The biogeochemistry of carbon across a gradient of streams and rivers within the Congo Basin

Dissolved organic carbon (DOC) and inorganic carbon (DIC and pCO2), lignin biomarkers and the optical properties of dissolved organic matter (DOM) were measured in a gradient of streams and rivers within the Congo Basin (Republic of Congo), with the aim of examining how vegetation cover and hydrology influences the composition and concentration of exported fluvial carbon (C). Three sampling campaigns (February 2010, November 2010 and August 2011) spanning 56 sites are compared by sub-basin watershed land cover type (savannah, tropical forest, and swamp) and hydrologic regime (high, intermediate, and low). Land cover properties predominately controlled the amount and quality of DOC, chromophoric DOM (CDOM) and lignin phenol concentrations (∑8) exported in streams and rivers throughout the Congo Basin. Higher DIC concentrations and changing DOM composition (lower molecular weight, less aromatic C) during periods of low hydrologic flow indicated a shift from rapid overland supply pathways in wet conditions to deeper groundwater inputs during drier periods. Lower DOC concentrations in forest and swamp sub-basins were apparent with increasing catchment area, indicating enhanced DOC loss with extended water residence time. Surface water pCO2 in savannah and tropical forest catchments ranged between 2600 and 11922 µatm, and swamp regions contained extremely high pCO2 (10598-15802 µatm), highlighting their potential as significant pathways for water-air efflux. Our data suggest that the quantity and quality of DOM exported to streams and rivers is largely driven by terrestrial ecosystem structure and that anthropogenic land-use or climate change may impact the composition and reactivity of fluvial C, with ramifications for regional C budgets and future climate scenarios

Age, extent and carbon storage of the central Congo Basin peatland complex

Peatlands are carbon-rich ecosystems that cover just three per cent of Earth's land surface, but store one-third of soil carbon. Peat soils are formed by the build-up of partially decomposed organic matter under waterlogged anoxic conditions. Most peat is found in cool climatic regions where unimpeded decomposition is slower, but deposits are also found under some tropical swamp forests. Here we present field measurements from one of the world's most extensive regions of swamp forest, the Cuvette Centrale depression in the central Congo Basin. We find extensive peat deposits beneath the swamp forest vegetation (peat defined as material with an organic matter content of at least 65 per cent to a depth of at least 0.3 metres). Radiocarbon dates indicate that peat began accumulating from about 10,600 years ago, coincident with the onset of more humid conditions in central Africa at the beginning of the Holocene. The peatlands occupy large interfluvial basins, and seem to be largely rain-fed and ombrotrophic-like (of low nutrient status) systems. Although the peat layer is relatively shallow (with a maximum depth of 5.9 metres and a median depth of 2.0 metres), by combining in situ and remotely sensed data, we estimate the area of peat to be approximately 145,500 square kilometres (95 per cent confidence interval of 131,900-156,400 square kilometres), making the Cuvette Centrale the most extensive peatland complex in the tropics. This area is more than five times the maximum possible area reported for the Congo Basin in a recent synthesis of pantropical peat extent. We estimate that the peatlands store approximately 30.6 petagrams (30.6 × 10(15) grams) of carbon belowground (95 per cent confidence interval of 6.3-46.8 petagrams of carbon)-a quantity that is similar to the above-ground carbon stocks of the tropical forests of the entire Congo Basin. Our result for the Cuvette Centrale increases the best estimate of global tropical peatland carbon stocks by 36 per cent, to 104.7 petagrams of carbon (minimum estimate of 69.6 petagrams of carbon; maximum estimate of 129.8 petagrams of carbon). This stored carbon is vulnerable to land-use change and any future reduction in precipitation

Lymphatic filariasis in the Democratic Republic of Congo; micro-stratification overlap mapping (MOM) as a prerequisite for control and surveillance

