Evaluating vegetation recovery following large-scale forest fires in Borneo and northeastern China using multi-temporal NOAA/AVHRR images

Idris, Muhamad Husni; Kuraji, Koichiro; Suzuki, Masatoshi

doi:10.1007/s10310-004-0106-y

Cited by 30 publications

(24 citation statements)

References 36 publications

(49 reference statements)

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“…The unburned control plot procedure, which is widely used in NDVI-based studies related to the post-fire vegetation recovery (Díaz-Delgado et al, 2002;Idris et al, 2005;Lhermitte et al, 2010Lhermitte et al, , 2011Li et al, 2008;van Leeuwen et al, 2010;Veraverbeke et al, 2011), has not to our knowledge been applied to fraction images. Our study validated its use with MESMA SGV fraction images.…”

Section: U N C O R R E C T E D P R O O Fmentioning

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

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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.

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Section: U N C O R R E C T E D P R O O Fmentioning

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

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“…The surge of new vegetation growth in the first year may seem incongruent with the initial burn severity classification due to a boost in plant-available nitrogen (Stephan et al 2012) and nutrients remaining in the ash layer (Neary et al 1999); thus, it is important to monitor vegetation and soil recovery characteristics over time (Sankey et al 2013;Berryman et al 2014). The resilience of the ecosystem to the fire disturbance depends on specific conditions of the pre-fire environment such as vegetation type and density, and forest health such as drought or insect kill, taken into consideration with the scale and intensity of effects of the fire on the vegetation and soil (Idris et al 2005;Schoennagel et al 2008;Bowman et al 2009;Perry et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Indicators of burn severity at extended temporal scales: a decade of ecosystem response in mixed-conifer forests of western Montana

Lewis

Hudak

Robichaud

et al. 2017

Int. J. Wildland Fire

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Abstract. We collected field and remotely sensed data spanning 10 years after three 2003 Montana wildfires to monitor ecological change across multiple temporal and spatial scales. Multiple endmember spectral mixture analysis was used to create post-fire maps of: char, soil, green (GV) and non-photosynthetic ( Initial estimates of char and NPV from the HS images were significantly correlated with their ground-measured counterparts (r ¼ 0.60 (P ¼ 0.03) and 0.68 (P ¼ 0.01) respectively), whereas HS GV and Landsat GV were correlated with canopy GV (r ¼ 0.75 and 0.70 (P ¼ 0.003) respectively). HS imagery had stronger direct correlations with all classes of fine-scale ground data than Landsat and also had stronger predictive correlations with 10-year canopy data (r ¼ 0.65 (P ¼ 0.02) to 0.84 (P ¼ 0.0003)). There was less than 5% understorey GV cover on the sites initially, but by 2013, it had increased to nearly 60% regardless of initial condition. The data suggest it took twice as long for understorey GV and NPV to replace char and soil as primary ground cover components on the highburn-severity sites compared with other sites.

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“…However, these remotely sensed data, NDVI for example, usually reach saturation levels prior to the point where an ecosystem fully recovers its maximum biomass after disturbance [204,225,228] (Table 9). Therefore, tracking recovery patterns in vegetation using vegetation indices might limit and underestimate the rate of recovery after disturbance [183].…”

Section: Tracking Patterns Of Forest Recovery After Firementioning

confidence: 99%

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

2013

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Abstract:The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre-and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions.

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Evaluating vegetation recovery following large-scale forest fires in Borneo and northeastern China using multi-temporal NOAA/AVHRR images

Cited by 30 publications

References 36 publications

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

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

Indicators of burn severity at extended temporal scales: a decade of ecosystem response in mixed-conifer forests of western Montana

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

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