Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices

Bwangoy, Jean-Robert B.; Hansen, Matthew C.; Roy, David P.; Grandi, G. De; Justice, Christopher O.

doi:10.1016/j.rse.2009.08.004

Cited by 291 publications

(188 citation statements)

References 62 publications

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“…This is greater than five times the 'maximum possible' area reported for this region in a recent synthesis of pantropical peat storage 2 . Comparing our estimated area of swamp vegetation that overlies peat with other remotely sensed estimates of the total regional wetland extent, including seasons wetlands 22 , suggests that peatlands account for ~40% of the total regional wetland extent.…”

Section: Main Textmentioning

confidence: 69%

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

Dargie

Lewis

Lawson

et al. 2017

Nature

502

535

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Section: Main Textmentioning

confidence: 69%

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

Dargie

Lewis

Lawson

et al. 2017

Nature

502

535

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“…The Congolese 'Cuvette Centrale' , with a flooded surface area of 360×10 3 km 2 (ref. 20), is the second largest tropical wetland area after the Amazon. The sum of FCO 2 and FCH 4 from the 'Cuvette Centrale' would correspond to 0.48 ± 0.08 PgCO 2 e yr −1 (based on the scaling of a subset of fluxes from the rivers, streams and navigation channels draining the 'Cuvette Centrale' and computed with the k recommended for flooded areas 2 ).…”

Section: Regional and Global Significance Of Ghg Fluxesmentioning

confidence: 99%

Globally significant greenhouse-gas emissions from African inland waters

et al. 2015

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Carbon dioxide emissions to the atmosphere from inland waters-streams, rivers, lakes and reservoirs-are nearly equivalent to ocean and land sinks globally. Inland waters can be an important source of methane and nitrous oxide emissions as well, but emissions are poorly quantified, especially in Africa. Here we report dissolved carbon dioxide, methane and nitrous oxide concentrations from 12 rivers in sub-Saharan Africa, including seasonally resolved sampling at 39 sites, acquired between 2006 and 2014. Fluxes were calculated from published gas transfer velocities, and upscaled to the area of all sub-Saharan African rivers using available spatial data sets. Carbon dioxide-equivalent emissions from river channels alone were about 0.4 Pg carbon per year, equivalent to two-thirds of the overall net carbon land sink previously reported for Africa. Including emissions from wetlands of the Congo river increases the total carbon dioxide-equivalent greenhouse-gas emissions to about 0.9 Pg carbon per year, equivalent to about one quarter of the global ocean and terrestrial combined carbon sink. Riverine carbon dioxide and methane emissions increase with wetland extent and upland biomass. We therefore suggest that future changes in wetland and upland cover could strongly a ect greenhouse-gas emissions from African inland waters.C limate predictions necessitate a full and robust account of natural and anthropogenic greenhouse-gas (GHG) fluxes, especially for CO 2 (refs 1-3), CH 4 (ref. 4) and N 2 O (ref. 5), which together accounted for 94% of the anthropogenic global radiative forcing by well-mixed GHGs in 2011 relative to 1750 (ref. 6). Inland waters (streams, rivers, lakes and reservoirs) are increasingly recognized as important sources of GHGs to the atmosphere, with global CO 2 and CH 4 emissions estimated at 2.1 PgC yr −1 (ref.3) and 0.7 PgC yr −1 (CO 2 -equivalents; CO 2 e) (ref. 4) (1 Pg = 10 15 g), respectively. Considering that the oceanic and land carbon (C) sinks correspond to ∼1.5 and ∼2.0 PgC yr −1 (ref. 7), respectively, the GHG flux from inland waters is significant in the global C budget.In a recent global compilation of inland CO 2 data 3 , <20 data points (out of 6,708, that is, <0.3%) represented African inland waters (with the exception of South Africa, which has been densely sampled), even though they account for ∼12% of both global freshwater discharge 8 and riverine surface area 3 , and include some of the largest rivers and lakes in the world. Equally for the global CH 4 database, there is a strong under-representation of tropical inland waters, whereby a recent synthesis 4 resorted to extrapolating CH 4 fluxes from temperate rivers.The prevailing large uncertainty involved in GHG flux estimates for inland waters, essentially due to the paucity of available data, is coupled to a poor understanding of underlying processes, both of which preclude gauging of future fluxes in response to human pressures. In particular, there is a need to further understand the link between inland water GHG fluxes and ...

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“…RF was selected for this study because it generally outperforms conventional classifiers such as the Gaussian maximum likelihood classifier [61,62], while performing favorably, or equally well, to other non-parametric approaches; e.g., CART [63,64], Support Vector Machines [32,65,66], Artificial Neural Networks [67], and K-Nearest Neighbor [68]. It is a powerful non-linear and non-parametric classifier that allows for fusion and aggregation of high-dimensional data from various sources (e.g., optical, SAR, and topography [30,69,70]; SAR and topography [21,58,71]; and optical and topography [72][73][74]). RF produces independently constructed classification trees, similar to the Classification and Regression (CART) method, using bootstrapped samples of the original data [75,76].…”

Section: Image Classificationmentioning

confidence: 99%

“…The integration of multi-source data to improve RF model performance has been reported in several studies in tropical environments [30,93,94]. Optical and SAR imagery provide complementary information and are often used in combination, while addition of topographic variables has also been shown to improve wetland and other land cover classification [15,21,95,96].…”

Section: Random Forest Classifier Performance and Variable Importancementioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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Abstract:The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82-89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

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Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices

Cited by 291 publications

References 62 publications

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

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

Globally significant greenhouse-gas emissions from African inland waters

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

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