Hasil untuk "Physical geography"

G. Fillenbaum, M. Smyer

A. Hargreaves

This paper introduces a new concept in educational research and social science: that of emotional geographies. Emotional geographies describe the patterns of closeness and distance in human interactions that shape the emotions we experience about relationships to ourselves, each other, and the world around us. Drawing on an interview-based study of 53 elementary and secondary teachers, the paper describes five emotional geographies of teacher-parent interactions—sociocultural, moral, professional, physical, and political—and their consequences.

E. Leslie, N. Coffee, L. Frank et al.

D. Duncan, J. Aldstadt, J. Whalen et al.

Neighborhood walkability can influence physical activity. We evaluated the validity of Walk Score® for assessing neighborhood walkability based on GIS (objective) indicators of neighborhood walkability with addresses from four US metropolitan areas with several street network buffer distances (i.e., 400-, 800-, and 1,600-meters). Address data come from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5–11 years and their families participating in YMCA-administered, after-school programs located in four geographically diverse metropolitan areas in the US (n = 733). GIS data were used to measure multiple objective indicators of neighborhood walkability. Walk Scores were also obtained for the participant’s residential addresses. Spearman correlations between Walk Scores and the GIS neighborhood walkability indicators were calculated as well as Spearman correlations accounting for spatial autocorrelation. There were many significant moderate correlations between Walk Scores and the GIS neighborhood walkability indicators such as density of retail destinations and intersection density (p < 0.05). The magnitude varied by the GIS indicator of neighborhood walkability. Correlations generally became stronger with a larger spatial scale, and there were some geographic differences. Walk Score® is free and publicly available for public health researchers and practitioners. Results from our study suggest that Walk Score® is a valid measure of estimating certain aspects of neighborhood walkability, particularly at the 1600-meter buffer. As such, our study confirms and extends the generalizability of previous findings demonstrating that Walk Score is a valid measure of estimating neighborhood walkability in multiple geographic locations and at multiple spatial scales.

K. Whittington, Jason Owen-Smith, W. Powell

A. Hornero, E. Prikaziuk, R. Hernandez-Clemente et al.

In the framework of vegetation modelling, the accurate representation of spatial heterogeneity is a challenge for remotely sensed radiative transfer models (RTMs). On the one hand, one-dimensional RTMs are highly realistic on homogeneous surfaces but have limitations when modelling complex structures, while three-dimensional RTMs, although physically detailed, have a high computational cost and greater complexity in parameterisation. In this paper, we present the SILVO (Simplified Light-Vegetation Overlay) model as a streamlined ray-tracing tool that allows the generation of basic spatial metrics (gap fraction, scene vegetation density) on a geometrical representation of the canopy. The model is driven by a simplified scene description and solar illumination conditions, and has been implemented in parallel with OpenMP to provide a computationally efficient solution. The results obtained in a real heterogeneous forest scene demonstrate the ability of the model to capture the essential aspects of the canopy’s spatial structure, facilitating the extension of 1D RTMs for more realistic representations of heterogeneous canopies. An indicative comparison with LiDAR-derived structural proxies is also reported (R2 = 0.774 for vertical vegetation density; NRMSE = 11.7% for gap fraction), while acknowledging that this does not constitute a full validation exercise. As it is an active project, and in search of integration with future developments, its code is available under the AGPL license in a public repository.

