Fire is an important natural disturbance that affects several ecosystems and is also one of the main factors of the terrestrial carbon cycle. As fire modifies the structure and composition of vegetation, it is considered to be an important land management tool. Burned area mapping is an essential step in forest fire research to investigate the relationship between forest fire and climate change and the effect of forest fire on carbon budgets. Traditional data collection of forest fires in field- which are statistically recorded are difficult to manipulate over a large area. The development of the remote sensing technique provides a labor-efficient method for research of land surface processes. At the regional or global scale, in order to obtain a long-time series of burned area maps, a moderate spatial resolution with high temporal resolution remote sensing data is considered as the best alternative. Currently, the most widely used remote sensing data are Advanced Very High Resolution Radiometer (AVHRR) images and Moderate-Resolution Imaging Spectroradiometer (MODIS) images. Although the AVHRR provides continuous observations for burned area analyses, some studies have identified several sources of potential errors in burned area discrimination from this sensor, mainly due to its radiometric instability, cloud obscuration, and transmission problems. Most of these problems have been notably reduced in the MODIS sensor, which offers greater spectral, spatial, and radiometric resolution than the AVHRR. This study proposes an algorithm to map areas burned by forest fire using MODIS time series data in Heilongjiang Valley, China. The algorithm is divided into two steps: First, the "core" pixels were extracted to represent the most possible burned pixels based on a comparison of the temporal change of the Global Environmental Monitoring Index (GEMI), the Burned Area Index (BAI), and the MODIS active fire products between pre- and post-fire spatial patterns. Second, a 15-km distance was set to extract the entire burned area near the "core" pixels. These more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fire spatial patterns, which were represented by the MODIS fire products and the spectral index, respectively. Heilongjiang province in China was selected as the typical study area to validate the accuracy of the algorithm. The results showed that with the use of the MODIS fire products, the accuracy of the algorithm was improved, with an overall accuracy of 71% and a highest accuracy of 84%. Consequently, the algorithm used in this study produced a long-time series of burned area maps of the study area from 2000 to 2011 with a relatively high accuracy. According to the burned area maps, the study area has been seriously affected by fire disasters on average of 0.53 million ha of burned land each year. The most affected years were 2003 and 2008 with burned areas exceeding 1 million ha. The least affected year was 2010 with a burned area of just 0.18 million ha. The relatively large disparity between the maximum and minimum values of the areas burned by forest fire indicates that there is a fluctuation in the severity of disaster during the studied period.