Monitoring forest regrowth following large scale fire using satellite data-A case study of Yellowstone National Park, USA-

Abstract

Monitoring forest regrowth following major fires is important for understanding controls on forest regeneration and succession and detecting change in postfire plant communities. In this study we examined the extent to which forest regrowth following the 1988 Yellowstone National Park fires can be characterized by optical remote sensing data, and the spatial patterns associated with regrowth. Using a near-annual time series of Landsat satellite imagery, several satellite-based metrics were compared with field-based data of burn extent and post-fire stand structure and function. While there was little correlation between lodgepole pine density and our satellite metrics, single year regressions produced results over 80% and trend analysis of multiyear sites were able to explain 60 to 70% of the variability found between the ground collected data and the satellite metrics.

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

“…Our results reveal that fire severity did not show significant impact on long-term forest recovery by the year 2011 (Figure 8), whereas the effects of environmental variations were more pronounced [56,71]. For the VCT mapped "non-recovered" class, the mean canopy cover for all burn severities were less than 10%, with slightly higher mean canopy cover following the low severity fires.…”

“…The GYE has been a focal point for many post-disturbance vegetation recovery studies [9,42,[54][55][56][57][58], yet most previous studies relied on plot-level data and were limited in their ability to densely sampling in the remote, high-elevation areas of the Yellowstone Caldera. No systematic measurement based assessment of forest recovery following disturbances has been conducted across the whole region in recent decades (1980s to the present).…”

Ten years of forest cover change in the Sierra Nevada detected using Landsat satellite image analysis

“…Advantages of NDVI for the purpose of post-fire vegetation monitoring have been cited in its mathematical simplicity and ease of comparability across numerous multispectral remote-sensing platforms. Nonetheless, several studies have found that although NDVI may be sensitive to early (herbaceous) post-fire recovery, vegetation indices that include the shortwave infrared band (SWIR) may be better correlated with subsequent woody (tree and shrub) regrowth trajectories Franks, Masek, and Turner 2013).…”

Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis

“…The graphical output of classification stability can be visually assessed as values are displayed for each image object in a range from dark green (1.0, non-ambiguous) to red (0.0, ambiguous). Classification accuracy matrices [25] for the three hierarchical classification levels, based on the manually selected assessment polygons, were produced and summarized. A confusion matrix for each hierarchical level was also developed [49], using the 62 field validated sites and additional imagery interpreted observations.…”

“…Additionally, conventional per-pixel classification methods of analyzing medium resolution remotely sensed imagery, are not necessarily suitable for hyperspatial imagery analysis [16,20]. For example, pixel-based supervised classification or unsupervised classification are not suitable for the analysis of hyperspatial images, including those captured by aerial multispectral sensors, because these fail to incorporate the high spatial content and associated information in the classification scheme [21][22][23][24][25][26]. Image segmentation in hyperspatial optical imagery is the first step to harnessing the spatial detail of such data and a preliminary step to OBIA [27].…”