<p>Abstract</p> <p>Background</p> <p>The Democratic Republic of Congo (DRC) has a significant burden of lymphatic filariasis (LF) caused by the parasite <it>Wuchereria bancrofti</it>. A major impediment to the expansion of the LF elimination programme is the risk of serious adverse events (SAEs) associated with the use of ivermectin in areas co-endemic for onchocerciasis and loiasis. It is important to analyse these and other factors, such as soil transmitted helminths (STH) and malaria co-endemicity, which will impact on LF elimination.</p> <p>Results</p> <p>We analysed maps of onchocerciasis community-directed treatment with ivermectin (CDTi) from the African Programme for Onchocerciasis Control (APOC); maps of predicted prevalence of <it>Loa loa</it>; planned STH control maps of albendazole (and mebendazole) from the Global Atlas of Helminth Infections (GAHI); and bed nets and insecticide treated nets (ITNs) distribution from Demographic and Health Surveys (DHS) as well as published historic data which were incorporated into overlay maps. We developed an approach we designate as micro-stratification overlap mapping (MOM) to identify areas that will assist the implementation of LF elimination in the DRC. The historic data on LF was found through an extensive review of the literature as no recently published information was available.</p> <p>Conclusions</p> <p>This paper identifies an approach that takes account of the various factors that will influence not only country strategies, but suggests that country plans will require a finer resolution mapping than usual, before implementation of LF activities can be efficiently deployed. This is because 1) distribution of ivermectin through APOC projects will already have had an impact of LF intensity and prevalence 2) DRC has been up scaling bed net distribution which will impact over time on transmission of <it>W. bancrofti </it>and 3) recently available predictive maps of <it>L. loa </it>allow higher risk areas to be identified, which allow LF implementation to be initiated with reduced risk where <it>L. loa </it>is considered non-endemic. We believe that using the proposed MOM approach is essential for planning the expanded distribution of drugs for LF programmes in countries co-endemic for filarial infections.</p

Congo Basin peatlands: threats and conservation priorities

The recent publication of the first spatially explicit map of peatlands in the Cuvette Centrale, central Congo Basin, reveals it to be the most extensive tropical peatland complex, at ca. 145,500 km2. With an estimated 30.6 Pg of carbon stored in these peatlands, there are now questions about whether these carbon stocks are under threat and, if so, what can be done to protect them. Here, we analyse the potential threats to Congo Basin peat carbon stocks and identify knowledge gaps in relation to these threats, and to how the peatland systems might respond. Climate change emerges as a particularly pressing concern, given its potential to destabilise carbon stocks across the whole area. Socio-economic developments are increasing across central Africa and, whilst much of the peatland area is protected on paper by some form of conservation designation, the potential exists for hydrocarbon exploration, logging, plantations and other forms of disturbance to significantly damage the peatland ecosystems. The low level of human intervention at present suggests that the opportunity still exists to protect the peatlands in a largely intact state, possibly drawing on climate change mitigation funding, which can be used not only to protect the peat carbon pool but also to improve the livelihoods of people living in and around these peatlands

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

Wetlands Characterization in the Congo River Basin Using Multi-source Remotely Sensed Data and Field Survey

