Unraveling near real-time spatial dynamics of population using geographical ensemble learning.

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Spatial-positional associations in short-term memory can vanish in long-term memory.

Studies on the SPoARC effect have shown that serial information is spatially processed in working memory. However, it remains unknown whether these spatial-positional associations are durable or only temporary. This study aimed at investigating whether spatialization would persist when a sequence presented repeatedly is expected to be chunked. If chunked, the items could be unified spatially and their spatialization could vanish. Thirty-seven participants performed a spatialization task which was remotely inspired by the Hebb repetition paradigm. A sequence of four stimuli presented individually in the middle of a computer screen was repeated throughout the task. After each sequence, participants had to decide whether a probe belonged to the series using two lateralized response keys. The results showed no spatialization for these repetitive sequences, on average. Moreover, further analysis revealed that the effect was detectable at the beginning of the task, suggesting that the more the sequence was repeated, the less participants spatialized information from left to right. These findings show that associations created in working memory between items and space can vanish in repeated sequences: we discuss the idea that working memory progressively saves on spatialization once a sequence is chunked in long-term memory.

Precise GDP Spatialization and Analysis in Built-Up Area by Combining the NPP-VIIRS-like Dataset and Sentinel-2 Images.

Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at -52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.

Overview of Health-Monitoring Technology for Long-Distance Transportation Pipeline and Progress in DAS Technology Application.

There are various health issues associated with the different stages of long-distance pipeline transportation. These issues pose potential risks to environmental pollution, resource waste, and the safety of human life and property. It is essential to have real-time knowledge of the overall health status of pipelines throughout their entire lifecycle. This article investigates various health-monitoring technologies for long-distance pipelines, providing references for addressing potential safety issues that may arise during long-term transportation. This review summarizes the factors and characteristics that affect pipeline health from the perspective of pipeline structure health. It introduces the principles of major pipeline health-monitoring technologies and their respective advantages and disadvantages. The review also focuses on the application of Distributed Acoustic Sensing (DAS) technology, specifically time and space continuous monitoring technology, in the field of pipeline structure health monitoring. This paper discusses the process of commercialization development of DAS technology, the main research progress in the experimental field, and the open research issues. DAS technology has broad application prospects in the field of long-distance transportation pipeline health monitoring.

Spatialized PM2.5 during COVID-19 pandemic in Brazil's most populous southern city: implications for post-pandemic era.

Brazil has experienced one of the highest COVID-19 fatality rates globally. While numerous studies have explored the potential connection between air pollution, specifically fine particulate matter (PM2.5), and the exacerbation of SARS-CoV-2 infection, the majority of this research has been conducted in foreign regions-Europe, the United States, and China-correlating generalized pollution levels with health-related scopes. In this study, our objective is to investigate the localized connection between exposure to air pollution exposure and its health implications within a specific Brazilian municipality, focusing on COVID-19 susceptibility. Our investigation involves assessing pollution levels through spatial interpolation of in situ PM2.5 measurements. A network of affordable sensors collected data across 9 regions in Curitiba, as well as its metropolitan counterpart, Araucaria. Our findings distinctly reveal a significant positive correlation (with r-values reaching up to 0.36, p-value < 0.01) between regions characterized by higher levels of pollution, particularly during the winter months (with r-values peaking at 0.40, p-value < 0.05), with both COVID-19 mortality and incidence rates. This correlation gains added significance due to the intricate interplay between urban atmospheric pollution and regional human development indices. Notably, heightened pollution aligns with industrial hubs and intensified vehicular activity. The spatial analysis performed in this study assumes a pivotal role by identifying priority regions that require targeted action post-COVID. By comprehending the localized dynamics between air pollution and its health repercussions, tailored strategies can be implemented to alleviate these effects and ensure the well-being of the public.

Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity.

Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.

Spatialization and driving factors of carbon budget at county level in the Yangtze River Delta of China.

