植被病虫害遥感监测预警研究进展与展望
Progress of vegetation pest and disease monitoring and forecasting
- 2025年29卷第6期 页码:2065-2082
收稿日期:2024-09-07,
纸质出版日期:2025-06-07
DOI: 10.11834/jrs.20254391
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收稿日期:2024-09-07,
纸质出版日期:2025-06-07
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植被病虫害作为农林业生产和生态系统健康的重要威胁,在全球气候变化的背景下其影响进一步加剧。病虫害的绿色、高效、精准防控有赖于高质量的监测和预警信息,相比传统植保测报手段,快速发展的遥感技术在病虫害测报中的潜力不断被各国科学家和政府关注和认知。本文以近年来遥感技术如何在植被病虫害监测和预警的研究和实践中得到系统应用为主线,介绍了技术、方法、模型的进展,分析了这一领域当前存在的主要挑战,展望了未来发展趋势。首先,模态不断丰富、性能和精度不断提升的卫星、航空、无人机遥感和近地传感器组成的多尺度遥感观测数据为病虫害测报提供了关键的数据源基础,能够越来越好地适应病虫害监测制图、生境监测等需求。在病虫害遥感监测方面,波谱分析和图像分析作为两类主要的方法,从不同角度提取遥感数据中对病虫害敏感的关键信息;同时,时相分析技术的应用为病虫害过程监测及不同胁迫间区分提供了有效手段。在病虫害遥感预警方面,多源遥感信息被用于各类生境因子的监测,进而与各类统计模型、机器学习模型、深度学习模型、机理性模型耦合,实现病虫害大范围预警;正因有了遥感信息的加入,使病虫害预警得以实现由点到面的扩展,从静态发展至动态,形成大范围时空动态预测的能力。未来,针对病虫害遥感监测和预警研究中仍存在的包括光谱特征复杂性、数据质量和处理效率、模型适用性和泛化能力等重要挑战,有必要进一步加强多源遥感数据的融合及综合运用,特别是积极探索荧光、热红外、激光雷达等技术在病虫害监测、生境评价方面应用的可能性;在此基础上,深入开展综合遥感技术、人工智能技术及植保理论模型的跨学科综合性研究,充分挖掘和释放蕴藏在多源遥感数据中的潜力和价值,为建立更加及时、准确、高效、动态的病虫害测报系统提供支撑,更好地服务于植被病虫害综合管理及绿色防控。
Vegetation diseases and pests pose significant threats to agricultural and forestry production
as well as to ecosystem health. In the context of global climate change
these impacts are further exacerbated. Effective
precise
and environmentally friendly control of diseases and pests relies heavily on high-quality monitoring and forecasting information. Compared to traditional vegetation protection methods
the rapidly advancing remote sensing technology is increasingly recognized by scientists and governments worldwide for its potential in disease and pest monitoring. This paper systematically reviews the recent applications of remote sensing technology in the monitoring and forecasting of vegetation diseases and pests
focusing on the advancements in techniques
methods
and models. It analyzes the current major challenges in this field and discuss future development trends. Firstly
the multi-scale remote sensing observation data composed of satellite
aviation
UAV remote sensing and near-ground sensors with increasingly enriched modes and improved performance and accuracy provide key data source for monitoring and habitat evaluation of vegetation diseases and pests. In terms of remote sensing monitoring tasks
spectral analysis and image analysis are two primary methods used to extract key information sensitive to diseases and pests from remote sensing data. Meanwhile
the application of temporal analysis techniques offers effective tools for monitoring disease and pest processes and discrimination among different stressors. For remote sensing-based forecasting of diseases and pests
multi-source remote sensing information is employed to monitor various habitat factors. The information coupled with various statistical models
machine learning models
deep learning models
or mechanistic models to achieve large-scale early warnings of diseases and pests. The integration of remote sensing information enables the expansion of the capability from specific points to wider areas
evolving from static to dynamic
and forming large-scale spatiotemporal dynamic predictions. In the future
it is necessary to further strengthen the fusion and comprehensive application of multi-source remote sensing data in view of the important challenges still existing in the research of remote sensing monitoring and forecasting of diseases and pests
including the complexity of spectral characteristics
data quality
data processing efficiency
model applicability and generalization ability. Particularly
it is important to explore the potential applications of fluorescence
thermal infrared
LiDAR
and other remote sensing ways in the monitoring and forecasting tasks. On this basis
it is essential to carry out interdisciplinary comprehensive research that integrates remote sensing technology
artificial intelligence
and plant protection theoretical models to fully explore and unleash the potential and value contained in multi-source remote sensing data. This will support the establishment of a more timely
accurate
efficient
and dynamic disease and pest monitoring and forecasting system
ultimately better serving the integrated management and environmentally friendly control of vegetation diseases and pests.
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