An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

International Journal of Applied Earth Observations and Geoinformation

Valentyn Tolpekin¹  S.K. Srivastav²  Prasun Kumar Gupta³  Raktim Ghosh⁴ 

[1] Corresponding author: Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands;Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun, India.;Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun, India;Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands;

关键词: STARFM;    ESTARFM;    Spatiotemporal fusion;    VIIRS;    Landsat;    Coal fire;   

DOI  :  

来源: DOAJ

【 摘 要 】

Many real-world applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellite-based monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat-8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modified-ESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsat-like images from different algorithms (STARFM, modified-STARFM, ESTARFM, modified-ESTARFM) indicate that the modified-ESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modified-ESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.

【 授权许可】

Unknown