Pub Date : 2025-02-27DOI: 10.1016/j.envsoft.2025.106404
W.D. Dimuth P. Welivitiya , G.R. Hancock
Aerial and ground-based survey routinely employs technology such as digital photogrammetry, Light Detecting and Ranging (LiDAR) and Terrestrial Laser Scanning (TLS). These systems produce huge data sets with varying accuracy and reliability. At present there are no guidelines for the grid size dimension needed to accurately and reliably represent common features such as rills, gullies and contour drains. Here, synthetic landscapes with a very high density of points (10,000 pt m−2) are created. Coordinate data capture error is also examined. Results demonstrate that for the reliable representation of a gully or contour drain, the DEM grid spacing needs to be at least 1/3 the width of the feature of interest. Typical coordinate errors inherent within the data do not significantly affect the definition of gullies or contour drains. The findings here provide a defensible guide for the coordinate density required to hydrologically and geomorphically represent a landscape surface.
{"title":"What is the optimal digital elevation model grid size to best capture hillslope gullies and contour drains?","authors":"W.D. Dimuth P. Welivitiya , G.R. Hancock","doi":"10.1016/j.envsoft.2025.106404","DOIUrl":"10.1016/j.envsoft.2025.106404","url":null,"abstract":"<div><div>Aerial and ground-based survey routinely employs technology such as digital photogrammetry, Light Detecting and Ranging (LiDAR) and Terrestrial Laser Scanning (TLS). These systems produce huge data sets with varying accuracy and reliability. At present there are no guidelines for the grid size dimension needed to accurately and reliably represent common features such as rills, gullies and contour drains. Here, synthetic landscapes with a very high density of points (10,000 pt m<sup>−2</sup>) are created. Coordinate data capture error is also examined. Results demonstrate that for the reliable representation of a gully or contour drain, the DEM grid spacing needs to be at least 1/3 the width of the feature of interest. Typical coordinate errors inherent within the data do not significantly affect the definition of gullies or contour drains. The findings here provide a defensible guide for the coordinate density required to hydrologically and geomorphically represent a landscape surface.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106404"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.envsoft.2025.106391
Junjie Yu , Yuan Sun , Sarah Lindley , Caroline Jay , David O. Topping , Keith W. Oleson , Zhonghua Zheng
The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. However, CLMU presents significant challenges due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, a toolkit was developed, including (1) an operating system-independent containerized application developed to streamline the execution of CLMU and (2) a Python-based tool used to interface with the containerized CLMU and create urban surface and atmospheric forcing data. This toolkit enables users to simulate urban climate and explore climate-related variables such as urban building energy consumption and human thermal stress. It also supports the simulation under future climate conditions and the exploration of urban climate responses to various surface properties, providing a foundation for evaluating urban climate adaptation strategies.
{"title":"Integration and execution of Community Land Model Urban (CLMU) in a containerized environment","authors":"Junjie Yu , Yuan Sun , Sarah Lindley , Caroline Jay , David O. Topping , Keith W. Oleson , Zhonghua Zheng","doi":"10.1016/j.envsoft.2025.106391","DOIUrl":"10.1016/j.envsoft.2025.106391","url":null,"abstract":"<div><div>The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. However, CLMU presents significant challenges due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, a toolkit was developed, including (1) an operating system-independent containerized application developed to streamline the execution of CLMU and (2) a Python-based tool used to interface with the containerized CLMU and create urban surface and atmospheric forcing data. This toolkit enables users to simulate urban climate and explore climate-related variables such as urban building energy consumption and human thermal stress. It also supports the simulation under future climate conditions and the exploration of urban climate responses to various surface properties, providing a foundation for evaluating urban climate adaptation strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106391"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1016/j.envsoft.2025.106402
Philipp Brun , Lucienne de Witte , Manuel Richard Popp , Damaris Zurell , Dirk Nikolaus Karger , Patrice Descombes , Riccardo de Lutio , Jan Dirk Wegner , Christophe Bornand , Stefan Eggenberg , Tasko Olevski , Niklaus E. Zimmermann
Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.
