Dataset Information

Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo.

ABSTRACT: National forest inventories in tropical regions are sparse and have large uncertainty in capturing the physiographical variations of forest carbon across landscapes. Here, we produce for the first time the spatial patterns of carbon stored in forests of Democratic Republic of Congo (DRC) by using airborne LiDAR inventory of more than 432,000 ha of forests based on a designed probability sampling methodology. The LiDAR mean top canopy height measurements were trained to develop an unbiased carbon estimator by using 92 1-ha ground plots distributed across key forest types in DRC. LiDAR samples provided estimates of mean and uncertainty of aboveground carbon density at provincial scales and were combined with optical and radar satellite imagery in a machine learning algorithm to map forest height and carbon density over the entire country. By using the forest definition of DRC, we found a total of 23.3 ± 1.6 GtC carbon with a mean carbon density of 140 ± 9 MgC ha^-1 in the aboveground and belowground live trees. The probability based LiDAR samples capture variations of structure and carbon across edaphic and climate conditions, and provide an alternative approach to national ground inventory for efficient and precise assessment of forest carbon resources for emission reduction (ER) programs.

SUBMITTER: Xu L

PROVIDER: S-EPMC5678085 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo.

Xu Liang L Saatchi Sassan S SS Shapiro Aurélie A Meyer Victoria V Ferraz Antonio A Yang Yan Y Bastin Jean-Francois JF Banks Norman N Boeckx Pascal P Verbeeck Hans H Lewis Simon L SL Muanza Elvis Tshibasu ET Bongwele Eddy E Kayembe Francois F Mbenza Daudet D Kalau Laurent L Mukendi Franck F Ilunga Francis F Ebuta Daniel D

Scientific reports 20171108 1

National forest inventories in tropical regions are sparse and have large uncertainty in capturing the physiographical variations of forest carbon across landscapes. Here, we produce for the first time the spatial patterns of carbon stored in forests of Democratic Republic of Congo (DRC) by using airborne LiDAR inventory of more than 432,000 ha of forests based on a designed probability sampling methodology. The LiDAR mean top canopy height measurements were trained to develop an unbiased carbon ...[more]

PMID: 29118358

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Project description:BackgroundHigh-risk fertility behaviour remains a major public health in the Democratic Republic of the Congo, like other sub-Saharan Africa countries, especially because Total Fertility Rate (TFR) is very high in the country, estimated at 6.6 children. Despite the commendable progress in reducing maternal deaths in the region, sub-Saharan Africa is still lagging very behind compared with other regions. Yet, high-risk fertility behaviours are pivotal to improve maternal and child health. This study aims to assess geographical variations of, and to identify risk factors associated with high-risk fertility behaviours among married women in the Democratic Republic of the Congo using the 2013-14 Demographic and Health Survey.MethodsOverall, 11,497 married women were selected from a nationally representative using a two-stage sampling design. Standard logistic regressions were performed to identify individual- and household/community-level factors associated with high-risk fertility behaviours. Additionally, interactions between women's age and education, and urban residence were tested. Bernoulli based spatial scan statistics were used to identify the presence of high-risk fertility behaviours spatial clusters using Kulldorff's SaTScan version 9.6 software. ArcGIS 10.7 was used to visualize the spatial variations of high-risk fertility behaviours. Geographically weighted regression (GWR) analysis was employed using Multiscale GWR version 2.0 software.ResultsFindings indicated that more than two-third of married women exhibited high-risk fertility behaviours in the Democratic Republic of the Congo. Multivariate logistic regression showed that education was negatively and significantly associated with the odds of high-risk fertility behaviours. In contrast, women's age significantly increased the odds of high-risk fertility behaviours. Interactions between urban residence and women's education and age confirmed the urban advantage identified from previous studies. Finally, high-risk fertility behaviours were highly clustered in the Northeastern provinces of the country.ConclusionThe study showed that there were significant geographical variations of high-risk fertility behaviours across provinces in the Democratic Republic of the Congo. The paper also identified significant-high hot spots of high-risk fertility behaviours in the Northeastern provinces of the country. To reduce high-risk fertility behaviours, and ultimately improve maternal and child outcomes in the country, policymakers and health planners need to strategically address these inequalities. Finally, this paper highlighted the persistent needs of country-specific studies due to differences across sub-Saharan African countries in terms of social development and cultures.

Project description:Most Central African rainforests are characterized by a remarkable abundance of light-demanding canopy species: long-lived pioneers (LLP) and non-pioneer light demanders (NPLD). A popular explanation is that these forests are still recovering from intense slash-and-burn farming activities, which abruptly ended in the 19th century. This "human disturbance" hypothesis has never been tested against spatial distribution patterns of these light demanders. Here, we focus on the 28 most abundant LLP and NPLD from 250 one-ha plots distributed along eight parallel transects (~50 km) in the Yangambi forest. Four species of short-lived pioneers (SLP) and a single abundant shade-tolerant species (Gilbertiodendron dewevrei) were used as reference because they are known to be strongly aggregated in recently disturbed patches (SLP) or along watercourses (G. dewevrei). Results show that SLP species are strongly aggregated with clear spatial autocorrelation of their diameter. This confirms that they colonized the patch following a one-time disturbance event. In contrast, LLP and NPLD species have random or weakly aggregated distribution, mostly without spatial autocorrelation of their diameter. This does not unambiguously confirm the "human disturbance" hypothesis. Alternatively, their abundance might be explained by their deciduousness, which gave them a competitive advantage during long-term drying of the late Holocene. Additionally, a canonical correspondence analysis showed that the observed LLP and NPLD distributions are not explained by environmental variables, strongly contrasting with the results for the reference species G. dewevrei, which is clearly aggregated along watercourses. We conclude that the abundance of LLP and NPLD species in Yangambi cannot be unambiguously attributed to past human disturbances or environmental variables. An alternative explanation is that present-day forest composition is a result of adaptation to late-Holocene drying. However, results are inconclusive and additional data are needed to confirm this alternative hypothesis.

Dataset Information

Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo.

Publications

Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo.

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