Hugo Tameirão Seixas

Remote sensing contributes to characterizing forest structure across heterogeneous tropical regions, yet structural parameters used to compare Atlantic Forest phytophysiognomies remain limited, especially in fragmented landscapes affected by multiple drivers of forest loss and degradation. This study used Global Ecosystem Dynamics Investigation (GEDI) data to compare the structure of old-growth candidate forest polygons in four Brazilian Atlantic Forest phytophysiognomies: Dense Ombrophilous Forest (DOF), Mixed Ombrophilous Forest (MOF), Seasonal Semideciduous Forest (SSdF), and Seasonal Deciduous Forest (SDF). We analyzed canopy height (H), canopy cover (COVER), foliage height diversity (FHD), plant area index (PAI), and aboveground biomass density (AGBD) from GEDI L2B and L4A footprints acquired between 2019 and 2024. Structural differences among phytophysiognomies were significant for all variables (Kruskal–Wallis, p < 0.001), with small-to-moderate effect sizes (ε² ≈ 0.05–0.15). The strongest pairwise contrasts occurred for SDF–SSdF and SSdF–DOF, whereas MOF showed greater overlap with the other groups. Across variables, AGBD and H were the most consistent discriminators, and polygon-level summaries strengthened among-group separation. These findings show that GEDI-derived polygon-level metrics can support regional comparisons of forest structure among Atlantic Forest phytophysiognomies and help identify the strongest contrasts in fragmented landscapes.

Land-use and land-cover (LULC) changes in the Amazon biome are key processes that influence the environment and societies at local, national, and global scales. Numerous studies have already relied on land-cover and land-use maps to analyze change processes. This study presents a new dataset created by calculating the time required for deforested areas to transition to agriculture (annual and permanent crops) in the Brazilian Amazon biome. The calculations were performed over MapBiomas land-cover data (version 7), which range from 1985 to 2021, at a spatial resolution of 30 m. The method consists of basic algebraic operation and recursion to identify every conversion from forest to agriculture between 1985 and 2021. The results show a correlation between environmental policies and the time required for the conversion to be completed, such as the adoption of the soy moratorium and the New Forest Code, that were followed by a search for old cleared areas for the establishment of new agricultural sites. The new data can be useful in interdisciplinary studies focused on land-use and land-cover change analysis in Brazil, such as planning of forest restoration initiatives, and the evaluation of carbon stocks according to conversion length. Our accuracy assessment shows an opportunity to improve conversion length calculations by reducing errors in the classification of agriculture establishment. The major innovation of this study is the establishment of explicit links between the deforestation year of a given pixel and its respective year of agriculture establishment, which can provide new insights into understanding long-term land-use conversion processes in tropical ecosystems.

With deforestation and associated fires ongoing at high rates, and amidst urgent need to preserve Amazonia, improving the understanding of biomass burning emissions drivers is essential. The use of orbital remote sensing data enables the estimate of both biomass burning emissions and deforestation. In this study, we have estimated emissions of particulate matter with diameter less than 2.5 µm (PM_2.5) associated with biomass burning, a primary human health risk, using the Brazilian Biomass Burning emission model with Fire Radiative Power (3BEM_FRP), and estimated deforestation based on the MapBiomas dataset. Using these estimates, we have assessed for the first time how deforestation drove biomass burning emissions in Amazonia over the last two decades at three scales of analysis: Amazonia-wide, country/state and pixel. Amazonia accounted for 48% of PM_2.5 emitted from biomass burning in South America and current deforestation rates have reached values on par with those of the early 21st Century. Emissions and deforestation were concentrated in the Eastern and Central-Southern portions of Amazonia. Amazonia-wide deforestation and emissions were linked through time (R = 0.65). Countries/states with the widest spread agriculture were less likely to be correlated at this scale, likely because of the importance of biomass burning in agricultural practices. Concentrated in regions of ongoing deforestation, in 18% of Amazonia grid cells PM_2.5 emissions associated with biomass burning and deforestation were significantly positively correlated. Deforestation is an important driver of emissions in Amazonia but does not explain biomass burning alone. Therefore, future work must link climate and other non-deforestation drivers to completely understand biomass burning emissions in Amazonia. The advance of anthropogenic activities over forested areas, which ultimately leads to more fires and deforestation, is expected to continue, worsening a crisis of dangerous emissions.

The Pantanal faced an unprecedented drought event in 2020. The hydrological year ended in July, 2020 had an annual average rainfall 26 % lower than the average from 1982 to 2020. Consequently, catastrophic wildfires burned out of control. Active fires during this year have also increased, and were 123 % higher than the 2002–2020 Pantanal’s average. Approximately 95 % of these active fires occurred in natural land covers with 28 % of them occurring in areas classified as wetlands that likely dried out due to the drought. Therefore, the development of a special policy is needed to minimize the impact of this crisis on the biodiversity, conservation, and traditional people of the Pantanal.

Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM_2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km²), which is clearly associated with an increase in the PM_2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM_2.5 was estimated to be 3594 ton year⁻¹, while the most recent interval of 2017–2019 had an average of 6258 ton year⁻¹. This represented an increase of 58% in the annual average of PM_2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential.

Vis-NIR-SWIR reflectance spectra of leaf samples, collected in the laboratory, allow the calibration of predictive models to quantify their physicochemical attributes in a practical manner and without producing chemical residues. This technique should enable the development of management strategies for intensification of pasture use. However, spectral analysis performed in the laboratory may be affected by the deterioration of plant material during transport from the field to the lab, so storage methods are necessary. This research aimed to evaluate the effects of different storage methods on the spectral response of Mombasa grass leaves. Three methods were evaluated: (i) artificially refrigerated environment, (ii) humid environment, and (iii) without microenvironment control. These methods were tested in five different storage times: 2 hours, 4 hours, 8 hours, 24 hours and 48 hours. The spectral behavior of the leaves still inserted in the plant was used as a quality reference. Results showed notable changes at the earliest storage time for the treatment without microenvironment control. Both methods with microenvironment control stabilized the occurrence of spectral changes over 48 hours of the samples storage, thus both were suggested for this species.