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Within the 2030 Sustainable Development Agenda, large hydropower dams are positioned as a sustainable energy source, notwithstanding their adverse impacts on societies and ecosystems. This study contributed to ongoing discussions about the persistence of critical social issues, even after the investments of large amounts of resources in areas impacted by the construction of large hydropower dams. Our study focused on food insecurity and evaluated this issue in the city of Altamira in the Brazilian Amazon, which has been profoundly socially and economically impacted by the construction, between 2011 and 2015, of Brazil's second-largest dam, namely, Belo Monte. A survey in Altamira city featured a 500-household random sample. Structural equation modeling showed conditioning factors of 60% of the population experiencing varying food insecurity degrees. Poverty, female-led households, lower education, youth, and unemployment were strongly linked to higher food insecurity. Crowded, officially impacted, and resettled households also faced heightened food insecurity. Our findings underscore the food insecurity conditions in the region impacted by the Belo Monte dam, emphasizing the need to take into account this crucial issue while planning and implementing hydropower dams.

Climate change mitigation and biodiversity conservation are two major environmental actions that need to be effectively performed this century, alongside ensuring food supply for a growing global human population. These three issues are highly interlinked through land management systems. Thus, major global food production regions located in biodiversity hotpots and with potential for carbon sequestration face trade-offs between these valuable land-based ecosystem services. The state of Mato Grosso in Brazil is one such region, where private lands that have been illegally used for agriculture could be restored to natural vegetation - with potential benefits for climate change mitigation and biodiversity conservation, although with potentially negative effects on food production. To address this challenge, in this study we used a multicriteria nexus modeling approach that considers carbon stocks, priority areas for biodiversity conservation, and the opportunity for food production, to develop scenarios of land allocation that aim to balance the benefits and drawbacks of ecosystem restoration. Results show that forcing landowners to restore their individual lands compromises the potential for a "green land market" throughout the Amazon biome in which private landowners with lower food production capacities (e.g., less connected to markets and infrastructure) would benefit from restoration programs that compensate them for the inclusion of environmental restoration among their economic activities, instead of taking large economic risks to produce more food. We additionally highlight that strategic ecosystem restoration can achieve higher gains in biodiversity and carbon with lower costs of restoration actions and with minimal impacts on agriculture. Analyses like ours demonstrate how scenarios of land allocation that simultaneously address climate mitigation and biodiversity conservation through ecosystem restoration, while also minimizing possible impacts on food production, can be sought to move the world towards a sustainable future., 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 B.V. All rights reserved.)

Large hydropower projects continue to be built in developing nations, despite their known negative impacts. Large-scale energy projects strain local infrastructure and reduce access to infrastructure for households that live near them. Here we investigate the link between large-scale hydropower projects and stress. Our results suggest that these projects create stress through two mechanisms: strains on community resources and through the process of displacement. We also ask how compensation and resettlement programs condition these relationships. Using data from the Madeira river basin in the Brazilian Amazon, we find that hydropower projects increase stress by reducing access to energy, water, sanitation and land. Compensation provided was not sufficient to moderate this effect., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Mayer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., (© 2023. The Author(s).)

Understanding social and environmental impacts and household adaptation strategies in the face of expansions in energy infrastructure projects is essential to inform mitigation and interventions programs that promote well-being. Here we conducted surveys in seven communities distributed across varying degrees of proximity to a hydropower dam complex in the Brazilian Amazon along about 250 km of the floodplain of the Madeira River. Based on interviews with 154 fishers from these communities, we examine how fishers perceived changes in fisheries yields, changes in the composition of fish species, and whether and how adaptation strategies had evolved 8-9 years after the dams' construction. Most respondents (91%) indicated declines in yields after the dams for both upstream and downstream zones. Multivariate analyses revealed statistically significant differences in the composition of species yields in pre-and post-dam periods for all communities and in both upstream and downstream zones (p < 0.001). The composition of yields diversified after the dams, with an apparent decline in yields of species of greatest market value (e.g., catfishes Brachyplatystoma spp., Pseudoplatystoma spp., and jatuarana Brycon spp.), and increases in yields of a set of other smaller bodied and faster growing species (e.g., 'branquinhas' Psectrogaster spp., Potamohinna spp., and sardines Triportheus spp.). Both downstream and upstream fishers indicated that fishing profits decreased since the dams' construction (76.8% and 67.9%, respectively). To cope with these changes, the majority of both upstream and downstream fishers (>70%) stated they have had to devote more time to fishing after the dams were built. The time fishers spend traveling to fishing locations also increased for upstream communities (77.1%), but not for downstream communities. Thirty-four percent of the interviewees changed the gear they use to fish after the dams construction, with twice as many mentioning uses of non-selective gear, such as gillnets, and declining use of traditional fishing gears such as castnets and a trap ("covi"). Fish consumption overall decreased: fish was consumed 'everyday' before the dams, but 1-2 times per week or rarely after the dams were built. Although the species that declined were those of high economic value, 53% of fishers stated fish prices have increased overall after the dams. These results shed light on the potential challenges faced by fishers and which adaptation strategies they have evolved to maintain livelihoods since the construction of the dams., 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 The Authors. Published by Elsevier Ltd.. All rights reserved.)

