A human-driven decline in global burned area Andela, N.; Morton, D. C.; Giglio, L. ...
Science (American Association for the Advancement of Science),
06/2017, Letnik:
356, Številka:
6345
Journal Article
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Fire is an essential Earth system process that alters ecosystem and atmospheric composition. Here we assessed long-term fire trends using multiple satellite data sets. We found that global burned ...area declined by 24.3 ± 8.8% over the past 18 years. The estimated decrease in burned area remained robust after adjusting for precipitation variability and was largest in savannas. Agricultural expansion and intensification were primary drivers of declining fire activity. Fewer and smaller fires reduced aerosol concentrations, modified vegetation structure, and increased the magnitude of the terrestrial carbon sink. Fire models were unable to reproduce the pattern and magnitude of observed declines, suggesting that they may overestimate fire emissions in future projections. Using economic and demographic variables, we developed a conceptual model for predicting fire in human-dominated landscapes.
The net flux of carbon from land use and land-cover change (LULCC) accounted for 12.5% of anthropogenic carbon emissions from 1990 to 2010. This net flux is the most uncertain term in the global ...carbon budget, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and not all analyses consider the same types of management activities. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. In addition to deforestation, all analyses considered changes in the area of agricultural lands (croplands and pastures). Some considered, also, forest management (wood harvest, shifting cultivation). None included emissions from the degradation of tropical peatlands. Means and standard deviations across the thirteen model estimates of annual emissions for the 1980s and 1990s, respectively, are 1.14 ± 0.23 and 1.12 ± 0.25 Pg C yr−1 (1 Pg = 1015 g carbon). Four studies also considered the period 2000–2009, and the mean and standard deviations across these four for the three decades are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The standard deviations across model means shown here are smaller than previous estimates of uncertainty as they do not account for the errors that result from data uncertainty and from an incomplete understanding of all the processes affecting the net flux of carbon from LULCC. Although these errors have not been systematically evaluated, based on partial analyses available in the literature and expert opinion, they are estimated to be on the order of ± 0.5 Pg C yr−1.
Systematic, operational, long‐term observations of the terrestrial carbon cycle (including its interactions with water, energy and nutrient cycles and ecosystem dynamics) are important for the ...prediction and management of climate, water resources, food resources, biodiversity and desertification. To contribute to these goals, a terrestrial carbon observing system requires the synthesis of several kinds of observation into terrestrial biosphere models encompassing the coupled cycles of carbon, water, energy and nutrients. Relevant observations include atmospheric composition (concentrations of CO2 and other gases); remote sensing; flux and process measurements from intensive study sites; in situ vegetation and soil monitoring; weather, climate and hydrological data; and contemporary and historical data on land use, land use change and disturbance (grazing, harvest, clearing, fire).
A review of model–data synthesis tools for terrestrial carbon observation identifies ‘nonsequential’ and ‘sequential’ approaches as major categories, differing according to whether data are treated all at once or sequentially. The structure underlying both approaches is reviewed, highlighting several basic commonalities in formalism and data requirements.
An essential commonality is that for all model–data synthesis problems, both nonsequential and sequential, data uncertainties are as important as data values themselves and have a comparable role in determining the outcome.
Given the importance of data uncertainties, there is an urgent need for soundly based uncertainty characterizations for the main kinds of data used in terrestrial carbon observation. The first requirement is a specification of the main properties of the error covariance matrix.
As a step towards this goal, semi‐quantitative estimates are made of the main properties of the error covariance matrix for four kinds of data essential for terrestrial carbon observation: remote sensing of land surface properties, atmospheric composition measurements, direct flux measurements, and measurements of carbon stores.
Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed ...an approach for representing synoptic‐ and diurnal‐scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003–2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)‐derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top‐down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.
Key Points
We developed an approach to distribute daily and hourly fire emissions
Daily and hourly patterns of fire activity varied among different land types
Daily and hourly fire emissions improved CO simulations
Drainage of peatlands and deforestation have led to large-scale fires in equatorial Asia, affecting regional air quality and global concentrations of greenhouse gases. Here we used several sources of ...satellite data with biogeochemical and atmospheric modeling to better understand and constrain fire emissions from Indonesia, Malaysia, and Papua New Guinea during 2000-2006. We found that average fire emissions from this region 128 ± 51 (1σ) Tg carbon (C) year⁻¹, T = 10¹² were comparable to fossil fuel emissions. In Borneo, carbon emissions from fires were highly variable, fluxes during the moderate 2006 El Niño more than 30 times greater than those during the 2000 La Niña (and with a 2000-2006 mean of 74 ± 33 Tg C yr⁻¹). Higher rates of forest loss and larger areas of peatland becoming vulnerable to fire in drought years caused a strong nonlinear relation between drought and fire emissions in southern Borneo. Fire emissions from Sumatra showed a positive linear trend, increasing at a rate of 8 Tg C year⁻² (approximately doubling during 2000-2006). These results highlight the importance of including deforestation in future climate agreements. They also imply that land manager responses to expected shifts in tropical precipitation may critically determine the strength of climate-carbon cycle feedbacks during the 21st century.
