Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems

García-Llamas, Paula; Suárez‐Seoane, Susana; Fernández‐Guisuraga, José Manuel; Fernández–Manso, Alfonso; Quintano, Carmen; Taboada, Ángela; Marcos, Elena

doi:10.1016/j.jag.2019.04.006

Cited by 52 publications

(42 citation statements)

References 44 publications

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“…This accuracy implies that the combination of GeoCBI and Sentinel-2A dNBR may be suitable for the burnt Mediterranean vegetation communities studied. Our finding was in agreement with two recent comparative studies on satellite ability to discriminate levels of burn severity in Mediterranean heterogeneous physiognomy [28,29].…”

Section: Burn Severity Mapsupporting

confidence: 93%

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

et al. 2020

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In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.

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Section: Burn Severity Mapsupporting

confidence: 93%

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

et al. 2020

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“…In this way, NBR, NDWI, NDVI 750 , and SR (contributing more than 94%) could be computed with Sentinel-2 MSI data, which are more easily available than EO-1 Hyperion data. Though the spectral and spatial resolution are different, Sentinel-2 MSI spectral indices have already shown their suitability for discriminating burned areas [19,54,55]. Thus, we believe that the proposed methodology may be generalized using Sentinel-2 MSI data; however, we leave this test for futures studies.…”

Section: Discussionmentioning

confidence: 97%

A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Fernández–Manso

Quintano

2020

Remote Sensing

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Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI 750 ), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.

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“…The images are freely available from the European Space Agency (ESA) Copernicus Open Access Hub (https://scihub.copernicus.eu/without/dhus/#/home). These images have been widely used in land cover detection [34,35], natural disaster monitoring [36], forest monitoring [37], and agricultural monitoring and management [38,39]. The Sentinel-2 Atmospheric Reflective Top (L1C) product was used in this study.…”

Section: Remote Sensing Data Acquisition and Preprocessingmentioning

confidence: 99%

Identification of Apple Orchard Planting Year Based on Spatiotemporally Fused Satellite Images and Clustering Analysis of Foliage Phenophase

Zhu

Yang

et al. 2020

Remote Sensing

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The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar outlines but different planting years, and it is, therefore, difficult to effectively determine the actual planting year based on textural or structural characteristics. Therefore, the monitoring method provided in this paper is not to monitor the growing year positively from the planting of orchard seedlings but to use time series remote sensing data to reverse determine the continuous growth age of each existing orchard. The city of Qixia, Shandong Province, China, was used as a case study. Firstly, the spatial distribution of apple orchards was accurately extracted using the Sentinel-2 normalized difference vegetation index (NDVI) spatiotemporally fused images and phenological vegetation information. Secondly, using region of interest (ROI) data for different vegetation types obtained from a field survey, NDVI time series were extracted from the Sentinel-2 NDVI spatiotemporally fused image. Among them, three characteristic phenological periods were selected, and the NDVI time series for apple orchards was used as a template to extract the apple orchard distribution area from 2000 to 2017. Then, the distribution area of apple orchards was defined as the area of interest in the planting year, combined with the Landsat NDVI time series image composed of three characteristic phenological periods each year from 2000 to 2017, and the apple orchard phenological curve. Subsequently, a Euclidean distance (ED) method was used to calculate the distribution area of apple orchards for each year between 2000 and 2017. Finally, a pixel-by-pixel inverse time series calculation method was used to obtain the planting year of apple orchards in the study area. This study provides a new way to accurately identify the planting year of apple orchards using satellite remote sensing images.

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Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems

Cited by 52 publications

References 44 publications

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Identification of Apple Orchard Planting Year Based on Spatiotemporally Fused Satellite Images and Clustering Analysis of Foliage Phenophase

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