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The synthesis of complex functional inorganic materials, such as oxides, can be successfully performed by using microwave irradiation as the source of heat. To achieve this, different routes and set-ups can be used: microwave-assisted synthesis may proceed in the solid state or in solution, aqueous or not, and the set ups may be as simple and accessible as domestic oven or quite sophisticated laboratory equipment. An obvious advantage of this innovative methodology is the considerable reduction in time—minutes rather than hours or days—and, as a consequence, energy saving. No less important is the fact that the particle growth is inhibited and the broad variety of different microwave or microwave-assisted synthesis techniques opens up opportunities for the preparation of inorganic nanoparticles and nanostructures. In this work, various microwave synthesis techniques have been employed: solid-state microwaves, single-mode microwaves using a TE10p cavity and microwave-assisted hydrothermal synthesis. Relevant examples are presented and discussed.

Abstract Private lands are important for conservation worldwide, but knowledge about their effectiveness is still insufficient. To help fill this important knowledge gap, we analyzed the impacts of a national policy for conservation on private lands in Brazil, a global biodiversity hotspot with high potential for nature-based climate solutions. Through the evaluation of over 4 million private rural properties from the Rural Environmental Cadastre, we found that the last policy review in 2012 mainly affected the Amazon Forest. The amnesty granted to 80% of landowners of small properties prevented the restoration of 14.6 million hectares of agricultural land with a carbon sequestration potential of 2.4 gigatonnes. We found that private lands exist within the limits of public conservation areas and that between 2003 and 2020 deforestation rates in these private lands were higher than those across all conservation areas. The Rural Environmental Cadastre can be an effective tool for managing forests within private lands, with potential to integrate governance approaches to control deforestation and mitigate climate change.

The world has entered the United Nations Decade on Ecosystem Restoration (2021–2030), yet many regions of the world still face environmental degradation. In this context a question arises: under what conditions may a given region shift from a trajectory of environmental degradation to environmental recovery? Answering this question constitutes an important endeavor for the scientific community, policymakers, and organizations leading the planning and implementation of restoration projects. This study examines some of the human-environment conditions underpinning the net gains in natural forest cover in a region that has experienced environmental degradation: the Atlantic Forest biome, Brazil. Using land-use/cover (LULC) data, we assessed the loss and successive gain in forest cover during the 1987–2001 and 2001–2015 periods. Municipality-level statistics on agriculture and economy, together with LULC and biophysical data, were used to develop models for assessing forest cover trajectories. Of the 1,972 municipalities experiencing net forest loss during the 1987–2001 period, 59% shifted their trajectory to a net gain during the 2001–2015 period. This shift, known as forest transition, followed a complex socio-economic pathway characterized by (i) the stagnation of traditional agricultural activities favoring the replacement of pasturelands by mechanized agriculture, and (ii) the emergence of non-agricultural rural activities together with multifunctional landscapes. Furthermore, a trend of decrease in precipitation seems to be associated with land abandonment, thus, favoring the return of natural vegetation. Our findings suggest that forest transition at the biome level is possible if environmental and land regulations take advantage of specific context-dependent situations that foster net forest gains.

This research aims to understand the difference of major land-cover change results caused in various time periods and to examine the impacts of human-induced factors on land-cover changes along the TransAmazon Highway region. The Landsat Thematic Mapper and Operational Land Imager data from 2011, 2014, and 2017 and our previous land-cover classification results in 1991, 2000, and 2008 were used to examine land-cover dynamics. A classification system consisting of five land-cover classes – primary forest (PF), secondary forest (SF), agropasture (AP), urban area, and water – were chosen. The hierarchical-based classification method was used to generate land-cover classification results, and the post-classification comparison approach was used to produce detailed “from-to” conversions for each detection period. The emphasis was on deforestation of PF, dynamic change of SF and AP, and urbanization over time. The impacts of human-induced factors such as population and economic conditions on urban expansion, AP expansion, and deforestation were examined. This research indicated that selection of a suitable time period was critical for effectively detecting land-cover changes; that is, too long time period (i.e., 9 years) cannot accurately capture some land-cover changes such as the AP and SF in this research. Although deforestation – the conversion from PF to SF and AP – accounted for a large proportion of land-cover changes, the changes between SF and AP became more important than PF conversion, and required a short time period (i.e., 3 years here) for effectively reflecting their dynamics. Human-induced factors play important roles in deforestation, dynamic changes between AP and SF, and urbanization.

