Analyzing post fire regrowth from Cavity Lake and Ham Lake wildfires using Landsat Analysis Ready Data (ARD)
Abstract
Forest fires have a significant role in shaping the coniferous forest of northern Minnesota and southern Ontario, Canada. The Cavity Lake (2006) and Ham Lake (2007) fires burned more than 100,000 acres of timber within the Superior National Forest (Minnesota, USA), Boundary Waters Canoe Area Wilderness (Minnesota, USA) and Quetico Provincial Park (Canada). Here, the recovery of the Cavity Lake and Ham Lake burn areas are quantified by examining vegetation response post fire utilizing a temporal stack of U.S. Landsat Analysis Ready Data (ARD). Derived from Landsat Collection-1 Level-1 scenes, Landsat ARD are gridded to a common tile system which is ideal for pixel-based studies, since a single pixel will be located in the same location during the temporal period. In addition, U.S. Landsat ARD are already corrected for surface reflectance, eliminating the need to conduct pre-processing. Methods included first assessing the severity of the fire with respect to vegetation using the Normalized Burn Ratio (NBR) Index. By comparing NBR from ARD images pre- and post-fire, the severity of burn was quantified. Next, to examine vegetation response post-fire, the Normalized Difference Vegetation Index (NDVI) for a subset of pixels identified as being most severely burned were tracked. While, the United States Forest Service produced a final report on these two fire events in 2007, to date, few follow-up study investigating post-fire recovery in these areas has been undertaken. This analysis leads to a more complete understanding of the recovery of vegetation post fire in coniferous forests, especially flora dependent on fire to start their life cycle, such as the jack pine (Pinus banksiana). Furthermore, this study provides insight into a potential application of leveraging U.S. Landsat ARD in multi-temporal change detection and monitoring analysis.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.B31L2641R
- Keywords:
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- 0434 Data sets;
- BIOGEOSCIENCESDE: 0480 Remote sensing;
- BIOGEOSCIENCESDE: 1632 Land cover change;
- GLOBAL CHANGEDE: 1640 Remote sensing;
- GLOBAL CHANGE