Mapping and Tracking Forest Burnt Areas in the Indio Maiz Biological Reserve Using Sentinel-3 SLSTR and VIIRS-DNB Imagery

Abstract

Wildfires are considered one of the most major hazards and environmental issues worldwide. Recently, Earth observation satellite (EOS) sensors have proven to be effective for wildfire detection, although the quality and usefulness of the data are often hindered by cloud presence. One practical workaround is to combine datasets from multiple sensors. This research presents a methodology that utilizes data of the recently-launched Sentinel-3 sea and land surface temperature radiometer (S3-SLSTR) to reflect its applicability for detecting wildfires. In addition, visible infrared imaging radiometer suite day night band (VIIRS-DNB) imagery was introduced to assure day-night tracking capabilities. The wildfire event in the Indio Maiz Biological Reserve, Nicaragua, during 3–13 April 2018, was the study case. Six S3-SLSTR images were processed to compute spectral indices, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized burn ratio (NBR), to perform image segmentation for estimating the burnt area. The results indicate that 5870.7 ha of forest was affected during the wildfire, close to the 5945 ha reported by local authorities. In this study, the fire expansion was delineated and tracked in the Indio Maiz Biological Reserve using a modified fast marching method on nighttime-sensed temporal VIIRS-DNB. This study shows the importance of S3-SLSRT for wildfire monitoring and how it can be complemented with VIIRS-DNB to track burning biomass at daytime and nighttime.

Impacts of the COVID-19 confinement on air quality, the Land Surface Temperature and the urban heat island in eight cities of Andalusia (Spain)

“…The coefficients of determination R 2 , SD, MBE and RMSE denote a good agreement of the values and an adequate general performance. These data are in line with other reports from a number of cities or territories ( Coppo et al, 2010 ; Ruescas et al, 2016 ; Sobrino et al, 2016 ; Prikaziuk and van der Tol, 2019 ; Chiang and Ivan, 2020 ; Yang et al, 2020b ), lending validity to the results obtained in this research.…”

“…The results presented on the level 2 products of Sentinel 3 for obtaining the LST and the SUHI give adequate yields, consistent with each other and similar to those of other studies ( Li et al, 2011 ; Tan and Li, 2015 ; Sobrino et al, 2016 ; Prikaziuk and van der Tol, 2019 ; Yang et al, 2019 ; Chiang and Ivan, 2020 ; Hu et al, 2020 ; Ye et al, 2020 ). The precision of this method gives differences between the recovered LST and environmental temperatures that range between 0.2 and 7.8 °C ( Gallo et al, 2011 ; Liu and Zhang, 2011 ; Avdan and Jovanovska, 2016 ; Rongali et al, 2018 ; Mukherjee and Singh, 2020 ).…”

Analysis of Urban Heat Island and Heat Waves Using Sentinel-3 Images: a Study of Andalusian Cities in Spain

“…The results presented here, for Sentinel day and night products used to obtain the LST and the SUHI in the cities of Andalusia, present adequate yields that are consistent with each other and similar to those provided by similar investigations (Li et al 2011;Tan and Li 2015;Sobrino et al 2016;Prikaziuk and van der Tol 2019;Yang et al 2019Yang et al , 2020bChiang and Ivan 2020;Venter et al 2020).…”

“…The existing SW algorithms that serve to gauge LST are based on the concept of differential absorption (McMillin 1975), whereby the difference between the two TIRS band wavelengths allows for correction of the atmospheric effects produced on the signal. Abundant studies report on the validation, use and precision of these algorithms in Sentinel-3 images (Coppo et al 2010;Wan 2013;Ruescas et al 2016;Sobrino et al 2016;Prikaziuk and van der Tol 2019;Chiang and Ivan 2020;Yang et al 2020b).…”

Wildfire Risk Assessment of Transmission-Line Corridors Based on Naïve Bayes Network and Remote Sensing Data

“…To improve the wildfire prevention capability of transmission lines, scholars carried out much research, including distribution analysis of widfire occurrence [6,7], transmissionline trip mechanisms caused by wildfire [8,9], fire-spot monitoring algorithms [10][11][12][13] and wildfire risk assessment methods [14][15][16]. Remote sensing satellite is an economical and effective way to monitor wildfires in transmission-line corridors continuously.…”