Since the 1970s, Brazilian soybean production has grown rapidly, driven by increasing national and international demand and rising prices. Consequently, soybean production has come to be perceived as an attractive opportunity, with the number of farms producing soybean across the country reaching around 240,000 in 2017. However, producers can become trapped by dependencies on moneylenders, traders and input supply companies, facing so-called' agricultural treadmills'. In this study we undertook interviews with individual soybean producers and representatives of trader companies, producers' associations, the processing industry, government and civil society, and used these data to analyze rural production operations related to the soybean supply chain and to interpret how agricultural treadmills persist in this sector. Based on literature review and fieldwork interviews, we found that producers have been able to add value to their products, but some strategies they use to cope with production costs lead them into high-risk situations. Our findings show that, sectoral (e.g., producers' associations) and collective actions (e.g., farmers' buying groups) are a useful strategy for producers to gain influence and bargaining power against transnational companies and input suppliers compared to acting as an individual within the soybean supply chain. Producers were emphatic that the current soybean business is now much more than just farming activities, and that education and training are highly valuable and important strategies to keep producers in the soybean business and out of traps. These findings are important given the neoliberal context in which individual producers find themselves and which presents challenges that alone they can do little to change. Policies that enable individual producers to make environmentally- and financially-sound agricultural decisions are vital to ensure a sustainable soybean system that does not trap producers in endless cycles of debt and investment.

Previous research has explored the potential to integrate lidar and optical data in aboveground biomass (AGB) estimation, but how different data sources, vegetation types, and modeling algorithms influence AGB estimation is poorly understood. This research conducts a comparative analysis of different data sources and modeling approaches in improving AGB estimation. RapidEye-based spectral responses and textures, lidar-derived metrics, and their combination were used to develop AGB estimation models. The results indicated that (1) overall, RapidEye data are not suitable for AGB estimation, but when AGB falls within 50–150 Mg/ha, support vector regression based on stratification of vegetation types provided good AGB estimation; (2) Lidar data provided stable and better estimations than RapidEye data; and stratification of vegetation types cannot improve estimation; (3) The combination of lidar and RapidEye data cannot provide better performance than lidar data alone; (4) AGB ranges affect the selection of the best AGB models, and a combination of different estimation results from the best model for each AGB range can improve AGB estimation; (5) This research implies that an optimal procedure for AGB estimation for a specific study exists, depending on the careful selection of data sources, modeling algorithms, forest types, and AGB ranges.

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

Agricultural systems are heterogeneous across temporal and spatial scales. Although much research has investigated farm size and economic output, the synergies and trade-offs across various agricultural and socioeconomic variables are unclear. This study applies a GIS-based approach to official Brazilian census data (Agricultural Censuses of 1995, 2006, and 2017) and surveys at the municipality level to (i) evaluate changes in the average soybean farm size across the country and (ii) compare agricultural and socioeconomic outcomes (i.e., soybean yield, agricultural production value, crop production diversity, and rural labor employment) relative to the average soybean farm size. Statistical tests (e.g., Kruskal–Wallis tests and Spearman’s correlation) were used to analyze variable outcomes in different classes of farm sizes and respective Agricultural Censuses. We found that agricultural and socioeconomic outcomes are spatially correlated with soybean farm size class. Therefore, based on the concepts of trade-offs and synergies, we show that municipalities with large soybean farm sizes had larger trade-offs (e.g., larger farm size was associated with lower crop diversity), while small and medium ones manifest greater synergies. These patterns are particularly strong for analysis using the Agricultural Census of 2017. Trade-off/synergy analysis across space and time is key for supporting long-term strategies aiming at alleviating unemployment and providing sustainable food production, essential to achieve the UN Sustainable Development Goals.

