Estimation and Spatio-temporal Patterns of Carbon Emissions from Grassland Fires in Inner Mongolia,China

Grassland fires results in carbon emissions,which directly affects the carbon cycle of ecosystems and the carbon balance.The grassland area of Inner Mongolia accounts for 22%of the total grassland area in China,and many fires occur in the area every year.However,there are few models for estimation of carbon emissions from grassland fires.Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon.In this study,the regression equations for aboveground biomass(AGB)of grassland in growing season and MODIS NDVI(Normalized Difference Vegetation Index)were established through field experiments,then AGB during Nov.–Apr.were retrieved based on that in Oct.and decline rate,finally surface fuel load was obtained for whole year.Based on controlled combustion experiments of different grassland types in Inner Mongolia,the carbon emission rate of grassland fires for each grassland type were determined,then carbon emission was estimated using proposed method and carbon emission rate.Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978×1012 kg.The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2,with the annual average area of 311.69 km2.The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia.The total carbon emissions from grassland fires amounted to 2.24×107 kg with an annual average of 1.32×106 for the study area.The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner.The spatial characteristics of carbon emission depend on the location of grassland fire,mainly in the northeast of Inner Mongolia include Hulunbuir City,Hinggan League,Xilin Gol League and Ulanqab City.The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions.This study provides a reference for estimating carbon emissions from steppe fires.The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.

Analysis of Landsat 8 detection of the interannual variability of grassland curing in Greater Melbourne,Australia

As one of the main components of Grassland Fire Danger Index,grassland curing degree provides crucial information for determining grassland fire danger.Accurate estimates of grassland curing are critical for determining grassland fire risk.This research focuses on the use of Landsat 8 to estimate grassland curing.Results demonstrate that Landsat 8 observations can be used to estimate curing percentages as assessed by visual and ground sampling measurements.Grassland interannual variability for the Greater Melbourne region using Landsat 8 imagery from 2013 to 2019 is examined.Slight differences in curing times and degree are observed for sample sites surrounding Greater Melbourne due to climatic differences across the region.Precipitation is regarded as an essential variable affecting curing degree and this relationship is evident for all five sample sites.Landsat 8 curing results are compared to both visual observations and destructive sampling,the most accurate method,for accuracy assessment.At 95%confidence level,Landsat 8 estimations are no different from destructive ground sampling estimations.Overall,this study validates the use of Landsat 8 data as an effective and accurate way for grassland curing monitoring.

Inter-satellite variability of grassland curing maps produced by different satellite sensors-Victoria,Australia

Grassland fires are a serious problem in Victoria,Australia due to large quantity of dry grass.Grassland curing degree(GCD)measures the dryness of the grass and is an important factor for assessing grassland fire danger.Grassland curing maps(GCMs)display the spatial distribution of GCDs,but the quality of GCMs varies depending on the spatial resolution of the observing satellite remote sensing system.The higher the spatial resolution,the finer the GCD details and more spatial variations the GCM can reveal.In this study,GCD calculation algorithm named MapVictoria based on MODIS data is tested for Landsat 8 Sentinel 2;GCMs generated from these three satellites are contrasted by their GCD differences,defined here as inter-satellite variability(ISV).ISV is used to identify areas where higher resolution satellite GCMs should be used.Results show that spatial resolution difference(ΔSR),seasonality and geographical locations affect the magnitude of the ISV.Based on these findings,this paper provides recommendations to decision makers on where and when to use which satellite for grassland observations.