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1. Droughts Amplify Differences Between the Energy Balance Components of Amazon Forests and Croplands

   https://doi.org/10.3390/rs12030525  

   Caioni, Charles; Silvério, Divino Vicente; Macedo, Marcia N.; Coe, Michael T.; Brando, Paulo M. (February 2020, Remote Sensing)

   Droughts can exert a strong influence on the regional energy balance of the Amazon and Cerrado, as can the replacement of native vegetation by croplands. What remains unclear is how these two forcing factors interact and whether land cover changes fundamentally alter the sensitivity of the energy balance components to drought events. To fill this gap, we used remote sensing data to evaluate the impacts of drought on evapotranspiration (ET), land surface temperature (LST), and albedo on cultivated areas, savannas, and forests. Our results (for seasonal drought) indicate that increases in monthly dryness across Mato Grosso state (southern Amazonia and northern Cerrado) drive greater increases in LST and albedo in croplands than in forests. Furthermore, during the 2007 and 2010 droughts, croplands became hotter (0.1–0.8 °C) than savannas (0.3–0.6 °C) and forests (0.2–0.3 °C). However, forest ET was consistently higher than ET in all other land uses. This finding likely indicates that forests can access deeper soil water during droughts. Overall, our findings suggest that forest remnants can play a fundamental role in the mitigation of the negative impacts of extreme drought events, contributing to a higher ET and lower LST. 

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2. An observational analysis of precipitation and deforestation age in the Brazilian Legal Amazon

   https://doi.org/10.1016/j.atmosres.2022.106122  

   Mu, Ye; Jones, Charles (March 2022, Atmospheric research)

   Rainfall in the Amazon is influenced by atmospheric circulation dynamics on multiple spatiotemporal scales. Anthropogenic influences such as deforestation, land-use changes, and global climate change are also critical factors in determining rainfall in South America. Modeling studies have projected a drier climate with the ongoing deforestation in the Amazon, but observational evaluation of the variability of rainfall and deforestation patterns has been limited. This study analyzes spatiotemporal trends in rainfall between 1981 and 2020 and relationships with deforestation age in the Brazilian Legal Amazon (BLA). An improved rainfall dataset is derived by calibrating the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data with observations from a rain gauge network in the BLA. Trend analysis is employed to identify significant changes in precipitation over the BLA. Satellite-based land cover data Mapbiomas and ET datasets are used to evaluate similar trends. While large spatial variability is observed, the results show coherent relationships between negative dry-season rainfall trends and old-age deforested areas. Deforestation aged up to a decade enhanced rainfall and older deforested regions have reduced rainfall during the dry season. These results suggest substantial changes in the hydroclimate of the BLA and increased vulnerability to future land cover change. 

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3. Complex relationships between soybean trade destination and tropical deforestation

   https://doi.org/10.1038/s41598-023-38405-1  

   da Silva, Ramon Felipe Bicudo; Moran, Emilio F.; Millington, James D. A.; Viña, Andrés; Liu, Jianguo (July 2023, Scientific Reports)

   Abstract Over the last few years, understanding of the effects of increasingly interconnected global flows of agricultural commodities on coupled human and natural systems has significantly improved. However, many important factors in environmental change that are influenced by these commodity flows are still not well understood. Here, we present an empirical spatial modelling approach to assess how changes in forest cover are influenced by trade destination. Using data for soybean-producing municipalities in the state of Mato Grosso, Brazil, between 2004 and 2017, we evaluated the relationships between forest cover change and the annual soybean trade destination. Results show that although most of the soybean produced in Mato Grosso during the study period (60%) was destined for international markets, municipalities with greater and more consistent soybean production not destined for international markets during the study period were more strongly associated with deforestation. In these municipalities, soybean production was also significantly correlated with cattle and pasture expansion. These results have important implications for the sustainable management of natural resources in the face of an increasingly interconnected world, while also helping to identify the most suitable locations for implementing policies to reduce deforestation risks. 

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4. High-fidelity representation of climate variations by Amburana cearensis tree-ring chronologies across a tropical forest transition in South America

   https://doi.org/10.1016/j.dendro.2022.125932  

   López, Lidio; Villalba, Ricardo; Stahle, David (April 2022, Dendrochronologia)

   Numerous ring-width chronologies from different species have recently been developed in diverse tropical forests across South America. However, the temporal and spatial climate signals in these tropical chronologies is less well known. In this work, annual growth rings of Amburana cearensis, a widely distributed tropical tree species, were employed to estimate temporal and spatial patterns of climate variability in the transition from the dry Chiquitano (16–17◦S) to the humid Guarayos-southern Amazon (14–15◦S) forests. Four well-replicated chronologies (16–21 trees, 22–28 radii) of A. cearensis were compared with temperature and precipitation records available in the region. The interannual variations in all four A. cearensis tree-ring chronologies are positively correlated with precipitation and negatively with temperature during the late dry-early wet season, the classic moisture response seen widely in trees from dry tropical and temperate forests worldwide. However, the chronologies from the dry Chiquitano forests of southern Bolivia reflect the regional reduction in precipitation during recent decades, while the chronologies from the tropical lowland moist forests in the north capture the recent increase in precipitation in the southern Amazon basin. These results indicate that A. cearensis tree growth is not only sensitive to the moisture balance of the growing season, it can also record subtle differences in regional precipitation trends across the dry to humid forest transition. Comparisons with previously developed Centrolobium microchaete chronologies in the region reveal a substantial common signal between chronologies in similar environments, suggesting that regional differences in climate are a major drivers of tree growth along the precipitation gradient. The difficulty of finding A. cearensis trees over 150-years old is the main limitation involved in the paleoclimate application of this species. The expansion of monocultures and intensive cattle ranching in the South American tropics are contributing to the loss of these old growth A. cearensis trees and the valuable records of climate variability and climate change that they contain. 

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5. Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

   https://doi.org/10.3390/rs12040688  

   Lee, Jacky; Cardille, Jeffrey A.; Coe, Michael T. (February 2020, Remote Sensing)

   Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems. 

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