Ntelekoa Masiane, Nnamdi Nwulu, Kowiyou Yessoufou

Universal electricity access remains elusive in Lesotho, with only a 53% connection rate. This statistic highlights a significant urban–rural gap of 60% to 18%, favouring urban areas mainly served by the main grid. The rugged terrain renders extending the grid to most rural areas impractical. To address this, the energy policy and electrification master plans aim to leverage abundant renewable energy resources and deploy mini-grids in rural regions. However, progress has been slow since the first advanced mini-grid projects began in 2018. The paper reviewed policy and framework documents from 2010 to 2025 that are pertinent to the deployment of mini-grids. It employed a hybrid qualitative-quantitative approach of SWOT-TOWS-AHP, which is rarely applied in energy policy analysis. It used the SWOT analysis tool to identify the Strengths, Weaknesses, Opportunities, and Threats faced in implementing sustainable renewable energy mini-grids. This was followed by the TOWS-AHP (Threats, Opportunities, Weaknesses, and Strengths-Analytical Hierarchy Process) method to develop strategies that utilize strengths and seize opportunities while tackling weaknesses and mitigating threats. These strategies were ranked based on their potential impact on mini-grid deployment. Despite supporting policies for mini-grids, the lack of political will from the government has emerged as a major obstacle. The three top strategies suggested to accelerate the deployment of sustainable mini-grids and advance efforts to achieve Sustainable Development Goal no. 7 by 2030 are establishing a mini-grid financing fund, reviewing the mini-grid regulatory framework, and reforming rural electrification institutions to improve coordination and collaboration. The top strategies carry weights of 8.5%, 7.8%, and 7.7%, respectively.

Mingqi Li, Pengxin Wang, Kevin Tansey et al.

Accurate crop yield estimation enables informed decisions that support efficient and sustainable food production systems. Despite some success in crop yield estimation using deep learning models, they are often referred to as “black boxes” due to their lack of interpretability. Meanwhile, most current models are designed to provide yield estimations without assessing the uncertainty and verifying the contribution of components within the models. This work developed a novel deep learning approach to estimate winter wheat yield in the Guanzhong Plain, PR China by using three remotely sensed indices, vegetation temperature condition index (VTCI), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) during main growth stages of winter wheat. The attention mechanism (AM) and an interpretable attribution method, backpropagation-based integrated gradients (IG), were incorporated into the deep learning approach to enhance interpretability. Additionally, to address uncertainty limitations the Monte Carlo (MC) dropout was applied to the deep learning approach to assess the uncertainty over time during data accumulation. The proposed approach (AM-CNN-LSTM) combined a one-dimensional convolutional neural network (1D-CNN) to capture local dependencies in sequences, the temporal data processing capability of long short-term memory (LSTM), and the interpretability of the AM. The AM-CNN-LSTM model had enhanced precision in yield estimation (R2 = 0.64, RMSE = 498.08 kg/ha) compared with the CNN, AM-CNN, and CNN-LSTM. The attention weights indicated that the most significant variable influencing wheat yield was FPAR, followed by LAI and VTCI. The results of IG showed that the FPAR at the jointing and heading-filling stages, LAI at the heading-filling and milk maturity stages, and VTCI at the jointing stage contributed more to the yield. The MC dropout results showed that the level of model uncertainty decreased steadily as time advanced from late March to late April and stabilized around mid-May.

Soon Ho Kwon, Suhyun Lim, Seungyub Lee

Study region: The study region is Yongseong Reservoir, located in Gyeongsangbuk-do, South Korea, a small agricultural reservoir primarily used for irrigation and is subject to pronounced hydrological seasonality. Study focus: This study proposes a novel framework for estimating water levels in ungauged agricultural reservoirs using images from CCTVs originally installed for security purposes. The method integrates a U-Net-based water-body segmentation model with four machine learning regression algorithms (support vector regression, SVR; random forest, RF; extreme gradient boosting, XGB; and light gradient boosting machine, LGBM) to predict reservoir water levels from segmented water pixel counts. Importantly, we assess the potential of region of interest (ROI) filtering to enhance prediction accuracy, demonstrating that surveillance camera imagery can be effectively repurposed for hydrological monitoring in data-scarce environments. New hydrological insights for the region: The results revealed that ROI filtering significantly improved prediction performance, increasing R² by 10–20 % and reducing root mean squared error by up to 0.197 (for RF). The RF model achieved the highest overall accuracy (R² = 0.964), while SVR performed best during no temporal variations. XGB and LGBM showed balanced residuals but slightly underestimated water levels during peak fluctuations. This study demonstrates the feasibility of image-based water-level estimation in ungauged agricultural reservoirs using security CCTVs. The results underscore the importance of spatial input refinement (ROI filtering) for reliable hydrological modeling.

