Impact of image acquisition lag-time on monitoring short-term postfire spectral dynamics in tropical savannas: the Campos Amazonicos Fire Experiment

• Autor(a) principal: Alves, Daniel Borini [UNESP]
• Data de Publicação: 2022
• Outros Autores: Fidelis, Alessandra [UNESP], Perez-Cabello, Fernando, Alvarado, Swanni T., Conciani, Dhemerson Estevao, Cambraia, Bruno Contursi, Pires da Silveira, Antonio Laffayete, Silva, Thiago Sanna Freire
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1117/1.JRS.16.034507; http://hdl.handle.net/11449/245475
• topic: fire experiments; spectral sensitivity; seasonality; Landsat; Sentinel-2; sensor harmonization; tropical savanna

The use of optical remote sensing to monitor short-term postfire dynamics in rapidly recovering ecosystems is a complex task, requiring strong spectral separability and high temporal resolution. Thus, characterizing the effects of temporal lag in image acquisitions in relation to fire occurrence is of fundamental importance to improve our understanding of how fire-related environmental and ecological processes are connected to multitemporal spectral information. We used a harmonized series of Sentinel-2A/2B and Landsat-7/8 images paired with a fire experiment specifically designed for remote sensing assessment to evaluate short-term spectral responses of fire in tropical savannas. Our experimental design included strategic geolocation of experimental plots and separate prescribed burnings during the early- and mid-dry seasons. The M-statistic was used to assess spectral separability between burned and control treatments, and the temporal stability was assessed using fire scar latency, a custom statistic adapted to capture scar spectral responses over time. Finally, linear regressions were fitted to explore the association between consumed fuel load and spectral indices. The variation of normalized burn ratio and mid-infrared bispectral index indices stood out with the highest separability (M-statistic > 3) in both treatments and moderate-high association with consumed fuel load (R-2 > 0.54). We observed that burned areas spectral sensitivity depends not only on timing of image acquisition but also on fire season and their interactions with land surface phenology. Our characterization provides a solid benchmark for future experimental assessments of fire spectral responses and provides new insights for improving fire monitoring algorithms. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)