{"title":"FlorID – A nationwide identification service for plants from photos and habitat information","authors":"Philipp Brun , Lucienne de Witte , Manuel Richard Popp , Damaris Zurell , Dirk Nikolaus Karger , Patrice Descombes , Riccardo de Lutio , Jan Dirk Wegner , Christophe Bornand , Stefan Eggenberg , Tasko Olevski , Niklaus E. Zimmermann","doi":"10.1016/j.envsoft.2025.106402","DOIUrl":"10.1016/j.envsoft.2025.106402","url":null,"abstract":"<div><div>Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106402"},"PeriodicalIF":4.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bushfires are one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analysing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analysing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia.
{"title":"Modelling bushfire severity and predicting future trends in Australia using remote sensing and machine learning","authors":"Shouthiri Partheepan , Farzad Sanati , Jahan Hassan","doi":"10.1016/j.envsoft.2025.106377","DOIUrl":"10.1016/j.envsoft.2025.106377","url":null,"abstract":"<div><div>Bushfires are one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analysing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analysing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106377"},"PeriodicalIF":4.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1016/j.envsoft.2025.106393
Lu Yang , Jingming Hou , Xinhong Wang , Pan Wang , Yongwei Wang
This paper presents a high-resolution 2D eco-hydraulics model accelerated by GPU technology, specifically designed for a spawning ground of Gymnocypris eckloni located downstream of the B hydropower station in the Upper Yellow River. The model evaluates the quality of the spawning habitat from April to June during a typical year. The calculation efficiency is improved by 12.1 times eco-hydraulics on a GPU device compared with the simulation on CPU device. The results indicate that while the concentration of dissolved oxygen (DO) meets the spawning requirements, water temperature significantly affects the habitat quality. Through the analysis of Weighted Useable Area (WUA) values and considering the impact of flood pulses on fish, an ecological scheduling scheme for the B hydropower station was developed to simulate the natural runoff process from April 24 to May 24. The implementation of this ecological scheduling scheme facilitates the coordinated development of economic activities and river ecological health. Additionally, it serves as an important reference for the development of ecological flow management measures and programs for other reservoirs.
{"title":"Application of the 2D high-resolution eco-hydraulics model based on GPU acceleration technology in the Upper Yellow River","authors":"Lu Yang , Jingming Hou , Xinhong Wang , Pan Wang , Yongwei Wang","doi":"10.1016/j.envsoft.2025.106393","DOIUrl":"10.1016/j.envsoft.2025.106393","url":null,"abstract":"<div><div>This paper presents a high-resolution 2D eco-hydraulics model accelerated by GPU technology, specifically designed for a spawning ground of <em>Gymnocypris eckloni</em> located downstream of the B hydropower station in the Upper Yellow River. The model evaluates the quality of the spawning habitat from April to June during a typical year. The calculation efficiency is improved by 12.1 times eco-hydraulics on a GPU device compared with the simulation on CPU device. The results indicate that while the concentration of dissolved oxygen (DO) meets the spawning requirements, water temperature significantly affects the habitat quality. Through the analysis of Weighted Useable Area (WUA) values and considering the impact of flood pulses on fish, an ecological scheduling scheme for the B hydropower station was developed to simulate the natural runoff process from April 24 to May 24. The implementation of this ecological scheduling scheme facilitates the coordinated development of economic activities and river ecological health. Additionally, it serves as an important reference for the development of ecological flow management measures and programs for other reservoirs.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106393"},"PeriodicalIF":4.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1016/j.envsoft.2025.106382
Miguel López-Otal , Fernando Domínguez-Castro , Borja Latorre , Javier Vela-Tambo , Jorge Gracia
Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics available for the analysis of droughts, assessing their impacts represents one of the best ways to understand their magnitude and extent. However, there are no systematic records outlining these impacts.