During the first two decades of the 21st century, Brazil carried out massive public investments on infrastructure projects, such as large hydropower dams, with potential impact on population health. Here we characterize local malaria transmission and its potential spread during the construction of three large hydropower dams in the Brazilian Amazon. We focus on Porto Velho (PVH), in Rondônia state, where the Santo Antônio and Jirau dams were built (2008-2013), and Altamira region (ATM), in Pará state, where the construction of the Belo Monte dam took place (2011-2016). Analyzed data cover 4 years before, 6 years during, and 4 years after each dam construction. In total, we utilized malaria case notifications entered into the electronic malaria notification system of the Ministry of Health of Brazil between January 2004 and December 2020 (n = 39,977,167 malaria notifications). First, we used Interrupted Time-Series Analysis (ITSA) to assess temporal changes in malaria notifications in the study sites. Then, we conducted a space-time cluster analysis to investigate the potential of malaria spread from the study sites (sources) to elsewhere (sinks). Finally, we present the sociodemographic characteristics of exported cases over time using multivariate logistic regressions. Our results show that there was no upsurge in malaria cases in the study sites and exported cases did not trigger outbreaks in other localities. Exported malaria infections originating from PVH and ATM were typically found in working age literate males involved in mining, farming or traveling. We suggest that efficient control measures, such as ensuring timely diagnosis and treatment; fostering integrated vector control; promoting health education; and prevention, detection and containment of outbreaks, if properly implemented and sustained, may prevent local and introduced malaria outbreaks during and after hydropower dam construction in the Amazon., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Johansen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Millions of people across the world live off-grid not by choice but because they live in rural areas, have low income, and have no political clout. Delivering sustainable energy solutions to such a substantial amount of the world's population requires more than a technological fix; it requires leveraging the knowledge of underserved populations working together with a transdisciplinary team to find holistically derived solutions. Our original research has resulted in an innovative Convergence Framework integrating the fields of engineering, social sciences, and communication, and is based on working together with communities and other stakeholders to address the challenges posed by delivering clean energy solutions. In this paper, we discuss the evolution of this Framework and illustrate how this Framework is being operationalized in our on-going research project, cocreating hybrid renewable energy systems for off-grid communities in the Brazilian Amazon. The research shows how this Framework can address clean energy transitions, strengthen emerging industries at local level, and foster Global North-South scholarly collaborations. We do so by the integration of social science and engineering and by focusing on community engagement, energy justice, and governance for underserved communities. Further, this solution-driven Framework leads to the emergence of unique approaches that advance scientific knowledge, while at the same time addressing community needs.

While there have been efforts to supply off-grid energy in the Amazon, these attempts have focused on low upfront costs and deployment rates. These "get-energy-quick" methods have almost solely adopted diesel generators, ignoring the environmental and social risks associated with the known noise and pollution of combustion engines. Alternatively, it is recommended, herein, to supply off-grid needs with renewable, distributed microgrids comprised of photovoltaics (PV) and in-stream generators (ISG). Utilization of a hybrid combination of renewable generators can provide an energetically, environmentally, and financially feasible alternative to typical electrification methods, depending on available solar irradiation and riverine characteristics, that with community engagement allows for a participatory codesign process that takes into consideration people's needs. A convergent solution development framework that includes designers-a team of social scientists, engineers, and communication specialists-and communities as well as the local industry is examined here, by which the future negative impacts at the human-machine-environment nexus can be minimized by iterative, continuous interaction between these key actors., (© The Author(s) 2022. Published by Oxford University Press on behalf of the National Academy of Sciences.)