The present dissertation reports wetlands characterization results in the Central African Congo River Basin. Multi-source and multi-resolution remote sensing data are used along with a state-of-the art decision tree classification method to map the wetlands extent in the Democratic Republic of the Congo (DRC). This map is then combined with vegetation and forest cover change maps published in Potapov et al. (2012) to derive a map including forested wetlands as a thematic class. Using population density data, we investigated whether high terra firma population density and low percent remaining terra firma forest are related at the lowest administrative level (Sector); specifically, we tested these two variables as predictors of wetland forest cover loss. Additionally, a network of 548 circular permanent carbon inventory plots of 15 meters radius were established in forested wetlands and dense semi-deciduous forest in order to estimate Above Ground Biomass (AGB) for each forest type and to compare values between forested wetlands and non-wetlands forests. Results showed that wetlands are a significant part of the landscape in the Cuvette Centrale and in the DRC. They cover approximately 32% of the Cuvette Centrale located in the western part of the Democratic Republic of the Congo and in the eastern part of the Republic of the Congo. They also cover an estimated 440,000 square kilometers, or 19.2% of the total area of the Democratic Republic of the Congo. They predominate in the Lake Tumba-Lake Tele area where they cover more than 55% of the land area. By quantifying available upland forest resources and overlaying with population density, it was possible to identify stressed areas inside of the forest domain (traditionally known for having generically high levels of forest resources). A polynomial regression relating population and primary terra firma forest to wetland forest cover loss yielded an r2 of 0.76, illustrating a nascent and significant land cover change dynamic in response to terra firma forest resources exhaustion. Areas most at risk for future wetland forest loss lie in the western Cuvette and coincide with the Lake Tumba and Lake Tele landscape that include (north to south) the Sud-Ubangi, Mongala, Equateur and Mai-Ndombe Districts. The scarcity of terra firma forests in these districts has forced local communities to develop cropping methods that allow for cultivation in periodically flooded areas, specifically in marginally elevated areas within the wetlands. Biomass estimates in the Lake Mai-Ndombe area have revealed that forested wetlands hold important AGB stocks. These stocks estimates varied significantly with the forest type and the allometric equation used to derive AGB from DBH measurements. A local allometric model developed herein has estimated AGB values from 308 to 554 Mg biomass per hectare in flooded forests. The lowest values were estimated for the open swamp forest (308 Mg/ha) and the AGB estimates were significantly higher in the dense swamp forest (554 Mg/ha). Compared to the two mature undisturbed terra firma forest classes (Forescom logged and unlogged), this equation showed that the dense flooded forest has significantly higher AGB stock (Forescom logged: 532.346 Mg/ha and unlogged: 526.718 Mg/ha). The Chave’s Pan-tropical allometric equation yielded substantially larger estimates of AGB values for the undisturbed mature forests. The Forescom logged area has the highest AGB estimate (634.5 Mg/ha), followed by unlogged forest stratum (549.9 Mg/ha) and the dense swamp forest (538.5 Mg/ha). However, there was no significant difference in AGB stocks between the dense flooded forest and the dense semi-deciduous unlogged forest. These results show the need for addressing issues related to wetland forest management and protection in the Congo Basin, especially where increasing populations are exhausting primary terra firma forest resources. Thus, regular forest monitoring and verification using remotely sensed data coupled with on the ground data collection such as forest inventories will be an asset for wetlands preservation and management

Using Geographic Information Systems and Remote Sensing for Sustainable Forest Resources Management in the Mai-Ndombe Region (Democratic Republic of the Congo)

This paper presents the methodology used to build a forest zoning map and to produce a vegetation map for the Mai-Ndombe District using Landsat Thematic Mapper (TM) and/or Enhanced Thematic Mapper (ETM+) images. Nine images with different cloud coverage covering a period from 1986 to 2001 were used. All the TM images used were radiometrically corrected and orthorectified, while ETM+ images required geometric corrections. Moreover, one ETM+ image required radiometric rectification to reduce variability of environmental conditions during the image acquisition. Some scenes had intense haze and thus could not be used for comparative studies. Principal component and scatterplot analyses were carried out for the nine images. The analyses revealed data redundancy between bands one and two; thus all the first bands were excluded from the analysis. Image classifications were performed on all the images separately using Isodata clustering and image segmentation techniques. The result of the classification shows that the study area is mostly covered by swampy forests (dense swampy forest, riparian palm forest, open swampy forest and swampy forest) with 29% of the total area, followed by semi-deciduous forests (single dominant and dense semi-deciduous) with a total 27.7%. The dense humid forest and grassland cover each 10% of the Mai-Ndombe region. Sensitive vegetation classes were extracted to produce various models for forest resources management and a zoning plan using spatial analytical techniques. This zoning map proposes priority allocation for various activities in the region, including local community agriculture land allocation, forest harvesting, and forest and natural resources conservation areas. The use of such models can help prevent conflicts in land utilization in the region