To help in their ongoing creation, we present a software framework that leverages raw newspaper articles, identifies any drought-related ones, and automatically classifies them according to a set of socioeconomic impacts. The information is provided to the user in a structured format, including geographical coordinates and their date of reporting. Our approach employs state-of-the-art Transformer-based Natural Language Processing (NLP) techniques, which achieve great accuracy. We currently support newspaper articles in the Spanish language within Spain, but our framework can be expanded to other countries and languages.
{"title":"SeqIA: A Python framework for extracting drought impacts from news archives","authors":"Miguel López-Otal , Fernando Domínguez-Castro , Borja Latorre , Javier Vela-Tambo , Jorge Gracia","doi":"10.1016/j.envsoft.2025.106382","DOIUrl":"10.1016/j.envsoft.2025.106382","url":null,"abstract":"<div><div>Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics available for the analysis of droughts, assessing their impacts represents one of the best ways to understand their magnitude and extent. However, there are no systematic records outlining these impacts.</div><div>To help in their ongoing creation, we present a software framework that leverages raw newspaper articles, identifies any drought-related ones, and automatically classifies them according to a set of socioeconomic impacts. The information is provided to the user in a structured format, including geographical coordinates and their date of reporting. Our approach employs state-of-the-art Transformer-based Natural Language Processing (NLP) techniques, which achieve great accuracy. We currently support newspaper articles in the Spanish language within Spain, but our framework can be expanded to other countries and languages.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106382"},"PeriodicalIF":4.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-23DOI: 10.1016/j.envsoft.2025.106396
Xueying Li , Xiaofan Yang
porousRTFoam v1.0 is a software developed to solve pore-scale hydro-bio-geochemical processes in porous media. It is developed based on OpenFOAM® by using the micro-continuum approach, which is adopted to solve a system of equations, including the Darcy-Brinkman-Stokes equation, the advection-diffusion equation with geochemical source terms, as well as biomass evolution with Monod kinetics for biofilm growth. Calcite dissolution and biofilm formation are used as benchmark cases to demonstrate the capabilities of porousRTFoam, with results compared against existing numerical packages and experimental data. The software is further demonstrated to be adaptable for building specific models, such as, mineral dissolution and precipitation in porous media and microbially induced calcite carbonate precipitation (MICP) in fractured media. The new software has the potential to promote fundamental understanding of various pore-scale reactive transport mechanisms, including but not limited to cell aggregation as well as cotransport of contaminants and bacteria in subsurface environments.
{"title":"porousRTFoam v1.0: An open-source numerical platform for simulating pore-scale reactive transport processes in porous media","authors":"Xueying Li , Xiaofan Yang","doi":"10.1016/j.envsoft.2025.106396","DOIUrl":"10.1016/j.envsoft.2025.106396","url":null,"abstract":"<div><div>porousRTFoam v1.0 is a software developed to solve pore-scale hydro-bio-geochemical processes in porous media. It is developed based on OpenFOAM® by using the micro-continuum approach, which is adopted to solve a system of equations, including the Darcy-Brinkman-Stokes equation, the advection-diffusion equation with geochemical source terms, as well as biomass evolution with Monod kinetics for biofilm growth. Calcite dissolution and biofilm formation are used as benchmark cases to demonstrate the capabilities of porousRTFoam, with results compared against existing numerical packages and experimental data. The software is further demonstrated to be adaptable for building specific models, such as, mineral dissolution and precipitation in porous media and microbially induced calcite carbonate precipitation (MICP) in fractured media. The new software has the potential to promote fundamental understanding of various pore-scale reactive transport mechanisms, including but not limited to cell aggregation as well as cotransport of contaminants and bacteria in subsurface environments.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106396"},"PeriodicalIF":4.8,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-22DOI: 10.1016/j.envsoft.2025.106398
Carmine Apollaro , Ilaria Fuoco , Giovanni Vespasiano , Rosanna De Rosa , Mauro F. La Russa , Daniele Cinti , Michela Ricca , Alessia Pantuso , Andrea Bloise
Reaction Path Modelling was used to calculate the fluxes in terms of solutes and CO2 consumption during the water-rock interaction process at the basin-scale, considering the current and future climate scenarios (temperature and atmospheric CO2 concentration) and two types of solid reagent (Silicate Solid Reagent-SSR and Carbonate-Silicate Reagent C-SSR). Two modelling were performed considering solid reagents and simulating their weathering in the current climate scenario and two other simulations were developed to consider the future climate scenario (Representative Concentration Pathways – RCP 8.5). The study highlights that although the higher temperature promotes an increase of total dissolved ions (TDS) into riverine waters, the higher temperature also causes a decrease in precipitation and, thus, in the runoff. This condition will lead to a reduction in weathering rate and CO2 consumption at the basin scale. The main indirect effect of a negative CO2 consumption budget is a further increase in CO2 atmospheric concentration.
