kunesm-blog

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Hudak, A., Robichaud, P., Evans, J., Clark, J., & Lannom K. 2004. Field Validation of Burned Area Reflectance Classification (BARC) Products for Post-Fire Assessment. USDA Forest Service / UNL Faculty Publications. 220.

In this study, Hudak et al. looked at post-fire effects of the Simi and Old fires in southern California, as well as the Wedge Canyon, Robert, Black Mountain 2, and Cooney Ridge fires of north-west Montana. They used normalized burn ratio (NBR), delta NBR (dNBR), and normalized difference vegetation index (NDVI) to analyze Landsat imagery. Once these values were calculated, they were compared to field data from 2003 at 35 sites across all six wildfires. The results showed more accurate numbers from NRB and dNBR when compared to NDVI values. Overall, the calculations from the satellite imagery were favored over the field surveys due the cost and time consumption.

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Mallinia, G., Maris, F., Kalinderis, I., & Koutsias, N. 2009. Assessment of Post-Fire Soil Erosion Risk in Fire-Affected Watersheds Using Remote Sensing and GIS. GIScience & Remote Sensing. 46(4): 388-410.

Mallinia et al. observe north-central Greece based on a 2006 fire. The goal of the study was to model post-fire potential soil erosion risk at the watershed level. They accomplished this by creating a burn severity map using satellite imagery, observing potential post-fire sediment loss, and estimating the risk assessment based on the erosional pattern of the variability of burn severity. Additionally, they mapped the watersheds to determine which ones would be most vulnerable. The results showed some errors, but overall accuracy of the methods used. It was determined that even with the errors, these analyses are much quicker and less expensive than traditional post-fire field surveys. They were able to create a model that showed areas of high potential erosion based on burn severity. This model can be used to determine which areas need quick action to protect the local watersheds.

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Mitri, G. & Gitas, I. 2013. Mapping Post-Fire Forest Regeneration and Vegetation Recovery Using a Combination of Very High Spatial Resolution and Hyperspectral Satellite Imagery. International Journal of Applied Earth Observation and Geoinformation. 20: 60-66.

Mitri and Gitas (2013) study the island of Thasos, Greece and two large fires that happened in 1985 and 1989. Hyperion and Landsat images were used to observe and analyze the data. Additionally, they used field survey data from 2003 and 2004 that focused on the visual assessment of the vegetation. The images were used to create segments of the burn areas, then each segment was classified at level 1, 2, or 3. The classification were broken out based on vegetation type and burn severity, such as brutia not burned, brutia regeneration, and other vegetation. By comparing the use of multispectral data to the sole use hyperspectral data, they found a 20% increase in classification accuracy of brutia not burned, 1% increase in the classification accuracy of brutia regeneration, 5% decrease in classification accuracy of nigra not burned, 21% increase in classification accuracy of nigra regeneration, and 12% increase in classification accuracy of other vegetation.

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Ireland, G. & Petropoulos, G. 2015. Exploring the Relationship Between Post-Fire Vegetation Regeneration Dynamics, Topography, and Burn Severity: A Case Study from the Montane Cordillera Ecozones of Western Canada. Applied Geography. 56: 232-248.

Ireland and Petropoulos (2015) looked at pre- and post-fire vegetation in Okanagan Mountain Park, British Columbia based on a 2003 fire. This study used normalized difference vegetation index (NDVI) and regeneration index (RI) to quantify vegetation patters found within the burn scars. They also looked at the relation between topography and vegetation regrowth dynamics. They accomplished these tasks by analyzing Landsat imagery in ENVI and ArcGIS. The results showed ~60% vegetation regrowth eight years post-fire. The RI values showed a strong correlation to the NDVI values, indicating accuracy for the method. Additionally, the topography and vegetation regrowth correlation showed that north facing areas had higher rates of vegetation regeneration compared to southern ones.

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Mitchell, M. & Yuan, F. 2010. Assessing Forest Fire and Vegetation Recovery in the Black Hills, South Dakota. GIScience & Remote Sensing. 47(2): 276-299.