Pengcheng Liu, Ziqin Shao, Tianyuan Xiao

The cluster patterns of features in map space represent a comprehensive reflection of individual feature geometric attributes and their spatial adjacency relationships. These patterns also embody spatial cognition results under the Gestalt principle. Describing non-linear spatial cluster patterns as effective regular structures is one of the fundamental tasks in deep learning for recognizing feature cluster patterns. In this study, based on the concept of texture co-occurrence matrices from regular gray-scale images, we utilized Voronoi diagrams to construct the tessellation structure of building polygons. Built upon the foundation of first-order texton co-occurrence matrices, we established three-dimensional texton co-occurrence matrices for building polygons, considered five attributes of building size, shape, orientation, and density, and encompassed 64 different combinations of second-order neighboring directions. This matrix concretizes the latent Gestalt spatial characteristics of building polygon clusters into a three-dimensional sparse matrix. It is then used as an input vector to construct a deep convolutional neural network for recognizing building polygon cluster patterns. Through adjustments and optimizations of neural network structure and strategies, along with validation through practical case studies and comparisons with other models, we have demonstrated the effectiveness of the second-order texton co-occurrence matrix in describing the characteristics of building polygon clusters.

Vámos, Ramóna

Gábor Mezősi was born on July 18, 1952, in Szeged and spent most of his life there. He graduated as a teacher in Mathematics and Geography in 1975. With a German state scholarship, he gained professional experience and, observing their education system, reformed the Hungarian geographer training. In 1991, he won the Academy Award, and as head of department, he helped to introduce geoinformatics training. In 1993, he became Doctor of the Hungarian Academy of Sciences in geoinformatics and landscape geography. After the turn of the millennium, he played an important role as dean of the Faculty of Science of the University of Szeged. In 2021, he was awarded Professor Emeritus of the University of Szeged and in 2023 the Civil Section of the Middle Cross of the Order of Merit of Hungary. In addition to his scientific work, Gábor Mezősi did a lot to reform geographer training as well. The conversation with the Professor gives an insight into the most important stages and experiences of his life.

Albenis Pérez-Alarcón, José C. Fernández-Alvarez, Ricardo M. Trigo et al.

In this study, we identified the moisture sources for the precipitation associated with tropical cyclones (TCs) during the rapid intensification (RI) process from 1980 to 2018 by applying a Lagrangian moisture source diagnostic method. We detected sixteen regions on a global scale for RI events distributed as follows: four in the North Atlantic (NATL), two in the Central and East Pacific Ocean (NEPAC), the North Indian Ocean (NIO) and South Indian Ocean (SIO), and three in the South Pacific Ocean (SPO) and the Western North Pacific Ocean (WNP). The moisture uptake (MU) mostly was from the regions where TCs underwent RI. The Western NATL, tropical NATL, Caribbean Sea, the Gulf of Mexico and the Central America and Mexico landmass supported ∼85.4% of the precipitating moisture in the NATL, while the latter source and the eastern North Pacific Ocean provided the higher amount of moisture in NEPAC (∼84.3%). The Arabian Sea, the Bay of Bengal and the Indian Peninsula were the major moisture sources in NIO, contributing approximately 81.3%. The eastern and western parts of the Indian Ocean supplied most of the atmospheric humidity in SIO (∼83.8%). The combined contributions (∼87.9%) from the western and central SPO and the Coral Sea were notably higher in SPO. Meanwhile, TCs in the WNP basin mostly received moisture from the western North Pacific Ocean, the Philippine Sea and the China Sea, accounting for 80.1%. The remaining moisture support in each basin came from the summed contributions of the remote sources. Overall, RI TCs gained more moisture up to 2500 km from the cyclone centre than those slow intensification (SI) and the total MU was approximately three times higher during RI than SI. Finally, the patterns of the MU differences respond to the typical pathways of moisture transport in each basin.