Fire‐driven deforestation is the major source of carbon emissions from Amazonia. Recent expansion of mechanized agriculture in forested regions of Amazonia has increased the average size of ...deforested areas, but related changes in fire dynamics remain poorly characterized. We estimated the contribution of fires from the deforestation process to total fire activity based on the local frequency of active fire detections from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. High‐confidence fire detections at the same ground location on 2 or more days per year are most common in areas of active deforestation, where trunks, branches, and stumps can be piled and burned many times before woody fuels are depleted. Across Amazonia, high‐frequency fires typical of deforestation accounted for more than 40% of the MODIS fire detections during 2003–2007. Active deforestation frontiers in Bolivia and the Brazilian states of Mato Grosso, Pará, and Rondônia contributed 84% of these high‐frequency fires during this period. Among deforested areas, the frequency and timing of fire activity vary according to postclearing land use. Fire usage for expansion of mechanized crop production in Mato Grosso is more intense and more evenly distributed throughout the dry season than forest clearing for cattle ranching (4.6 vs. 1.7 fire days per deforested area, respectively), even for clearings >200 ha in size. Fires for deforestation may continue for several years, increasing the combustion completeness of cropland deforestation to nearly 100% and pasture deforestation to 50–90% over 1–3‐year timescales typical of forest conversion. Our results demonstrate that there is no uniform relation between satellite‐based fire detections and carbon emissions. Improved understanding of deforestation carbon losses in Amazonia will require models that capture interannual variation in the deforested area that contributes to fire activity and variable combustion completeness of individual clearings as a function of fire frequency or other evidence of postclearing land use.
The continuous fields Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products are 500-m sub-pixel representations of basic vegetation characteristics including tree, herbaceous and ...bare ground cover. Our previous approach to deriving continuous fields used a linear mixture model based on spectral endmembers of forest, grassland and bare ground training. We present here a new approach for estimating percent tree cover employing continuous training data over the whole range of tree cover. The continuous training data set is derived by aggregating high-resolution tree cover to coarse scales and is used with multi-temporal metrics based on a full year of coarse resolution satellite data. A regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multi-temporal metrics. The automated algorithm was tested globally using Advanced Very High Resolution Radiometer (AVHRR) data, as a full year of MODIS data has not yet been collected. A root mean square error (rmse) of 9.06% tree cover was found from the global training data set. Preliminary MODIS products are also presented, including a 250-m map of the lower 48 United States and 500-m maps of tree cover and leaf type for North America. Results show that the new approach used with MODIS data offers an improved characterization of land cover.
Operational monitoring of land cover from satellite data will require automated procedures for analyzing large volumes of data. We propose multiple criteria for assessing algorithms for this task. In ...addition to standard classification accuracy measures, we propose criteria to account for computational resources required by the algorithms, stability of the algorithms, and robustness to noise in the training data. We also propose that classification accuracy take account, through estimation of misclassification costs, of unequal consequences to the user depending on which cover types are confused. In this article, we apply these criteria to three variants of decision tree classifiers, a standard decision tree implemented in C5.0 and two techniques recently proposed in the machine learning literature known as “bagging” and “boosting.” Each of these algorithms are applied to two data sets, a global land cover classification from 8 km AVHRR data and a Landsat Thematic Mapper scene in Peru. Results indicate comparable accuracy of the three variants of the decision tree algorithms on the two data sets, with boosting providing marginally higher accuracies. The bagging and boosting algorithms, however, are both substantially more stable and more robust to noise in the training data compared with the standard C5.0 decision tree. The bagging algorithm is most costly in terms of computational resources while the standard decision tree is least costly. The results illustrate that the choice of the most suitable algorithm requires consideration of a suite of criteria in addition to the traditional accuracy measures and that there are likely to be trade-offs between algorithm performance and required computational resources.
Conversion of land to grow crops, raise animals, obtain timber, and build cities is one of the foundations of human civilization. While land use provides these essential ecosystem goods, it alters a ...range of other ecosystem functions, such as the provisioning of freshwater, regulation of climate and biogeochemical cycles, and maintenance of soil fertility. It also alters habitat for biological diversity. Balancing the inherent trade-offs between satisfying immediate human needs and maintaining other ecosystem functions requires quantitative knowledge about ecosystem responses to land use. These responses vary according to the type of land-use change and the ecological setting, and have local, short-term as well as global, long-term effects. Land-use decisions ultimately weigh the need to satisfy human demands and the unintended ecosystem responses based on societal values, but ecological knowledge can provide a basis for assessing the trade-offs.
This study uses a global terrestrial carbon cycle model (the Carnegie‐Ames‐Stanford Approach (CASA) model), a satellite‐derived map of existing vegetation, and global maps of natural vegetation to ...estimate the effects of human‐induced land cover change on carbon emissions to the atmosphere and net primary production. We derived two maps approximating global land cover that would exist for current climate in the absence of human disturbance of the landscape, using a procedure that minimizes disagreements between maps of existing and natural vegetation that represent artifacts in the data. Similarly, we simulated monthly fields of the Normalized Difference Vegetation Index, required as input to CASA, for the undisturbed land cover case. Model results estimate total carbon losses from human‐induced land cover changes of 182 and 199 Pg for the two simulations, compared with an estimate of 124 Pg for total flux between 1850 and 1990 Houghton, 1999, suggesting that land cover change prior to 1850 accounted for approximately one‐third of total carbon emissions from land use change. Estimates of global carbon loss from the two independent methods, the modeling approach used in this paper and the accounting approach of Houghton 1999, are comparable taking into account carbon losses from agricultural expansion prior to 1850 estimated at 48–57 Pg. However, estimates of regional carbon losses vary considerably, notably in temperate midlatitudes where our estimates indicate higher cumulative carbon loss. Overall, land cover changes reduced global annual net primary productivity (NPP) by approximately 5%, with large regional variations. High‐input agriculture in North America and Europe display higher annual NPP than the natural vegetation that would exist in the absence of cropland. However, NPP has been depleted in localized areas in South Asia and Africa by up to 90%. These results provide initial crude estimates, limited by the spatial resolution of the data sets used as input to the model and by the lack of information about transient changes in land cover. The results suggest that a modeling approach can be used to estimate spatially‐explicit effects of land cover change on biosphere‐atmosphere interactions.
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