The county is the basic administrative unit of China, and the spatialization of carbon budget at the county scale plays an irreplaceable role in deepening the understanding of the carbon emission mechanism and spatial pattern. Yueqing County, an economically developed county in the Yangtze River Delta of China, was selected as the study area, the spatial pattern of the carbon budget and the optimal resolution of the spatialization at the county level were dissected on the basis of accurate accounting, and driving factors of carbon emissions were further identified using the geographically weighted regression model. The results indicated that (1) the carbon emissions were mainly generated from fossil fuel combustion related to energy, accounting for 98.8% of the total carbon budget in the study area; (2) the optimal resolution of spatialization was 200 m and carbon emissions were concentrated in the southeast of the study area; (3) energy intensity, energy structure, per capita GDP, and urbanization rate were positively correlated with carbon emissions, while population played a bidirectional role in carbon emissions. This study not only strengthens the understanding of the patterns and drivers of the carbon budget but also establishes a theoretical framework and operational tools for policymakers to formulate solutions to mitigate the carbon crisis.

Terroir Influence on Polyphenol Metabolism from Grape Canes: A Spatial Metabolomic Study at Parcel Scale.

The composition of bioactive polyphenols from grape canes, an important viticultural byproduct, was shown to be varietal-dependent; however, the influence of soil-related terroir factors remains unexplored. Using spatial metabolomics and correlation-based networks, we investigated how continuous changes in soil features and topography may impact the polyphenol composition in grape canes. Soil properties, topography, and grape cane extracts were analyzed at georeferenced points over 3 consecutive years, followed by UPLC-DAD-MS-based metabolomic analysis targeting 42 metabolites. Principal component analyses on intra-vintage metabolomic data presented a good reproducibility in relation to geographic coordinates. A correlation-driven approach was used to explore the combined influence of soil and topographic variables on metabolomic responses. As a result, a metabolic cluster including flavonoids was correlated with elevation and curvature. Spatial metabolomics driven by correlation-based networks represents a powerful approach to spatialize field-omics data and may serve as new field-phenotyping tool in precision agriculture.

An integrated model chain for future flood risk prediction under land-use changes.

Flood is a very destructive natural disaster in the world that is strongly influenced by land-use change. Therefore, a comprehensive flood risk modeling considering the change in land-use is essential for understanding, predicting, and mitigating flood risk. However, most existing single modeling ignored the derivative effect of land-use change, which may reduce the reality of results. To further address the issue, this study presented an integrated model chain by coupling the Markov-FLUS model, the multiple linear regression and the improved TOPSIS model. By applying it in Guangdong Province, the future land-use simulation, spatialization of hazard-bearing bodies, and determination of flood risk were realized. The results show that the coupled model chain allows for good prediction of flood risk under different scenarios, which could be expressed by flood risk composite index (FRSI). In the natural growth scenario, the flood risk will show markedly increasing trend from 2020 to 2030 (FRSI = 2.06), with the high and highest risk zones will expand significantly. Spatially, these increased high flood risk zones mainly distributed on the periphery of existing built-up lands. On the contrary, the flood risk in ecological protection scenario tends to stabilize (FRSI = 1.98), which may be a reference for alternative development paths. These dynamic information identified by this model chain provides a deeper insight into the spatiotemporal characteristics of future high flood risk areas, which can facilitate reasonable flood mitigation measures to be developed at the most critical locations in the region. In further applications, more efficient spatialization models and climate factor are suggested to be introduced.

Cross-modal correspondence enhances elevation localization in visual-to-auditory sensory substitution.