Shahid Nawaz Khan, Dapeng Li, Maitiniyazi Maimaitijiang

Estimating crop yield prior to harvest plays a crucial role in agricultural decision making. Machine learning models that use remote sensing (RS) data can provide quick and early estimates of crop yield. Previously, timeseries gross primary production (GPP) synthesized from RS data has been employed in agricultural applications. However, relevant research on using GPP data and deep learning models in crop yield prediction is scarce. The aim of this study was to assess the capability of MODIS-derived GPP data and deep transfer learning to predict crop yield. We developed and trained deep neural network (DNN), one-dimensional convolutional neural network (1D-CNN), and gated recurrent unit (GRU) models to predict county-level corn and soybean yields. Then we evaluated the performance of crop-type and location transfer learning strategies in crop yield prediction. The results of this study can be summarized as follows: (1) GPP data and deep transfer learning have the potential to predict crop yield with reasonable prediction accuracy; (2) in the context of transfer learning, crop-type transfer learning produced a coefficient of determination (R2) ranging from 0.521 to 0.784 for soybean and from 0.644 to 0.903 for corn. Meanwhile, location transfer learning yielded R2 values ranging from 0.690 to 0.931 for the Great Plains (GP) climatic zone and 0.480 to 0.801 for the Eastern Temperate Forest (ETF) climatic zone; and (3) For corn and soybean yield predictions, the 1D-CNN model (R2 = 0.864 for corn and 0.750 for soybean) outperformed the DNN and GRU models. Our results reveal that MODIS-derived GPP data and deep transfer learning can be used to effectively predict county-level crop yield.

Shu Lan, Yao Zhang, Tingyao Gao et al.

Tiller number serves as a crucial indicator of total yield in agriculture. Accurately monitoring tiller numbers is essential for guiding variable fertilization to optimize inputs and enhance final yields. Currently, most of the existing winter wheat tillering sensing monitoring methods cannot meet the three requirements of fast, accurate and efficient at the same time. In order to improve the monitoring accuracy and efficiency, this paper proposes a fast UAV remote sensing method for winter wheat tiller number estimation, combined with self-adaptive segmentation framework and multi-feature selection method. The self-adaptive segmentation framework is designed from the perspective of amplifying the difference of spectral information of ground objects. This framework fully considers the influence of complex environment, especially vegetation coverage, on parameter setting while using mathematical function means and polarization ideas. It aims to solve the problem of insufficient applicability of traditional segmentation methods under different environmental conditions. Subsequently, an optimized grey wolf search algorithm CMI-IGWO with multi-strategy fusion is proposed. Its novel evaluation criteria and iterative mechanism provide important algorithm support for the determination of the best inversion feature combination. The search algorithm combines statistics and information theory, comprehensively considers the linear and nonlinear relationships between variables, and guides the gray wolf to select the optimal features. Experiments show that the mixed features covering spectral, texture, color and shape information can make the final tiller number prediction model work best, achieving an R2 value exceeding 0.75. It affirms the efficacy and robustness of the proposed method in estimating winter wheat tiller numbers, providing valuable guidance for precise topdressing. This study provides a new feasible solution for predicting crop tiller number with high accuracy and efficiency using UAV remote sensing technology.

M. Galea

Telerehabilitation refers to the virtual delivery of rehabilitation services into the patient's home. This methodology has shown to be advantageous when used to enhance or replace conventional therapy to overcome geographic, physical, and cognitive barriers. The exponential growth of technology has led to the development of new applications that enable health care providers to monitor, educate, treat, and support patients in their own environment. Best practices and well-designed Telerehabilitation studies are needed to build and sustain a strong Telerehabilitation system that is integrated in the current health care structure and is cost-effective.

Min Chen, A. Voinov, D. Ames et al.

Abstract: Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of existing resources and can attract diverse participants. With this approach, participants from different physical locations or domains of expertise can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated modelling and simulation systems, highlighting the associated development challenges when moving to an open web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design perspective, with potential use cases provided. The four components of this conceptual framework - a set of standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed simulation environment - are also discussed in detail with the goal of advancing this emerging field.

L. Fischer, J. Honold, Alexandra Botzat et al.

Taehoon Hong, Minhyun Lee, Choongwan Koo et al.

Liuyi Zhang, Anni Wang, Xia Xie et al.

L. Parrino