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

Abstract

Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. In this study, the LTSS-VCT approach was applied to examine long-term (up to 24 years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results also indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. With the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S.

A Review of Free Optical Satellite Imagery for Watershed-Scale Landscape Analysis

“…e freely available Global Forest Change product over British Columbia (Hansen et al, 2013), indicates a total of 85,900 km 2 has been lost (including wildfire and forestry) and 43,800 km 2 has been gained, for a net forest cover change of -42,100 km 2 between 2000 and 2015. Forest disturbance, change attribution, and recovery mapping are common applications of FOSI (Hermosilla et al, 2015b;Zhao et al, 2016;Zhu et al, 2012). Researchers have also endeavoured to associate changes in forest health to changes observed across the spectrum of satellitebased remote sensing wavelengths (Meng et al, 2016).…”

“…Forest disturbance, change attribution, and recovery mapping are common applications of FOSI (Hermosilla et al, 2015b;Zhao et al, 2016;Zhu et al, 2012). Researchers have also endeavoured to associate changes in forest health to changes observed across the spectrum of satellitebased remote sensing wavelengths (Meng et al, 2016). Recent studies have shown evidence for the accurate mapping of grey- (Hart & Veblen, 2015) and green- (Foster et al, 2017) attack spruce stands using spectral indices, while others have shown evidence for the use of thermal remote sensing (Hais & Kučera, 2008).…”

Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection

“…The potentially variable duration of disturbance effect raises the possibility that our single-year representation of the disturbed category may be insufficient and additional effort should be made to track recovery through time. This is, currently, not a trivial matter since land cover will transition to a number of different states until it returns to a condition similar to pre-disturbance [61].…”

Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

“…The widespread availability of data and methods that enable dense time series analyses allows for remote sensing to characterize vegetation recovery post-disturbance, including Wittenberg et al (2007), Bastos et al (2011), Kennedy et al (2012), Chu and Guo (2014), Griffiths et al (2014), Frazier et al (2015, Zhao et al (2016), Bartels et al (2016), and Yang et al (2017). All these studies used a variety of approaches to investigate forest recovery, and most have been focused on the spectral recovery of vegetation greenness, by using vegetation indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index, Normalized Burn Ratio, and tasseled cap greenness (Pickell et al, 2016;Yang et al, 2017).…”