scholarly journals Modeling Burned Areas in Indonesia: The FLAM Approach

Forests ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 437 ◽  
Author(s):  
Andrey Krasovskii ◽  
Nikolay Khabarov ◽  
Johannes Pirker ◽  
Florian Kraxner ◽  
Ping Yowargana ◽  
...  
Keyword(s):  
Large Scale ◽  
Correlation Coefficients ◽  
Grid Cell ◽  
Climate Impacts ◽  
Burned Area ◽  
Time Step ◽  
Fire Emissions ◽  
Burned Areas ◽  
Haze Pollution ◽  
Spatio Temporal

Large-scale wildfires affect millions of hectares of land in Indonesia annually and produce severe smoke haze pollution and carbon emissions, with negative impacts on climate change, health, the economy and biodiversity. In this study, we apply a mechanistic fire model to estimate burned area in Indonesia for the first time. We use the Wildfire Climate Impacts and Adaptation Model (FLAM) that operates with a daily time step on the grid cell of 0.25 arc degrees, the same spatio-temporal resolution as in the Global Fire Emissions Database v4 (GFED). GFED data accumulated from 2000–2009 are used for calibrating spatially-explicit suppression efficiency in FLAM. Very low suppression levels are found in peatland of Kalimantan and Sumatra, where individual fires can burn for very long periods of time despite extensive rains and fire-fighting attempts. For 2010–2016, we validate FLAM estimated burned area temporally and spatially using annual GFED observations. From the validation for burned areas aggregated over Indonesia, we obtain Pearson’s correlation coefficient separately for wildfires and peat fires, which equals 0.988 in both cases. Spatial correlation analysis shows that in areas where around 70% is burned, the correlation coefficients are above 0.6, and in those where 30% is burned, above 0.9.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Estimates of biomass burning emissions in tropical Asia based on satellite-derived data

2010 ◽  
Vol 10 (5) ◽  
pp. 2335-2351 ◽  
Author(s):  
D. Chang ◽  
Y. Song
Keyword(s):  
Biomass Burning ◽  
Atmospheric Chemistry ◽  
Crop Residue ◽  
Fire Season ◽  
Burned Area ◽  
Published Data ◽  
Tropical Asia ◽  
Fire Emissions ◽  
Burned Areas ◽  
In Fire

Abstract. Biomass burning in tropical Asia emits large amounts of trace gases and particulate matter into the atmosphere, which has significant implications for atmospheric chemistry and climatic change. In this study, emissions from open biomass burning over tropical Asia were evaluated during seven fire years from 2000 to 2006 (1 March 2000–31 February 2007). The size of the burned areas was estimated from newly published 1-km L3JRC and 500-m MODIS burned area products (MCD45A1). Available fuel loads and emission factors were assigned to each vegetation type in a GlobCover characterisation map, and fuel moisture content was taken into account when calculating combustion factors. Over the whole period, both burned areas and fire emissions showed clear spatial and seasonal variations. The size of the L3JRC burned areas ranged from 36 031 km2 in fire year 2005 to 52 303 km2 in 2001, and the MCD45A1 burned areas ranged from 54 790 km2 in fire year 2001 to 148 967 km2 in 2004. Comparisons of L3JRC and MCD45A1 burned areas using ground-based measurements and other satellite data were made in several major burning regions, and the results suggest that MCD45A1 generally performed better than L3JRC, although with a certain degree of underestimation in forest areas. The average annual L3JRC-based emissions were 123 (102–152), 12 (9–15), 1.0 (0.7–1.3), 1.9 (1.4–2.6), 0.11 (0.09–0.12), 0.89 (0.63–1.21), 0.043 (0.036–0.053), 0.021 (0.021–0.023), 0.41 (0.34–0.52), 3.4 (2.6–4.3), and 3.6 (2.8–4.7) Tg yr−1 for CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10, respectively, whereas MCD45A1-based emissions were 122 (108–144), 9.3 (7.7–11.7), 0.63 (0.46–0.86), 1.1 (0.8–1.6), 0.11 (0.10–0.13), 0.54 (0.38–0.76), 0.043 (0.038–0.051), 0.033 (0.032–0.037), 0.39 (0.34–0.47), 3.0 (2.6–3.7), and 3.3 (2.8–4.0) Tg yr−1. Forest burning was identified as the major source of the fire emissions due to its high carbon density. Although agricultural burning was the second highest contributor, it is possible that some crop residue combustion was missed by satellite observations. This possibility is supported by comparisons with previously published data, and this result may be due to the small size of the field crop residue burning. Fire emissions were mainly concentrated in Indonesia, India, Myanmar, and Cambodia. Furthermore, the peak in the size of the burned area was generally found in the early fire season, whereas the maximum fire emissions often occurred in the late fire season.

