of an ecosystem, and influence nutrient cycles and energy flow. However, fire can also destroy human property, life, and ecosystems if it is not controlled [9]. As a result, better fire management methods are needed to accurately report the extent of human induced fire, the burn severity, and the recovery process after fire. These goals can be achieved by mapping the burned area using remote sensing techniques, especially extracting the reflectance of the vegetation and soil of the burned areas [10]. The spectral reflectance of vegetation and soil properties within burned areas is characterized largely, so the burnt area is distinguishable through the Near Infrared (NIR) and Short Wave Infrared (SWIR) region in remote sensing imagery [11]. In large forest fire assessment, use of multi-spectral analysis has been proven to be beneficial in detecting risk zones [12]. Landsat data is best suited for this type of study [13; 14]. The mid-infrared regions typically have the greatest response to burning among the reflective spectral regions [15; 16]. Landsat 4 images are available since 1982, so the burn severity of historical fires may be estimated using these data. TM and ETM+ are often used because of the transformed reflectance (\(\rho\)) values of two bands, NIR (band 4: 750-900 nm), and mid-infrared (MIR; band 7: 2090-2350 nm), represented by R\({}_{4}\) and R\({}_{7}\). These are particularly sensitive to fire-induced changes in surface conditions mostly in vegetation and soil [15; 17]. A decrease in green vegetation and moisture content causes R\({}_{4}\) to decrease with burn, while R\({}_{7}\) increases because of the decrease in soil moisture and increased exposure of soil, rock, and fewer shadows from trees [17]. The Normalized Burn Ratio (NBR) of these bands, was used along with differenced Normalized Burn Ratio (dNBR) and relativized dNBR (RdNBR) as a viable method in this study [17]. However, it is important to use filtering methods to extract accurate burned endmembers before using NBR and dNBR models. In addition to the existing dNBR method, the RdNBR method outperformed for classifications, which produces high accuracies for the high burn severity category [18; 19]. For example, RdNBR based classification were more accurate in vegetation with a mix of grass and shrubland or with a mix of forest and non-forest [18; 20; 21; 22]. Studies done by [PERSON] [23] and [PERSON] et al. [24] used a spectral mixture model with hyperspectral data to determine the correlation between burn severity and high surface reflectance resulting from burned vegetation, implying reflectance was high when the soil was covered in white ash after fire. A spectral mixture filtering analysis known as Mixture Tuned Matched Filtering (MTMF) is usually performed to identify only a single, user-defined endmember at a time [25]. The endmember of interest is extracted and the unknown background is suppressed [26]. The result is similar to traditional spectral linear mixing analysis by the grayscale images (scale 0-1, where 1 is a perfect match) of the endmember of interest are produced. The MTMF is an efficient and advantageous technique for vegetation mapping [27; 28; 29; 30; 31]. Often after a fire, dominant spectral signatures, such as charred blackness or exposed soil make it difficult to detect minor vegetation changes due to sub-pixel occurrence. This can be determined by MTMF [25; 32]. Minimum Noise Fraction (MNF) is a traditional method also used to determine the inherent dimensionality of image data to segregate noise in the data and to reduce the computational requirements for subsequent processing [33]. The ability to distinguish even small patches of remaining vegetation or patches of ash would provide a better indication of fire effects on surface conditions. Thus, MNF was used in conjunction with MTMF to segregate noise as well as extract endmembers of interest in this study. In general, the study area of Guanacaste is a mosaic of national parks, wildlife refuge, and biological reserve.