Introduction: Visual-to-auditory sensory substitution devices are assistive devices for the blind that convert visual images into auditory images (or soundscapes) by mapping visual features with acoustic cues. To convey spatial information with sounds, several sensory substitution devices use a Virtual Acoustic Space (VAS) using Head Related Transfer Functions (HRTFs) to synthesize natural acoustic cues used for sound localization. However, the perception of the elevation is known to be inaccurate with generic spatialization since it is based on notches in the audio spectrum that are specific to each individual. Another method used to convey elevation information is based on the audiovisual cross-modal correspondence between pitch and visual elevation. The main drawback of this second method is caused by the limitation of the ability to perceive elevation through HRTFs due to the spectral narrowband of the sounds. Method: In this study we compared the early ability to localize objects with a visual-to-auditory sensory substitution device where elevation is either conveyed using a spatialization-based only method (Noise encoding) or using pitch-based methods with different spectral complexities (Monotonic and Harmonic encodings). Thirty eight blindfolded participants had to localize a virtual target using soundscapes before and after having been familiarized with the visual-to-auditory encodings. Results: Participants were more accurate to localize elevation with pitch-based encodings than with the spatialization-based only method. Only slight differences in azimuth localization performance were found between the encodings. Discussion: This study suggests the intuitiveness of a pitch-based encoding with a facilitation effect of the cross-modal correspondence when a non-individualized sound spatialization is used.

Ecotoxicology and geostatistical techniques employed in subtropical reservoirs sediments after decades of copper sulfate application.

Spatial distribution linked to geostatistical techniques contributes to sum up information into an easier-to-comprehend knowledge. This study compares copper spatial distribution in surface sediments and subsequent categorization according to its toxicological potential in two reservoirs, Rio Grande (RG) and Itupararanga (ITU) (São Paulo-Brazil), where copper sulfate is applied and not applied, respectively. Sediments from 47 sites in RG and 52 sites in ITU were collected, and then, copper concentrations were interpolated using geostatistical techniques (kriging). The resulting sediment distributions were classified in categories based on sediment quality guides: threshold effect level and probable effect level; regional reference values (RRVs) and enrichment factor (EF). Copper presented a heterogenic distribution and higher concentrations in RG (2283.00 ± 1308.75 mg/kg) especially on the upstream downstream, associated with algicide application as well as the sediment grain size, contrary to ITU (21.81 ± 8.28 mg/kg) where a no-clear pattern of distribution was observed. Sediments in RG are predominantly categorized as "Very Bad", whereas sediments in ITU are mainly categorized as "Good", showing values higher than RRV. The classification is supported by the EF categorization, which in RG is primarily categorized as "Very High" contrasting to ITU classified as "Absent/Very Low". Copper total stock in superficial sediment estimated for RG is 4515.35 Ton of Cu and for ITU is 27.45 Ton of Cu.

Social Resistance and Spatial Knowledge: Protest Against Cruise Ships in Venice.

Cruise ships are at the same time among the most popular and most controversial means of travel. Photos of oversized ships, passing through the historic center of Venice, have become iconic. This paper explores the background of the debate over cruise ships in Venice. Using research at the intersection of culture and technology, the history of technology, urban anthropology, and social movement theory, it sheds light on how the spatialization of the cruise industry through infrastructures affects Venice and the lagoon. In this paper, I will retrace the development of these interdependencies to show how activists, associations, and citizen campaigners address and perform these entanglements. Protest has turned the ship into a powerful symbol for the infrastructural appropriation and transformation of natural and urban space. Since transportation and traffic routes influence people's everyday lives, it is important to consider their impacts on practices, spaces, and relations, especially in a city like Venice, where footpaths and waterways form an important element of the identity of both the city and its inhabitants. Through its actions, its tacit knowledge of local space, and the explicit knowledge the protest network produces, it both opposes and adds to hegemonic discourses. I argue that the cruise ship has been transformed into a metaphor of global capitalism, which in turn renders it a symbol with transnational impact.

How the 5G Enabled the COVID-19 Pandemic Prevention and Control: Materiality, Affordance, and (De-)Spatialization.

