Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates

Mataveli, Guilherme; Pereira, Gabriel; Sanchez, Alber; de Oliveira, Gabriel; Jones, Matthew W.; Freitas, Saulo R.; Aragão, Luiz E. O. C.

doi:10.3390/fire6110426

Cited by 2 publications

(2 citation statements)

References 56 publications

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“…These species spread after fire (Xaud et al, 2013;Numata et al, 2017;Silva et al, 2021), and remain in the area in constant cycles of mortality and growth of new individuals. Due to aboveground biomass decrease in burned areas and its committed emissions through decomposition, this process further increases carbon emissions to the atmosphere and climate warming (Aragão et al, 2018;Mataveli et al, 2023), which is an urgent topic of global interest.…”

Section: Discussionmentioning

confidence: 99%

Fire effect on bamboo-dominated forests in Southwestern Amazon: impacts on tree diversity and forest structure

Silva,

Miranda,

Anderson

et al. 2024

Rev. Bras. Ciênc. Ambient.

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Severe droughts increase the forest flammability, especially if fires are recurrent. Considering that fires tend to alter the forest structure and reduce biological diversity, we analyzed the fire effect on the tree plant community and forest structure over a 10-year post-fire period. The study was carried out in two tropical forest fragments located in the eastern Acre State in southwestern Brazilian Amazon. In each fragment, we established three plots of 250 × 10 m2 in an unburned forest and three in a burned forest. In these plots, we collected all tree individuals with DBH≥10 following the RAINFOR protocol, with censuses made in 2011, 2014, 2016, 2017, 2019, 2020 and 2021. The fire significantly reduced the abundance, basal area, and aboveground biomass of tree species, and altered the species composition along the post-fire temporal gradient. The absence of differences in the species richness and species diversity between unburned and burned forests is probably related to the life cycle of bamboo. The results suggest that, 10 years after the fire, the structure and phytosociology of the forest have not yet fully recovered.

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Section: Discussionmentioning

confidence: 99%

Fire effect on bamboo-dominated forests in Southwestern Amazon: impacts on tree diversity and forest structure

Silva,

Miranda,

Anderson

et al. 2024

Rev. Bras. Ciênc. Ambient.

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“…Effects on soil erosion and sedimentation of water bodies have been related to land cover change [ 12 , 13 ]. Drought, fires, natural disasters, and the positive feedback from climate change have also played a role in driving land cover change [ 14 , 15 , 16 ]. The interconnection of land cover and land cover change demonstrates the importance of accurate methods to map land cover around the world.…”

Section: Introductionmentioning

confidence: 99%

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data

Lasko,

O’Neill,

Sava

2024

Sensors

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A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery.

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Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates

Cited by 2 publications

References 56 publications

Fire effect on bamboo-dominated forests in Southwestern Amazon: impacts on tree diversity and forest structure

Fire effect on bamboo-dominated forests in Southwestern Amazon: impacts on tree diversity and forest structure

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data

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