Estimation of Carbon Sequestration; Using Allometric Equations; in Azrou Cedar Forests (Cedrus atlantica Manetti) in the Central Middle Atlas of Morocco under Climate Change

Mderssa, Mohamed El; Belghazi, Bakhyi; Benjelloun, Hassan; Zennouhi, Omar; Nassiri, Laila; Ibijbijen, Jamal

doi:10.4236/ojf.2019.93011

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Analyses of 30 tree samples showed that the largest carbon stock allocation (93%) was in the trunk, followed by branches (5%) and leaves (2%) [2]. Seedlings and understory plants had the lowest carbon stores.…”

Section: Total Carbon In Pt Kojo's Forest Based On Carbon Poolsmentioning

confidence: 99%

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Estimation of aboveground carbon stock in PT KOJO’s forest in Riau, Indonesia

Pebriandi,

Yoza,

Sukmantoro

et al. 2024

BIO Web Conf.

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The government of Indonesia is committed to reducing carbon emissions through the Forestry and Other Land Uses (FOLU) Net Sink scheme, e.g., by measuring and calculating potential aboveground carbon in PT KOJO’s forest. This research aims to measure biomass and aboveground biomass in PT KOJO’s forest, Pinggir District, Bengkalis. Non-destructive sampling was applied by making plots of 20 m × 20 m (trees), 10 m × 10 m (poles), and 5 m × 5 m (saplings). Destructive sampling was carried out in a plot of 1 m × 1 m for seedlings and understory plants. Results showed that potential carbon stock at tree, pole, sapling, seedling, understory plant, litter, and necromass levels were 160.62 tC/ha, 34.60 tC/ha, 20.88 tons/ha, 1.54 tC/ha, 11.59 tC/ha, and 1.47 tC/Ha, respectively. The total carbon stock in PT KOJO’s forest was 160.69 tC/ha. Carbon stock in PT KOJO’s forest is classified as medium, therefore efforts are needed to increase carbon stocks and efforts to protect PT KOJO’s forest area so that stored carbon is not released into the air.

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Section: Total Carbon In Pt Kojo's Forest Based On Carbon Poolsmentioning

confidence: 99%

“…Carbon sequestration in forest ecosystems has recently come to the attention of many parties due to concerns over global climate change [2,3]. As regards land cover change in Jambi, the estimated net emission in 2009-2011 was 4.8 Mt CO2-eq/year [4].…”

Section: Introductionmentioning

confidence: 99%

Estimation of aboveground carbon stock in PT KOJO’s forest in Riau, Indonesia

Pebriandi,

Yoza,

Sukmantoro

et al. 2024

BIO Web Conf.

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“…To assess the initial biomass Carbon stock, the use of carbomass models developed at the level of Azrou forest [43] was relevant instead of taking into account the default parameters of the IPCC. These models were used to estimate the amount of carbon per compartment (i.e.…”

Section: Model Initializationmentioning

confidence: 99%

Calibrating a Process-based Model to Enhance Robustness in Carbon Sequestration Simulations: the Sase of Cedrus Atlantica (Endl.) Manetti ex Carrière

Boukhris¹,

Lahssini²,

Collalti³

et al. 2022

Preprint

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To assess the degree to which it has met its commitments under the Paris Agreement, Morocco is called upon to carry out carbon assessments and transparent evaluations. Within the forestry sector, little is known about the role of Morocco’s forests in contributing to carbon uptake. With this aim, we applied for the first time in literature the 3-PG model to Cedrus atlantica ((Endl.) Manetti ex Carrière, 1855), which represents about 131.800 ha of Morocco’s forest area (i.e. Azrou forest). Through the Differential Evolution - Markov Chains (DE-MC) we tested and assessed the sensitivity and calibrated the 3-PG model. This process-based model provided significant results regarding the carbon sequestration capacity. The results showed the following: i- Parameters related to stand properties, canopy structure, and processes, as well as biomass partitioning, are the most important or sensitive for the performance of the model; ii- The DE-MC method optimized the values of the 3-PG parameters which was confirmed by the means of Gelman-Rubin convergence test; iii- According to the predictions of the calibrated 3-PG, the Net Primary Production in the pure Azrou forest varies between 0.32 and 7.88 tC.ha−1.yr−1, it is equal in average to 4.9 tC.ha−1.yr−1, which given the total area corresponds to 7078 tC.yr−1.

