Comment on 'A first map of tropical Africa's above-ground biomass derived from satellite imagery'

Copyright Institute of Physics © 2011We present a critical evaluation of the above-ground biomass (AGB) map of Africa published in this journal by Baccini et al (2008 Environ. Res. Lett. 3 045011). We first test their map against an independent dataset of 1154 scientific inventory plots from 16 African countries, and find only weak correspondence between our field plots and the AGB value given for the surrounding 1 km pixel by Baccini et al. Separating our field data using a continental landcover classification suggests that the Baccini et al map underestimates the AGB of forests and woodlands, while overestimating the AGB of savannas and grasslands. Secondly, we compare their map to 216 000 × 0.25 ha spaceborne LiDAR footprints. A comparison between Lorey's height (basal-area-weighted average height) derived from the LiDAR data for 1 km pixels containing at least five LiDAR footprints again does not support the hypothesis that the Baccini et al map is accurate, and suggests that it significantly underestimates the AGB of higher AGB areas. We conclude that this is due to the unsuitability of some of the field data used by Baccini et al to create their map, and overfitting in their model, resulting in low accuracies outside the small areas from which their field data are drawn

Understanding ‘saturation’ of radar signals over forests

There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain ‘saturation’ point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points

A framework for estimating forest disturbance intensity from successive remotely sensed biomass maps:Moving beyond average biomass loss estimates

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Background The success of satellites in mapping deforestation has been invaluable for improving our understanding of the impacts and nature of land cover change and carbon balance. However, current satellite approaches struggle to quantify the intensity of forest disturbance, i.e. whether the average rate of biomass loss for a region arises from heavy disturbance focused in a few locations, or the less severe disturbance of a wider area. The ability to distinguish between these, very different, disturbance regimes remains critical for forest managers and ecologists. Results We put forward a framework for describing all intensities of forest disturbance, from deforestation, to widespread low intensity disturbance. By grouping satellite observations into ensembles with a common disturbance regime, the framework is able to mitigate the impacts of poor signal-to-noise ratio that limits current satellite observations. Using an observation system simulation experiment we demonstrate that the framework can be applied to provide estimates of the mean biomass loss rate, as well as distinguish the intensity of the disturbance. The approach is robust despite the large random and systematic errors typical of biomass maps derived from radar. The best accuracies are achieved with ensembles of ≥1600 pixels (≥1 km 2 with 25 by 25 m pixels). Summary The framework we describe provides a novel way to describe and quantify the intensity of forest disturbance, which could help to provide information on the causes of both natural and anthropogenic forest loss—such information is vital for effective forest and climate policy formulation.ESANERC National Centre for Earth ObservationNERC CarbonFusion projectMpingo Conservation and Development InitiativeEU Framework 7 I-REDD + projec

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100

Africa and the global carbon cycle

The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications. However, the sparse observation network in and around the African continent means that Africa is one of the weakest links in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major source of interannual variability in global atmospheric CO(2). Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability are likely to undergo substantial increases through the 21st century

Uncertainty in the spatial distribution of tropical forest biomass:a comparison of pan-tropical maps

BACKGROUND: Mapping the aboveground biomass of tropical forests is essential both for implementing conservation policy and reducing uncertainties in the global carbon cycle. Two medium resolution (500 m – 1000 m) pantropical maps of vegetation biomass have been recently published, and have been widely used by sub-national and national-level activities in relation to Reducing Emissions from Deforestation and forest Degradation (REDD+). Both maps use similar input data layers, and are driven by the same spaceborne LiDAR dataset providing systematic forest height and canopy structure estimates, but use different ground datasets for calibration and different spatial modelling methodologies. Here, we compare these two maps to each other, to the FAO’s Forest Resource Assessment (FRA) 2010 country-level data, and to a high resolution (100 m) biomass map generated for a portion of the Colombian Amazon. RESULTS: We find substantial differences between the two maps, in particular in central Amazonia, the Congo basin, the south of Papua New Guinea, the Miombo woodlands of Africa, and the dry forests and savannas of South America. There is little consistency in the direction of the difference. However, when the maps are aggregated to the country or biome scale there is greater agreement, with differences cancelling out to a certain extent. When comparing country level biomass stocks, the two maps agree with each other to a much greater extent than to the FRA 2010 estimates. In the Colombian Amazon, both pantropical maps estimate higher biomass than the independent high resolution map, but show a similar spatial distribution of this biomass. CONCLUSIONS: Biomass mapping has progressed enormously over the past decade, to the stage where we can produce globally consistent maps of aboveground biomass. We show that there are still large uncertainties in these maps, in particular in areas with little field data. However, when used at a regional scale, different maps appear to converge, suggesting we can provide reasonable stock estimates when aggregated over large regions. Therefore we believe the largest uncertainties for REDD+ activities relate to the spatial distribution of biomass and to the spatial pattern of forest cover change, rather than to total globally or nationally summed carbon density