Hydropower dams produce huge impacts on renewable energy production, water resources, and economic development, particularly in the Global South, where accelerated dam construction has made it a global hotspot. We do not fully understand the multiple impacts that dams have in the nearby areas from a global perspective, including the spatial differentiations. In this study, we examined the impacts of hydropower dam construction in nearby areas. We first found that more than one-third of global gross domestic production (GDP) and almost one-third of global population fall within 50 km of the world's 7,155 hydropower dams (<10% of the global land area sans the Antarctic). We further analyzed impacts of 631 hydropower dams (≥1-megawatt capacity) constructed since 2001 and commissioned before 2015 for their effects on economy, population, and environment in nearby areas and examined the results in five regions (i.e., Africa, Asia, Europe, North America, and South America) and by different dam sizes. We found that recently constructed dams were associated with increased GDP in North America and urban areas in Europe but with decreased GDP, urban land, and population in the Global South and greenness in Africa in nearby areas. Globally, these dams were linked with reduced economic production, population, and greenness of areas within 50 km of the dams. While large dams were related with reduced GDP and greenness significantly, small and medium dams were coupled with lowered population and urban land substantially, and large and medium dams were connected to diminished nighttime light noticeably in nearby areas., Competing Interests: The authors declare no competing interest., (Copyright © 2022 the Author(s). Published by PNAS.)

Human-environment interactions within and across borders are now more influential than ever, posing unprecedented sustainability challenges. The framework of metacoupling (interactions within and across adjacent and distant coupled human-environment systems) provides a useful tool to evaluate them at diverse temporal and spatial scales. While most metacoupling studies have so far addressed the impacts of distant interactions (telecouplings), few have addressed the complementary and interdependent effects of the interactions within coupled systems (intracouplings) and between adjacent systems (pericouplings). Using the production and trade of a major commodity (soybean) as a demonstration, this paper empirically evaluates the complex effects on deforestation and economic growth across a globally important soybean producing region (Mato Grosso in Brazil). Although this region is influenced by a strong telecoupling process (i.e., soybean trade with national and international markets), intracouplings pose significant effects on deforestation and economic growth within focal municipalities. Furthermore, it generates pericoupling effects (e.g., deforestation) on adjacent municipalities, which precede economic benefits on adjacent systems, and may occur during and after the soybean production takes place. These results show that while economic benefits of the production of agricultural commodities for global markets tend to be localized, their environmental costs tend to be spatially widespread. As deforestation also occurred in adjacent areas beyond focal areas with economic development, this study has significant implications for sustainability in an increasingly metacoupled world., (© 2021. The Author(s).)

Understanding the regime shifts of social-ecological systems (SES) and their local and spillover effects over a long time frame is important for future sustainability. We provide a perspective of processes unfolding over time to identify the regime shifts of a SES based on changes in the relationships between SES components while also addressing their drivers and local and spillover effects. The applicability of this approach has been demonstrated by analyzing the evolution over the past 1000 years of the SES in China's Loess Plateau (LP). Five evolutionary phases were identified: "fast expansion of cultivation," "slow expansion of cultivation," "landscape engineering for higher production," "transition from cultivation to ecological conservation," and "revegetation for environment." Our study establishes empirical links between the state (phase) of a SES to its drivers and effects. Lessons of single-goal driven and locally focused SES management in the LP, which did not consider these links, have important implications to long-term planning and policy formulation of SES., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Ozone oxidation has allowed the stabilization of a very high iron oxidation state in the FeSr 2 YCu 2 O 7.85 cuprate, in which a long-range magnetic ordering of the high valent iron cations coexists with the superconducting interactions ( magnetic ordering temperature T N = 110 K > superconducting critical temperature T c = 70 K). The somewhat unexpected A-type AFM structure, with a μ(Fe) ∼ 2 μ B magnetic saturation moment associated with the hypervalent iron sublattice, suggests an unusual low spin state for the iron cations, while the low dimensionality of the magnetic structure results in a soft switching toward ferromagnetism under small external magnetic fields. The role of the crystal structure and of the high charge concentration in the stabilization of this unusual electronic configuration for the iron cations is discussed.