5G, the most disruptive innovation, had played a significant role in the COVID-19 pandemic prevention and control. However, as a novel technology and context, we have little knowledge about how 5G enabled pandemic prevention and control. This study collected 212 cases and conducted qualitative research to explore how the 5G worked in prevention and control. Based on the concepts of materiality and affordance, we grounded two affordances of spatialization and de-spatialization from the data. Spatialization provides non-contact ways to complete the tasks which are supposed to be completed in contact, and de-spatialization provides remote operations to complete the tasks which are supposed to be completed on-site. Spatialization and de-spatialization enabled the diagnosis and treatment of the infectors to relieve the unbalance of medical staff, cutting the infectious route to contain the viral spread, and logistic supply to support the prevention and control. Our study offers theoretical contributions to digital pandemic prevention and control, and the literature on 5G also offers practical implications.

A SPoARC of Music: Musicians Spatialize Melodies but not All-Comers.

Recent studies on the spatial positional associated response codes (SPoARC) effect have shown that when Western adults are asked to keep in mind sequences of verbal items, they mentally spatialize them along the horizontal axis, with the initial items being associated with the left and the last items being associated with the right. The origin of this mental line is still debated, but it has been theorized that it necessitates specific spatial cognitive structures to emerge, which are built through expertise. This hypothesis is examined by testing for the first time whether Western individuals spatialize melodies from left to right and whether expertise in the musical domain is necessary for this effect to emerge. Two groups (musicians and non-musicians) of participants were asked to memorize sequences of four musical notes and to indicate if a subsequent probe was part of the sequence by pressing a "yes" key or a "no" key with the left or right index finger. Left/right-hand key assignment was reversed at mid-experiment. The results showed a SPoARC effect only for the group of musicians. Moreover, no association between pitch and hand responses was observed in either of the two groups. These findings suggest a crucial role of expertise in the SPoARC effect.

Modeling spatially resolved characterization factors for eutrophication potential in life cycle assessment.

PURPOSE: Prior versions of the Tool for Reduction and Assessment of Chemical and other environmental Impacts (TRACI) have recognized the need for spatial variability when characterizing eutrophication. However, the method's underlying environmental models had not been updated to reflect the latest science. This new research provides the ability to differentiate locations with a high level of detail within the USA and provides global values at the country level. METHODS: In previous research (Morelli et al. 2018), the authors reviewed a broad range of domain-specific models and life cycle assessment methods for characterization of eutrophication and ranked these by levels of importance to the field and readiness for further development. The current research is rooted in the decision outcome of Morelli et al. (2018) to separate freshwater and marine eutrophication to allow for the most tailored characterization of each category individually. The current research also assumes that freshwater systems are limited by phosphorus and marine systems are limited by nitrogen. Using a combination of spatial modeling methods for soil, air, and water, we calculate midpoint characterization factors for freshwater and marine eutrophication categories and evaluate the results through a US-based case application. RESULTS AND DISCUSSION: Maps of the nutrient inventories, characterization factors, and overall impacts of the case application illustrate the spatial variation and patterns in the results. The importance of variation in geographic location is demonstrated using nutrient-based activity likelihood categories of agricultural (rural fertilizer), non-agricultural (urban fertilizer), and general (human waste processing). Proximity to large bodies of water, as well as individual hydraulic residence times, was shown to affect the comparative values of characterization factors across the USA. CONCLUSIONS: In this paper, we have calculated and applied finely resolved freshwater and marine eutrophication characterization factors for the USA and country-level factors for the rest of the globe. Additional research is needed to provide similarly resolved characterization factors for the entire globe, which would require expansion of publicly available data and further development of applicable fate and transport models. Further scientific advances may also be considered as computing capabilities become more sophisticated and widely accessible.

Spatial-temporal variability and extreme climate indices of precipitation in a coastal watershed of southeastern Brazil.

