Hasil untuk "Physical geography"

J. Sallis, M. Floyd, D. Rodriguez et al.

A. Cooper, A. Goodman, A. Page et al.

BackgroundPhysical activity and sedentary behaviour in youth have been reported to vary by sex, age, weight status and country. However, supporting data are often self-reported and/or do not encompass a wide range of ages or geographical locations. This study aimed to describe objectively-measured physical activity and sedentary time patterns in youth.MethodsThe International Children’s Accelerometry Database (ICAD) consists of ActiGraph accelerometer data from 20 studies in ten countries, processed using common data reduction procedures. Analyses were conducted on 27,637 participants (2.8–18.4 years) who provided at least three days of valid accelerometer data. Linear regression was used to examine associations between age, sex, weight status, country and physical activity outcomes.ResultsBoys were less sedentary and more active than girls at all ages. After 5 years of age there was an average cross-sectional decrease of 4.2 % in total physical activity with each additional year of age, due mainly to lower levels of light-intensity physical activity and greater time spent sedentary. Physical activity did not differ by weight status in the youngest children, but from age seven onwards, overweight/obese participants were less active than their normal weight counterparts. Physical activity varied between samples from different countries, with a 15–20 % difference between the highest and lowest countries at age 9–10 and a 26–28 % difference at age 12–13.ConclusionsPhysical activity differed between samples from different countries, but the associations between demographic characteristics and physical activity were consistently observed. Further research is needed to explore environmental and sociocultural explanations for these differences.

R. Cann

P. Gordon-Larsen, Melissa C. Nelson, P. Page et al.

L. Frank, T. Schmid, J. Sallis et al.

R. Hulteen, P. Morgan, L. Barnett et al.

C. Ballabio, Panos Panagos, Luca Monatanarella

Abstract The Land Use and Cover Area frame Statistical survey (LUCAS) aimed at the collecting harmonised data about the state of land use/cover over the extent of European Union (EU). Among these 2 · 10 5 land use/cover observations selected for validation, a topsoil survey was conducted at about 10% of these sites. Topsoil sampling locations were selected as to be representative of European landscape using a Latin hypercube stratified random sampling, taking into account CORINE land cover 2000, the Shuttle Radar Topography Mission (SRTM) DEM and its derived slope, aspect and curvature. In this study we will discuss how the LUCAS topsoil database can be used to map soil properties at continental scale over the geographical extent of Europe. Several soil properties were predicted using hybrid approaches like regression kriging. In this paper we describe the prediction of topsoil texture and related derived physical properties. Regression models were fitted using, along other variables, remotely sensed data coming from the MODIS sensor. The high temporal resolution of MODIS allowed detecting changes in the vegetative response due to soil properties, which can then be used to map soil features distribution. We will also discuss the prediction of intrinsically collinear variables like soil texture which required the use of models capable of dealing with multivariate constrained dependent variables like Multivariate Adaptive Regression Splines (MARS). Cross validation of the fitted models proved that the LUCAS dataset constitutes a good sample for mapping purposes leading to cross-validation R 2 between 0.47 and 0.50 for soil texture and normalized errors between 4 and 10%.

Wuhua Wang, Jiakui Tang, Na Zhang et al.

Accurate estimation of grassland aboveground biomass (AGB) is crucial for terrestrial carbon cycling, global climate change research, degradation assessment, and sustainable land management. This study employs XGBoost model, combined with feature selection via Random Forest & Pearson correlation, alongside SHapley Additive exPlanations (SHAP), to enhance AGB predictions across diverse grassland ecosystems in China. Results indicate that incorporating vegetation height significantly improves model performance, increasing test R2 values by 0.01–0.07 (final range: 0.59 to 0.68), and reducing the errors nRMSE to ≤ 0.04. This underscores the critical role of vegetation height in improving biomass estimation accuracy. SHAP analysis further reveals the relative importance of key predictors, offering insights into their individual contributions to model performances. Spatiotemporal analysis (2001–2021) reveals rising AGB trends in highly productive regions, whereas arid and degraded grasslands exhibit stability or continue to decline, highlighting their vulnerability to climatic changes and anthropogenic pressures. Although the model demonstrates strong predictive capability, regional heterogeneity and complex feature interactions warrant further investigation. This research highlights the effectiveness of machine learning combined with remote sensing in monitoring grassland degradation, providing valuable insights for ecosystem restoration, carbon sequestration strategies, and policy-driven conservation efforts.

