Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index
Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics...
Ausführliche Beschreibung
Autor*in: |
Kewei Li [verfasserIn] Erqi Xu [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: GIScience & Remote Sensing - Taylor & Francis Group, 2022, 60(2023), 1 |
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Übergeordnetes Werk: |
volume:60 ; year:2023 ; number:1 |
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Link aufrufen |
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DOI / URN: |
10.1080/15481603.2023.2188674 |
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Katalog-ID: |
DOAJ09819416X |
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520 | |a Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. | ||
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10.1080/15481603.2023.2188674 doi (DE-627)DOAJ09819416X (DE-599)DOAJced0815135144f62a5f8432a8989354e DE-627 ger DE-627 rakwb eng GA1-1776 GE1-350 Kewei Li verfasserin aut Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. landsat time series post-fire salvage logging biological legacy recovery pattern boreal forest greater hinggan mountain region Mathematical geography. Cartography Environmental sciences Erqi Xu verfasserin aut In GIScience & Remote Sensing Taylor & Francis Group, 2022 60(2023), 1 (DE-627)502921471 (DE-600)2209042-3 19437226 nnns volume:60 year:2023 number:1 https://doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/article/ced0815135144f62a5f8432a8989354e kostenfrei http://dx.doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/toc/1548-1603 Journal toc kostenfrei https://doaj.org/toc/1943-7226 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 60 2023 1 |
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10.1080/15481603.2023.2188674 doi (DE-627)DOAJ09819416X (DE-599)DOAJced0815135144f62a5f8432a8989354e DE-627 ger DE-627 rakwb eng GA1-1776 GE1-350 Kewei Li verfasserin aut Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. landsat time series post-fire salvage logging biological legacy recovery pattern boreal forest greater hinggan mountain region Mathematical geography. Cartography Environmental sciences Erqi Xu verfasserin aut In GIScience & Remote Sensing Taylor & Francis Group, 2022 60(2023), 1 (DE-627)502921471 (DE-600)2209042-3 19437226 nnns volume:60 year:2023 number:1 https://doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/article/ced0815135144f62a5f8432a8989354e kostenfrei http://dx.doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/toc/1548-1603 Journal toc kostenfrei https://doaj.org/toc/1943-7226 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 60 2023 1 |
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10.1080/15481603.2023.2188674 doi (DE-627)DOAJ09819416X (DE-599)DOAJced0815135144f62a5f8432a8989354e DE-627 ger DE-627 rakwb eng GA1-1776 GE1-350 Kewei Li verfasserin aut Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. landsat time series post-fire salvage logging biological legacy recovery pattern boreal forest greater hinggan mountain region Mathematical geography. Cartography Environmental sciences Erqi Xu verfasserin aut In GIScience & Remote Sensing Taylor & Francis Group, 2022 60(2023), 1 (DE-627)502921471 (DE-600)2209042-3 19437226 nnns volume:60 year:2023 number:1 https://doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/article/ced0815135144f62a5f8432a8989354e kostenfrei http://dx.doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/toc/1548-1603 Journal toc kostenfrei https://doaj.org/toc/1943-7226 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 60 2023 1 |
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10.1080/15481603.2023.2188674 doi (DE-627)DOAJ09819416X (DE-599)DOAJced0815135144f62a5f8432a8989354e DE-627 ger DE-627 rakwb eng GA1-1776 GE1-350 Kewei Li verfasserin aut Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. landsat time series post-fire salvage logging biological legacy recovery pattern boreal forest greater hinggan mountain region Mathematical geography. Cartography Environmental sciences Erqi Xu verfasserin aut In GIScience & Remote Sensing Taylor & Francis Group, 2022 60(2023), 1 (DE-627)502921471 (DE-600)2209042-3 19437226 nnns volume:60 year:2023 number:1 https://doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/article/ced0815135144f62a5f8432a8989354e kostenfrei http://dx.doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/toc/1548-1603 Journal toc kostenfrei https://doaj.org/toc/1943-7226 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 60 2023 1 |
