坦桑尼亚干燥常绿山地森林的体积和地上生物量模型

Mponie Leison Mwaluseke, E. Mwakalukwa, S. Maliondo
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引用次数: 1

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摘要Mwaluseke ML, Mwakalukwa EE, Maliondo SMS。2023. 坦桑尼亚干燥常绿山地森林的体积和地上生物量模型。亚洲J: 7:45 -53。可用于准确估计坦桑尼亚干燥常绿山地森林的乔灌木体积和地上生物量的模型在很大程度上是缺乏的。因此,本研究对坦桑尼亚北部Lendikinya森林保护区的干常绿山地森林进行了体积和地上生物量模型的建立。本研究共破坏性采伐30株乔灌木样本,直径范围为5 ~ 58.5 cm。具体而言,研究开发了(i)高度-直径模型和(ii)总体积和地上生物量模型。以下高度,体积,和生物量模型似乎适合估计树高,体积,和树和灌木物种的生物量研究网站:高度(米)= 2.3249 + 6.6101 /胸径+ 0.2847胸径(R2 = 0.78, RMSE = 1.79,工商局= 164.37),ln(体积,m3) = -9.845 + 1.915 ln(胸径)+ 1.089 ln (Ht) (R2 = 0.97, RMSE = 0.296,工商局= -144.18)和ln(生物量、公斤)= -1.666 + 0.853 ln (WD DBH2 x Ht) (R2 = 0.95, RMSE = 0.324,工商局= 224.13)。两个模型的偏差都很低,因此表明拟合很好。这些模型将有助于了解森林的状况和森林储存碳的潜力,从而有可能从正在进行的REDD+计划谈判中受益,通过对保护区的可持续管理来支付避免砍伐森林和退化的费用。
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Volume and aboveground biomass models for a dry evergreen montane forest in Tanzania
Abstract. Mwaluseke ML, Mwakalukwa EE, Maliondo SMS. 2023. Volume and aboveground biomass models for a dry evergreen montane forest in Tanzania. Asian J For 7: 45-53. Models available for accurately estimating trees and shrubs' volume and aboveground biomass from dry evergreen montane forests in Tanzania are largely lacking. Therefore, this study was conducted to develop volume, and aboveground biomass models for a dry evergreen montane forest of Lendikinya Forest Reserve found in Northern Tanzania. A total of thirty sample trees and shrubs with a diameter range of 5-58.5 cm were destructively harvested and used in this study. Specifically, the study developed (i) the height-diameter model and (ii) the total volume and aboveground biomass models. The following height, volume, and biomass models appear to be suitable for estimating tree height, volume, and biomass of tree and shrub species found in the study site: Height (m) =2.3249 + 6.6101/DBH + 0.2847DBH (R2 = 0.78, RMSE = 1.79, AIC = 164.37), ln(Volume, m3) = -9.845 + 1.915 ln(DBH) + 1.089ln(Ht) (R2 = 0.97, RMSE = 0.296, AIC = -144.18) and ln(Biomass, kg) = -1.666 + 0.853ln(WD x DBH2 x Ht) (R2 = 0.95, RMSE = 0.324, AIC = 224.13). Both models yielded low bias, hence indicating an excellent fit. These models will be useful in understanding the condition of the forest and the potential of this forest in storing carbon hence, the possibility of benefiting from the ongoing negotiations of REDD+ schemes for payment for avoided deforestation and degradation through sustainable management of the reserve.
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