The analysis of multi-temporal and spatial trends of rainfall in a river basin is an essential approach for water resource planning and management approach. In this study, a combination of trend analysis and spatial-temporal variability of the rainfall for 1970-2017 was applied to examine rainfall distribution patterns in a coastal watershed, Santa Maria da Vitória River Basin (southeastern Brazil). Data from 42 meteorological stations were analyzed using kriging as a geostatistical tool for point data interpolation. Trends in rainfall were computed using the RClimDex package with eleven extreme climate indices. The results have shown spatial and temporal rainfall variability, with drought events becoming more persistent in recent years in the upper sector of the basin, where agricultural land use prevails. Water shortage may impact crops and threatening the water supply and hydropower production. Trend analysis suggests that the annual total wet-day precipitation (PRCPTOT) increases in the coastal section and decreases in the upper basin sector. Consecutive dry days (CDD) and consecutive wet days (CWD) show a strong positive tendency in the lower basin section, where the metropolitan area is located, flooding risks increase in response to positive trends of intensive short-term rainfall events. These results support managers developing and planning sustainability strategies to assure water security and subsidize adaptative responses to extreme hydrological events.

A Commodity Supply Mix for More Regionalized Life Cycle Assessments.

Supply chain information is invaluable to further regionalize product life cycle assessments (LCAs), but detailed information linking production and consumption centers is not always available. We introduce the commodity supply mix (CSM) defined as the trade-volume-weighted average representing the combined geographic areas for the production of a commodity exported to a given market with the goal of (1) enhancing the relevance of inventory and impact regionalization and (2) allocating these impacts to specific markets. We apply the CSM to the Brazilian soybean supply chain mapped by Trase to obtain the mix of ecoregions and river basins linked to domestic consumption and exports to China, EU, France, and the rest of the world, before quantifying damage to biodiversity, and water scarcity footprints. The EU had the lowest potential biodiversity damage but the largest water scarcity footprint following respective sourcing patterns in 12 ecoregions and 18 river basins. These results differed from the average impact scores obtained from Brazilian soybean production information alone. The CSM can be derived at different scales (subnationally, internationally) using existing supply chain information and constitutes an additional step toward greater regionalization in LCAs, particularly for impacts with greater spatial variability such as biodiversity and water scarcity.

Spatial distribution of Brazilian bovine taurine breeds associated with climatic, physical and socioeconomic variables / [Distribuição espacial de raças taurinas bovinas brasileiras associada a variáveis climáticas, físicas e socioeconômicas]

The objective of this study was to evaluate the spatial distribution of purebred Bos taurus taurus bovine breeds raised in Brazil in association with climatic, physical and socioeconomic variables. The breeds Aberdeen Angus, Ayrshire, Braford, Brangus, Charolais, Devon, Flemish, Hereford, Pinzgauer, Shorthorn and Simental were classified according to their aptitude (milk, meat or dual-purpose). They were spatialized according to their aptitude using state and municipal information. The milk breeds were found in the states of Rio Grande do Sul and Santa Catarina, while the dual-purpose breeds were found in Minas Gerais and Rio Grande do Sul states and the beef breeds were concentrated in the southern region. Only the Aberdeen Angus meat breed showed higher dispersion in other regions. Meat and dual-purpose breeds tended to be raised in regions with lower maximum temperature, average temperature, thermal amplitude and temperature-humidity index. Dual-purpose breeds were found in municipalities with high humidity and altitude, but with a low gross domestic product, little technical guidance received from cooperatives and the government, low control of diseases and parasites, as well as low use of pasture rotation systems. The spatial distribution of Brazilian bovine taurine breeds, regardless of aptitude, was related to climatic, physical and socioeconomic factors.(AU)

