Quantifying spatial-temporal changes of aboveground carbon stocks using Landsat time series data: A case study of Miombo woodlands

Lembani, Reuben; Knight, Jasper; Adam, Elhadi; Kalaba, Felix Kanungwe

doi:10.1080/00207233.2019.1698870

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…According to (Lembani et al, 2020), the influence of forest disturbance on aboveground carbon stores in the Miombo woodlands is highlighted by field evidence of variations in tree size at different sites and the related spatial changes in AGLB. Xu, (2016), argued that, internal community-level mechanisms like as competition and natural succession, rather than external environmental factors, appear to be more important in driving forest biomass C variation at the regional scale.…”

Section: Factors Affecting Carbon Stockmentioning

confidence: 99%

Estimation of the Aboveground Carbon Stock in Kanonge Local Forest of Kaputa District in Northern Province, Zambia

Nyirenda

2023

IJES

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Purpose: The majority of district-level carbon stock estimates for the local forests are obsolete and incomplete. Because of this, decisions made by policymakers have an impact on forest regions. This study establishes some baseline information for estimates of the above-ground carbon stocks in the nearby Kanonge forest. Methodology: The use of a sequential explanatory design allowed the quantitative data to be collected and analysed first, and the qualitative data to be collected and analysed later to explain why the variables identified in the first phase are significant predictors of the aboveground carbon stocks. As a result, 15 sample plots were used in the forest, 286 households were sampled on villages within 5km radius to the forest, 8 key informants were sampled from Forestry Department, 8 from Ministry of Agriculture, 10 village head persons and 10 forest users. The quantitative data was collected using forest inventories and household surveys while qualitative data was collected using key informants’ interviews. The allometric equations were used to analyse forest inventory data, logistic regression was used to analyse household survey data and the key informant interviews were analysed using thematic analysis. Findings: The results show that, the above ground biomass and carbon stock were 276.19 ± 7.41 and 155.76 ± 4.18 t ha-1 respectively. The logistic regression results shows that Forest products use, farm size, household size and forest clearing where the significant predictors of the reduction of the aboveground carbon stocks. The results further reveals that there is inadequate intersectoral coordination at district and local level. Unique Contribution to Theory, Practice and Policy: Further study should be done to find strategies to sustain carbon stocks in the forest while maintaining the use of the forest by the local communities. This study provides accurate estimates of forest and tree carbon stocks and supports in the development of the district integrated plan in accordance with decentralized policy. This information can therefore be used at the district and federal levels in various carbon programs like REDD+, offering the district the chance to participate in the fight against climate change and global warming.

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Section: Factors Affecting Carbon Stockmentioning

confidence: 99%