scholarly journals Modelling African biomass burning emissions and the effect of spatial resolution

2019 ◽  
Author(s):  
Dave van Wees ◽  
Guido R. van der Werf
Keyword(s):  
Spatial Resolution ◽  
Input Data ◽  
Large Scale ◽  
Sub Saharan Africa ◽  
Fuel Load ◽  
Burned Area ◽  
Fire Emissions ◽  
Continental Scale ◽  
Coarse Resolution ◽  
The Impact

Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. In this paper, we developed a model using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500-meter spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050°, 0.125°, 0.250°). We estimated fire emissions of 0.68 PgC yr−1 at 500-meter resolution and 0.82 PgC yr−1 at 0.25° resolution; a difference of 24 %. At 0.25° resolution, our model results were relatively similar to GFED4, which also runs at 0.25° resolution, whereas our 500-meter estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 PgC yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500-meter and 0.25° resolution simulations, besides those stemming from representation errors in the calibration process, namely: 1. biome misclassification leading to errors in parameterization, 2. errors due to the averaging of input data and the associated reduction in variability, and 3. a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations up to a factor 4, and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse resolution models and suggest the need for increased spatial resolution in global fire emission models.

scholarly journals FABM-NflexPD 1.0: assessing an instantaneous acclimation approach for modeling phytoplankton growth

2021 ◽  
Vol 14 (10) ◽  
pp. 6025-6047
Author(s):  
Onur Kerimoglu ◽  
Prima Anugerahanti ◽  
Sherwood Lan Smith
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Grid Cell ◽  
Cost Effective ◽  
State Variables ◽  
Time Step ◽  
Ocean Turbulence ◽  
State Variable ◽  
Biogeochemical Models ◽  
Additional State

Abstract. Coupled physical–biogeochemical models can generally reproduce large-scale patterns of primary production and biogeochemistry, but they often underestimate observed variability and gradients. This is partially caused by insufficient representation of systematic variations in the elemental composition and pigment density of phytoplankton. Although progress has been made through approaches accounting for the dynamics of phytoplankton composition with additional state variables, formidable computational challenges arise when these are applied in spatially explicit setups. The instantaneous acclimation (IA) approach addresses these challenges by assuming that Chl:C:nutrient ratios are instantly optimized locally (within each modeled grid cell, at each time step), such that they can be resolved as diagnostic variables. Here, we present the first tests of IA in an idealized 1-D setup: we implemented the IA in the Framework for Aquatic Biogeochemical Models (FABM) and coupled it with the General Ocean Turbulence Model (GOTM) to simulate the spatiotemporal dynamics in a 1-D water column. We compare the IA model against a fully dynamic, otherwise equivalently acclimative (dynamic acclimation; DA) variant with an additional state variable and a third, non-acclimative and fixed-stoichiometry (FS) variant. We find that the IA and DA variants, which require the same parameter set, behave similarly in many respects, although some differences do emerge especially during the winter–spring and autumn–winter transitions. These differences however are relatively small in comparison to the differences between the DA and FS variants, suggesting that the IA approach can be used as a cost-effective improvement over a fixed-stoichiometry approach. Our analysis provides insights into the roles of acclimative flexibilities in simulated primary production and nutrient drawdown rates, seasonal and vertical distribution of phytoplankton biomass, formation of thin chlorophyll layers and stoichiometry of detrital material.

scholarly journals Modelling biomass burning emissions and the effect of spatial resolution: a case study for Africa based on the Global Fire Emissions Database (GFED)

2019 ◽  
Vol 12 (11) ◽  
pp. 4681-4703
Author(s):  
Dave van Wees ◽  
Guido R. van der Werf
Keyword(s):  
Spatial Resolution ◽  
Input Data ◽  
Large Scale ◽  
Sub Saharan Africa ◽  
Fuel Load ◽  
Burned Area ◽  
Fire Emissions ◽  
Continental Scale ◽  
Coarse Resolution ◽  
The Impact

Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. We developed a model using MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500 m spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050, 0.125, 0.250∘). We estimated fire emissions of 0.68 Pg C yr−1 at 500 m resolution and 0.82 Pg C yr−1 at 0.25∘ resolution; a difference of 24 %. At 0.25∘ resolution, our model results were relatively similar to GFED4, which also runs at 0.25∘ resolution, whereas our 500 m estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 Pg C yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse-resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500 m and 0.25∘ resolution simulations, besides those stemming from representation errors in the calibration process, namely (1) biome misclassification leading to errors in parameterization, (2) errors due to the averaging of input data and the associated reduction in variability, and (3) a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations of up to a factor 4 and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse-resolution models and suggest the need for increased spatial resolution in global fire emission models.

scholarly journals Global Wildfire Outlook Forecast with Neural Networks

2020 ◽  
Vol 12 (14) ◽  
pp. 2246
Author(s):  
Yongjia Song ◽  
Yuhang Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
South America ◽  
Land Cover ◽  
Southern Africa ◽  
Burned Area ◽  
Good Prediction ◽  
Wildfire Occurrence ◽  
Fire Emissions ◽  
Burned Areas

Wildfire occurrence and spread are affected by atmospheric and land-cover conditions, and therefore meteorological and land-cover parameters can be used in area burned prediction. We apply three forecast methods, a generalized linear model, regression trees, and neural networks (Levenberg–Marquardt backpropagation) to produce monthly wildfire predictions 1 year in advance. The models are trained using the Global Fire Emissions Database version 4 with small fires (GFEDv4s). Continuous 1-year monthly fire predictions from 2011 to 2015 are evaluated with GFEDs data for 10 major fire regions around the globe. The predictions by the neural network method are superior. The 1-year moving predictions have good prediction skills over these regions, especially over the tropics and the southern hemisphere. The temporal refined index of agreement (IOA) between predictions and GFEDv4s regional burned areas are 0.82, 0.82, 0.8, 0.75, and 0.56 for northern and southern Africa, South America, equatorial Asia and Australia, respectively. The spatial refined IOA for 5-year averaged monthly burned area range from 0.69 in low-fire months to 0.86 in high-fire months over South America, 0.3–0.93 over northern Africa, 0.69–0.93 over southern Africa, 0.47–0.85 over equatorial Asia, and 0.53–0.8 over Australia. For fire regions in the northern temperate and boreal regions, the temporal and spatial IOA between predictions and GFEDv4s data in fire seasons are 0.7–0.79 and 0.24–0.83, respectively. The predictions in high-fire months are better than low-fire months. This study illustrates the feasibility of global fire activity outlook forecasts using a neural network model and the method can be applied to quickly assess the potential effects of climate change on wildfires.

scholarly journals Estimates of biomass burning emissions in tropical Asia based on satellite-derived data

2009 ◽  
Vol 9 (5) ◽  
pp. 19599-19640 ◽  
Author(s):  
D. Chang ◽  
Y. Song
Keyword(s):  
Biomass Burning ◽  
Atmospheric Chemistry ◽  
Vegetation Type ◽  
Burned Area ◽  
Tropical Asia ◽  
Fire Emissions ◽  
Burned Areas ◽  
Fuel Moisture Content ◽  
In Fire ◽  
Derived Data

Abstract. Biomass burning in tropical Asia emits large amounts of trace gases and particulate matters into the atmosphere, which has significant implications for atmospheric chemistry and climatic change. In this study, emissions from open biomass burning over tropical Asia were evaluated during seven fire years from 2000–2006 (1 April 2000–31 March 2007). Burned areas were estimated from newly published 1-km L3JRC and 500-m MODIS burned area products (MCD45A1). Available fuel loads and emission factors were assigned for each vegetation type in a GlobCover characterisation map, and fuel moisture content was taken into account when calculating combustion factors. Over the whole period, both burned areas and fire emissions clearly showed spatial and seasonal variations. The L3JRC burned areas ranged from 31 165 km2 in fire year 2005 to 57 313 km2 in 2000, while the MCD45A1 burned areas ranged from 54 260 km2 in fire year 2001 to 127 068 km2 in 2004. Comparisons of L3JRC and MCD45A1 burned areas with ground-based measurements and other satellite information were constructed in several major burning regions, and results suggested that MCD45A1 performed better in most areas than L3JRC did although with a certain degree of underestimation of burned forest areas. The average annual L3JRC-based emissions were 125, 12, 0.98, 1.91, 0.11, 0.89, 0.044, 0.022, 0.42, 3.40, and 3.68 Tg yr

scholarly journals Environmental Factors Affecting Wildfire Burned Area In South-Eastern France, 1970–2019

