Minimal effect of prescribed burning on fire spread rate and intensity in savanna ecosystems (original) (raw)
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Prescribed burning effects on savanna fire spread, intensity, and predictability
2020
Fire is an integral part of the Earth for millennia. Recent wildfires exhibited an unprecedented spatial and temporal extend and their control is beyond national firefighting capabilities. Prescribed or controlled burning treatments are debated as a potential measure for ameliorating the spread and intensity of wildfires. Machine learning analysis using random forests was performed in a spatio-temporal data set comprising a large number of savanna fires across 22 years. Results indicate that controlled fire return interval accounts of 3.5% of fire spread and 3.5% of fire intensity. Manipulating burn seasonality accounted for 5% of fire spread and 6% of fire intensity. While manipulated fire return interval and seasonality moderated both fire spread and intensity, their overall effects were low in comparison with hydrological and climatic variables. Predicting fire spread and intensity has been a poor endeavour thus far and we show that more data of the variables already monitored wo...
Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility
Predictive Modeling of Wildfire Occurrence and Damage in a Tropical Savanna Ecosystem of West Africa
Fire, 2020
Wildfires are a major environmental, economic, and social threat. In Central Côte d'Ivoire, they are among the biggest environmental and forestry problems during the dry season. National authorities do not have tools and methods to predict spatial and temporal fire proneness over large areas. This study, based on the use of satellite historical data, aims to develop an appropriate model to forecast wildfire occurrence and burnt areas in each ecoregion of the N'Zi River Watershed. We used an autoregressive integrated moving average (ARIMA) model to simulate and forecast the number of wildfires and burnt area time series in each ecoregion. Nineteen years of monthly datasets were trained and tested. The model performance assessment combined Ljung-Box statistics, residuals, and autocorrelation analysis coupled with cross-validation using three forecast errors-namely, root mean square error, mean absolute error, and mean absolute scaled error-and observed-simulated data analysis. The results showed that the ARIMA models yielded accurate forecasts of the test dataset in all ecoregions and highlighted the effectiveness of the ARIMA models to forecast the total number of wildfires and total burnt area estimation in the future. The forecasts of possible wildfire occurrence and extent of damages in the next four years will help decision-makers and wildfire managers to take actions to reduce the exposure and the vulnerability of ecosystems and local populations to current and future pyro-climatic hazards.
Ecological Informatics, 2021
Abstract The recurrent forest fires have been a serious management concern in southern Western Ghats, India. This study investigates the applicability of various geospatial data, machine learning techniques (MLTs) and spatial statistical tools to demarcate the forest fire susceptible regions of the forested landscape of the Wayanad district in the southern Western Ghats (Kerala, India). The inventory map of 279 forest fire locations (period = 2001–2018) was developed via Sentinel 2A satellite images, NASA fire archives, and field visits. The forest fire susceptibility modelling involves twelve influencing factors, such as ambient air temperature, wind speed, rainfall, relative humidity, atmospheric water vapor pressure (WVP), elevation, slope angle, topographical wetness index (TWI), slope aspect, land use/land cover (LU/LC), distance from the road and distance from the villages. Considering the varying level of performances (i.e., receiver operating characteristics-area under curve (ROC-AUC) values ranging from 0.869 to 0.924 in the testing phase) of the MLTs, viz., artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), Naive Bayesian classifier (NBC), K-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting (AdaBoost) and maximum entropy (MaxEnt), we propose a weighted approach to characterize the forest fire susceptibility of the region using the outputs of the different MLTs. The proposed method demonstrates improvement in accuracy (AUC = 89%) for mapping the forest fire susceptibility of the region compared to the individual MLTs (AUC = 71.5 to 86.9%) while validating with the recent forest fire data (i.e., 2019–2021). This study suggests that roughly one-third of the study area is highly susceptible to the occurrence of forest fires, implying the severity of the disturbance regime. The analysis also indicates the role of anthropogenic factors in the occurrence of forest fires in the region. It is expected that the demarcation and prioritization of the forest fire susceptibility zones in the region, which is a part of one of the global biodiversity hotspots, have significant implications on biodiversity conservation at a regional scale.
Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems
IEEE Transactions on Geoscience and Remote Sensing, 2006
The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity ( = 0 78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satelliteand meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.
2020
Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month, and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. We showed that the inclusion of antecedent vegetation conditions on timescales > 1 yr had no impact on burnt area, but inclusion of antecedent vegetation conditions representi...
2014
We present a method for developing spatially explicit probability maps for the presence of wildfire residuals within a burned landscape. Using the Random Forest method, we learn rules that explain the formation of wildfire residuals based on selected physical predictors. We then implement the rules (akin to inverting the learning algorithm) to build maps of likely residual stand locations. First, satellite derived data from eleven fire events (from the same ecoregion) are partitioned into training and validation using a hold-out approach. The performance of the model is then assessed using an independent and extensive fire event and using thresholdindependent measures at 4, 8, 16, 32, and 64 m spatial resolutions. The model has a reasonable or high predictive performance (‘marginal’ or strong’ model outcome) for most of the fire events within the same ecoregion. However, the predictive power of the model is lower for the independent fire event. We further characterize the relative i...
Remote Sensing of Environment, 2007
This paper evaluated the capacity of SPOT VEGETATION time-series to monitor herbaceous fuel moisture content (FMC) in order to improve fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. In situ herbaceous FMC data were used to assess the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Vegetation Dryness Index (VDI), Improved VDI (IVDI), and Accumulated Relative NDVI Decrement (ARND) during the dry season. The effect of increasing amounts of dead vegetation on the monitoring capacity of derived indices was studied by sampling mixed live and dead FMC. The IVDI was proposed as an improvement of the VDI to monitor herbaceous FMC during the dry season. The IVDI is derived by replacing NDVI with the integrated Relative Vegetation Index (iRVI), as an approximation of yearly herbaceous biomass, when analyzing the 2-dimensional space with NDWI. It was shown that the iRVI offered more information than the NDVI in combination with NDWI to monitor FMC. The VDI and IVDI exhibited a significant relation to FMC with R 2 of 0.25 and 0.73, respectively. The NDWI, however, correlated best with FMC (R 2 = 0.75), while the correlation of ARND and FMC was weaker (R 2 = 0.60) than that found for NDVI, NDWI, and IVDI. The use of in situ herbaceous FMC consequently indicated that NDWI is appropriate as spatio-temporal information source of herbaceous FMC variation which can be used to optimize fire risk and behavior assessment for fire management in savanna ecosystems.
Global Ecology and Biogeography, 2019
Aim: An emerging framework for tropical ecosystems states that fire activity is either 'fuel build-up limited' or 'fuel moisture limited' i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture. Location: Pantropical savannas and grasslands Time period: 2002-2016 Methods: We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how fuel moisture and fuel buildup effect (accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships. Results: Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were fuel build-up limited against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr-1 , Africa at 800 mm yr-1 and Australia at 1000 mm yr-1 of mean annual rainfall. Main conclusions: In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regionslocal solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.