{"title":"Climate change effects at basin-scale: Weathering rates and CO2 consumption assessment by using the reaction path modelling","authors":"Carmine Apollaro , Ilaria Fuoco , Giovanni Vespasiano , Rosanna De Rosa , Mauro F. La Russa , Daniele Cinti , Michela Ricca , Alessia Pantuso , Andrea Bloise","doi":"10.1016/j.envsoft.2025.106398","DOIUrl":"10.1016/j.envsoft.2025.106398","url":null,"abstract":"<div><div><em>Reaction Path Modelling</em> was used to calculate the fluxes in terms of solutes and CO<sub>2</sub> consumption during the water-rock interaction process at the basin-scale, considering the current and future climate scenarios (temperature and atmospheric CO<sub>2</sub> concentration) and two types of solid reagent (Silicate Solid Reagent-SSR and Carbonate-Silicate Reagent C-SSR). Two modelling were performed considering solid reagents and simulating their weathering in the current climate scenario and two other simulations were developed to consider the future climate scenario (Representative Concentration Pathways – RCP 8.5). The study highlights that although the higher temperature promotes an increase of total dissolved ions (TDS) into riverine waters, the higher temperature also causes a decrease in precipitation and, thus, in the runoff. This condition will lead to a reduction in weathering rate and CO<sub>2</sub> consumption at the basin scale. The main indirect effect of a negative CO<sub>2</sub> consumption budget is a further increase in CO<sub>2</sub> atmospheric concentration.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106398"},"PeriodicalIF":4.8,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-22DOI: 10.1016/j.envsoft.2025.106392
Shuangjin Wang , Puxuan Wang , Richard Cebula , Maggie Foley , Chen Liang
Digital agriculture has transformed the landscape of agricultural technology innovation and has led to increased attention towards managing innovation in this domain. This study seeks to provide a comprehensive understanding of digital agriculture innovation management by proposing a new retrieval strategy and constructing a dataset of 1878 research papers from the WoS-SSCI core collection spanning the years 2000 through 2023. The research employs scientific methods and tools to analyze the overall development, collaboration networks, frontier hotspots, and contribution paths in the Chinese context, as well as future opportunities for research in digital agriculture innovation management. The study reveals that digital agriculture innovation management research has experienced accelorated growth since 2020 and is expected to undergo further changes in the near future. The keywords extracted from the WoS-SSCI core collection and CNKI (China National Knowledge Infrastructure) core database exhibit the characteristics of Zipf's Law, indicating certain terms are more frequently used than others. The analysis identifies 44 frontier hotspots in digital agriculture innovation management research within the WoS-SSCI, with topics such as “precision agriculture”, “remote sensing”, and “food security” displaying notable prominence in different sub-disciplines due to their high centrality and density. This scientometric analysis not only provides strategic guidance and methodological inspiration for theoretical research and disciplinary development in digital agriculture innovation management but also offers practical recommendations for implementing digital agriculture strategies and promoting rural development. The findings of this study lay a solid foundation for future research in digital agriculture innovation management and emphasize the potential for further advancements in this field.