This article discusses the history of fire among the United States and the historical methods of tacking fire data. Previously, fire data was collected via hand-drawn polygons taken from low-flying aircraft, GPS coordinates of field surveyors, and ground-based methods combined with low-level digital photos. These methods require a significant amount of time and tend to be costly. Eventually, methods became more evolved such as normalized burn ratio (NBR) and normalized difference vegetation index (NDVI) using Landsat imagery. With a combination of field surveys and remotely sensed imagery, Mitchell et al. studied the vegetation of the 2000 Jasper Fire in Black Hill, SD. It was determined that satellite imagery was the best method used in this study due to the limitation of access, expense, and time consumption of field surveys. The NDVI results showed that ~38% of the area was severely burned, compared to 39% in a previous field-based study.

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Tonbul, H., Kavzoglu, T., & Kaya, S. 2016. Assessment of Fire Severity and Post-Fire Regeneration Based on Topographical Features Using Multitemporal Landsat Imagery: A Case Study in Mersin, Turkey. The International Archives of the Photogrammetry, Remote Sensing and Spatial Sciences. Volume XLI-B8.

In a 2008 fire, Mersin, Turkey saw a devastating travesty resulting in two deaths and hundreds homeless. This study using multitemporal images to observe the fire severity and the vegetation regrowth. Tonbul et al. using normalized difference vegetation index (NDVI) and soil adjust vegetation index (SAVI) to come to their conclusions. This study looks at pre-fire vegetation and up to six years of post-fire imagery. The results broke up the fire severity of the study area into four difference classes: low severity (6%), moderate-low severity (26%), moderate-high severity (40%), and high severity (28%). Six-year post-fire the NDVI level increased 57% and the SAVI levels increased 63%, both showing accurate representations. It was determined that SAVI would be an acceptable alternative to NDVI when observing vegetation.

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Fox, D.M., Maselli, F., & Carrega, P. 2008. Using SPOT Images and Field Sampling to Map Burn Severity and Vegetation Factors Affecting Post Forest Fire Erosion Risk. Catena. 75: 326-335.

Fox et al. dive deeper into a 2003 fire in south-east France. They look at pre-fire vegetation density, burn severity, soil erodibility, and slope via a DEM. The results were achieved by using field data, a DEM, and SPOT multispectral images. Fox et al. used the multispectral images to calculate the NDVI for the burn severity map and pre-fire vegetation. In comparison, the study found that GIS was a much more practical way to observe post-fire vegetation when compared to collecting field data. The GIS results were promising enough when compared to the expensive, long duration field data collection process.

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Nioti, F., Xystrakis, F., Koutsias, N., & Dimopoulos, P. 2015. A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece. Remote Sens. 7: 7712 – 7731.

In this study, Nioti et al. takes a close look at the Pinus brutia woodlands on Karpathos Aegean Island, Greece. This study looked at Landsat data from 1982 – 2012, and the purpose of it was to identify landcover changes and environmental parameters surrounding a 1983 fire. The post-fire images showed a decrease in dense and open woodland, but an increase in scrubland and garrigues. Additionally, ~58% of the original dense P. brutia woodland was classified as belonging to the same class 26 years later, but only ~15% of the original open P. brutia woodland was classified as dense woodland. The study concluded stating the port-fire vegetation recovery was affected by pre-fire vegetation conditions, fire history, climate, landscape, and human-induced changes.

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Stankova, N. & Nedkov, R. 2015. Monitoring the Dynamics and Post-Fire Recovery Processes of Different Vegetation Communities Using MODIS Satellite Images. Journal of Environment Protection and Sustainable Development. 1(3): 182-192.

Stankova and Nedkov (2015) used multi-angular images from the MODIS sensor on the Terra and Aqua satellites. The region of study was south-eastern Bulgaria, and it was observed from 2007 – 2014. This study utilizes normalized difference vegetation index (NDVI) and perpendicular vegetation index (PVI) to monitor the post-fire vegetation. The results showed a higher NDVI in 2010 than pre-fire in 2007. This may be due to factors that brought in new vegetation species. The PVI results did not show as drastic of a change as the NDVI but was more subtle over a longer period.

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Riano, D., Chuvieco, E., Ustin, S., Zomer, R., Dennison, P., Roberts, D., & Salas, J. 2002. Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sensing of Environment. 79: 60-71.

