Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. Furthermore, the continuous nature of the product's outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines. 

/pdf/dynamic-world-near-real-time-global-10-m-land-use-land-cover-17m77jj5.pdf

Dynamic World, Near real-time global 10 m land use land cover mapping

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b). 

/pdf/global-carbon-budget-2022-7mmsmyg8.pdf

Global Carbon Budget 2022

The Pantanal region in South America is one of the world’s largest wetlands. Since 2019, the Pantanal has suffered a prolonged drought that has spelled disaster for the region, and subsequent fires have engulfed hundreds of thousands of hectares. The lack of rainfall during the summers of 2019 and 2020 was caused by reduced transport of warm and humid summer air from Amazonia into the Pantanal. Instead, a predominance of warmer and drier air masses from subtropical latitudes contributed to a scarcity of summer rainfall at the peak of the monsoon season. This led to prolonged extreme drought conditions across the region. This drought had severe impacts on the hydrology of the Pantanal. Hydrometric levels fell all along the Paraguay River. In 2020, river levels reached extremely low values, and in some sections of this river, transportation had to be restricted. Very low river levels affected the mobility of people and shipping of soybeans and minerals to the Atlantic Ocean by the Hidrovia -Parana-Paraguai (Parana-Paraguay Waterway). This study is directed to better understand the hydroclimatic aspects of the current drought in the Brazilian Pantanal and their impacts on natural and human systems. As a consequence of the drought, fires spread and affected natural biodiversity as well as the agribusiness and cattle ranching sectors. While fires had serious socioecological and economic consequences, we do not intend to investigate the effect of the downstream low-level waters on the Pantanal ecosystems or the drought in the risk of fire.

/pdf/extreme-drought-in-the-brazilian-pantanal-in-2019-2020-3ehnml39ah.pdf

Extreme Drought in the Brazilian Pantanal in 2019–2020: Characterization, Causes, and Impacts

The COVID-19 pandemic as an opportunity to weaken environmental protection in Brazil

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr−1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.

https://www.mdpi.com/2072-4292/12/6/924/pdf

Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

Fire can change gallery forest vegetation structure, thereby altering nutrient fluxes between terrestrial and aquatic ecosystems. In Brazilian savannas, which are fire-prone ecosystems, the fire regime is changing due to human activities such as converting native vegetation to farmland and urban areas. Uncontrolled wildfires in these savannas can reach gallery forests, which are more sensitive to fire impacts, leading to concerns about the effects of fire on gallery forest vegetation structure and freshwater ecosystems. We analysed the relationships between fire severity (the degree to which the fire has changed an area), percentage of burned area, variation in precipitation with gallery forest and savanna vegetation structure recovery and input of nutrients (NH4, NO 2 − , NO 3 − and PO 4 3 ) to streams for 16 months after a fire event in five watersheds associated with small streams. One year after the fire, vegetation recovery (NDVI) was lower in gallery forest areas than in savanna woodlands, despite the more severe fires in savannas. The short-term effects of fire on gallery forest vegetation included increased nitrate concentrations in streams, which were also influenced by increased precipitation and the extent of the burned area. The nutrient inputs into the stream stabilized within 1 year. However, gallery forest vegetation did not fully recover at that time and may continue to alter the functioning of the aquatic ecosystem. Together, these results demonstrate the need for an integrated fire management plan that considers both gallery forests and the surrounding savannas in the landscape to address consequences to aquatic ecosystems. O fogo pode alterar a estrutura da vegetação de florestas ripárias, alterando assim os fluxos de nutrientes entre os ecossistemas terrestres e aquáticos. Nas savanas brasileiras, que são ecossistemas propensos ao fogo, o regime de fogo está mudando devido a atividades humanas tais como a conversão de vegetação nativa em áreas agrícolas e urbanas. Eventos de fogo descontrolado em savanas podem atingir as florestas ripárias, que são mais sensíveis aos impactos do fogo, levando a preocupações sobre os efeitos do fogo na estrutura da vegetação de tais florestas e nos ecossistemas aquáticos associados. Nós analisamos as relações entre a severidade do fogo (o grau em que o fogo alterou uma área), percentagem de área queimada, e a variação da precipitação com a recuperação da estrutura de florestas ripárias e da vegetação em áreas de savana, e a entrada de nutrientes (NH4, NO 2 − , NO 3 − , e PO 4 3 − ) nos cursos de água ao longo de 16 meses após um evento de passagem do fogo em cinco bacias hidrográficas associadas a pequenos cursos de água. Um ano após a passagem do fogo, a recuperação da vegetação (NDVI) foi menor nas áreas de florestas ripárias do que nas savanas, apesar do fogo mais intenso nas savanas. Os efeitos em curto prazo do fogo em florestas ripárias incluíram o aumento das concentrações de nitrato nos cursos de água, o que também foi influenciado pelo aumento da precipitação e pela extensão da área queimada. As entradas de nutrientes nos córregos estabilizaram no prazo de um ano. Contudo, a vegetação de florestas ripárias não havia se recuperado totalmente após esse período e podendo continuar a alterar o funcionamento dos ecossistemas aquáticos. Juntos, estes resultados demonstram a necessidade de um plano integrado de gestão de incêndios que considere tanto as formações florestais ripárias como as savanas circundantes na paisagem, a fim de abordar as consequências para os ecossistemas aquáticos. The authors declare that they have no known conflicts of interest. Appendix S1. Cerrado physiognomies definition according to Ribeiro and Walter (2008). Appendix S2. Nutrient concentrations of each stream according to month and year (2011 or 2012), per cent burned area and fire severity (postfire normalized burn ratio [NBR]). Appendix S3. Accumulated precipitation recorded by Brazilian Institute of Geography and Statistics Ecological Reserve weather station of the National Institute of Meteorology (Roncador station) 30 days before sampling streamwater. Appendix S4. Results of Dunn test for pairwise multiple comparisons of nitrate concentrations between streams. Appendix S5. Results of Dunn test for pairwise multiple comparisons of dissolved inorganic carbon (DIC), conductivity and turbidity between streams. Appendix S6. Generalized linear mixed model analysis of changes in physicochemical parameters of streamwater. Appendix S7. Total drainage area (TD) and catchment area (CA) of each stream. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. 

Felipe E. B. Lenti

Papers

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

Fire effects on riparian vegetation recovery and nutrient fluxes in Brazilian Cerrado