The impact of land use on soil carbon in Miombo Woodlands of Malawi

Walker, Sarah; Desanker, Paul V.

doi:10.1016/j.foreco.2004.08.004

Cited by 141 publications

(101 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, there was no discernible increase in soil C stocks within the period of re-growth, suggesting that the rate of accumulation of organic matter in these soils was very slow. On the other hand, [58] observed that agricultural soils in Malawi had 40% less carbon than mature Miombo Woodlands. The authors stated that as the area of land converted to agriculture increases in the region, land in this re-growth state will most likely become the dominant form of Miombo.…”

Section: Biomass and Carbon Dynamicsmentioning

confidence: 99%

See 1 more Smart Citation

Miombo Woodlands Research Towards the Sustainable Use of Ecosystem Services in Southern Africa

Ribeiro¹,

Syampungani²,

Matakala³

et al. 2015

Biodiversity in Ecosystems - Linking Structure and Function

View full text Add to dashboard Cite

Section: Biomass and Carbon Dynamicsmentioning

confidence: 99%

“…Although soil is one of the main carbon pools in Miombo, studies that deal with this component are limited [28,48,[58][59]. [28] observed that woodland soils were capable of storing >100 t C ha -1 , whereas in re-growing areas soil carbon stocks did not exceed 74 t C ha -1 .…”

Section: Biomass and Carbon Dynamicsmentioning

confidence: 99%

Miombo Woodlands Research Towards the Sustainable Use of Ecosystem Services in Southern Africa

Ribeiro¹,

Syampungani²,

Matakala³

et al. 2015

Biodiversity in Ecosystems - Linking Structure and Function

View full text Add to dashboard Cite

“…Donato et al [8], studying Indo-Pacific mangrove soils, found lower soil bulk densities (0.35-0.55 g·cm ) with no significant changes from the top soil down to 100 cm depth, but higher bulk densities in deeper layers in a depth of up to 150 cm. According to Wendling [50], the reduction of carbon concentration with depth is more common in terrestrial forests due to high concentrations of biological activity, particularly litter deposition and decomposition near the soil surface, while deposition of sediments from the river stream constitute an important source of organic matter in mangrove soils. The average soil carbon content (depth 0-100 cm) was estimated as 160 Mg·ha .…”

Section: Soil Carbonmentioning

confidence: 99%

Biomass and Carbon Stocks of Sofala Bay Mangrove Forests

Sitoe

Mandlate

Guedes

2014

Forests

108

View full text Add to dashboard Cite

Mangroves could be key ecosystems in strategies addressing the mitigation of climate changes through carbon storage. However, little is known regarding the carbon stocks of these ecosystems, particularly below-ground. This study was carried out in the mangrove forests of Sofala Bay, Central Mozambique, with the aim of quantifying carbon stocks of live and dead plant and soil components. The methods followed the procedures developed by the Center for International Forestry Research (CIFOR) for mangrove forests. In this study, we developed a general allometric equation to estimate individual tree biomass and soil carbon content (up to 100 cm depth). We estimated the carbon in the whole mangrove ecosystem of Sofala Bay, including dead trees, wood debris, herbaceous, pneumatophores, litter and soil. The general allometric equation for live trees derived was [Above-ground tree dry weight (kg) = 3.254 × exp(0.065 × DBH)], root mean square error (RMSE = 4.244), and coefficient of determination (R 2 = 0.89). The average total carbon storage of Sofala Bay mangrove was 218.5 Mg·ha , of which around 73% are stored in the soil. Mangrove conservation has the potential for REDD+ programs, especially in regions like Mozambique, which contains extensive mangrove areas with high deforestation and degradation rates.

show abstract

“…Miombo woodlands are extensive in the Democratic Republic of Congo (DRC), Angola, Tanzania, Mozambique, Malawi, Zambia and Zimbabwe [1,2]. These broad-leaved deciduous woodlands-dominated by tree species, such as Brachystegia, Julbernardia, and Isoberlinia-provide important ecosystem, socioeconomic and cultural services in Central and Southern Africa [3].…”

Section: Introductionmentioning

confidence: 99%

Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

Kamusoko¹,

Gamba²,

Murakami

2014

Land

View full text Add to dashboard Cite

Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT), random forests (RF), and support vector machines (SVM) in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran's I) variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.

show abstract

The impact of land use on soil carbon in Miombo Woodlands of Malawi

Cited by 141 publications

References 50 publications

Miombo Woodlands Research Towards the Sustainable Use of Ecosystem Services in Southern Africa

Miombo Woodlands Research Towards the Sustainable Use of Ecosystem Services in Southern Africa

Biomass and Carbon Stocks of Sofala Bay Mangrove Forests

Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

Contact Info

Product

Resources

About