Assessing postfire recovery of chamise chaparral using multi-temporal spectral vegetation index trajectories derived from Landsat imagery

Storey, Emanuel A.; Stow, Douglas A.; O’Leary, John F.

doi:10.1016/j.rse.2016.05.018

Cited by 69 publications

(67 citation statements)

References 39 publications

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“…Other studies suggest that TCW is well suited to observe forest recovery due to its ability to track overall moisture content [43], however, in our study, we found it less reliable because of its low level of separation directly following a fire. Like Storey et al [21], we found that NBR2 has extended recovery timeframes, and may be worth considering for future post-fire recovery studies. In southeast Australia, many eucalypts have the ability to survive low and moderate fire through epicormic resprouting (Figure 9), whereas after high intensity stand replacement fires, forest regrowth is dependent on new seedlings ( Figure 10) [58], which naturally thin out as the forest matures.…”

Section: Discussionsupporting

confidence: 79%

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Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

et al. 2018

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Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass's delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy 'greenness', are not as reliable, with values returning to pre-fire levels in 3-5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8-10 years) recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to understand the advantages and limitations of the various indices that these methods rely on.

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Section: Discussionsupporting

confidence: 79%

“…It is provided as a standard product by the United States Geological Survey (USGS), but is rarely used in the literature. Storey et al [21] found it useful for post-fire recovery assessment in chamise chaparral vegetation in southern California, while Stroppiana et al [22] used it as part of an ensemble to map burned areas.…”

Section: Introductionmentioning

confidence: 99%

Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

et al. 2018

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“…, in the study that developed the harmonic modeling approach we adapted to detect microrefugia, demonstrated that dense time series of all available Landsat data enabled detection of subtle forest thinning. Other studies, more commonly using time series of annual images (including composite images; eg, Roy et al, 2010), emphasize the power of LTS to detect gradual processes including forest decline due to diffuse disturbances such as insect outbreaks or drought (Ahmed et al, 2017;Cohen et al, 2016;Deel et al, 2012;Kennedy et al, 2010), forest succession and woodland densification (Vogelmann et al, 2012), and variation in ecosystem recovery following disturbance (Kennedy et al, 2007(Kennedy et al, , 2010Lawrence and Ripple, 1999;Storey et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Andrew

Warrener

2017

Remote Sensing of Environment

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(in press). Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics. Remote Sensing of Environment. Accepted 4 August 2017. DOI:https: //doi.org/10.1016/j.rse.2017.08.005 Disclaimer:The PDF document is a copy of the final version of this manuscript that was subsequently accepted by the journal for publication. The paper has been through peer review, but it has not been subject to any additional copy-editing or journal specific formatting (so will look different from the final version of record, which may be accessed following the DOI above depending on your access situation). 2 AbstractMicrorefugia are sites with stable, high quality habitat within landscapes characterized by dynamic environmental conditions driven by climate variability or ecological disturbances. There is considerable interest in the potential of microrefugia to provide climate change resilience to landscapes and to biodiversity conservation. Although attractive conceptually, there is yet little guidance on how to identify climate change microrefugia in order to study and protect them, and the data required to do so are often lacking. This study demonstrates how time series remote sensing, using all available Landsat images of a study area, can be used to directly detect microrefugia maintained by water subsidies in a semi-arid landscape in southwest Western Australia.Microrefugia were identified as pixels with abundant vegetation and consistent vegetation dynamics between wet and dry years. At every pixel, a harmonic model was fit to the intra-annual time series of vegetation index values compiled from the wettest years in the Landsat-5 Thematic Mapper (TM) archive. This model was then used to predict the phenological cycle of the driest years at that pixel. Candidate microrefugia were defined to be those pixels with (1) high vegetation activity in dry years and (2) highly predictable phenologies that are consistent regardless of the weather conditions experienced in a given year. Spatial relationships between candidate microrefugia and landscape features associated with elevated moisture availability (thought to drive climate microrefugia in these semi-arid landscapes) were assessed. The candidate microrefugia show great promise. Evaluations against high-resolution imagery reveal that candidate microrefugia most likely buffer against drought, although refugia from other disturbances, especially fire, were also detected. In contrast, spatial proxies of the physical features expected to maintain microrefugia failed to adequately represent the distribution of microrefugia across the landscape, likely due to data quality and the heterogeneity of microrefugia. Direct detection of microrefugia with Earth observation data is a promising solution in data limited regions. Landsat time series analyses are well suited to this application as they can characterize both the habitat quality and stability aspects of microrefugia.

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“…Landsat imagery covering the entire globe has been collected regularly for over 45 years and is now freely available for public use [8]. Recent studies have taken advantage of this data availability by developing time series data sets to monitor ecosystem recovery after wildfires [9][10][11]. The recovery trajectories derived from these data sets are an improvement over simple two-date change detection models because they capture the temporal variability that occurs after fires, including inter-and intra-annual trends [12].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series

et al. 2019

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Recovery trajectories derived from remote sensing data are widely used to monitor ecosystem recovery after disturbance events, but these trajectories are often retrieved without a precise understanding of the land cover within a scene. As a result, the sources of variability in post-disturbance recovery trajectories are poorly understood. In this study, we monitored the recovery of chaparral and conifer species following the 2007 Zaca Fire, which burned 97,270 ha in Santa Barbara County, California. We combined field survey data with two time series remote sensing products: the relative delta normalized burn ratio (RdNBR) and green vegetation (GV) fractions derived from spectral mixture analysis. Recovery trajectories were retrieved for stands dominated by six different chaparral species. We also retrieved recovery trajectories for stands of mixed conifer forest. We found that the two remote sensing products were equally effective at mapping vegetation cover across the burn scar. The GV fractions (r(78) = 0.552, p < 0.001) and normalized burn ratio (r(78) = 0.555, p < 0.001) had nearly identical correlations with ground reference data of green vegetation cover. Recovery of the chaparral species was substantially affected by the 2011-2017 California drought. GV fractions for the chaparral species generally declined between 2011 and 2016. Physiological responses to fire and drought were important sources of variability between the species. The conifer stands did not exhibit a drought signal that was directly correlated with annual precipitation, but the drought likely delayed the return to pre-fire conditions. As of 2018, 545 of the 756 conifer stands had not recovered to their pre-fire GV fractions. Spatial and temporal variation in species composition were important sources of spectral variability in the chaparral and conifer stands. The chaparral stands in particular had highly heterogeneous species composition. Dominant species accounted for between 30% and 53% of the land cover in the surveyed chaparral patches, so non-dominant land cover types strongly influenced remote sensing signals. Our study reveals that prolonged drought can delay or alter the post-fire recovery of Mediterranean ecosystems. It is also the first study to critically examine how fine-scale variability in land cover affects time series remote sensing analyses.

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Assessing postfire recovery of chamise chaparral using multi-temporal spectral vegetation index trajectories derived from Landsat imagery

Cited by 69 publications

References 39 publications

Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series

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