Estimating Burned Severity With Multiple Methods In Da Hinggan Mountains

Forest fire, as an important part of forest ecosystem, has an critical effect on species composition, age structure and spatial pattern of forest ecosystem with a variety of forms from surface fire to canopy fire. It destroyed a large amount of trees, caused tremendous harm to human life, property and the environment at the same time. Its affecting on the global carbon cycle is one of the most serious natural disasters faced by the mankind. Forest fire is also very severe in China. Merely in Da Hinggan Mountains district,649forest fires occurred from2003to2009, with a burned area of752,000hectares. Remote sensing estimation of burn severity was studied in this paper, which would contribute to the quantitative evaluation of tree losses caused by forest fires and impact of forest fires on forest ecosystem health, so that to provide scientific basis for post-disaster vegetation recovery and selection on proper forest ecosystem recovery mode. At the same time, it would provide technical methods and supportive data for the correct estimation of carbon emissions caused by forest fire and its contribution to ecological deterioration.Burned district of2006in South Urn River forestry center of Da Hinggan Mountains was selected as the study area. With the application of forest resource inventory data, the ground field survey data and Landsat5TM remote sensing data of the area before and after the fire, the accuracy of fire intensity estimation from non-linear and linear model based on the normalized burn ratio (NBR) and delta normalized burn ratio (dNBR) was tested. Multivariate comparison of burn severity was made through advanced mathematical modeling, partial least squares regression, neural networks and vector supporting machines. In order to simplify the model, the efficiency of the screening model was also compared, such as the importance of the variable projection (VIP), and orthogonal signal correction (OSC) and mean impact value (MIV). Preliminary results were as follow:While using a single variable to estimate the burn severity, the accuracy of the quadratic polynomial model was the highest. The two nonlinear models performed better in abroad study did not get effective results. Further evaluation of the results by confusion matrix showed that the three models (linear, polynomial, exponential) didn’t have a high accuracy in estimating the overall accuracy burn severity, with the estimation accuracy rate of less than65%. It reveals that other remote sensing factors and new methods also need to be explored to estimate the burn severity.There are three kinds of data processing forms in applications of remote sensing data:DN value, radiant brightness and atmosphere apparent reflectance., three linear regression model, through using three kinds of data processing then extracting NBR to estimate burn severity, had similar accuracy, R2from0.67to0.69. But in inspection of the regression coefficients and constant term, there was a significant difference between every two of the three models. The atmosphere apparent reflectance is closest to the perpendicular incidence reflectivity of ground objects, and is the basic of quantitative remote sensing of biophysical parameters foundation.Building multivariable model to estimate burn severity was based on20remote sensing factors. A4-fold cross-validation method was used for evaluation model estimation accuracy. Vector supporting machine had the highest accuracy, with the highest estimation accuracy rate of85%. The estimation accuracy of different kernel function vector supporting machine was different, and the accuracy of the RBF kernel function vector supporting machine was the highest. When the penalty parameter C was16and the kernel function parameter gama was0.25, RBF kernel function vector supporting classifier machine had the highest accuracy. The generalized regression neural network model to estimate the accuracy of fire intensity was inferior while comparing with the vector supporting classifier machine. In addition to the effect from neural network that came with radial velocity of propagation parameters(spread), model accuracy was also impacted by different modeling approach. And modeling data standardization by [-1,1] standardized way gave the model the highest accuracy, model estimation accuracy was80%when the spread was0.9. Estimating the accuracy of burn severity with partial least squares regression method had the minimum accuracy, with a maximum accuracy of forecasting is only65%. The standardizing way to make partial least squares regression model predict with the highest accurate data was [0,1] standardizing way.10variables were selected by VIP method, while3were selected by both the OSC and MIV with each having overlaps. Average accuracy rate of61.01percent were worked out from two selected variables with VIP method, and the highest prediction accuracy was70%which was higher than the full-variable model. The three variables selected by the OSC method was the original band of near-infrared and mid-infrared as well as variables of their combination. The established PLS model increased the average and the highest correct prediction rate. GRNN model established with variables selected by the MIV method and all variables GRNN had a same prediction accuracy, which was higher than the PLS model accuracy established by the other two methods selected variables. But PLS model established with variables selected by MIV had a low accuracy, indicating MIV method as quite depending on the model. It also revealed that GRNN model was robust, and could excavate linear and nonlinear relationships between the variables correctly. While at the same time, the PLS regression model was only sensitive to the linear relationship between the variables.In short, forest fire intensity can be estimated by remote sensing data, especially, multi-variable models perform better than single variable model, and vector supporting machines, neural networks and partial least squares regression model are all good choices of multi- variable models. They do not only save investigating time and labor intensity of post-disaster field surveys, but also provide accurate real-time basic data for comprehensive assessment of the correct loss of forest fire.