scholarly journals Evaluation of Composite Burn Index and Land Surface Temperature for Assessing Soil Burn Severity in Mediterranean Fire-Prone Pine Ecosystems

Forests ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 494 ◽  
Author(s):  
Elena Marcos ◽  
Víctor Fernández-García ◽  
Alfonso Fernández-Manso ◽  
Carmen Quintano ◽  
Luz Valbuena ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Surface Temperature ◽  
Soil Properties ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Burn Severity ◽  
Soil Burn Severity ◽  
Changes In Soil Properties

We analysed the relationship between burn severity indicators, from remote sensing and field observations, and soil properties after a wildfire in a fire-prone Mediterranean ecosystem. Our study area was a large wildfire in a Pinus pinaster forest. Burn severity from remote sensing was identified by studying immediate post-fire Land Surface Temperature (LST). We also evaluated burn severity in the field applying the Composite Burn Index (CBI) in a total of 84 plots (30 m diameter). In each plot we evaluated litter consumption, ash colour and char depth as visual indicators. We collected soil samples and pH, soil organic carbon, dry aggregate size distribution (MWD), aggregate stability and water repellency were analysed. A controlled heating of soil was also carried out in the laboratory, with soil from the control plots, to compare with the changes produced in soils affected by different severity levels in the field. Our results shown that changes in soil properties affected by wildfire were only observed in soil aggregation in the high severity situation. The laboratory-controlled heating showed that temperatures of about 300 °C result in a significant reduction in soil organic carbon and MWD. Furthermore, soil organic carbon showed a significant decrease when LST values increased. Char depth was the best visual indicator to show changes in soil properties (mainly physical properties) in large fires that occur in Mediterranean pine forests. We conclude that CBI and post-fire LST can be considered good indicators of soil burn severity since both indicate the impact of fire on soil properties.

Application of ECOSTRESS multispectral LWIR images to assess topsoil properties: preliminary results on agricultural test sites in Central Italy

2020 ◽  
Author(s):  
Anvesh Rangisetty ◽  
Raffaele Casa ◽  
Victoria Ionca ◽  
Giovanni Laneve ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Central Italy ◽  
Preliminary Results ◽  
Medium Resolution

<p>Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a thermal infrared sensor, developed by NASA-JPL, launched in June 2018. ECOSTRESS acquires five LWIR spectral channels between 8 and 12 μm, with 70 m of spatial resolution at different times of the day and night.</p><p>The availability of multispectral TIR bands allows the retrieval of Land Surface Temperature (LST) and Land Surface Emissivity (LSE) by using well known procedures, like Temperature and Emissivity Separation (TES). The availability of LSE images in the LWIR atmospheric window at a medium resolution allows to estimate some topsoil/rock properties, for example those related to quartz diagnostic absorption features.</p><p>Furthermore, recent studies have shown that multispectral data in the LWIR region allows to retrieve quantitative information on topsoil properties, such as texture, carbon and nitrogen content, especially when applying multivariate statistical models [1] [2]. This study intends to verify the potential of night and day ECOSTRESS images for topsoil properties estimation.</p><p>To this aim, on specific experimental fields in Central Italy, soil sampling campaigns have been conducted to assess the topsoil properties like soil texture (clay, silt, sand) and soil organic carbon (SOC).</p><p>First, on these experimental fields, ECOSTRESS archive images were explored to identify the images in which the sampled fields are ploughed (i.e. bare soil conditions). Second, the ECO2LSTE products [3], containing the land surface temperature and emissivity, were downloaded from the USGS web site (https://ecostress.jpl.nasa.gov/data) and atmospherically corrected. Third, the TES algorithm was applied providing emissivity images at a spatial resolution of 70 m.</p><p>Last, the emissivity images were used to define a prediction model (calibration and validation) by using both Partial Least Squares Regression (PLSR) and Random Forest (RF).</p><p>The preliminary results seem to confirm: i) the potential of ECOSTRESS LWIR data to retrieve topsoil properties valuable for agronomical purposes at the regional scale, ii) the preliminary result of the multivariate analysis like PLSR and RF to derive model for topsoil properties (mainly clay and organic content) prediction  at a medium resolution scale.</p><p>References</p><ul><li>[1] Notesco, G., Weksler, S., & Ben-Dor, E. (2019). Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data. Remote Sensing, 11(12), 1429.</li> <li>[2] Pascucci, S., Casa, R., Belviso, C., Palombo, A., Pignatti, S., & Castaldi, F. (2014). Estimation of soil organic carbon from airborne hyperspectral thermal infrared data: A case study.European journal of soil science, 65(6), 865-875.</li> <li>[3] Silvestri, M., Romaniello, V., Hook, S., Musacchio, M., Teggi, S., & Buongiorno, M. F. (2020). First Comparisons of Surface Temperature Estimations between ECOSTRESS, ASTER and Landsat 8 over Italian Volcanic and Geothermal Areas. Remote Sensing, 12(1), 184.</li> </ul>

Algorithm for remote sensing of land surface temperature

2008 ◽  
Author(s):  
Sultan AlSultan ◽  
H. S. Lim ◽  
M. Z. MatJafri ◽  
K. Abdullah
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface
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