As part of the global effort to plant billion trees,an afforestation project is launched in Pakistan in Khyber Pakhtunkhwa(KP)province to conserve existing forests and to increase area under forest cover.The present s...As part of the global effort to plant billion trees,an afforestation project is launched in Pakistan in Khyber Pakhtunkhwa(KP)province to conserve existing forests and to increase area under forest cover.The present study is designed to build a Systems'model by incorporating major activities of the Billion Tree Tsunami Afforestation Project(BTTAP)with special focus on afforestation activities to estimate the growth in forest area of KP.Availability of complete dataset was a challenge.To fix the model,the raw data taken from the project office has been utilized.Planning Commission Form 1-Phase I&II helped us with additional information.We relied on the data available for one and half period of the project as rest of the data is subject to the completion of the project.Our results show that the project target to enhance area under forest differs from the target to afforest area under the project.The system dynamics'model projection shows that the forest area of KP would be 23.59 million hectares at the end of the BTTA project,thus having an increase of 3.29%instead of 2%that has been initially proposed.However,the results show that the progress to meet the target in some afforestation classes is slow as compared to other categories.Farm forestry,plantation on communal lands and owners'plantation need special focus of the authority.Deforestation would affect 0.02 million hectares area of the project.The model under study may be used as a reference model that can be replicated to other areas where billion tree campaigns are going on.展开更多
使用日本对地观测卫星(advanced land observing satellite,ALOS)2008—2011年的影像数据,解译云南现状植被分布.使用分类回归树模型(classification and regression trees,CART)预测云南主要森林植被的潜在分布区,估算云南森林植被的...使用日本对地观测卫星(advanced land observing satellite,ALOS)2008—2011年的影像数据,解译云南现状植被分布.使用分类回归树模型(classification and regression trees,CART)预测云南主要森林植被的潜在分布区,估算云南森林植被的碳储量和固碳潜力.结果显示:云南省林地总面积是2.0×107hm2,森林覆盖率为52.49%,主要森林植被碳储量为871.14 Tg;不同森林植被碳储量及固碳潜力不同,碳储量较高的是季风常绿阔叶林、暖温性针叶林和暖热性针叶林,分别为205.42、172.72 Tg和137.78 Tg,而固碳潜力较大的是暖热性针叶林、暖温性针叶林和温凉性针叶林,分别为788.53、119.00 Tg和156.78 Tg,分别是现实碳储量的5.7倍、2.1倍和0.91倍;云南主要森林植被总固碳潜力为1 321.52 Tg,约为现实碳储量的1.52倍.总体上,云南省针叶林的固碳潜力远大于阔叶林,适当的人为干扰可提高暖热性针叶林的固碳潜力,暖热性针叶林具有较强的清洁发展机制(clean development mechanism,CDM)潜力,云南省主要森林植被在整体上是一个碳汇.展开更多
