In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of ...aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (>
100
t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was
r²
=
0.53 with an
RMSE of 79
t/ha. The model is valid up to 307
t/ha with an accuracy requirement of 50
t/ha and up to 614
t/ha with an accuracy requirement of 100
t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.
► We analyzed X- and L-band SAR data to estimate tropical forest aboveground biomass. ► Field biomass reference data were upscaled using LiDAR measurements. ► The multi-temporal L- and X-band combined model achieved the best result. ►The model is valid up to 307
t/ha. ►Spatial distribution of AGB is indicated over the whole biomass range up to 614
t/ha.
Theoretical uncertainties in the simulation of tt¯bb¯ production represent one of the main obstacles that still hamper the observation of Higgs-boson production in association with top-quark pairs in ...the H→bb¯ channel. In this Letter we present a next-to-leading order (NLO) simulation of tt¯bb¯ production with massive b-quarks matched to the Sherpa parton shower. This allows one to extend NLO predictions to arbitrary tt¯bb¯ kinematics, including the case where one or both b-jets arise from collinear g→bb¯ splittings. We find that this splitting mechanism plays an important role for the tt¯H(bb¯) analysis.
A
bstract
We present precise predictions for four-lepton plus jets production at the LHC obtained within the fully automated S
herpa
+ O
pen
L
oops
framework. Off-shell intermediate vector bosons and ...related interferences are consistently included using the complex-mass scheme. Four-lepton plus 0- and 1-jet final states are described at NLO accuracy, and the precision of the simulation is further increased by squared quark-loop NNLO contributions in the gg → 4
ℓ
, gg → 4
ℓ
+ g, g
q
→ 4
ℓ
+
q
, and
→ 4
ℓ
+ g channels. These NLO and NNLO contributions are matched to the S
herpa
parton shower, and the 0- and 1-jet final states are consistently merged using the M
eps
@N
lo
technique. Thanks to Sudakov resummation, the parton shower provides improved predictions and uncertainty estimates for exclusive observables. This is important when jet vetoes or jet bins are used to separate four-lepton final states arising from Higgs decays, diboson production, and top-pair production. Detailed predictions are presented for the Atlas and C
ms
H → WW
*
analyses at 8 TeV in the 0- and 1-jet bins. Assessing renormalisation-, factorisation- and resummationscale uncertainties, which reflect also unknown subleading Sudakov logarithms in jet bins, we find that residual perturbative uncertainties are as small as a few percent.
Extensive peatlands in Indonesia are a major store of carbon. Deforestation, conversion to other land uses, especially plantations of oil palm and pulpwood trees, and recurrent fires have recently ...caused the release of large amounts of this carbon to the atmosphere. If these large emissions from degrading peatlands are taken into account Indonesia is one of the largest emitters of CO
2 worldwide. To improve estimates of the amount of carbon stored in Indonesian peatlands we applied 3D modelling based on the combined analysis of satellite imagery (Landsat ETM+, SRTM) and 750 in situ peat thickness measurements. We demonstrate that SRTM radar data can be used to determine the extent and topography of the dome shaped surface of a selection of peatlands in Central Kalimantan, South Sumatra and West Papua. A strong correlation was obtained between the convex peat dome surface and the underlying mineral ground, which was used to calculate the peat volume and carbon store. Conservatively, we estimate that at least 55
±
10 Gt of carbon are stored in Indonesia's peatlands. This amount is higher than previous results published because it takes into account the biconvex nature of the tropical peatlands. With this huge carbon storage and the current rate of degradation the tropical peatlands of Indonesia have the power to negatively influence the global climate.
We present theoretical predictions for the production of top-quark pairs with up to three jets at the next-to leading order in perturbative QCD. The relevant calculations are performed with
Sherpa
...and
OpenLoops
. To address the issue of scale choices and related uncertainties in the presence of multiple scales, we compare results obtained with the standard scale
H
T
/
2
at fixed order and the M
i
NLO procedure. Analyzing various cross sections and distributions for
t
t
¯
+
0
,
1
,
2
,
3
jets at the 13 TeV LHC we find a remarkable overall agreement between fixed-order and M
i
NLO results. The differences are typically below the respective factor-two scale variations, suggesting that for all considered jet multiplicities missing higher-order effects should not exceed the ten percent level.
In 1997-98, fires associated with an exceptional drought caused by the El Niño/Southern Oscillation (ENSO) devastated large areas of tropical rain forests worldwide. Evidence suggests that in ...tropical rainforest environments selective logging may lead to an increased susceptibility of forests to fire. We investigated whether this was true in the Indonesian fires, the largest fire disaster ever observed. We performed a multiscale analysis using coarse- and high-resolution optical and radar satellite imagery assisted by ground and aerial surveys to assess the extent of the fire-damaged area and the effect on vegetation in East Kalimantan on the island of Borneo. A total of 5.2 ± 0.3 million hectares including 2.6 million hectares of forest was burned with varying degrees of damage. Forest fires primarily affected recently logged forests; primary forests or those logged long ago were less affected. These results support the hypothesis of positive feedback between logging and fire occurrence. The fires severely damaged the remaining forests and significantly increased the risk of recurrent fire disasters by leaving huge amounts of dead flammable wood.
Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, ...biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.
In the context of the ongoing climate change discussions the importance of peatlands as carbon stores is increasingly recognised in the public. Drainage, deforestation and peat fires are the main ...reasons for the release of huge amounts of carbon from peatlands. Successful restoration of degraded tropical peatlands is of high interest due to their huge carbon store and sequestration potential. The blocking of drainage canals by dam building has become one of the most important measures to restore the hydrology and the ecological function of the peat domes. This study investigates the capability of using multitemporal radar remote sensing imagery for monitoring the hydrological effects of these measures. The study area is the former Mega Rice Project area in Central Kalimantan, Indonesia, where peat drainage and forest degradation is especially intense. Restoration measures started in July 2004 by building 30 large dams until June 2008. We applied change detection analysis with more than 80 ENVISAT ASAR and ALOS PALSAR images, acquired between 2004 and 2009. Radar signal increases of up to 1.36 dB show that high frequency multitemporal radar satellite imagery can be used to detect an increase in peat soil moisture after dam construction, especially in deforested areas with a high density of dams. Furthermore, a strong correlation between cross-polarised radar backscatter coefficients and groundwater levels above −50 cm was found. Monitoring peatland rewetting and quantifying groundwater level variations is important information for vegetation re-establishment, fire hazard warning and making carbon emission mitigation tradable under the voluntary carbon market or REDD (Reducing Emissions from Deforestation and Degradation) mechanism.
Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is ...challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.
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