Hydropower has been the leading source of renewable energy across the world, accounting for up to 71% of this supply as of 2016. This capacity was built up in North America and Europe between 1920 and 1970 when thousands of dams were built. Big dams stopped being built in developed nations, because the best sites for dams were already developed and environmental and social concerns made the costs unacceptable. Nowadays, more dams are being removed in North America and Europe than are being built. The hydropower industry moved to building dams in the developing world and since the 1970s, began to build even larger hydropower dams along the Mekong River Basin, the Amazon River Basin, and the Congo River Basin. The same problems are being repeated: disrupting river ecology, deforestation, losing aquatic and terrestrial biodiversity, releasing substantial greenhouse gases, displacing thousands of people, and altering people's livelihoods plus affecting the food systems, water quality, and agriculture near them. This paper studies the proliferation of large dams in developing countries and the importance of incorporating climate change into considerations of whether to build a dam along with some of the governance and compensation challenges. We also examine the overestimation of benefits and underestimation of costs along with changes that are needed to address the legitimate social and environmental concerns of people living in areas where dams are planned. Finally, we propose innovative solutions that can move hydropower toward sustainable practices together with solar, wind, and other renewable sources., Competing Interests: The authors declare no conflict of interest., (Copyright © 2018 the Author(s). Published by PNAS.)

Human-environment research in the 21st century will need to change in major ways. It will need to integrate the natural and the social sciences; it will need to engage stakeholders and citizens in the design of research and in the delivery of science for the benefit of society; it will need to address ethical and democratic goals; and it will need to address a myriad of important theoretical and methodological challenges that continue to impede progress in the advance of sustainability science., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Impervious surface area (ISA) is an important parameter related to environmental change and socioeconomic conditions, and has been given increasing attention in the past two decades. However, mapping ISA using remote sensing data is still a challenge due to the variety and complexity of materials comprising ISA and the limitations of remote sensing data spectral and spatial resolution. This paper examines ISA mapping with Landsat Thematic Mapper (TM) images in urban and urban-rural landscapes in the Brazilian Amazon. A fractional-based method and a per-pixel based method were used to map ISA distribution, and their results were evaluated with QuickBird images based on the 2010 Brazilian census at the sector scale of analysis for examining the mapping performance. This research showed that the fraction-based method improved the ISA estimation, especially in urban-rural frontiers and in a landscape with a small urban extent. Large errors were mainly located at the sites having ISA proportions of 0.2-0.4 in a census sector. Calibration with high spatial resolution data is valuable for improving Landsat-based ISA estimates.

This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes - forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.

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. Of 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, has 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.

A hierarchical-based classification method was designed to develop time series land-use/land-cover datasets from Landsat images between 1977 and 2008 in Lucas do Rio Verde, Mato Grosso, Brazil. A post-classification comparison approach was used to examine land-use/land-cover change trajectories, which emphasis is on the conversions from vegetation or agropasture to impervious surface area, from vegetation to agropasture, and from agropasture to regenerating vegetation. Results of this research indicated that increase in impervious surface area mainly resulted from the loss of cerrado in the initial decade of the study period and from loss of agricultural lands in the last two decades. Increase in agropasture was mainly at the expense of losing cerrado in the first two decades and relatively evenly from the loss of primary forest and cerrado in the last decade. When impervious surface area was less than approximately 40 km 2 before 1999, impervious surface area was negatively related to cerrado and forest, and positively related to agropasture areas, but after impervious surface area reached 40 km 2 in 1999, no obvious relationship exists between them.

Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem.

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of "salt-and-pepper" pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance.

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance.

Accurately detecting urban expansion with remote sensing techniques is a challenge due to the complexity of urban landscapes. This paper explored methods for detecting urban expansion with multitemporal QuickBird images in Lucas do Rio Verde, Mato Grosso, Brazil. Different techniques, including image differencing, principal component analysis (PCA), and comparison of classified impervious surface images with the matched filtering method, were used to examine urbanization detection. An impervious surface image classified with the hybrid method was used to modify the urbanization detection results. As a comparison, the original multispectral image and segmentation-based mean-spectral images were used during the detection of urbanization. This research indicates that the comparison of classified impervious surface images with matched filtering method provides the best change detection performance, followed by the image differencing method based on segmentation-based mean spectral images. The PCA is not a good method for urban change detection in this study. Shadows and high spectral variation within the impervious surfaces represent major challenges to the detection of urban expansion when high spatial resolution images are used.

Net primary productivity (NPP) is one of the major ecosystem products on which human societies rely heavily. However, rapid urban sprawl and its associated dense population and economic conditions have generated great pressure on natural resources, food security, and environments. It is valuable to understand how urban expansion and associated demographic and economic conditions affect ecosystem functions. This research conducted a case study in Southeastern China to examine the impacts of urban expansion and demographic and economic conditions on NPP. The data sources used in research include human settlement developed through a combination of MODIS, DMSP-OLS and Landsat ETM+ images, the annual NPP from MODIS, and the population and gross domestic product (GDP) from the 2000 census data. Multiple regression analysis and nonlinear regression analysis were used to examine the relationships of NPP with settlement, population and GDP. This research indicates that settlement, population and GDP have strongly negative correlation with NPP in Southeastern China, but the outcomes were nonlinear when population or GDP reached certain thresholds.

