Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar

Hess, Laura L.; Mélack, John M.; Affonso, Adriana Gomes; Barbosa, Cláudio Clemente Faria; Gastil-Buhl, M.; Novo, Evlyn Márcia Leão de Moraes

doi:10.1007/s13157-015-0666-y

Cited by 219 publications

(266 citation statements)

References 57 publications

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“…The contribution of PALSAR L-band HH and HV data to wetland classification has been well documented [33,34,58,100]. For example, large-area mapping of the Pantanal using PALSAR L-band FBD [33], achieved 80% accuracy across the entire area.…”

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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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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“…It might not be appropriate to assume a uniform vegetation height for all the DEM pixels within the grid cell of the vegetation height dataset. Hess et al (2003Hess et al ( , 2015a) developed a high-resolution (3 arcsec) land cover dataset for floodplains (or wetlands) located in the lowland Amazon Basin (i.e., areas with elevations lower than 500 m). This land cover dataset was used in our DEM correction process.…”

Section: Vegetation-caused Biases In Demmentioning

confidence: 99%

“…This result suggests that flood extent discrepancies were also caused by other factors such as (1) uncertainties in model parameters including floodplain topography, channel cross-sectional geometry, channel Manning coefficients, the riverbed slope, etc. ; (2) surface water bodies (e.g., lakes and swamps) not represented by the model that were lumped into the inundated floodplains; (3) subsurface processes and wetlands sustained by groundwater that were not simulated; and (4) inundation that could be underestimated or overestimated in the GIEMS data which were of comparatively low resolution (Hess et al, 2015a;Prigent et al, 2007). The effects of model parameters (including floodplain topography, channel cross-sectional geometry and channel Manning coefficients) on the inundation results were investigated in the sensitivity study.…”

Section: Flood Extentmentioning

confidence: 99%

“…Seasonal floods occur every year and wetlands occupy a considerable fraction of the total area in the basin (Hess et al, 2003(Hess et al, , 2015a. River and inundation dynamics were simulated by using 2-D hydrodynamic models in the central Amazon Basin (e.g., Baugh et al, 2013;Wilson et al, 2007).…”

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confidence: 99%

“…To alleviate those biases, we used a method similar to that of Baugh et al (2013). Besides the vegetation height map by Simard et al (2011), we also used a land cover dataset for wetlands of the lowland Amazon Basin developed by Hess et al (2003Hess et al ( , 2015a. A "bare-earth" DEM of the Amazon Basin was created and employed in the hydrologic modeling for the entire basin.…”

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confidence: 99%

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Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation v1.0: impacts of geomorphological parameters and river flow representation

Luo

Leung

et al. 2017

Geosci. Model Dev.

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Abstract. In the Amazon Basin, floodplain inundation is a key component of surface water dynamics and plays an important role in water, energy and carbon cycles. The Model for Scale Adaptive River Transport (MOSART) was extended with a macroscale inundation scheme for representing floodplain inundation. The extended model, named MOSART-Inundation, was used to simulate surface hydrology of the entire Amazon Basin. Previous hydrologic modeling studies in the Amazon Basin identified and addressed a few challenges in simulating surface hydrology of this basin, including uncertainties of floodplain topography and channel geometry, and the representation of river flow in reaches with mild slopes. This study further addressed four aspects of these challenges. First, the spatial variability of vegetationcaused biases embedded in the HydroSHEDS digital elevation model (DEM) data was explicitly addressed. A vegetation height map of about 1 km resolution and a land cover dataset of about 90 m resolution were used in a DEM correction procedure that resulted in an average elevation reduction of 13.2 m for the entire basin and led to evident changes in the floodplain topography. Second, basin-wide empirical formulae for channel cross-sectional dimensions were refined for various subregions to improve the representation of spatial variability in channel geometry. Third, the channel Manning roughness coefficient was allowed to vary with the channel depth, as the effect of riverbed resistance on river flow generally declines with increasing river size. Lastly, backwater effects were accounted for to better represent river flow in mild-slope reaches. The model was evaluated against in situ streamflow records and remotely sensed Envisat altimetry data and Global Inundation Extent from Multi-Satellites (GIEMS) inundation data. In a sensitivity study, seven simulations were compared to evaluate the impacts of the five modeling aspects addressed in this study. The comparisons showed that representing floodplain inundation could significantly improve the simulated streamflow and river stages. Refining floodplain topography, channel geometry and Manning roughness coefficients, as well as accounting for backwater effects had notable impacts on the simulated surface water dynamics in the Amazon Basin. The understanding obtained in this study could be helpful in improving modeling of surface hydrology in basins with evident inundation, especially at regional to continental scales.

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Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon

Longo

Keller

dos-Santos

et al. 2016

Global Biogeochemical Cycles

125

165

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Deforestation rates have declined in the Brazilian Amazon since 2005, yet degradation from logging, fire, and fragmentation has continued in frontier forests. In this study we quantified the aboveground carbon density (ACD) in intact and degraded forests using the largest data set of integrated forest inventory plots (n = 359) and airborne lidar data (18,000 ha) assembled to date for the Brazilian Amazon. We developed statistical models relating inventory ACD estimates to lidar metrics that explained 70% of the variance across forest types. Airborne lidar‐ACD estimates for intact forests ranged between 5.0 ± 2.5 and 31.9 ± 10.8 kg C m−2. Degradation carbon losses were large and persistent. Sites that burned multiple times within a decade lost up to 15.0 ± 0.7 kg C m−2 (94%) of ACD. Forests that burned nearly 15 years ago had between 4.1 ± 0.5 and 6.8 ± 0.3 kg C m−2 (22–40%) less ACD than intact forests. Even for low‐impact logging disturbances, ACD was between 0.7 ± 0.3 and 4.4 ± 0.4 kg C m−2 (4–21%) lower than unlogged forests. Comparing biomass estimates from airborne lidar to existing biomass maps, we found that regional and pantropical products consistently overestimated ACD in degraded forests, underestimated ACD in intact forests, and showed little sensitivity to fires and logging. Fine‐scale heterogeneity in ACD across intact and degraded forests highlights the benefits of airborne lidar for carbon mapping. Differences between airborne lidar and regional biomass maps underscore the need to improve and update biomass estimates for dynamic land use frontiers, to better characterize deforestation and degradation carbon emissions for regional carbon budgets and Reduce Emissions from Deforestation and forest Degradation (REDD+).

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Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar

Cited by 219 publications

References 57 publications

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

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

Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation v1.0: impacts of geomorphological parameters and river flow representation

Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon

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