{"title":"Scientometric analysis of development and opportunities for research in digital agriculture innovation management","authors":"Shuangjin Wang , Puxuan Wang , Richard Cebula , Maggie Foley , Chen Liang","doi":"10.1016/j.envsoft.2025.106392","DOIUrl":"10.1016/j.envsoft.2025.106392","url":null,"abstract":"<div><div>Digital agriculture has transformed the landscape of agricultural technology innovation and has led to increased attention towards managing innovation in this domain. This study seeks to provide a comprehensive understanding of digital agriculture innovation management by proposing a new retrieval strategy and constructing a dataset of 1878 research papers from the WoS-SSCI core collection spanning the years 2000 through 2023. The research employs scientific methods and tools to analyze the overall development, collaboration networks, frontier hotspots, and contribution paths in the Chinese context, as well as future opportunities for research in digital agriculture innovation management. The study reveals that digital agriculture innovation management research has experienced accelorated growth since 2020 and is expected to undergo further changes in the near future. The keywords extracted from the WoS-SSCI core collection and CNKI (China National Knowledge Infrastructure) core database exhibit the characteristics of Zipf's Law, indicating certain terms are more frequently used than others. The analysis identifies 44 frontier hotspots in digital agriculture innovation management research within the WoS-SSCI, with topics such as “precision agriculture”, “remote sensing”, and “food security” displaying notable prominence in different sub-disciplines due to their high centrality and density. This scientometric analysis not only provides strategic guidance and methodological inspiration for theoretical research and disciplinary development in digital agriculture innovation management but also offers practical recommendations for implementing digital agriculture strategies and promoting rural development. The findings of this study lay a solid foundation for future research in digital agriculture innovation management and emphasize the potential for further advancements in this field.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106392"},"PeriodicalIF":4.8,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-22DOI: 10.1016/j.envsoft.2025.106399
Pei Dang , Jun Zhu , Chao Dang , Heng Zhang
Parametric geographic scene modeling serves as the primary method for achieving large-scale rapid spatial visualization. However, balancing modeling efficiency and specificity of geographic entities poses significant challenges due to the complexity and diversity of real-world geographic environments. This study proposes a novel 3D geographic scene modeling approach that integrates knowledge graphs and large language models (LLMs). The method leverages the extensive pre-trained knowledge and inference capabilities of LLMs to autonomously infer and enhance semantic information of unknown geographic entities. Through progressive knowledge graphs, it transforms the semantic information of geographic entities into modeling parameters, ultimately achieving more intelligent 3D geographic scene modeling. Our approach addresses current limitations in parametric modeling by offering a flexible and adaptive solution capable of efficiently handling diverse geographic entities. Through case studies and comparative analyses, we examine the inference results and modeling effects under various prompt ratios, validating the effectiveness and advantages of this method.
{"title":"Semantic-driven parametric 3D geographic scene modeling: Integrating knowledge graphs and large language models","authors":"Pei Dang , Jun Zhu , Chao Dang , Heng Zhang","doi":"10.1016/j.envsoft.2025.106399","DOIUrl":"10.1016/j.envsoft.2025.106399","url":null,"abstract":"<div><div>Parametric geographic scene modeling serves as the primary method for achieving large-scale rapid spatial visualization. However, balancing modeling efficiency and specificity of geographic entities poses significant challenges due to the complexity and diversity of real-world geographic environments. This study proposes a novel 3D geographic scene modeling approach that integrates knowledge graphs and large language models (LLMs). The method leverages the extensive pre-trained knowledge and inference capabilities of LLMs to autonomously infer and enhance semantic information of unknown geographic entities. Through progressive knowledge graphs, it transforms the semantic information of geographic entities into modeling parameters, ultimately achieving more intelligent 3D geographic scene modeling. Our approach addresses current limitations in parametric modeling by offering a flexible and adaptive solution capable of efficiently handling diverse geographic entities. Through case studies and comparative analyses, we examine the inference results and modeling effects under various prompt ratios, validating the effectiveness and advantages of this method.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106399"},"PeriodicalIF":4.8,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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