In this study, the combination of surface reflectance products from Terra- Moderate Resolution Imaging Spectroradiometer and Landsat-Enhanced Thematic Mapper Plus sensors are explored through the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm within the framework of forest fire studies over tropical savannah environments. Thus, 60 fusion-derived images were generated from four spectral bands [red, near-infrared, shortwave infrared (SWIR₁ and SWIR₂)] and six spectral indices [normalized difference vegetation index, normalized difference moisture index, global environment monitoring index, soil-adjusted vegetation index, normalized burn ratio (NBR), and differenced normalized burn ratio (dNBR)] over two selected study sites. For all fusion processes performed, the actual Landsat images for the corresponding dates are available, which supports validation of the blended images. Additionally, integration of blended spectral indices in the immediate post-fire evaluation and the generation of fire severity were analysed. The blended bands presented correlation and Structure Similarity Index Measure (SSIM) values that were consistently higher than 0.819 and root mean square error values of less than 0.027, which confirms good accuracy levels obtained from the model. Similar correlation and SSIM accuracy levels were observed in the blended indices assessment for both study sites, which enables its values to be well-integrated for an analysis of the immediately post-fire date. However, the fire severity mapping from fused images needs to be carefully implemented since the dNBR index is generally less accurate than other blended indices. FSDAF fusion proved to be a useful alternative to retrieving multispectral information from savannah environments affected by fires.

This article aims to analyze the accuracy of the latest versions of burned area (BA) products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) series (MCD45A1 v051, MCD64A1 v051, and MCD64A1 v006), quantifying their progression in the annual/seasonal mapping of fire scars on a regional scale over tropical savanna environments. To this end, a validation was processed comparing each product with a BA reference covering the period of 2001 to 2016, available for one of the largest savanna vegetation enclaves located in the southern Brazilian Amazon, the Campos Amazônicos Savanna Enclave. Three indices derived from cross-tabulated error matrices were used in the comparison [commission error, omission error (Oe), and Dice coefficient]. Pareto boundaries were also computed to explore the influence of the spatial resolution of the MODIS products on detecting BAs. The overall assessment found a clear decrease in Oes, varying from 0.797 and 0.499 in the MCD45A1 v051 and MCD64A1 v051, respectively, to a value of 0.280 in the MCD64A1 v006. Improvements obtained with MCD64A1 v006 in the middle and late dry season periods were significant. The results validate the higher performance of MCD64A1 v006 and confirm it as a useful instrument for the regional mapping of BAs over tropical savanna areas.

This study aims to analyse the dynamics of land-use and land-cover (LULC) in a selected southern Amazonian area (Brazil), monitoring and distinguishing trajectories in NDVI (Normalized Difference Vegetation Index) variations for the last three decades. The area, with a total of 17336 km², has been subject to significant LULC changes associated with deforestation progress and use of fire. Considering available Landsat time series, it was selected an image per year from 1984 to 2013 (path/row -231/66), at a particular period of year, atmospherically corrected using LEDAPS tools. NDVIs values were generated for each selected image. Furthermore, the images of 1984 and 2010 still underwent a classification of LULC differentiate five categories: water, forest, secondary/degraded forest, savannah/pasture and crop/bare soil. The trajectories in NDVI variation values were analysed by R software, considering intersections of classified categories. The pixels identified as forests on the images of 1984 and 2010 displayed stable trajectories of NDVI values, with average value 0.824 and coefficient of variation 3.9%. While the pixels of savannah/pasture, which was periodically affected by fire, had an average NDVI value 0.585 and coefficient of variation 15,1%. The main regressive trajectory was the transition “forest to crop/bare soil", identifying 1999 as the starting point in the drop in NDVI values, associated with an increase of the deforested areas. Therefore, the results show distinct trajectories associated with NDVIs and LULC changes that assist in better understanding the dynamics of ecological processes and the human impacts operating in the area.

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 (R2 > 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.

Monitoring and understanding vegetation responses to fire in Amazonian savanna ecosystems remains a very important scientific challenge to improve the landscape management practices of these areas. In this sense, the present study analyzes the dynamics of spectral separability as well as the postfire vegetation recovery process related to fire experiments carried out in open savanna ecosystems of the Campos Amazônicos National Park (Brazil). For this purpose, a harmonized Landsat and Sentinel-2 dataset was processed and analyzed. The time series of the Normalized Difference Vegetation Index (NDVI) and the Normalized Burned Ratio 2 (NBR2) spectral indices were also generated from this same dataset for the period from 2019 to 2023 and evaluated in combination with fine fuel load in-situ measurements. M-Statistics and mean absolute difference were calculated comparing data from burned and unburned plots, considering different treatments of fire seasonality (Early-Dry Season – EDS; Middle-Dry Season – MDS fires) and time since last fire (2-year-old fuel age; 3-year-old fuel age; and 10-year-old or older fuel age fires). The combined use of Sentinel-2 and Landsat resulted in an availability of cloud-free or partially cloud-free images ≈0.6 times greater than that obtained when using Landsat images exclusively. The potential of the NBR2 stood out, generating statistically significant mean absolute difference values when comparing EDS and MDS fires, and also when comparing 2-year-old fuel age areas with 3-year-old or 10-year-old or older fuel age areas. Satellite and field information converged in the detection of a rapid response of vegetation to fire in these ecosystems, demonstrating that conditions similar to those observed before the fire were reached after three rainy seasons. The results reinforce the potential of Landsat and Sentinel-2 harmonized remote sensing datasets to assess and monitor fire-affected areas over Amazonian savanna ecosystems, providing ecological meaning and establishing connections between remote sensing and field datasets.