开展气候变化下森林恢复措施对西南亚高山森林景观的恢复效果评价,对于该地区未来森林生态恢复技术研发具有重要参考意义.以岷江杂谷脑河上游流域为研究对象,基于森林景观模型LANDIS PRO 7.0模拟、预测气候变化下不同森林恢复措施(自然...开展气候变化下森林恢复措施对西南亚高山森林景观的恢复效果评价,对于该地区未来森林生态恢复技术研发具有重要参考意义.以岷江杂谷脑河上游流域为研究对象,基于森林景观模型LANDIS PRO 7.0模拟、预测气候变化下不同森林恢复措施(自然和人工)对流域森林景观演替过程及森林景观的影响并对比分析两种恢复情景的森林结构和组成恢复效果的差异.结果表明:模型模拟结果具有较高的可靠性,模型的初始化物种信息与野外调查数据相吻合.根据模型预测结果(2010-2060年),人工恢复情景下冷杉的平均林分林木胸高断面积在模拟的前期和中期低于自然恢复情景,在后期则高于自然恢复;云杉和华山松的平均林分林木胸高断面积在模拟的前期低于自然恢复情景,在中、后期则高于自然恢复;桦木、高山栎和落叶松的平均林分林木胸高断面积在整个模拟期均低于自然恢复情景.从空间尺度分析,人工恢复情景与自然恢复情景相比,冷杉林在干旱河谷、北坡和高山区的占比分别增加3.66%、1.60%、0.50%,而在南坡的占比有所减少;云杉林在干旱河谷区、南坡地区的占比分别增加0.72%、1.23%,而在北坡和高山区的占比略有减少;桦木和落叶松在不同的立地类型上占比降低.在自然恢复下,受气候变暖的影响,杂谷脑河上游流域森林优势物种(冷杉)的胸高断面积在模拟后期呈现逐渐下降的趋势,而人工恢复措施的实施能够促进区域优势树种(云、冷杉)的优势扩大.由此可见,在气候变暖的背景下对杂谷脑河上游流域森林施以适宜的人工恢复措施,能够有效地促进森林向顶级群落演替,提高杂谷脑河流域森林生态弹性,更好地应对未来的气候变化.展开更多
文摘As part of the global effort to plant billion trees,an afforestation project is launched in Pakistan in Khyber Pakhtunkhwa(KP)province to conserve existing forests and to increase area under forest cover.The present study is designed to build a Systems'model by incorporating major activities of the Billion Tree Tsunami Afforestation Project(BTTAP)with special focus on afforestation activities to estimate the growth in forest area of KP.Availability of complete dataset was a challenge.To fix the model,the raw data taken from the project office has been utilized.Planning Commission Form 1-Phase I&II helped us with additional information.We relied on the data available for one and half period of the project as rest of the data is subject to the completion of the project.Our results show that the project target to enhance area under forest differs from the target to afforest area under the project.The system dynamics'model projection shows that the forest area of KP would be 23.59 million hectares at the end of the BTTA project,thus having an increase of 3.29%instead of 2%that has been initially proposed.However,the results show that the progress to meet the target in some afforestation classes is slow as compared to other categories.Farm forestry,plantation on communal lands and owners'plantation need special focus of the authority.Deforestation would affect 0.02 million hectares area of the project.The model under study may be used as a reference model that can be replicated to other areas where billion tree campaigns are going on.
文摘开展气候变化下森林恢复措施对西南亚高山森林景观的恢复效果评价,对于该地区未来森林生态恢复技术研发具有重要参考意义.以岷江杂谷脑河上游流域为研究对象,基于森林景观模型LANDIS PRO 7.0模拟、预测气候变化下不同森林恢复措施(自然和人工)对流域森林景观演替过程及森林景观的影响并对比分析两种恢复情景的森林结构和组成恢复效果的差异.结果表明:模型模拟结果具有较高的可靠性,模型的初始化物种信息与野外调查数据相吻合.根据模型预测结果(2010-2060年),人工恢复情景下冷杉的平均林分林木胸高断面积在模拟的前期和中期低于自然恢复情景,在后期则高于自然恢复;云杉和华山松的平均林分林木胸高断面积在模拟的前期低于自然恢复情景,在中、后期则高于自然恢复;桦木、高山栎和落叶松的平均林分林木胸高断面积在整个模拟期均低于自然恢复情景.从空间尺度分析,人工恢复情景与自然恢复情景相比,冷杉林在干旱河谷、北坡和高山区的占比分别增加3.66%、1.60%、0.50%,而在南坡的占比有所减少;云杉林在干旱河谷区、南坡地区的占比分别增加0.72%、1.23%,而在北坡和高山区的占比略有减少;桦木和落叶松在不同的立地类型上占比降低.在自然恢复下,受气候变暖的影响,杂谷脑河上游流域森林优势物种(冷杉)的胸高断面积在模拟后期呈现逐渐下降的趋势,而人工恢复措施的实施能够促进区域优势树种(云、冷杉)的优势扩大.由此可见,在气候变暖的背景下对杂谷脑河上游流域森林施以适宜的人工恢复措施,能够有效地促进森林向顶级群落演替,提高杂谷脑河流域森林生态弹性,更好地应对未来的气候变化.
