Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data.

Willem Van Leeuwen

doi:10.3390/s8032017

Abstract

This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs of wildfire affected sites and a nearby unburned reference site were chosen to measure the post-disturbance recovery in relation to climate variation. All site pairs were chosen in forested uplands in Arizona and were restricted to the area of the Rodeo-Chediski fire that occurred in 2002. Fuel reduction treatments were performed in 1999 and 2001. The inter-annual and seasonal vegetation dynamics before, during, and after wildfire events can be monitored using a time series of biweekly composited MODIS NDVI (Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index) data. Time series analysis methods included difference metrics, smoothing filters, and fitting functions that were applied to extract seasonal and inter-annual change and phenological metrics from the NDVI time series data from 2000 to 2007. Pre- and post-fire Landsat data were used to compute the Normalized Burn Ratio (NBR) and examine burn severity at the selected sites. The phenological metrics (pheno-metrics) included the timing and greenness (i.e. NDVI) for the start, peak and end of the growing season as well as proxy measures for the rate of green-up and senescence and the annual vegetation productivity. Pre-fire fuel reduction treatments resulted in lower fire severity, which reduced annual productivity much less than untreated areas within the Rodeo-Chediski fire perimeter. The seasonal metrics were shown to be useful for estimating the rate of post-fire disturbance recovery and the timing of phenological greenness phases. The use of satellite time series NDVI data and derived pheno-metrics show potential for tracking vegetation cover dynamics and successional changes in response to drought, wildfire disturbances, and forest restoration treatments in fire-suppressed forests.

Highlights

Recurring drought and wildfire are widespread stressor and disturbance events that affect forest ecosystem functioning and processes and can result in significant soil erosion, and changes in land cover and carbon dynamics
Current climate variability coupled with a buildup of forest fuel loads in the Southwestern US has resulted in an increasing number of large and severe wildfires [1]
Fire severity data along with associated biophysical and phenological characteristics provide an analytical basis for assessing future resources, the risks posed by invasive species, and threats to biodiversity

Summary

Introduction

Recurring drought and wildfire are widespread stressor and disturbance events that affect forest ecosystem functioning and processes and can result in significant soil erosion, and changes in land cover and carbon dynamics. Current climate variability coupled with a buildup of forest fuel loads in the Southwestern US has resulted in an increasing number of large and severe wildfires [1]. There is a need to develop tools to evaluate post fire recovery trajectories and assess the effectiveness of land management decisions. The use of fire severity and vegetation recovery data, at local, state and national levels, could provide a baseline that can be used to monitor vegetation recovery and the health of fire affected landscapes over time. Fire severity data along with associated biophysical and phenological characteristics provide an analytical basis for assessing future resources, the risks posed by invasive species, and threats to biodiversity

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