Instead of using space images, this study uses Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). There are several advantages of using airborne images over space images, including more specific study area, possibility for higher temporal studies, and possibility for smaller swath, creating higher resolution. Through the study of post-fire vegetation evolution, this team compared the accuracy of hyperspectral data to NDVI. The study area is the Santa Monica Mountains of Southern California. The results of the study showed that hyperspectral images were more accurate at capturing certain vegetation species, but NDVI was accurate for the overall vegetation recovery.

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White, J., Ryan, K., Key, C., & Running, S. 1996. Remote Sensing of Forest Fire Severity and Vegetation Recovery. Int. J. Wildland Fire. 6(3): 125-136.

Using Landsat satellite images, White et al. (1996) looked at the burn severity of the Red Bench fire, which spread from Flathead National Forest to Glacier National Park. This study used both normalized difference vegetation index (NDVI) and a plotted map based on the areas burn severity. The pots were used for supervised and unsupervised classification purposes to make a complete map. The study conclude that multiple analyses are needed to monitor pre- and post-fire vegetation, but NDVI was more accurate than the mapping functions.

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Petropoulos, G., Griffiths, H., & Kalivas, D. 2014. Quantifying Spatial and Temporal Vegetation Recovery Dynamics Following a Wildfire Event in a Mediterranean Landscape using EO Data and GIS. Applied Geography. 50:120-131.

Petropoulos, Griffiths, & Kalivas (2014) calculated normalized difference vegetation index (NDVI) to compare post-fire vegetation and pre-fire vegetation. They chose to look at a Mediterranean landscape because of the potential heavy rainfalls and the topography. It was determined that the 2007 fire on Mt. Parnitha in Greece would be the best fit for this study, and Landsat images would be the best fit to calculate the NDVI. One pre-fire image from early 2007 and four post-fire images from 2007-2011 were used to observe vegetation. The results showed a steady increase in NDVI throughout the post-fire vegetation growth, but four years post-fire still did not equate to the pre-fire NDVI.

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Diaz-Delgado, R. & Pons, X. 2001. Spatial patterns of the forest fires in Catalonia (NE of Spain) along the period 1975 – 1995: Analysis of vegetation recovery after fire. Forest Ecology and Management. 147: 67-74.

Diaz-Delgado and Pons (2001) used Landsat images to calculate the normalized difference vegetation index (NDVI) of vegetation recovery in Catalonia during 1975 – 1995. The images were used to identify fire scars, then compared to a municipal fire data base to confirm or collect additional data. Once the NDVI was calculated, the information was compared to the municipal fire data base for accuracy. They found that NDVI was an acceptable comparison to the data base but worked better for smaller areas (equal to or greater than 0.3 kilometers squared).

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Daldegan, G., Junior, O., Guimaraes, R., Gomes, R., Ribeiro, F., & McManus, C. 2014. Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil. Remote Sensing. 6: 9873-9894.

This study looks at Landsat images between the summer months of 2001 – 2011 of the Serra Tombador Natural Reserve (STNR). Through these images, the study observes fire scars and land cover of effected fire areas. The results showed ~69% of the study area had been burned during the studied time period, and 2004 had the largest burn area of ~34%. The results also showed that savanna and grassland cover burned the most hectare within the study area throughout the studied time period. This study provides insight into which areas and land cover should have additional fire management during the summer months.

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Arianoutsou, M., Koukoulas, S., & Kazanis, D. 2011. Evaluation Post-Fire Forest Resilience Using GIS and Multi-Criteria Analysis: An Example from Cape Sounion National Park. Environmental Management. 47: 384-397.

Arianoutsou, Koukoulas, and Kazanis (2011) use GIS to analyze vegetation resilience post-fire. The sample area they chose is a portion of Cape Sounion National Park in Central Greece. They used four map layers for this studying, including vegetation map, fire history, parent rock material, and area topography. The study found that slope and rock type were factors in vegetation recovery. Areas with limestone, a harder rock, had less vegetation post-fire, most likely due to shallower soil depths. Additionally, areas with higher slope have less vegetation. Though the vegetation is not dense at high slopes, the variation in vegetation is high.