Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

scholarly journals Direct estimation of Byram's fire intensity from infrared remote sensing imagery

2017 ◽  
Vol 26 (8) ◽  
pp. 668 ◽  
Author(s):  
Joshua M. Johnston ◽  
Martin J. Wooster ◽  
Ronan Paugam ◽  
Xianli Wang ◽  
Timothy J. Lynham ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Ancillary Data ◽  
Remote Sensing Imagery ◽  
Method Of Calculation ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Thermal Infrared Imagery ◽  
Remote Sensing Methods

Byram’s fire intensity (IB,tot; kWm–1) is one the most important and widely accepted metrics for quantifying wildfire behaviour. Calculation of IB,tot requires measurement of fuel consumption, heat of combustion and rate of spread; existing methods for obtaining these measurements are either inexact or at times impossible to obtain in the field. This paper presents and evaluates a series of remote sensing methods for directly deriving radiative fire intensity (IB,rad; kWm–1) using the Fire Radiative Power (FRP) approach applied to thermal infrared imagery of spreading vegetation fires. Comparisons between the remote sensing data and ground-sampled measurements were used to evaluate the various estimates of IB,tot, and to determine the radiative fraction (radF) of a fire’s emitted energy. Results indicate that the IB,tot along an advancing flame front can be reasonably estimated (and agrees with traditional methods of estimation (R2=0.34–0.73)) from appropriately collected time-series of remote sensing imagery without the need for ground sampling or ancillary data. We further estimate that the radF of the fire’s emitted energy varies between 0.15 and 0.20 depending on the method of calculation, which is similar to previous estimates.

scholarly journals Forecasting the regional fire radiative power for regularly ignited vegetation fires

2021 ◽  
Author(s):  
Tero M. Partanen ◽  
Mikhail Sofiev
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
Grid Cell ◽  
Radiative Energy ◽  
Fire Intensity ◽  
High Temporal Resolution ◽  
South Central ◽  
Radiative Power ◽  
Cloud Mask ◽  
Fire Radiative Power

Abstract. This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, which cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely-sensed high temporal resolution fire radiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e., the weather forecast. The method is tested retrospectively for south-central African savannah areas with grid cell size of 1.5° × 1.5°. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG Fire Radiative Power and Cloud Mask. It has been found that in the areas with large numbers of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.

scholarly journals High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

2020 ◽  
Vol 12 (3) ◽  
pp. 460 ◽  
Author(s):  
Mingyu Liu ◽  
Chuan Xiong ◽  
Jinmei Pan ◽  
Tianxing Wang ◽  
Jiancheng Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Mountainous Areas ◽  
Snow Water ◽  
Sentinel 2

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.

scholarly journals Catching Geomorphological Response to Volcanic Activity on Steep Slope Volcanoes Using Multi-Platform Remote Sensing

2020 ◽  
Vol 12 (3) ◽  
pp. 438 ◽  
Author(s):  
Federico Di Traglia ◽  
Alessandro Fornaciai ◽  
Massimiliano Favalli ◽  
Teresa Nolesini ◽  
Nicola Casagli
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Morphological Changes ◽  
Remote Sensing Data ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Geomorphological Evolution ◽  
Sentinel 2 ◽  
Sciara Del Fuoco

The geomorphological evolution of the volcanic Island of Stromboli (Italy) between July 2010 and June 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLÉIADES-1 tri-stereo images and from Light Detection and Ranging (LiDAR) acquisitions allowed for topographic changes estimation. Data were comprised of high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images that allowed for the mapping of areas that were affected by major lithological and morphological changes. PLÉIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared and, although there are artefacts in the smaller structures (e.g., ridges and valleys), there is still a clear consistency between the two DEMs for the larger structures (as the main valleys and ridges). The period between July 2010 and May 2012 showed only minor changes consisting of volcanoclastic sedimentation and some overflows outside the crater. Otherwise, between May 2012 and May 2017, large topographic changes occurred that were related to the emplacement of the 2014 lava flow in the NE part of the Sciara del Fuoco and to the accumulation of a volcaniclastic wedge in the central part of the Sciara del Fuoco. Between 2017 and 2019, minor changes were again detected due to small accumulation next to the crater terrace and the erosion in lower Sciara del Fuoco.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

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