Carbon sequestration is one of the important ecosystem services provided by forested landscapes. Dry forests have high potential for carbon storage. However, their potential to store and sequester carbon is poorly und...Carbon sequestration is one of the important ecosystem services provided by forested landscapes. Dry forests have high potential for carbon storage. However, their potential to store and sequester carbon is poorly understood in Kenya. Moreover, past attempts to estimate carbon stock have ignored drylands ecosystem heterogeneity. This study assessed the potential of Mukogodo dryland forest-landscape in offsetting carbon dioxide through carbon sequestration and storage. Four carbon pools (above and below ground biomass, soil, dead wood and litter) were analyzed. A total of 51<span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">(400</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">m<sup>2</sup>) sample plots were established using stratified-random sampling technique to estimate biomass across six vegetation classes in three landscape types (forest reserve, ranches and conservancies) using nested-plot design. Above ground biomass was determined using generalized multispecies model with diameter at breast height, height and wood density as variables. Below ground, soil, litter and dead wood biomass;carbon stocks and carbon dioxide equivalents (CO<sub>2eq</sub>) were estimated using secondary information. The CO<sub>2eq</sub> was multiplied by current prices of carbon trade to compute carbon sequestration value. Mean ± SE of biomass and carbon was determined across vegetation and landscape types and mean differences tested by one-way Analysis of Variance. Mean biomass and carbon was about 79.15 ± 40.22</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TB</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha</span></span></span><span style="font-size:10px;"><span style="vertical-align:super;">-</span></span><span><span><span style="font-family:;" "=""><sup>1</sup> and 37.25 ± 18.89</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;">-</span>1</sup> respectively. Cumulative carbon stock was estimated at 682.08</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;">-</span>1</sup>;forest reserve (251.57</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup>-1</sup>) had significantly high levels of carbon stocks compared to ranches (209.78</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;white-space:normal;">-</span>1</sup>) and conservancies (220.73</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;white-space:normal;">-</span>1</sup>, <i>P</i> = 0.000). Further, closed forest significantly contributed to the overall biomass and carbon stock (58%). The carbon sequestration potential was about 19.9MTCO<sub>2eq</sub> with most conservative worth of KES 39.9B (US$40M) per annum. The high carbon stock in the landscape shows the potential of dryland ecosystems as carbon sink for climate change mitigation. However, for communities to benefit from bio-carbon funds in future, sustainable landscape management and restorative measures should be practiced to enhance carbon storage and provision of other ecosystem services.</span></span></span>展开更多
认识城市扩展过程的区位因素特征对旱区城市可持续发展具有重要意义。为此,以中国呼包鄂榆城市群为例,利用随机森林方法量化区位因素对城市扩展过程的影响。研究发现:随机森林方法能够有效地量化旱区城市扩展过程区位因素的基本特征,模...认识城市扩展过程的区位因素特征对旱区城市可持续发展具有重要意义。为此,以中国呼包鄂榆城市群为例,利用随机森林方法量化区位因素对城市扩展过程的影响。研究发现:随机森林方法能够有效地量化旱区城市扩展过程区位因素的基本特征,模型的AUC(area under curve)值达到0.97。同时,到城市中心距离对区域1980—2017年城市扩展过程影响最大,重要性达到42.62%。国道、高速公路和铁路等交通因素也有重要的影响,重要性均大于10%。此外,所有区位因素对区域城市扩展过程的影响均存在尺度效应,其中地形、气候和河流对城市扩展过程影响的尺度效应相对比较明显。地形、气候和河流对大城市影响的重要性分别为27.17%、20.23%和8.12%,分别是其对小城市影响的4.02倍、3.91倍和2.36倍。因此,建议在旱区城市建设中,应该高度重视地形、气候和河流等自然要素的约束作用,因地制宜地进行城市规划和建设。展开更多
文摘Carbon sequestration is one of the important ecosystem services provided by forested landscapes. Dry forests have high potential for carbon storage. However, their potential to store and sequester carbon is poorly understood in Kenya. Moreover, past attempts to estimate carbon stock have ignored drylands ecosystem heterogeneity. This study assessed the potential of Mukogodo dryland forest-landscape in offsetting carbon dioxide through carbon sequestration and storage. Four carbon pools (above and below ground biomass, soil, dead wood and litter) were analyzed. A total of 51<span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">(400</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">m<sup>2</sup>) sample plots were established using stratified-random sampling technique to estimate biomass across six vegetation classes in three landscape types (forest reserve, ranches and conservancies) using nested-plot design. Above ground biomass was determined using generalized multispecies model with diameter at breast height, height and wood density as variables. Below ground, soil, litter and dead wood biomass;carbon stocks and carbon dioxide equivalents (CO<sub>2eq</sub>) were estimated using secondary information. The CO<sub>2eq</sub> was multiplied by current prices of carbon trade to compute carbon sequestration value. Mean ± SE of biomass and carbon was determined across vegetation and landscape types and mean differences tested by one-way Analysis of Variance. Mean biomass and carbon was about 79.15 ± 40.22</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TB</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha</span></span></span><span style="font-size:10px;"><span style="vertical-align:super;">-</span></span><span><span><span style="font-family:;" "=""><sup>1</sup> and 37.25 ± 18.89</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;">-</span>1</sup> respectively. Cumulative carbon stock was estimated at 682.08</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;">-</span>1</sup>;forest reserve (251.57</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup>-1</sup>) had significantly high levels of carbon stocks compared to ranches (209.78</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;white-space:normal;">-</span>1</sup>) and conservancies (220.73</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">TC</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">ha<sup><span style="font-size:10px;white-space:normal;">-</span>1</sup>, <i>P</i> = 0.000). Further, closed forest significantly contributed to the overall biomass and carbon stock (58%). The carbon sequestration potential was about 19.9MTCO<sub>2eq</sub> with most conservative worth of KES 39.9B (US$40M) per annum. The high carbon stock in the landscape shows the potential of dryland ecosystems as carbon sink for climate change mitigation. However, for communities to benefit from bio-carbon funds in future, sustainable landscape management and restorative measures should be practiced to enhance carbon storage and provision of other ecosystem services.</span></span></span>
文摘认识城市扩展过程的区位因素特征对旱区城市可持续发展具有重要意义。为此,以中国呼包鄂榆城市群为例,利用随机森林方法量化区位因素对城市扩展过程的影响。研究发现:随机森林方法能够有效地量化旱区城市扩展过程区位因素的基本特征,模型的AUC(area under curve)值达到0.97。同时,到城市中心距离对区域1980—2017年城市扩展过程影响最大,重要性达到42.62%。国道、高速公路和铁路等交通因素也有重要的影响,重要性均大于10%。此外,所有区位因素对区域城市扩展过程的影响均存在尺度效应,其中地形、气候和河流对城市扩展过程影响的尺度效应相对比较明显。地形、气候和河流对大城市影响的重要性分别为27.17%、20.23%和8.12%,分别是其对小城市影响的4.02倍、3.91倍和2.36倍。因此,建议在旱区城市建设中,应该高度重视地形、气候和河流等自然要素的约束作用,因地制宜地进行城市规划和建设。