## 4 Discussion
Future research will concentrate on the spectral characteristics of the seasonal and regional sample sets of varying pre-fire vegetation types [22; 32], which can further reveal the distinguishing capacity of the spectral regions and indices to a burned area. The BPNN used in the method can learn the spectral differences based on the input samples of spectral indices for known types and can use that training to predict the unknown type of each pixel. The complex nonlinear relationship between the inputs and targets can be modeled using a BPNN. However, the traditional empirical formula method needs to analyze the spectral characteristics of the burned area and unburned types based on the extracted samples to find an empirical formula to separate them. Finding the formula is time-consuming and limited to the region where the data is extracted. Besides, the empirical formula is determined by the specific sample sets, which means it needs to be re-established in other regions. Thus, the proposed method is more applicable and effective than the traditional empirical formula method. Considering that the threshold T set for the output layer of the BPNN is an important parameter, the impact of threshold selection on the errors of results was verified in comparison to the reference OLI-based results. Intrinsic restrictions cause a tradeoff which is indicated by the change trends of the two kinds of errors (CE and OE) [25]; in other words, if the threshold value choice decreases one error, the other error will increase. On the one hand, selection of an appropriate threshold T can balance the CE and OE of the results so both are at a relatively low level. On the other hand, the setting of different thresholds can meet the demands of different users who prioritize CE and OE differently. If the user needs to identify all the burned areas, a low threshold can be applied. However, if the user needs to guarantee that all identified areas are actually burned, then a conservative threshold is required. In this study, we adopted a conservative threshold T = 0.8 in the analysis of the four wildfire cases to demonstrate the effectiveness of our method. However, the current method has a limitation of threshold selection for the output value in BPNN. The structure of BPNN can be further improved as more land cover types and more samples are collected to the training sets. Overall, the new method presents a better capacity for burned area detection based on a single MODIS image. The most intuitive benefit is that the proposed method can accurately and promptly determine the burned regions that are burned areas. In addition, the range of wildfire spread can also be estimated based on the premise that the wildfire can be captured by the MODIS sensor. Since the MODIS sensor has a large viewing swath of 2330 km with a revisit cycle of 1 or 2 days [70], and it has four overpasses per day (two daytime and two nighttime) when combining Aqua and Terra, it is reasonable to believe that our method is significant for the assessment of the burned regions. In addition to the application to MODIS data, the new burned area mapping method has good prospects for extension to other polar-orbiting and synchronous satellite data as well as Unmanned Aerial Vehicle (UAV) data for wildfire assessment. Furthermore, taking note of the possible spectral differences of training samples in varying seasons, regions and vegetation types [22; 32], we may establish seasonal, regional, and vegetation-based sample sets to improve the method further. Moreover, the deep learning techniques with powerful learning ability may be introduced to the data-processing of the abundant satellite data expected in the future.