Investigation of post fire vegetation regrowth under different burn severities based on satellite observations

Roodsarabi, Z.; Sam-Khaniani, Ali; Kiani, Abbas

doi:10.1007/s13762-022-04500-5

Cited by 3 publications

(2 citation statements)

References 68 publications

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“…Some areas were badly burnt, with a thick residue of fire ash. In Figure 2, we have categorized them based on field sampling data into three fire severity levels: low severity, moderate severity, and high severity [48][49][50]. We randomly selected 100 sample points from the field survey and UAV images.…”

Section: Threshold Methodsmentioning

confidence: 99%

Forest Fire Mapping Using Multi-Source Remote Sensing Data: A Case Study in Chongqing

et al. 2023

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Forest fires are one of the most severe natural disasters facing global ecosystems, as they have a significant impact on ecological security and social development. As remote sensing technology has developed, burned areas can now be quickly extracted to support fire monitoring and post-disaster recovery. This study focused on monitoring forest fires that occurred in Chongqing, China, in August 2022. The burned area was identified using various satellite images, including Sentinel-2, Landsat8, Environmental Mitigation II A (HJ2A), and Gaofen-6 (GF-6). The burned area was extracted using visual interpretation, differenced Normalized Difference Vegetation Index (dNDVI), and differenced Normalized Burnup Ratio (dNBR). The results showed that: (1) The results of the three monitoring methods were very consistent, with a coefficient of determination R2 > 0.96. (2) A threshold method based on the dNBR-extracted burned area was used to analyze fire severity, with moderate-severity fires making up the majority (58.05%) of the fires. (3) Different topographic factors had some influence on the severity of the forest fires. High elevation, steep slopes and the northwestern aspect had the largest percentage of burned area.

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Section: Threshold Methodsmentioning

confidence: 99%

Forest Fire Mapping Using Multi-Source Remote Sensing Data: A Case Study in Chongqing

et al. 2023

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“…These data fusion methods provide a more thorough understanding of the features of the fire (Mohsenifar et al, 2021). Time series analysis is also possible due to satellite networks' ability to collect data over time and this technique is employed to determine fire development, spread, and recovery over days, weeks, or years (Hostert et al, 2015;Woodcock et al, 2020;Kotawadekar, 2021;Roodsarabi et al, 2023). Artificial intelligence and machine learning may also be applied to satellite data to automatically detect and anticipate fires.…”

Section: Introductionmentioning

confidence: 99%

A case study about the forest fire occurred on 05 July 2021 over Khenchela province, Algeria, using space-borne remote sensing

Guehaz,

Sivakumar

2023

Front. Remote Sens.

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In this study, space-borne remote sensing (Landsat-8, MODIS) was employed to evaluate the effects of forest fires occurring on 05 July 2021, over Khenchela province, Algeria. Our objective is to understand the severity of damage caused by the fire and its implications for vegetation and land cover. Utilizing the Normalized Difference Vegetation Index (NDVI) from MODIS data and Landsat-8 imagery, we report changes in vegetation health and land cover. To identify areas affected by forest fires and evaluate the severity of damage, the Normalized Burn Ratio (NBR) and Differenced Normalized Burn Ratio (dNBR) were calculated. Analysis showed that −1825.11 ha (1.21%) of the total area experienced severe burns, 3843.54 ha (2.54%) moderate to high severity burns, 3927.97 ha (2.59%) moderate to low severity burns and 9864.45 ha (6.51%) low severity burns. The area covered by vegetation decreased from 2014 to 2021, indicating a negative trend in vegetation cover over the study period.

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Assessment of Post-Fire Phenological Changes Using MODIS-Derived Vegetative Indices in the Semiarid Oak Forests

Karimi

Heydari

Mirzaei

et al. 2023

Forests

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Wildfire has significant impact on plant phenology. The plants’ phenological variables, derived from time series satellite data, can be monitored and the changes in satellite imagery may be used to identify the beginning, peak, and end of the growing season. This study investigated the use of remote sensing data and land surface phenology (LSP) parameters to evaluate the impacts of fire. The LSP parameters included the start of growing season (SOS), the length of the growing season (LOS), the end of the growing season (EOS), maximum greenness of the season (Gmax), and minimum greenery in the season (Gmin) in the fire-impacted, semiarid oak forests of Iran. These LSP parameters were extracted from multitemporal normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2) data, acquired from MODIS sensor images in Zagros of the Ilam province in western Iran. By extracting LSP indices from the NDVI and EVI2 data, the indices were compared between burned forest areas, areas surrounding the burned forests, and unburned areas and for timesteps representing pre-fire, fire (i.e., year of fire), and post-fire (i.e., 2 years) conditions. It was found that for the burned area, there were significant differences in Gmax and the day that Gmax occurred. Furthermore, there was also a significant difference in Gmin between the pre- and post-fire conditions when NDVI was used and a significant difference between Gmax when EVI2 was used. The results also showed that in both time series there was a significant difference between the burned and control area in terms of Gmax. In general, the results showed that the fire had a negative effect on LSP, but in the two years after the fire, there were signs of forest restoration. This study provides necessary information to inform forest and resource conservation and restoration programs.

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Investigation of post fire vegetation regrowth under different burn severities based on satellite observations

Cited by 3 publications

References 68 publications

Forest Fire Mapping Using Multi-Source Remote Sensing Data: A Case Study in Chongqing

Forest Fire Mapping Using Multi-Source Remote Sensing Data: A Case Study in Chongqing

A case study about the forest fire occurred on 05 July 2021 over Khenchela province, Algeria, using space-borne remote sensing

Assessment of Post-Fire Phenological Changes Using MODIS-Derived Vegetative Indices in the Semiarid Oak Forests

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