The Contribution of Agriculture, Forestry and other Land Use activities to Global Warming, 1990–2012

Tubiello, Francesco N.; Salvatore, Mirella; Ferrara, Alessandro; House, Joanna Isobel; Federici, Sandro; Rossi, Simone; Biancalani, Riccardo; Golec, Rocio D. Condor; Jacobs, Heather; Flammini, Alessandro; Prosperi, Paolo; Cardenas-Galindo, Paola; Schmidhuber, Josef; Sanz, María José; Srivastava, Nalin.; Smith, Pete

doi:10.1111/gcb.12865

Cited by 458 publications

(316 citation statements)

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“…Anthropogenic emissions of non-CO 2 gases from peat and forest fires were 300 million tCO 2 eq yr -1 on average over the period , with prescribed burning of savanna adding 200 million tCO 2 eq yr -1 (Tubiello et al 2014). Slash and burn practices associated with deforestation were furthermore responsible for over 1.5 billion tCO 2 eq yr -1 as direct CO 2 gas ).…”

Section: Introductionmentioning

confidence: 99%

“…Specific fire emission categories are significant in terms of their overall regional impacts. For instance, in Africa, prescribed burning of savanna represents 25 % of total agricultural emissions (Tubiello et al 2014). In South-east Asia, fires in drained tropical peatland, often linked to palm oil cultivation, can lead to significant emissions in specific years, with impacts seen at large regional and even global scales (Hayasaka et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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FAOSTAT estimates of greenhouse gas emissions from biomass and peat fires

et al. 2016

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The Global Fire Emissions Database (GFED3) and the FAOSTAT Emissions database, containing estimates of greenhouse gas (GHG) emissions from biomass burning and peat fires, are compared. The two datasets formed the basis for several analyses in the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5), and thus represent a critical source of information for emissions inventories at national, regional and global level. The two databases differ in their level of computational complexity in estimating emissions. While both use the same burned area information from remote sensing, estimates of available biomass are computed in GFED3 at tier 3 using a complex dynamic vegetation model, while they are computed in FAOSTAT using default, tier 1 parameters from the Intergovernmental Panel on Climate Change (IPCC). Over the analysis period 1997-2011, the two methods were found to produce very similar global GHG emissions estimates for each of the five GFED aggregated biomass fire classes: i) Savanna; ii) Woodland; iii) Forest; iv) Deforestation; v) Peatlands; with total emissions ranging 6-8 Gt CO2eq yr -1 . The main differences between the two datasets were found with respect to peat fires, with FAOSTAT showing a lower 1997-1998 peak in emissions compared with GFED3, within an otherwise good agreement for the rest of the study period, when limited to the three tropical countries covered by GFED. Conversely, FAOSTAT global emissions from peat fires, including both boreal and tropical regions, were several times larger than those currently estimated by GFED3. Results Climatic Change (2016) 135:699-711

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FAOSTAT estimates of greenhouse gas emissions from biomass and peat fires

et al. 2016

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“…FAO uses the IPCC approach called "Tier 1" (e.g. Tubiello et al, 2015) to produce a "Land use -forest land" estimate from the Forest Resources Assessment data used in the bookkeeping method described in Sect. 2.2.1 (MacDicken, 2015).…”

Section: Other Published E Luc Methodsmentioning

confidence: 99%

Global Carbon Budget 2015

Quéré

Moriarty

Andrew

et al. 2015

Earth Syst. Sci. Data

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Abstract. Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry (E FF ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth (G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink (S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink (S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover change (some including nitrogen-carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), E FF was 9.0 ± 0.5 GtC yr −1 , E LUC was 0.9 ± 0.5 GtC yr −1 , G ATM was 4.4 ± 0.1 GtC yr −1 , S OCEAN was 2.6 ± 0.5 GtC yr −1 , and S LAND was 3.0 ± 0.8 GtC yr −1 . For the year 2014 alone, E FF grew to 9.8 ± 0.5 GtC yr −1 , 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr −1 that took place during 2005-2014. Also, for 2014, E LUC was 1.1 ± 0.5 GtC yr −1 , G ATM was 3.9 ± 0.2 GtC yr −1 , S OCEAN was 2.9 ± 0.5 GtC yr −1 , and S LAND was 4.1 ± 0.9 GtC yr −1 . G ATM was lower in 2014 compared to the past decade (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), reflecting a larger S LAND for that year. The global atmospheric CO 2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in E FF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0....

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“…According to the Intergovernmental Panel on Climate Change (IPCC) 5 th Assessment Report (AR5), Agriculture, Forestry, and Other Land Use (AFOLU) sectors contributed 24% of total anthropogenic emissions in 2010 (IPCC 2014;Tubiello et al 2015). While all sectors excluding AFOLU increased Greenhouse Gas (GHG) emissions, land use sectors including forestry maintained similar levels as before due to decreasing deforestation and increasing reforestation.…”

Section: Introductionmentioning

confidence: 99%

Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

Kim¹,

Lee²,

Yoon³

et al. 2016

Terr. Atmos. Ocean. Sci.

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Forest carbon is accurately quantified by observing individual tree positions and heights. This paper proposes a novel algorithm for individual tree detection using Light Detection and Ranging (LiDAR) data in the Chollipo arboretum, South Korea. The proposed algorithm does not need to specify a proper window size for operation, taking advantage over the mostly used local maxima (LM) filtering for forest analysis. Four hundred twenty-nine treetops were detected and the average height and standard errors were 12.74 ± 0.24 m. Reference data were collected from two sources for verifying accuracy: field survey and visual interpretation. Overall, the result was overestimated but showed relatively high accuracy. The field survey detected 87% of the trees with a coefficient of determination (R 2 ) and root mean square error (RMSE) of 0.77 and 1.57 m, respectively. The accuracy index (AI), which examines the correspondence between LiDAR detected and visually interpreted trees, was 91%. The average tree height error between on-site and LiDAR derived data was -1.42 ± 0.64 m and between visually interpreted and LiDAR derived data was -0.84 ± 0.10 m. This study emphasized the choice of algorithm and its parameters depending on forest conditions may influence the individual tree detection result. By comparing our work against previous studies, we found the tree location and height identification accuracy could be improved if different algorithms were used for different types of forests, as well as the LiDAR point density with each algorithm. This study suggests that more accurate individual tree detection could be obtained with different applications based on forest conditions.

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The Contribution of Agriculture, Forestry and other Land Use activities to Global Warming, 1990–2012

Cited by 458 publications

References 10 publications

FAOSTAT estimates of greenhouse gas emissions from biomass and peat fires

FAOSTAT estimates of greenhouse gas emissions from biomass and peat fires

Global Carbon Budget 2015

Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

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