Ecosystem services provided by urban vegetation can ameliorate problems common to urban environments while improving the quality of life of urban residents. Much research in urban ecology has analyzed urban environmental dynamics in the global north; rapidly urbanizing areas in the global south have not received commensurate attention. The land cover dynamics of mid-sized cities in the global south remain under-explored in particular. In this article, we investigate the spatial patterns and socioeconomic contexts of urban vegetation in Altamira, Brazil, a mid-sized but rapidly expanding city in the Amazon. Using time series remotely sensed imagery, we profile changes in urban land cover, and link them to socioeconomic indicators at the census sector (tract) level. While studies of urban environmental justice in the global north largely report that greener urban landscapes prevail in affluent neighborhoods, our analysis reveals significantly lower vegetative cover in higher-income sectors of Altamira. Vegetative cover is also significantly lower in sectors with higher housing density, time since urbanization and better infrastructure, and appears linked to housing tenure. Studies of vegetative outcomes in similar urban environments should investigate socioeconomic and demographic contexts while also integrating recent infrastructure development and density-dependent growth patterns.

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles (Shanghai and Shenzhen in China, and Ho Chi Minh City in Vietnam) and inland metropoles (Ulaanbaatar in Mongolia, Lanzhou in China, and Vientiane in Laos) with various developing stages and physical conditions for examining the spatiotemporal patterns of urban expansions in the past 25 years (1990–2015). Multitemporal Landsat images with 30 m spatial resolution were used to develop urban impervious surface area (ISA) distributions and examine their dynamic changes. The impacts of elevation, slope, and rivers on spatial patterns of urban expansion were examined. This research indicates that ISA is an important variable for examining urban expansion. Coastal metropoles had much faster urbanization rates than inland metropoles. The spatial patterns of urban ISA distribution and expansion are greatly influenced by physical conditions; that is, ISA is mainly distributed in the areas with slopes of less than 10 degrees. Rivers are important geographical factors constraining urban expansion, especially in developing stages, while bridges across the rivers promote urban expansion patterns and rates. The relationships of spatial patterns of urban ISA distribution and dynamics with physical conditions provide scientific data for urban planning, management, and sustainability.

It has been common to attribute tropical deforestation to population growth and/or migration. This paper finds that this is true only at large and aggregated spatial and temporal scales. When one examines regional-scaled processes, there are numberous mediating factors and more complex demographic processes that account for differences in rates of deforestation. Based upon three years of research in the Altamira region, Xingu Basin, Brazilian Amazon, we differenttiate between period and cohort effects in trajectories of deforestation. We find that every cohort of migrants follows the same overall trajectory of deforestation but that the magnitude of deforestation along a 20 year trajectory is dependent on period effects (such as hyperinflation, credit policy, land policy changes). Moreover, we find that the 20-year trajectory does indeed follow the constraints posed by the development cycle of the domestic group-refleting as it does the changing supply of labor.Tem sido comum atribuir o desflorestamento tropical ao crescimento populacional ou à migração. Esse texto aponta que isso ocorre apenas em amplas escalas temporais e espaciais agregadas. Quando se examina processos em escala regional, existem vários fatores de mediação e processos demográficos mais complexos que implicam em diferenças nas taxas de desflorestamento. Baseados em 3 anos de pesquisa na região de Altamira, na Bacia Amazônica brasileira, nós diferenciamos efeitos grupais e temporais nas trajetórias de desflorestamento. Descobrimos que todo grupo de migrantes segue a mesma trajetória de desflorestamento mas a magnitude do desflorestamento dentro de um período de 20 anos depende de certas contingências (como hiperinflação, políticas de crédito, mudanças na política fundiária). Além disso encontramos que a trajetória de 20 anos acompanha as condições colocadas pelo ciclo de desenvolvimento dos grupos domésticos - refletindo as alterações na oferta de trabalho.

