Several indices and simple empirical models and ratios of single band from pre-and post-fire Landsat images have been developed to estimate and/or map burn severity.However,these models and indices are usually site-,t...Several indices and simple empirical models and ratios of single band from pre-and post-fire Landsat images have been developed to estimate and/or map burn severity.However,these models and indices are usually site-,time-and vegetation-dependent and their applications are limited.The Daxing'an Mountains range has the largest forested area in China and is prone to wildfires.Whether or not the existing models can effectively characterize the burn severity over a large region is unclear.In this study,we used the orthogonal signal correction method based on partial least squares regression(PLSR)to select those variables that better interpret the variance of burn severity.A new index and other commonly used indices were used to construct a new,multivariate PLSR model which was compared with the popular single variable models,according to three assessment indices:relative root mean square error(RMSE%),relative bias(R E%)and Nash–Sutcliffe efficiency(NSE%).The results indicate that the multivariate PLSR model performed better than the other single variable models with higher NSE%(68.2%vs.67.8%)and less RE%(3.7%vs.-8.7%),while achieving almost the same R MSE%.We also discuss the spectral characteristics of the four selected variables for constructing the multivariate PLSR model and their correlation with the field burn severity data.The new model developed from this study should help to better understand the patterns of forest burn severity and assist in vegetation restoration efforts in the region.展开更多
Wild-land fires are a dynamic and destructive force in natural ecosystems. In recent decades, fire disturbances have increased concerns and awareness over significant economic loss and landscape change. The focus of t...Wild-land fires are a dynamic and destructive force in natural ecosystems. In recent decades, fire disturbances have increased concerns and awareness over significant economic loss and landscape change. The focus of this research was to study two northern California wild-land fires: Butte Humboldt Complex and Butte Lightning Complex of 2008 and assessment of vegetation recovery after the fires via ground based measurements and utilization of Landsat 5 imagery and analysis software to assess landscape change. Multi-temporal and burn severity dynamics and assessment through satellite imagery were used to visually ascertain levels of landscape change, under two temporal scales. Visual interpretation indicated noticeable levels of landscape change and relevant insight into the magnitude and impact of both wild-land fires. Normalized Burn Ratio (NBR) and delta NBR (DNBR) data allowed for quantitative analysis of burn severity levels. DNBR results indicate low severity and low re-growth for Butte Humboldt Complex “burned center” subplots. In contrast, DNBR values for Butte Lightning Complex “burned center” subplots indicated low-moderate burn severity levels.展开更多
This study was performed to estimate the emission of non-CO 2 greenhouse gases(GHGs) from biomass burning at a large fire area.The extended methodology adopted the IPCC Guidelines(2003) equation for use on data from t...This study was performed to estimate the emission of non-CO 2 greenhouse gases(GHGs) from biomass burning at a large fire area.The extended methodology adopted the IPCC Guidelines(2003) equation for use on data from the Samcheok forest fire gathered using 30 m resolution Landsat TM satellite imagery,digital forest type maps,and growing stock information per hectare by forest type in 1999.Normalized burn ratio(NBR) technique was employed to analyze the area and severity of the Samcheok forest fire that occurred in 2000.The differences between NBR from pre-and post-fire datasets are examined to determine the extent and degree of change detected from burning.The results of burn severity analysis by dNBR of the Samcheok forest fire area revealed that a total of 16,200 ha of forest were burned.The proportion of the area characterized by a 'Low' burn severity(dNBR below 152) was 35%,with 'Moderate'(dNBR 153-190) and 'High'(dNBR 191-255) areas were at 33% and 32%,respectively.The combustion efficiency for burn severity was calculated as 0.43 for crown fire where burn severity was 'High',as 0.40 for 'Moderate' severity,and 0.15 for 'Low' severity surface fire.The emission factors for estimating non-CO 2 GHGs were separately applied to CO 130,CH 4 9,NO x 0.7 and N 2 O 0.11.Non-CO 2 GHGs emissions from biomass burning in the Samcheok forest fire area were estimated to be CO 44.100,CH 4 3.053,NO x 0.238 and N 2 O 0.038 Gg.展开更多
Fire,especially wildfire,which can be considered as one of the main threats to vegetation cover and animals’life,has attracted lots of attention from environmental researchers.To better manage the fire crisis and tak...Fire,especially wildfire,which can be considered as one of the main threats to vegetation cover and animals’life,has attracted lots of attention from environmental researchers.To better manage the fire crisis and take the necessary measures to compensate for its damages,it is essential to have detailed information about the burn severity levels.Accordingly,satellite images and their spectral indices have been widely considered in the literature as powerful tools in producing burn severity information.Despite the efficiency of the previously proposed methods,the necessity of ground reference data for their thresholding step faces them with serious challenges.To address this problem,in this study,an automatic procedure based on the change-point analysis is presented for thresholding differenced normalized burn ratio(dNBR)and its another version,dNBR2.In this procedure,a mean-shift based change-point analysis is performed on the dNBR and dNBR2 images for classifying them into burn severity levels.Experiments,conducted on some parts of Alaska and California in the United States,illustrated the high efficiency of the proposed method.Moreover,as an applied experiment,the severity of the fires,occurred in 2020 in the Khaeiz protected area in Iran,was estimated and compared with local reports.展开更多
