The entire burned area and occurrence of large, naturally exploded fires has increased dramatically over the past four decades in North American called boreal forests (Kasischke & Turetsky, 2006).quickly increasing surface temperatures in Arctic and boreal regions (Hinzman et al., 2005) which increases the fire season and cause changes in the fire regime (Flannigan et al., 2005; Gillett et al., 2004; Kasischke & Turetsky, 2006; Westerling et al., 2006). Aerosol and gas emissions generated through the strong fire which influences the climate with surface albedo feedbacks (Bowman et al., 2009; Flanner et al., 2011; McGuire et al., 2006; Randerson et al., 2006). This is a complex factor which influences the vegetation recovery and the long-term effects of fire on radiative forcing. Some quantitative valuations recommend that due to enlarged snow extent albedo changes post-fire, , may be important enough to counter the opening carbon release, and for that fires will be speed up the climate warming in northern regions (Bala et al., 2007; Brovkin et al., 2004; Randerson et al., 2006). By analyzing the albedo dynamics the complex relationships within the burned area in a fire can be better understood with the help of spatial distribution and heterogeneity of the fire severity and the multiple factors affecting recovery. Overall, albedo recovery depends on the size, frequency, and burn severity of the fire. In particular, burn severity, a measure of the degree to which an area is disrupted by fire (NWCG, 2005), has vital consequences for post-fire ecosystem recovery in boreal forests (Beck et al., 2011; French et al., 2008; Goetz et al., 2007; Johnstone & Chapin, 2006; Mack et al., 2008). this study investigate the influence of burn severity and post-fire vegetation dynamics on early spring surface albedo at the fire mark level by using the finer spatial resolution (30 m) and improved radiometric fidelity (12 bit) of Landsat-8 data to highlight the result of surface heterogeneity on snow albedo across Alaskan boreal forests(Zhuosen Wang & Yanmin Shuai 2016). The arrangement developments of Landsat-8 permit for new valuations of snow albedo heterogeneity and the primary spring albedo recovery at the distinguishing scale of ecosystem disturbance by fire. An algorithm developed by Shuai et al. (2011, 2014) which give validation for non-snow covers by Román et al. (2013) is used to compute Landsat-8 snow albedo by joining Landsat-8 surface reflectance with MODIS BRDF data. In this paper authors generated Landsat-8 full appearance blue sky snow albedo by bearing in mind that surface multi-scattering consequence which is significant over snow-covered surfaces (Román et al., 2010). Huang et al. (2013) endeavored to capture fine scale snow-free albedo underlying forces using a Landsat albedo that assumes a Lambertian surface. During BRDF shape correction the surface albedo estimates are either under or overestimated which is neglected (Lucht & Lewis, 2000; Román et al., 2011). The actual impact of post-fire albedo dynamics is directed by the amount of visible snow cover during the early spring snow season (Liu et al., 2005; Amiro et al., 2006; Lyons et al., 2008; O’Halloran et al., 2014). values of Snow albedo vary with the vegetation height. Albedo is in elevation in bare areas with low profile vegetation and low over forested regions, since the canopy reductions the signal from understory snow. The presented data describe the advantage of the improved radiometric resolution of Landsat-8, which does not douse over snow and provides improved difference between snow and clouds(Zhuosen Wang & Yanmin Shuai 2016). The study conduct using the advantages of recently improved satellite Landsat-8 OLI which does not saturate over snow and it can easily use in the application of fire recovery progress at b100 m landscape scale(Zhuosen Wang & Yanmin Shuai 2016). Landsat-8 albedo capture the true reflective and layered character of snow cover over which includes land surface conditions and vegetation densities(Amiro & Randerson, 2006). The newly adopted techniques increase the capabilities of post-fire vegetation dynamics across low- to high-burn severity gradients in Arctic and boreal regions (Epting & Verbyla, 2005; Johnstone & Chapin, 2006; Johnstone et al., 2010; Shenoy et al., 2011; Zasada et al., 1983, 1987). The application is done during the early spring recovery of albedos show the greatest variation. Use of MODIS and Landsat-8 which represent surface Bidirectional Reflectance Distribution Functions (BRDF) products and surface reflectance’s for producing high resolution values in the surface of albedo. Shortwave blue sky albedo product accomplishes well with an overall RMSE of 0.0267 under both snow-free and snow-covered conditions(Zhuosen Wang & Yanmin Shuai 2016). Post-fire albedo recovery describe using MODIS albedo product which is contain with regional and global scales boundaries. The main objective of the study is to find out the significant importance of early spring post-fire albedo recovery by considering the significant spatial heterogeneity of burn severity at the landscape scale and secondly to find the significant impact of snow on the early spring albedo of various vegetation recovery types. Findings from the study is they find variation in early spring albedo which is larger than 0.6 and within a single MODIS gridded pixel(Zhuosen Wang & Yanmin Shuai 2016). The rapid surface change of albedo have a significant impact on rapid surface changes and thus make an impact in energy balance and distributions. Surface radiation which is derived from Landsat-8 data thus play a very important role in characterizing the carbon cycle which will also effect in the process of ecosystem(Zhuosen Wang & Yanmin Shuai 2016).