Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne lidar data

Bolton, Douglas K.; Coops, Nicholas C.; Wulder, Michael A.

doi:10.1016/j.rse.2015.03.004

Cited by 114 publications

(96 citation statements)

References 72 publications

(112 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…R 2 = 0.22; RMSE 5.7 percent), HT (Adj. R 2 = 0.42; RMSE 25.5 cm), and other structure estimates (mapped to the pixellevel for a specific stand age (2 year)) are not directly comparable to these results, Bolton et al (2015) did recognize (as we found) that substantial variability among structure estimates was common. Hence, our detection, modeling, and mapping of specific aspen structural attributes two years after wildfire disturbance using optical imaging remote sensing data marks a first in the literature.…”

Section: Discussion Mapping Early-seral Forest Compositioncontrasting

confidence: 71%

“…characterization of specific structural variables has lagged behind. Airborne light detection and ranging (lidar) transect data were used recently with Landsat time series data in Canada to model 75 th quantile heights and percent canopy cover (above two meters) for regenerating aspen patches in five-year increments up to 25 years following fire disturbance (Bolton et al, 2015), which represented a significant advance over past remote sensing efforts. The cross-validated accuracy results for percent aspen canopy across all age classes was reported as 86.7 percent, while direct measures of aspen density were not reported.…”

Section: Discussion Mapping Early-seral Forest Compositionmentioning

confidence: 99%

“…Indeed, fusion of either high resolution SPOT data or hyperspectral sensor data with discrete-return or waveform lidar sensor data are certainly promising such applications (Anderson et al, 2008;Bolton et al, 2015). However, for regional monitoring efforts, the development of methods which take advantage of existing space borne assets will likely be more practical.…”

Section: Multi-resolution Approach -What Did We Gain?mentioning

confidence: 99%

See 2 more Smart Citations

Quantifying Early-Seral Forest Composition with Remote Sensing

Cooley

Wolter

Sturtevant

2016

photogramm eng remote sensing

View full text Add to dashboard Cite

Section: Discussion Mapping Early-seral Forest Compositioncontrasting

confidence: 71%

Section: Discussion Mapping Early-seral Forest Compositionmentioning

confidence: 99%

Section: Multi-resolution Approach -What Did We Gain?mentioning

confidence: 99%

See 1 more Smart Citation

Quantifying Early-Seral Forest Composition with Remote Sensing

Cooley

Wolter

Sturtevant

2016

photogramm eng remote sensing

View full text Add to dashboard Cite

“…As burned areas often have limited accessibility and cover large areas, satellite remote sensing and geographic information systems are considered essential for gathering and analyzing spatially explicit information, enabling the assessment of forest recovery after fire (Chuvieco, 2009;Gitas et al, 2012). With nearly 40 years of continuous observation, free Landsat imagery has become the most widely used information source to monitor vegetation dynamics (Bolton et al, 2015). Its spectral and spatial resolutions have demonstrated significant utility and the existence of a large number of images ac ⁎ ⁎ Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

View full text Add to dashboard Cite

Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999-2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km 2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forests.

show abstract

“…From field measures to remote sensing and modeling, assessing burn severity presents diverse challenges [14]. Within remote sensing, various data (e.g., optical, RADAR, LiDAR) and approaches for assessing burned areas exist [15][16][17]. Burn severity assessments have attempted to develop predictive models relating multispectral satellite data to field measured burn severity measures [18], compare conceptual historical fire regimes to current fire regimes [19], evaluate effectiveness of fuel treatments [20,21], and monitor trends over time [22,23].…”

Section: Introductionmentioning

confidence: 99%

Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands

2015

View full text Add to dashboard Cite

Burn severity metrics and classification have yet to be tested for many eastern U.S. deciduous vegetation types, but, if suitable, would be valuable for documenting and monitoring landscape-scale restoration projects that employ prescribed fire treatments. Here we present a performance analysis of the Composite Burn Index (CBI) and its relationship to spectral data (differenced Normalized Burn Ratio (dNBR) and its relative form (RdNBR)) across an oak woodland -grassland landscape in southwestern Oklahoma, USA. Correlation and regression analyses were used to compare CBI strata, assess models describing burn severity, and determine thresholds for burn severity classes. Confusion matrices were used to assess burn severity classification accuracy. Our findings suggest that dNBR and RdNBR, thresholded using total CBI, can produce an accurate burn severity map in oak woodlands, particularly from an initial assessment period. Lower accuracies occurred for burn severity classifications of grasslands and raises questions related to definitions and detection of burn severity for grasslands, particularly in transition to more densely treed structures such as savannas and woodlands.

show abstract

Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne lidar data

Cited by 114 publications

References 72 publications

Quantifying Early-Seral Forest Composition with Remote Sensing

Quantifying Early-Seral Forest Composition with Remote Sensing

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands

Contact Info

Product

Resources

About