In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to t...In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.展开更多
The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine l...The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.展开更多
在显著气候变化叠加人类活动干扰的背景下,可持续的湿地生态系统管理对于湿地空间信息的需求不断提升,湿地遥感作为重要的交叉学科方向,研究成果日益增多。本文以Web of Science核心合集为数据库,通过检索过去50年湿地遥感论文成果,总...在显著气候变化叠加人类活动干扰的背景下,可持续的湿地生态系统管理对于湿地空间信息的需求不断提升,湿地遥感作为重要的交叉学科方向,研究成果日益增多。本文以Web of Science核心合集为数据库,通过检索过去50年湿地遥感论文成果,总结了湿地遥感研究全球发文量和引文量的变化情况;进行文献计量分析,探讨湿地遥感研究的发展历程和发展趋势。论文将湿地遥感研究划分为潜力探索期、框架成形期、快速增长期3个阶段,进而总结分析了不同阶段湿地遥感的研究主题和主要数据源;最后基于VOCviewer软件对湿地遥感研究热点关键词进行综述,从大数据时代背景下的湿地遥感分类及景观动态、精细化的湿地生态参量遥感观测、湿地可持续管理空间决策支持3个方面进行了未来研究趋势的展望。本研究将为理解国际湿地遥感研究发展历史、把握湿地遥感研究国际前沿、进行国内湿地遥感研究布局提供借鉴。展开更多
文摘In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.
文摘The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.
文摘在显著气候变化叠加人类活动干扰的背景下,可持续的湿地生态系统管理对于湿地空间信息的需求不断提升,湿地遥感作为重要的交叉学科方向,研究成果日益增多。本文以Web of Science核心合集为数据库,通过检索过去50年湿地遥感论文成果,总结了湿地遥感研究全球发文量和引文量的变化情况;进行文献计量分析,探讨湿地遥感研究的发展历程和发展趋势。论文将湿地遥感研究划分为潜力探索期、框架成形期、快速增长期3个阶段,进而总结分析了不同阶段湿地遥感的研究主题和主要数据源;最后基于VOCviewer软件对湿地遥感研究热点关键词进行综述,从大数据时代背景下的湿地遥感分类及景观动态、精细化的湿地生态参量遥感观测、湿地可持续管理空间决策支持3个方面进行了未来研究趋势的展望。本研究将为理解国际湿地遥感研究发展历史、把握湿地遥感研究国际前沿、进行国内湿地遥感研究布局提供借鉴。