This paper argues for a twofold perspective on human adaptation to climate change in the Amazon. First, we need to understand the processes that mediate perceptions of environmental change and the behavioural responses at the levels of the individual and the local population. Second, we should take into account the process of production and dissemination of global and national climate information and models to regional and local populations, especially small farmers. We discuss the sociocultural and environmental diversity of small farmers in the Amazon and their susceptibility to climate change associated with drought, flooding and accidental fire. Using survey, ethnographic and archival data from study areas in the state of Pará, we discuss farmers' sources of knowledge and long-term memory of climatic events, drought and accidental fire; their sources of climate information; their responses to drought and fire events and the impact of changing rainfall patterns on land use. We highlight the challenges of adaptation to climate change created by the influence of migration and family turnover on collective action and memory, the mismatch of scales used to monitor and disseminate climate data and the lack of extension services to translate large-scale forecasts to local needs. We found that for most farmers, memories of extended drought tend to decrease significantly after 3 years. Over 50% of the farmers interviewed in 2002 did not remember as significant the El Niño Southern Oscillation (ENSO) drought of 1997/1998. This helps explain why approximately 40% of the farmers have not changed their land-use behaviours in the face of the strongest ENSO event of the twentieth century.

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process.

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal component analysis was used to integrate TM and HRG panchromatic data. Vegetation change/non-change was detected with the image differencing approach based on the TM and HRG fused image and the corresponding TM image. A rule-based approach was used to classify the TM and HRG multispectral images into thematic maps with three coarse land-cover classes: forest, non-forest vegetation, and non-vegetation lands. A hybrid approach combining image differencing and post-classification comparison was used to detect vegetation change trajectories. This research indicates promising vegetation change techniques, especially for vegetation gain and loss, even if very limited reference data are available.

Integrated studies of coupled human and natural systems reveal new and complex patterns and processes not evident when studied by social or natural scientists separately. Synthesis of six case studies from around the world shows that couplings between human and natural systems vary across space, time, and organizational units. They also exhibit nonlinear dynamics with thresholds, reciprocal feedback loops, time lags, resilience, heterogeneity, and surprises. Furthermore, past couplings have legacy effects on present conditions and future possibilities.

Introductions: Location of the accessory pathway (AP) in Wolff-Parkinson-White (WPW) syndrome can be determined accurately by the QRS polarity on resting ECG. These ECG characteristics may be different in children, and no algorithm has yet been tested., Methods and Results: A total of 153 resting ECGs of symptomatic children with WPW syndrome were retrospectively analyzed. The anatomic AP location had been established fluoroscopically at eight possible sites during radiofrequency catheter ablation. Two independent observers predicted AP location on blinded ECGs with a QRS polarity algorithm for adults using leads II, III, aVL, V1, and V2. Subsequently, the QRS polarity for all individual ECG leads was evaluated and a new algorithm for children was devised. With the adult algorithm, the observers correctly predicted only 55% to 58% of AP locations. The septal and right-sided pathways often were inseparable, and mid-septal and parahisian pathways were missed. In the new children's algorithm, left lateral, left posteroseptal, and posteroseptal pathways shared a positive or intermediate QRS polarity on V1, with the left lateral pathway separated by a positive QRS polarity on lead III. Negative QRS polarity on lead V1 and positive QRS polarity on lead V3 were shared by right posteroseptal, mid-septal, parahisian, and anteroseptal pathways, with the latter two having a positive QRS polarity on lead aVF. Right lateral pathways had negative QRS polarity on lead V1 and negative or intermediate QRS polarity on lead V3. Overall accuracy for these five regions was 90%., Conclusion: AP characterization by QRS polarity in children with WPW syndrome is more diverse than in adults and requires other ECG leads to establish five AP regions.

This case describes "double counting" of intrinsic conduction due to the combination of intraventricular conduction delay and the merging of the electrograms from the right and left ventricle (coronary sinus) leads in a biventricular cardioverter defibrillator. This situation prompted asymptomatic antitachycardia pacing. Lengthening of AV conduction by betablocker solved the problem.