Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters...Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.展开更多
森林火灾是一种危害极大的自然灾害,是森林扰动的主要类型之一,直接影响森林生态系统结构、碳循环甚至全球气候的变化。近年来,航空平台和传感器的技术进步有效地提升了机载遥感系统探测和监测森林火灾的能力,推动了机载遥感在森林可燃...森林火灾是一种危害极大的自然灾害,是森林扰动的主要类型之一,直接影响森林生态系统结构、碳循环甚至全球气候的变化。近年来,航空平台和传感器的技术进步有效地提升了机载遥感系统探测和监测森林火灾的能力,推动了机载遥感在森林可燃物调查及载量评估、火险测报预测、火场态势及火情监测、灾害损失评估以及火烧迹地生态修复治理等方面的应用。本文首先介绍了中国林业科学研究院机载光学全谱段遥感系统CAF-LiTCHy(Chinese Academy of Forestry’s LiDAR,Thermal,CCD and Hyperspectral airborne observation system),描述了激光雷达扫描仪、热红外相机、CCD相机和高光谱传感器等传感器的参数;然后,阐明了集成方案和观测数据的处理方法;最后,以四川省西昌市"3.30森林火灾"作为该系统火后灾情遥感调查和灾情评估应用示例,综合多传感器数据特征,进行森林火烧程度评价,分析该系统采集的正射影像、冠层高度模型、高光谱影像、热红外影像在森林火灾监测评价中的潜力。研究结果表明CAF-LiTCHy机载遥感观测系统能有效获取森林火灾的灾情信息、火场及火环境参数,可为预防、预报预警、扑救指挥、灾害评估和生态修复提供支持。展开更多
IS1921 VF-256 type ground object spectrometer was used to extract the spectral data of the meadow grassland and bare land to obtain their refleotivity spectral characteristics. The experiment was carried out on the lo...IS1921 VF-256 type ground object spectrometer was used to extract the spectral data of the meadow grassland and bare land to obtain their refleotivity spectral characteristics. The experiment was carried out on the low mountain meadow steppe in the Saiwundu Village, Hargentai Town, West Ujumqin Banner, Xilin Gol League, Inner Mongolia. The results showed that different ground objects had different reflectances. The spectral reflectance curve of the meadow steppe plant communities had obvious characteristics of peak and valley in the visible spectrum band, and had strong reflection in the near-infrared band. The reflection curve of the bare lands in the visible spectrum band was higher than that of the meadow grassland communities while inthe near-infrared band it was lower than that of the meadow grassland communities. Under different degradation gradients, the spectral reflectivity of the meadow steppe grassland communities increased with the enhancement of the degradation gradients. Under the same degradation gradient, the Stipa grandis communities had a lower visible light reflectivity than the Artemisia frigida communities but had a higher near-infrared reflectivity than the Artemisia frigida communities; different ground objects on the meadow steppe had different spectrum characteristic, and showed a certain discrepancies with the changes of the degradation level.展开更多
With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concer...With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.展开更多
文摘Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.
文摘森林火灾是一种危害极大的自然灾害,是森林扰动的主要类型之一,直接影响森林生态系统结构、碳循环甚至全球气候的变化。近年来,航空平台和传感器的技术进步有效地提升了机载遥感系统探测和监测森林火灾的能力,推动了机载遥感在森林可燃物调查及载量评估、火险测报预测、火场态势及火情监测、灾害损失评估以及火烧迹地生态修复治理等方面的应用。本文首先介绍了中国林业科学研究院机载光学全谱段遥感系统CAF-LiTCHy(Chinese Academy of Forestry’s LiDAR,Thermal,CCD and Hyperspectral airborne observation system),描述了激光雷达扫描仪、热红外相机、CCD相机和高光谱传感器等传感器的参数;然后,阐明了集成方案和观测数据的处理方法;最后,以四川省西昌市"3.30森林火灾"作为该系统火后灾情遥感调查和灾情评估应用示例,综合多传感器数据特征,进行森林火烧程度评价,分析该系统采集的正射影像、冠层高度模型、高光谱影像、热红外影像在森林火灾监测评价中的潜力。研究结果表明CAF-LiTCHy机载遥感观测系统能有效获取森林火灾的灾情信息、火场及火环境参数,可为预防、预报预警、扑救指挥、灾害评估和生态修复提供支持。
基金Supported by Inner Mongolia Meteorological Brueau Technology ResearchProject(200618)~~
文摘IS1921 VF-256 type ground object spectrometer was used to extract the spectral data of the meadow grassland and bare land to obtain their refleotivity spectral characteristics. The experiment was carried out on the low mountain meadow steppe in the Saiwundu Village, Hargentai Town, West Ujumqin Banner, Xilin Gol League, Inner Mongolia. The results showed that different ground objects had different reflectances. The spectral reflectance curve of the meadow steppe plant communities had obvious characteristics of peak and valley in the visible spectrum band, and had strong reflection in the near-infrared band. The reflection curve of the bare lands in the visible spectrum band was higher than that of the meadow grassland communities while inthe near-infrared band it was lower than that of the meadow grassland communities. Under different degradation gradients, the spectral reflectivity of the meadow steppe grassland communities increased with the enhancement of the degradation gradients. Under the same degradation gradient, the Stipa grandis communities had a lower visible light reflectivity than the Artemisia frigida communities but had a higher near-infrared reflectivity than the Artemisia frigida communities; different ground objects on the meadow steppe had different spectrum characteristic, and showed a certain discrepancies with the changes of the degradation level.
基金Supported by the central university basic scientific research fund(XDJK2009C006)from Ministry of Educationthe National Youth Science Fund(41201436)from National Science Counci~~
文摘With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.
