Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

Dragozi, Eleni; Gitas, Ioannis Z.; Bajocco, Sofia; Stavrakoudis, Dimitris G.

doi:10.3390/rs8070566

Cited by 19 publications

(16 citation statements)

References 46 publications

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“…Topographical factors, that were not significant in this study, have clear influence on fire behaviour although the association between topography and burn severity cannot be generalized for all ecosystems and regions (Dragozi et al, 2016;Gonzalez-Olabarria et al, 2006;González et al, 2007) due to the prevalence of determined local conditions on the studied regions (for instance, variation on humidity between different elevations and aspects), or how the slope influences fire behaviour (higher slopes increase the rate of spread and intensity, but usually reduce the residence time of fire). Burn severity is expected to be more severe in coniferous forests than in broadleaved forests but in our study it did not appear as significant variable.…”

Section: Discussioncontrasting

confidence: 57%

“…Burn severity is expected to be more severe in coniferous forests than in broadleaved forests but in our study it did not appear as significant variable. The pre-fire tree species composition could play an important role in determining post-fire reflectance and recovery and, subsequently, burn severity (Dragozi et al, 2016). It is clear that, depending on the fire intensity and the specific response of the tree species to fire, different ecosystem responses such as resprouting and regeneration can be expected or be accentuated as time goes on (Keeley et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

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Aplicación de imágenes de satélites y datos LiDAR en la modelización de recursos forestales

Casado¹

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Section: Discussioncontrasting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Aplicación de imágenes de satélites y datos LiDAR en la modelización de recursos forestales

Casado¹

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“…Biodiversity and related ecosystem services issues resulted as connected, also, to studies about environmental disturbance phenomena, like fire. Many papers in rs+pheno studies analyzed the role of wildfires' behavior both as a driver of vegetation recovery patterns [101][102][103] and as influenced by fuel phenology and flammability [104,105]. In this latter case, a high potential exists for characterizing, classifying, and mapping fuel based on rs+pheno dynamics.…”

Section: Emerging Research Topicsmentioning

confidence: 99%

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

et al. 2019

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As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.

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“…Escuin et al [19] analyzed the capacity of NBR and NDVI indices derived from Landsat TM/ETM images for fire severity assessment in three fires that occurred in southern Spain. Dragozi et al [20] investigated the potential of GeoEye images on detecting the field-measured burn severity of a forest fire in Evros (Greece) during 2011. Results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time.…”

Section: Introductionmentioning

confidence: 99%

A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data

Teodoro

Amaral

2019

Environments

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Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data.

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Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

Cited by 19 publications

References 46 publications

Aplicación de imágenes de satélites y datos LiDAR en la modelización de recursos forestales

Aplicación de imágenes de satélites y datos LiDAR en la modelización de recursos forestales

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data

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