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“…Traditional methods for estimating above‐ground forest biomass rely mainly on direct destructive sampling and weighing measurements from field surveys (Liu, Zhou, et al, 2020). Usually, parameters such as height and diameter at the breast height of trees in sample plots are recorded and substituted into the categorical anisotropic growth equation (Wang et al, 2015; Xiang et al, 2016) or the stock‐biomass conversion equation (El Mderssa et al, 2019; Fang et al, 2002). This method could obtain accurate results, but it is quite time‐consuming, labor‐intensive and limited to the sample plot scale, making it hard to assess biomass over a large area directly.…”

Section: Introductionmentioning

confidence: 99%

Estimation of forest above‐ground biomass in Guangxi, China, by integrating forest age and stack learning

Liu

Yue

et al. 2023

Land Degrad Dev

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With the implementation of large‐scale ecological restoration projects, Southwest China has become one of the fastest forest growth areas in the world in terms of vegetation cover and above‐ground biomass (AGB). It is expected to be a potential area for achieving the carbon neutrality target in China. Accurate estimation of forest AGB is becoming an increasingly urgent necessity for carbon neutrality and forest management. However, due to the complex geological background, there is significant uncertainty in estimating forest AGB in the southwestern region, which generally results in underestimating carbon sinks from forest restoration. To address the issue, we propose a method by incorporating forest age information and stack learning technique to estimate forest AGB. Based on remote sensing, forest inventory and in situ forest biomass data, three fundamental methods (Multiple regression, Random forest and Support vector machine) are employed and compared to build the AGB estimation model with vegetation indices, texture feature factors and forest age. Optimal basic models are further enhanced by integration learning to improve the estimation performance and then applied to the study area Guangxi to obtain regional AGB information of different forest types. The results show that: (1) forest age plays a vital role in reducing the uncertainty of AGB estimation. By incorporating forest age information, R2 of AGB estimation is improved by 0.07–0.27 and RMSE is decreased by 16.35%–47.47% for different forest types; (2) with R2 value >0.78, random forest model outperforms support vector machine and multiple linear regression models. Compared with the single optimal model, integration model by stack learning further enhances R2 of estimation by 0.02–0.03 and decreases RMSE by 5.20%–14.89%. (3) The total forest AGB in Guangxi is 988.17 Tg and the average forest AGB level is 73.30 t/ha. Natural broadleaf forest has the highest AGB level (86.75 t/ha), followed by natural coniferous forest (81.19 t/ha), planted coniferous forest (63.23 t/ha) and planted eucalyptus forest (49.71 t/ha). AGB level in karst areas is lower than that in non‐karst areas due to soil and water constraints. The majority of plantation forests in Guangxi is in the early and middle stages of forest succession, with the rapid growth of forest AGB, and has hence significant potential as carbon sinks. This study indicates that stack learning and incorporation of forest age could significantly decrease the uncertainty of forest AGB estimation. Our study helps to provide more accurate AGB information for karst ecological project management and regional carbon neutrality assessment.

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Estimation of Carbon Sequestration; Using Allometric Equations; in Azrou Cedar Forests (Cedrus atlantica Manetti) in the Central Middle Atlas of Morocco under Climate Change

Cited by 4 publications

References 3 publications

Estimation of aboveground carbon stock in PT KOJO’s forest in Riau, Indonesia

Estimation of aboveground carbon stock in PT KOJO’s forest in Riau, Indonesia

Calibrating a Process-based Model to Enhance Robustness in Carbon Sequestration Simulations: the Sase of Cedrus Atlantica (Endl.) Manetti ex Carrière

Estimation of forest above‐ground biomass in Guangxi, China, by integrating forest age and stack learning

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