would increase steadily. (2) Conversely, conversion among different landscape element types would occur frequently and have high transition proportions. (3) Remarkable decrease of the extent of approximated primary forest, together with the conversion from degraded forest land to secondary forest, would probably result in the decline of forest volume. (4) Forest productivity in the meantime will not be maintained or enhanced because of the conversion from secondary forest to planted forest. These results suggest that the direct and underlying driving force of landscape dynamics should be understood and addressed in the upcoming studies for remnant approximated primary forest protection, secondary forest management and degraded forest land rehabilitation. The conclusion is that the Markov model can be used to analyze the forest landscape dynamics for FLR based on sample-plot survey data of Continuous Forest Inventory at a county level. |
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| title_short |
Analysis of forest landscape dynamics based on Forest Landscape Restoration: a case study of Yong’an city, Fujian province, China |
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https://dx.doi.org/10.1007/s10342-009-0351-x |
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| author2 |
Huang, Qing Lin Zhang, Chao |
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Huang, Qing Lin Zhang, Chao |
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| doi_str |
10.1007/s10342-009-0351-x |
| up_date |
2024-07-04T02:51:38.002Z |
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| score |
7.3995275 |