O objetivo deste estudo foi avaliar a distribuição espacial de raças bovinas puras Bos taurus taurus criadas no Brasil, associadas a variáveis climáticas, físicas e socioeconômicas. As raças Aberdeen Angus, Ayrshire, Braford, Brangus, Charolês, Devon, Flamenga, Hereford, Pinzgauer, Shorthorn e Simental foram classificadas de acordo com sua aptidão (leite, carne ou duplo-propósito). Elas foram espacializadas de acordo com sua aptidão a partir de informações estaduais e municipais. As raças leiteiras foram encontradas nos estados do Rio Grande do Sul e de Santa Catarina, enquanto as raças de duplo-propósito foram encontradas nos estados de Minas Gerais e Rio Grande do Sul, e as raças de carne concentraram-se na região Sul. Apenas a raça de carne Aberdeen Angus apresentou maior dispersão nas demais regiões. As raças de carne e de duplo-propósito tendem a ser criadas em regiões com menores temperatura máxima, temperatura média, amplitude térmica e índice de temperatura e umidade. As raças de duplo-propósito foram encontradas em municípios com alta umidade e altitude, mas com baixo produto interno bruto, pouca orientação técnica recebida de cooperativas e do governo, baixo controle de doenças e de parasitas e baixo uso de sistema de rotação de pastagens. A distribuição espacial das raças taurinas bovinas brasileiras, independentemente da aptidão, foi relacionada a fatores climáticos, físicos e socioeconômicos.(AU)

Mapping research topics at multiple levels of detail.

The institutional review of interdisciplinary bodies of research lacks methods to systematically produce higher-level abstractions. Abstraction methods, like the "distant reading" of corpora, are increasingly important for knowledge discovery in the sciences and humanities. We demonstrate how abstraction methods complement the metrics on which research reviews currently rely. We model cross-disciplinary topics of research publications and projects emerging at multiple levels of detail in the context of an institutional review of the Earth Research Institute (ERI) at the University of California at Santa Barbara. From these, we design science maps that reveal the latent thematic structure of ERI's interdisciplinary research and enable reviewers to "read" a body of research at multiple levels of detail. We find that our approach provides decision support and reveals trends that strengthen the institutional review process by exposing regions of thematic expertise, distributions and clusters of work, and the evolution of these aspects.

Pattern and spatial associations of commercial trees in the Amazon / Padrão e associações espaciais de árvores comerciais da Amazônia

ABSTRACT: This study was conducted to establish the pattern and the spatial associations of the Amazon commercial tree groups. In a region extending across 2,000 ha in the Tapajós National Forest (FNT), Pará, a forest census was conducted. The census included commercial trees of perimeter at breast height (PBH), equal to or greater than 158 cm and the Cartesian coordinates of their bearings. Using Ripley's K function, the spatial pattern was discerned, with 5 m radius being assumed, and maximum distances varying up to 1,500 m. Then, with 99.8% probability for both, 500 Monte Carlo simulations were performed for the univariate K function, and 500 toroidal simulations were done for the bivariate function.The commercial tree groups, which revealed an aggregated spatial pattern in a minimum of approximately 50% of the analysis distance were namely, Astronium lecointei, Bagassa guianensis, Couratari guianensis, Manilkara huberi, Mezilaurus itauba, and Vochysia maxima. In Brazil, the commercial tree groups, based on the commercialization criteria for tropical trees show random and aggregated spatial patterns with associations of spatial independence.

RESUMO: O objetivo do trabalho foi determinar o padrão espacial e a associação espacial de grupos de árvores comerciais da Amazônia.Realizou-se um censo florestal em uma área de 2.000 ha localizada na Floresta Nacional do Tapajós (FNT), Pará. No censo coletou-se a circunferência à altura do peito (CAP), igualou superior a 158 cm e as coordenadas cartesianas das árvores comerciais. Para determinar o padrão espacial foi utilizada a função K de Ripley assumindo um raio de 5 m, variando a uma distância máxima de 1.500 m. Para a função K univariada foram realizadas 500 simulações Monte Carlo e para a função bivariada foram realizadas 500 simulações toroidais, ambas com 99,8% de probabilidade. Os grupos de árvores comerciais que possuíram padrão espacial agregado em no mínimo 50% da distância de análise foram Astronium lecointei, Bagassa guianensis, Couratari guianensis, Manilkara huberi, Mezilaurus itauba, Vochysia maxima. Grupos de árvores comerciais, segundo critério de comercialização para árvores tropicais no Brasil, seguem um padrão espacial aleatório e agregado e possuem associações espaciais de independência espacial.