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10.1080/15481603.2023.2188674 doi (DE-627)DOAJ09819416X (DE-599)DOAJced0815135144f62a5f8432a8989354e DE-627 ger DE-627 rakwb eng GA1-1776 GE1-350 Kewei Li verfasserin aut Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. landsat time series post-fire salvage logging biological legacy recovery pattern boreal forest greater hinggan mountain region Mathematical geography. Cartography Environmental sciences Erqi Xu verfasserin aut In GIScience & Remote Sensing Taylor & Francis Group, 2022 60(2023), 1 (DE-627)502921471 (DE-600)2209042-3 19437226 nnns volume:60 year:2023 number:1 https://doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/article/ced0815135144f62a5f8432a8989354e kostenfrei http://dx.doi.org/10.1080/15481603.2023.2188674 kostenfrei https://doaj.org/toc/1548-1603 Journal toc kostenfrei https://doaj.org/toc/1943-7226 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 60 2023 1 |
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GA1-1776 GE1-350 Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index landsat time series post-fire salvage logging biological legacy recovery pattern boreal forest greater hinggan mountain region |
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Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index |
abstract |
Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. |
abstractGer |
Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. |
abstract_unstemmed |
Forests are resilient to a range of disturbances, but combinations of severe natural and anthropogenic disturbances (e.g. wildfire and logging) may inhibit forest recovery and lead to forest degradation. Recent studies have explored long-term forest-disturbance detection and forest-recovery dynamics by using free and open-access remote-sensing images. However, mapping consecutive multiple disturbance agents is challenging using existing automated change-detection algorithms because the reduced canopy reflectance and the smoothing of consecutive disturbance signals mean that the initial disturbance cannot be spectrally separated from the second disturbance. Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). This contributes to biases in long-term forest-recovery monitoring, which are not conducive to the guidance of post-fire vegetation recovery. Here, we propose a modified disturbance index to separate the spectral characteristics of fire and forest logging using normalized tasseled-cap components (brightness and wetness) and detect the spatiotemporal distribution of post-fire logging by means of an index threshold and image differencing. On this basis, the recovery patterns of the post-fire forest are differentiated by considering the cumulative effect of fire, post-fire logging, and recovery approaches. The method is tested in the burn areas of the 5.6 Fire in the Greater Hinggan Mountain area (the largest forest fire in recorded history in China), giving an overall accuracy of 85% in post-fire forest logging mapping. Our results confirm that biological legacies (i.e. trees, logs, and snags) were removed across many areas in the fire, with activities peaking in the second year after the fire and located chiefly in areas of moderate and high burn severity. By identifying post-fire logging, the fluctuation and high disturbance index of the conventional temporal trajectory in the early stage of forest recovery are explained. The large-scale salvage logging slowed the recovery of the post-fire forest ecosystem and influenced the recovery process through the interaction of burn severity and active forest restoration. In areas of high burn severity, assisted natural regeneration (i.e. natural regeneration with artificial aids such as clearing the snags, weeding, digging pits, and supplemental planting) preserved biological legacies and achieved a higher proportion of forest recovery, second only to non-logged areas where natural regeneration (i.e. forestry recovering naturally without any artificial intervention) occurred. By contrast, salvage logging followed by artificial regeneration (i.e. clear-cutting all dead or damaged trees followed by tree planting or artificial seeding) hindered vegetation recovery in the early stage, but it improved the recovery rate in years 10–20 and approached the recovery proportion of non-logged areas where natural regeneration occurred as the recovery progressed and habitat conditions improved. The proposed method is shown to offer important advantages in detecting post-fire salvage logging, and it provides improved guidance for forest managers in developing strategies for forest recovery. |
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title_short |
Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index |
url |
https://doi.org/10.1080/15481603.2023.2188674 https://doaj.org/article/ced0815135144f62a5f8432a8989354e http://dx.doi.org/10.1080/15481603.2023.2188674 https://doaj.org/toc/1548-1603 https://doaj.org/toc/1943-7226 |
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Erqi Xu |
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