Johannes Leonhardt, Juergen Gall, Ribana Roscher

Climatic conditions have a strong impact on the Earth’s surface, especially in terms of how different land cover classes appear and the way they are distributed. Satellite images are valuable data for studying these effects. However, disentangling the specific influence of climate remains a complex challenge. This paper proposes ClimSat, an image editing model designed to realistically simulate prescribed climate conditions on satellite imagery. The proposed ClimSat model is constructed as a diffusion autoencoder, and it incorporates contextual information through multi-conditional batch normalization and classifier-free guidance. The technical capabilities of ClimSat were first validated by demonstrating its ability to generate high-quality images which remain faithful to the prescribed conditions. The experimental results further show that ClimSat outperforms other models in terms of both criteria. ClimSat’s practical utility is demonstrated in two downstream applications, i.e., data augmentation for land cover classification, where training on ClimSat-augmented datasets improves classifier generalizability beyond regionally limited datasets, and climate change visualization, where the effects of forecast climate change are simulated under two socioeconomic pathways for protected regions in Finland and Italy.

Pangyin Li, Zhe Chen, Chen Long et al.

Semantic segmentation of urban point clouds captured by Airborne Laser Scanning (ALS) is essential for understanding complex 3D environments, serving as a robust underlying data foundation for digital twin applications. The fusion of multimodal data has been proven to significantly improve the performance of ALS semantic segmentation by fully mining rich complementary information in each modality. However, existing fusion-based ALS semantic segmentation methods face critical limitations due to the reliance on multiple sensors, which constrains their applicability. To this end, we propose a novel multimodal framework Elevation Guidance Adaptive Fused Network, termed EGAFNet, that integrates naturally formed top-view projection images from ALS to enhance the information perception of the point cloud. The framework focuses on utilizing projection images, structured around two key components: input representation and feature representation. Specifically, to generate highly discriminative input representation, we propose a novel projection method that accurately preserves the relative height relationships between objects and develop a Height Adaptive Scaling Module (HASM) to adaptively adjust object heights, enhancing the expressive capability of elevation information in the projection images. As for feature representation, we design a dual-branch network that effectively captures local and global context from the projection images within a large receptive field. Meanwhile, we propose an Elevation Guidance Adaptive Fusion Module (EGAFM) that adaptively fuses 2D and 3D features based on occlusion relationships to reduce feature confusion caused by occlusion in elevation projection, ensuring meaningful fusion between multimodal features. Extensive experiments on three public datasets demonstrate that our EGAFNet outperforms current state-of-the-art methods.

M. Browning, K. Lee

Is the amount of “greenness” within a 250-m, 500-m, 1000-m or a 2000-m buffer surrounding a person’s home a good predictor of their physical health? The evidence is inconclusive. We reviewed Web of Science articles that used geographic information system buffer analyses to identify trends between physical health, greenness, and distance within which greenness is measured. Our inclusion criteria were: (1) use of buffers to estimate residential greenness; (2) statistical analyses that calculated significance of the greenness-physical health relationship; and (3) peer-reviewed articles published in English between 2007 and 2017. To capture multiple findings from a single article, we selected our unit of inquiry as the analysis, not the article. Our final sample included 260 analyses in 47 articles. All aspects of the review were in accordance with PRISMA guidelines. Analyses were independently judged as more, less, or least likely to be biased based on the inclusion of objective health measures and income/education controls. We found evidence that larger buffer sizes, up to 2000 m, better predicted physical health than smaller ones. We recommend that future analyses use nested rather than overlapping buffers to evaluate to what extent greenness not immediately around a person’s home (i.e., within 1000–2000 m) predicts physical health.

Haoyan Wu, Zhijie Li, Brian King et al.

Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market share. Current SC information systems are unable to provide validated, pseudo real-time shipment tracking during the distribution phase. This information is available from a single source, often the carrier, and is shared with other stakeholders on an as-needed basis. This paper introduces an independent, crowd-validated, online shipment tracking framework that complements current enterprise-based SC management solutions. The proposed framework consists of a set of private distributed ledgers and a single blockchain public ledger. Each private ledger allows the private sharing of custody events among the trading partners in a given shipment. Privacy is necessary, for example, when trading high-end products or chemical and pharmaceutical products. The second type of ledger is a blockchain public ledger. It consists of the hash code of each private event in addition to monitoring events. The latter provide an independently validated immutable record of the pseudo real-time geolocation status of the shipment from a large number of sources using commuters-sourcing.