Soy occupies the largest area of agricultural land in Brazil, spreading from southern states to the Amazon region. Soy is also the most important agricultural commodity among Brazilian exports affecting food security and land use nationally and internationally. Here we pose the question of whether soy expansion affects only economic growth or whether it also boosts socioeconomic development, fostering education and health improvements in Brazilian municipalities where it is planted. To achieve this objective, we divided more than 5000 municipalities into two groups: those with >300 ha of soy (soy municipalities) and those with

The global food market makes international players intrinsically connected through the flow of commodities, demand, production, and consumption. Local decisions, such as new economic policies or dietary shifts, can foster changes in coupled human–natural systems across long distances. Understanding the causes and effects of these changes is essential for agricultural-export countries, such as Brazil. Since 2000, Brazil has led the expansion of soybean planted area—19 million hectares, or 47.5% of the world’s increase. Soybean is among the major crop commodities traded globally. We use the telecoupling framework to analyze (i) the international trade dynamics between Brazil and China as the cause of the increased production of Brazilian soybean since 2000; (ii) and the cascading effects of the Sino-Brazilian telecoupled soybean system for Brazilian maize production and exports, with attention to consequences on domestic prices, availability, and risks associated with climatic extreme events. Census-based data at state and county levels, policy analysis, and interviews with producers and stakeholders guided our methodological approach. We identified that the Brazilian soybean production decreased maize single crop production and accelerated maize as a second crop following soybean, a practice that makes farmers more vulnerable to precipitation anomalies (e.g., rainfall shortage). In addition, the two-crop system of soybean/maize pressures the Brazilian maize market when unexpected events such as extreme droughts strike and when this results in a failed maize harvest in the second crop, most of which is for domestic consumption rather than export. Our study suggests the need to incorporate the telecoupling framework in land use decision-making and understanding landscape changes.

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

A detailed study of the structure-composition-properties correlation is reported for the as-prepared (AP) and two oxygenated (oxygenation carried out at ambient pressure and under high pressure) Mo0.3Cu0.7Sr2ErCu2Oy samples. Their crystal structures were characterized by combining the x-ray/neutron powder diffraction (NPD) and electron diffraction techniques. All the samples show tetragonal symmetry, crystallizing in the P4/mmm space group. The influence of oxygenation in the electronic states for the Mo0.3Cu0.7Sr2ErCu2Oy system associated with an oxidation reaction leading from a non-superconducting to a superconducting state has also been investigated by means of x-ray photoelectron spectroscopy (XPS). XPS measurements show the predominance of the MoV oxidation state over the MoVI one in the AP sample; annealing under flowing oxygen enhances both the MoVI and CuII amounts. The AP sample shows the existence of ferromagnetic clusters originated from the short-range magnetic correlations of the paramagnetic MoV cations. On the other hand, all the oxygenated samples are not magnetic but superconducting. The high-pressure oxygenated sample shows the highest superconducting transition temperature of TC = 84 K. A partial oxygen ordering in the (Mo/Cu)O1+δ chain and a decrease in the charge transfer energy after oxygenation induces superconductivity in the oxygenated samples. [ABSTRACT FROM AUTHOR]

In this paper I review my experience as Charles Wagley's Ph.D. student and later as a faculty colleague at the University of Indiana. In addition to his deep humanism and personal warmth, Wagley also had an uncanny ability to foresee important emerging issues in social sciences, especially within Latin American and Brazilian Studies. With his flexible, personable style he found ways to direct students and colleagues towards the issues he considered important, and which later became truly major issues for these fields. For example, he helped to create the interdisciplinary field of Latin American Studies while in New York, focused on Latin American race relations while at Columbia University, and created the Amazonian Studies program at University of Florida with its focus on impacts of development and infrastructure projects. He helped create scholarship programs for such studies through the Title VI mechanism. Through all of his scholarly contributions, Wagley led by inspiring with a rare social consciousness and a deep concern for the human costs of social and economic change

El objeto de este trabajo es vincular la actividad de los museos y su patrimonio histórico militar en la conformación de identidades, como parte de un proyecto que se inició hacia fines del siglo XIX. Abordaremos la cuestión desde una visión amplia, incluyendo todos aquellos bienes que por su historia, y la función que ocupan en esa construcción identitaria, guardan estrecha relación con la historia militar. Para tal fin, se tomarán cuatro casos: el Museo Histórico Nacional, el Buque Museo Corbeta Uruguay, la Casa Museo Luis Piedra Buena y la Casa Amarilla (sede del Departamento de Estudios Históricos Navales y del Instituto Nacional Browniano) Todos ellos bienes patrimoniales e instituciones que surgen o se re-significan con el objeto de facilitar a la comunidad nacional e internacional el acceso al conocimiento de la historia del país.