2021 ◽  
Author(s):  
Christos Bountzouklis ◽  
Dennis Michael Fox ◽  
Elena Di Bernardino
Keyword(s):  
Environmental Factors ◽  
Forest Fires ◽  
Vegetation Type ◽  
Fire Suppression ◽  
Sharp Decrease ◽  
Burned Area ◽  
Factors Affecting ◽  
Burned Areas ◽  
South Eastern ◽  
Spatio Temporal

Abstract. Forest fires burn an average of about 440,000 ha each year in southern Europe. These fires cause numerous casualties and deaths and destroy houses and other infrastructures. In order to elaborate suitable fire-fighting strategies, complex interactions between human and environmental factors must be taken into account. In this study, we investigated the spatio-temporal evolution in burned area over a 50-year period (1970–2019) and its interactions between topography (slope inclination and aspect) and vegetation type in south-eastern France by exploiting Geographic Information System databases. Burned area decreased sharply after 1994, with the advent of the new fire suppression policy which focused on rapid extinction of fires in their early phase. The geographic distribution of burned area has also changed in the last 25 years, mainly in regions where large fires occurred (Var department). In other parts, even though forest fires are still frequent and occur in the same geographic locations, the total extent of the burned area is significantly reduced. Slope orientation presents an increasingly important role every decade, S-facing slopes have the greatest burned areas and increase their proportion each decade, while the opposite is observed for N-facing and W-facing ones. Fire increasingly favors low and intermediate slopes after the sharp decrease of burned area in 1990. The largest part of the BA is strongly associated with the location of sclerophyllous vegetation clusters, which exhibit high fire proneness while simultaneously expanding the region. On the contrary, natural grassland numbers decline through time as the proportion of area burned increases.

scholarly journals Fire and forage quality: post-fire regrowth quality and pyric herbivory in subtropical grasslands of Nepal.

2022 ◽  
Author(s):  
Shyam Kumar Thapa ◽  
Joost de Jong ◽  
Anouschka Hof ◽  
Naresh Subedi ◽  
Laxmi Joshi ◽  
...  
Keyword(s):  
Crude Protein ◽  
Large Scale ◽  
Forage Quality ◽  
Chemical Properties ◽  
Grazing Intensity ◽  
Mean Value ◽  
Burned Areas ◽  
Short Period ◽  
Pyric Herbivory ◽  
Spatio Temporal

Indiscriminate fire is rampant throughout subtropical South and Southeast Asian grasslands. However, very little is known about the role of fire and pyric herbivory on the functioning of highly productive subtropical monsoon grasslands lying within Cwa-climatic region. We collected grass samples from 60 m x 60 m plots and determined vegetation physical and chemical properties at regular 30-day intervals from April to July 2020, starting from 30 days after fire to assess post-fire regrowth forage quality. We counted pellet groups for the same four months from 2 m x 2 m quadrats that were permanently marked with pegs along the diagonal of each 60 m x 60 m plot to estimate grazing intensity to the progression of post-fire regrowth. We observed strong and significant reductions in crude protein (mean value 9.1 to 4.1 [55% decrease]) and phosphorus (mean value 0.2 to 0.11 [45% decrease]) in forage collected during different time intervals i.e., from 30 days to 120 days after fire. Mesofaunal deer utilised the burned areas extensively for a short period, i.e., up to two months after fire when the burned areas contained short grasses with a higher level of crude protein and phosphorus. Grazing intensity of chital (Axis axis) to post-fire regrowth differed significantly over time since fire, with higher intensity of use at 30 days after fire. Grazing intensity of swamp deer (Rucervus duvaucelii) did not differ significantly until 90 days after fire, however, decreased significantly after 90 days since fire. Large-scale indiscriminate single event fires thus may not fulfil nutritional requirements of all species in mesofaunal deer community in these subtropical monsoon grasslands. We recommend for a spatio-temporal manipulation of fire to reinforce grazing feedback and to yield for the longest possible period a reasonably good food supply for the conservation of mesofaunal deer.

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