J. Chacón-Duque, K. Adhikari, M. Fuentes-Guajardo et al.

Historical records and genetic analyses indicate that Latin Americans trace their ancestry mainly to the intermixing (admixture) of Native Americans, Europeans and Sub-Saharan Africans. Using novel haplotype-based methods, here we infer sub-continental ancestry in over 6,500 Latin Americans and evaluate the impact of regional ancestry variation on physical appearance. We find that Native American ancestry components in Latin Americans correspond geographically to the present-day genetic structure of Native groups, and that sources of non-Native ancestry, and admixture timings, match documented migratory flows. We also detect South/East Mediterranean ancestry across Latin America, probably stemming mostly from the clandestine colonial migration of Christian converts of non-European origin (Conversos). Furthermore, we find that ancestry related to highland (Central Andean) versus lowland (Mapuche) Natives is associated with variation in facial features, particularly nose morphology, and detect significant differences in allele frequencies between these groups at loci previously associated with nose morphology in this sample. Latin Americans trace their ancestry to the admixture of Native Americans, Europeans and Sub-Saharan Africans. Here, the authors develop a novel haplotype-based approach and analyse over 6,500 Latin Americans to infer the geographically-detailed genetic structure of this population.

Enkhmanlai Amarsaikhan, Nyamjargal Erdenebaatar, Damdinsuren Amarsaikhan et al.

Mongolian pasture plays an essential role in the national economy. Reliable pasture biomass estimation is indispensable to support the agricultural sector and also sustainable livelihood in the country. The aim of this study is to determine an appropriate method to estimate and map pasture biomass in a forest-steppe area of Mongolia. For this purpose, machine learning methods such as random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) were compared. As data sources, spectral indices derived from Sentinel-2B image of 2019 and field-measured biomass sample datasets were used. To determine the optimal spectral predictor variables, initially, 20 spectral indices were evaluated using the PLSR. Of these, five indices (i.e. ATSAVI2, EVI, GRVI, IPVI and MSR) with the highest correlation coefficients (r ≥ 0.94) were considered for further analysis. These indices were also examined and validated by a variable importance analysis. Then, the RF, SVM, and PLSR models were applied to predict and map pasture biomass using the selected five indices. The PLSR method demonstrated the highest accuracy with coefficient of determination (R2) =0.899 and root mean square error (RMSE)=10.560 g/m2. The SVM technique showed the second highest accuracy with R2=0.837 and RMSE = 12.881 g/m2. The RF model gave the lowest accuracy with R2=0.823 and RMSE = 13.430g/m2. Our research showed that different machine learning models might be applied (because in all cases R2>0.82) for a pasture biomass estimation and mapping in the selected test site, but the best result could be achieved by the use of the PLSR.

E. Dupont, R. Koppelaar, H. Jeanmart

Mário Sebastião Tuzine, Daniel Dantas, Arão Raimundo Finiasse et al.

O conhecimento da variabilidade espacial das características dendrométricas pode auxiliar no planejamento e estratificação de inventários florestais. Este trabalho teve como objetivo verificar a dependência da distribuição espacial, num fragmento de Mecrusse, Androstachys johnsonii, da altura dominante, volume, área basal e densidade de: (a) todas as espécies, (b) espécie dominante e (c) todas as espécies excluindo a dominante. Foram usados dados de 79 unidades amostrais na Província de Gaza, sul de Moçambique, onde foram medidas todas as árvores com diâmetro à altura do peito (DAP) maior ou igual a 10 cm. As variáveis foram associadas à coordenada geográfica do ponto de coleta para o processamento dos dados por meio de geoestatística. Foram ajustados e testados os modelos Esférico Exponencial e Gaussiano, pelo método dos mínimos quadrados ordinários. O modelo exponencial teve melhor ajuste e foi selecionado para estimar as características dendrométricas. A variável altura dominante não apresentou dependência espacial quando analisada para todas as espécies da floresta e para a espécie de Androstachys johnsonii. A dominância da espécie estudada apresentou dependência espacial. A interpolação por meio da krigagem ordinária mostrou a distribuição espacial da área basal variando de 4 a 42 m.ha-1 e as regiões Norte e Nordeste com maiores concentrações que a região Sul.

