Data Evaluation or Data Analysis for Fire Modeling
Fuels Inventory & Monitoring
Landsat Normalized Difference Vegetation Index (NDVI) is commonly used to monitor post-fire green-up; however, most studies do not distinguish new growth of conifer from deciduous or herbaceous species, despite potential consequences for local climate, carbon and wildlife. We found that dual season (growing and snow cover) NDVI improved our ability to distinguish conifer tree presence and density. We then examined the post-fire pattern (1984–2017) in Landsat NDVI for fires that occurred a minimum of 20 years ago (1986–1997). Points were classified into four categories depending on whether NDVI, 20 years post-fire, had returned to pre-fire values in only the growing season, only under snow cover, in both seasons or neither. We found that each category of points showed distinct patterns of NDVI change that could be used to characterize the average pre-fire and post-fire vegetation condition Of the points analyzed, 43% showed a between-season disagreement if NDVI had returned to pre-fire values, suggesting that using dual-season NDVI can modify our interpretations of post-fire conditions. We also found an improved correlation between 5- and 20-year NDVI change under snow cover, potentially attributable to snow masking fast-growing herbaceous vegetation. This study suggests that snow-cover Landsat imagery can enhance characterizations of forest recovery following fire.