Forest as a vital natural resource,is closely related to human production and life. Fire is the most serious hazard to forests. Moreover,the problems of frequent fires caused by massive accumulation of forest combustible materials and global warming,etc. are prominent. Most of remote sensing monitoring on fire are conducted on the basis of polar orbit satellites; yet the monitoring frequency and precision are to be improved. In this paper, focusing on the a forest fire occurred in Dandong,Liaoning from 11am to 6pm (Beijing time) on April 11,2017, intensive fire monitoring,burned areas and the damage of forest burning monitoring are conducted based on Himawari- 8 (H8) and Gaofen No. 1 (GF-1) satellites. A fire point recognition algorithm that is contextual fire detection algorithm is adopted to recognize fire points of the 7-track H8 data. Meanwhile,the central ignition temperature and the burning range are also extracted. The near-infrared spectra (B4) and the normalized vegetation index were used before and after the fire. B4,The NDVI and GEMI methods are utilized to identify burning areas with the use of 2- scene GF-1 before and after the fire. A Gaussian function fitting model is proposed in this paper to simulate the growth of healthy forests due to the long time resolution of GF satellites and the close relationship between the burning damage and the growth of forest. On account of spectral values before the fire reconstructed by current imaging data,a standard deviation standardization method is employed to score the damaging condition with the B4 decrement as the evaluation index. Results show that: it's clear that the position of the central ignition point is changed from the ignition point 1 to the ignition point 2,the range changed from 2 pixels to 4 pixels,and the temperature decreased from 321K to 314K according to the intensive monitoring of H8; Comparing the three methods for extracting burned areas,it can be concluded that the decay is changed the most obvious in the B4 band; the GEMI model is second; while the decay of NDVI is not obvious. Therefore,the B4 decay method is the best way to extract burned areas. The growth curve of healthy woodland is simulated based on the spectral index of 368 discrete forest lands,and the equation is fitted. The growth curve of the healthy forest is simulated based on forest spectral indexes of 368 discrete points with a correlation coefficient of the fitting equation is 0.89. Also,burning damage is monitored with data of a fire occurred 8 days ago in 3rd,April,whose omission error of the severity of damage is 90%. However,the precision of data severity,the precision of mild damage increased and the overall classification precision after simulated are increased by 24%,10% and 69%,respectively,with the kappa coefficient of 0.44. Therefore,it's necessary to reconstruct the spectral spectrum of remote sensing images for forests before fire.