Jonas Olsson, Anita Verpe Dyrrdal, Erika Médus et al.

Short-duration rainfall extremes are associated with a range of societal hazards, notably pluvial flooding but in addition, e.g., erosion-driven nutrient transport and point-source contamination. Fundamental for all analysis, modelling and risk assessment related to short-duration rainfall extremes is the access to and analysis of high-resolution observations. In this study, sub-daily rainfall observations from 543 meteorological stations in the Nordic–Baltic region were collected, quality-controlled and consistently analyzed in terms of records, return levels, geographical and climatic dependencies, time of occurrence of maxima and trends. The results reflect the highly heterogeneous rainfall climate in the region, with longitudinal and latitudinal gradients as well as local variability, and overall agree with previous national investigations. Trend analyses in Norway and Denmark indicated predominantly positive trends in the period 1980–2018, in line with previous investigations. Gridded data sets with estimated return levels and dates of occurrence (of annual maxima) are provided open access. We encourage further efforts towards international exchange of sub-daily rainfall observations as well as consistent regional analyses in order to attain the best possible knowledge on which rainfall extremes are to be expected in present as well as future climates. HIGHLIGHTS Sub-daily annual rainfall maxima have been collected from national observation networks in the Nordic–Baltic region, including a total of 543 stations.; A consistent regional analysis of records, return levels, geographical and climatic dependencies, time of occurrence of maxima and trends is performed.; Gridded data sets with return levels and time of occurrence are provided open access.;

R. You, Ninghua Zhu, X. Deng et al.

Chinese fir is one of the most important commercial timber species in China, with many geographic sources. However, little is known of the variation in wood physical properties among them. To explore the differences in wood physical properties and their influencing factors, five geographic sources of Chinese fir were selected. The variance inflation factor, stepwise regression, and principle component analysis were used to reduce multicollinearity and dimensions of the 19 wood physical properties (including density, shrinkage, and mechanical properties). The results showed that the wood density differed significantly among five geographic sources. The tangential shrinkage rate and radial shrinkage rate reached maximum values in black-heart Chinese fir (HNYX-T) but accompanied by the lowest value for difference dry shrinkage. The wood density and mechanical properties of HNYX-T was exceeded to that of others geographic sources. Fast-growth Chinese fir (FJYK-P) had the lowest value for all mechanical properties. The precipitation and temperature had significant correlations with the wood physical properties of this five geographic sources. The temperature in summer was mainly positive correlated with physical properties, while precipitation was negatively correlated with them. HNYX-T had the highest comprehensive score of PCA, followed by JXCS-R, emerged as higher-quality geographic source, which is important for selecting and utilizing geographic sources in forest management.

J. Schipperijn, E. Cerin, M. Adams et al.

Several systematic reviews have reported mixed associations between access to parks and physical activity, and suggest that this is due to inconsistencies in the study methods or differences across countries. An international study using consistent methods is needed to investigate the association between access to parks and physical activity. The International Physical Activity and Environment Network (IPEN) Adult Study is a multi-country cross-sectional study using a common design and consistent methods. Accelerometer, survey and Geographic Information Systems (GIS) data for 6,181 participants from 12 cities in 8 countries (Belgium, Brazil, Czech Republic, Denmark, Mexico, New Zealand, UK, USA) were used to estimate the strength and shape of associations of 11 measures of park access (1 perceived and 10 GIS-based measures) with accelerometer-based moderate-to-vigorous physical activity (MVPA) and four types of self-reported leisure-time physical activity. Associations were estimated using generalized additive mixed models. More parks within 1 km from participants' homes were associated with greater leisure-time physical activity and accelerometer-measured MVPA. Respondents who lived in the neighborhoods with the most parks did on average 24 minutes more MVPA per week than those living in the neighborhoods with the lowest number of parks. Perceived proximity to a park was positively associated with multiple leisure-time physical activity outcomes. Associations were homogeneous across all cities studied. Living in neighborhoods with many parks could contribute with up to 1/6 of the recommended weekly Having multiple parks nearby was the strongest positive correlate of PA. To increase comparability and validity of park access measures, we recommend that researchers, planners and policy makers use the number of parks within 1 km travel distance of homes as an objective indicator for park access in relation to physical activity.

Christopher Ball, J. Francis, Kuo-Ting Huang et al.