Search

Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO₂) flux (FCO₂), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO₂ data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO₂ for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO₂ emission and the eucalypt species, providing a distinction of values between the species in the statistical data., (© 2024. The Author(s).)

The emission of soil carbon dioxide (CO 2 ) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of soil CO 2 emissions and its relationship with related variables, such as the CO 2 flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO 2 emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO 2 and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO 2 flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO 2 flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO 2 emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO 2 emissions and soil temperature (-0.240) and CO 2 flux model (-0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO 2 flux model variable. Soil CO 2 emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 μmol m -2 s -1 ) and low-yield potential soybean areas (from 0.943 to 5.686 μmol m -2 s -1 ) and higher for native forests (from 2.279 to 8.131 μmol m -2 s -1 ), whereas the opposite was true for the CO 2 flux model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO 2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study evaluates the CO 2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO 2) dry air mole fraction in the atmosphere, set as X CO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO 2 flux in the SMT. The results showed that the temporal variability of CO 2 flux is correlated positively with rainfall, while X CO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO 2 occurred in the dry and rainy months, respectively, for X CO2 , which might be the result of differences in the vertical resolution of the CO 2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only X CO2 but also CO 2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration. • X CO2 is inversely related to rainfall, with highest concentration in drier periods. • Human actions in land use and land cover change increase atmospherical CO 2. • Remote sensing to locate and understand the sources and sinks of carbon dioxide. [ABSTRACT FROM AUTHOR]