基金partially supported by the Fundamental Research Funds for the Central Universities(DL12CA12,2572017PZ05)in part by the Research Foundation for Junior Teachers from the Ministry of Education of China(20110062120010)。
文摘Several indices and simple empirical models and ratios of single band from pre-and post-fire Landsat images have been developed to estimate and/or map burn severity.However,these models and indices are usually site-,time-and vegetation-dependent and their applications are limited.The Daxing'an Mountains range has the largest forested area in China and is prone to wildfires.Whether or not the existing models can effectively characterize the burn severity over a large region is unclear.In this study,we used the orthogonal signal correction method based on partial least squares regression(PLSR)to select those variables that better interpret the variance of burn severity.A new index and other commonly used indices were used to construct a new,multivariate PLSR model which was compared with the popular single variable models,according to three assessment indices:relative root mean square error(RMSE%),relative bias(R E%)and Nash–Sutcliffe efficiency(NSE%).The results indicate that the multivariate PLSR model performed better than the other single variable models with higher NSE%(68.2%vs.67.8%)and less RE%(3.7%vs.-8.7%),while achieving almost the same R MSE%.We also discuss the spectral characteristics of the four selected variables for constructing the multivariate PLSR model and their correlation with the field burn severity data.The new model developed from this study should help to better understand the patterns of forest burn severity and assist in vegetation restoration efforts in the region.
文摘Wild-land fires are a dynamic and destructive force in natural ecosystems. In recent decades, fire disturbances have increased concerns and awareness over significant economic loss and landscape change. The focus of this research was to study two northern California wild-land fires: Butte Humboldt Complex and Butte Lightning Complex of 2008 and assessment of vegetation recovery after the fires via ground based measurements and utilization of Landsat 5 imagery and analysis software to assess landscape change. Multi-temporal and burn severity dynamics and assessment through satellite imagery were used to visually ascertain levels of landscape change, under two temporal scales. Visual interpretation indicated noticeable levels of landscape change and relevant insight into the magnitude and impact of both wild-land fires. Normalized Burn Ratio (NBR) and delta NBR (DNBR) data allowed for quantitative analysis of burn severity levels. DNBR results indicate low severity and low re-growth for Butte Humboldt Complex “burned center” subplots. In contrast, DNBR values for Butte Lightning Complex “burned center” subplots indicated low-moderate burn severity levels.
文摘This study was performed to estimate the emission of non-CO 2 greenhouse gases(GHGs) from biomass burning at a large fire area.The extended methodology adopted the IPCC Guidelines(2003) equation for use on data from the Samcheok forest fire gathered using 30 m resolution Landsat TM satellite imagery,digital forest type maps,and growing stock information per hectare by forest type in 1999.Normalized burn ratio(NBR) technique was employed to analyze the area and severity of the Samcheok forest fire that occurred in 2000.The differences between NBR from pre-and post-fire datasets are examined to determine the extent and degree of change detected from burning.The results of burn severity analysis by dNBR of the Samcheok forest fire area revealed that a total of 16,200 ha of forest were burned.The proportion of the area characterized by a 'Low' burn severity(dNBR below 152) was 35%,with 'Moderate'(dNBR 153-190) and 'High'(dNBR 191-255) areas were at 33% and 32%,respectively.The combustion efficiency for burn severity was calculated as 0.43 for crown fire where burn severity was 'High',as 0.40 for 'Moderate' severity,and 0.15 for 'Low' severity surface fire.The emission factors for estimating non-CO 2 GHGs were separately applied to CO 130,CH 4 9,NO x 0.7 and N 2 O 0.11.Non-CO 2 GHGs emissions from biomass burning in the Samcheok forest fire area were estimated to be CO 44.100,CH 4 3.053,NO x 0.238 and N 2 O 0.038 Gg.
文摘Fire,especially wildfire,which can be considered as one of the main threats to vegetation cover and animals’life,has attracted lots of attention from environmental researchers.To better manage the fire crisis and take the necessary measures to compensate for its damages,it is essential to have detailed information about the burn severity levels.Accordingly,satellite images and their spectral indices have been widely considered in the literature as powerful tools in producing burn severity information.Despite the efficiency of the previously proposed methods,the necessity of ground reference data for their thresholding step faces them with serious challenges.To address this problem,in this study,an automatic procedure based on the change-point analysis is presented for thresholding differenced normalized burn ratio(dNBR)and its another version,dNBR2.In this procedure,a mean-shift based change-point analysis is performed on the dNBR and dNBR2 images for classifying them into burn severity levels.Experiments,conducted on some parts of Alaska and California in the United States,illustrated the high efficiency of the proposed method.Moreover,as an applied experiment,the severity of the fires,occurred in 2020 in the Khaeiz protected area in Iran,was estimated and compared with local reports.
