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作物胁迫无人机遥感监测研究评述
The Use of UAV Remote Sensing Technology to Identify Crop Stress:A Review

查看参考文献63篇

黄耀欢 1,2   李中华 1,2 *   朱海涛 3  
文摘 作物胁迫是全球农业发展的一个重要制约因素,实现快速、大范围、实时的作物胁迫监测对于农业生产具有重要意义。传统的作物胁迫监测方式,如田间调查、理化检测和卫星遥感监测总是受到各种田间条件或大气条件的制约。随着无人机和各种轻量化传感器的快速发展,其凭借高频、迅捷等优势为各种作物胁迫监测提供了一套全新的解决方案。本文在介绍了目前主流的多种无人机和传感器的基础上,首先对目前无人机遥感用于作物监测的主要胁迫类型进行了梳理,然后重点阐述了基于光谱成像和热红外传感器进行作物胁迫无人机遥感监测的应用和技术方法,最后提出了作物胁迫无人机遥感监测尚需解决的关键问题,并展望了未来无人机遥感用于作物胁迫监测的前景。
其他语种文摘 Crop stress is an important factor restricting global agricultural development.Monitoring and understanding rapid,large-scale and real-time crop stress is of great significance for agricultural production.However,traditional methods of crop stress monitoring (such as fields surveys,physical and chemical detection,and satellite remote sensing),are strongly influenced by field and atmospheric conditions,temporal and spatial resolution,and labor costs.Rapid development of UAV platforms and various lightweight sensors,provide new solutions for various crop stress monitoring.These offer multiple advantages,primarily high frequency and speed.The introduction of various mainstream UAV platforms such as multi-rotor and fixed-wing,and sensors such as visible light digital camera,multispectral camera,hyperspectral camera,and thermal infrared camera has allowed for more efficient crop monitoring.This review explores the main biotic and abiotic stress types used by UAV remote sensing systems for crop monitoring.Biotic stressors mainly include miscellaneous grass stress,plant diseases,and insect pests stress.Abiotic stressors predominantly include water and nutrient stress.The application and technical methods of UAV remote sensing system monitoring of crop stress,based on spectral imaging and thermal infrared sensor technology are discussed.Sensitive bands and common vegetation indices used for crop stress monitoring are identified.Finally,key issues associated with UAV remote sensing and the future use of UAV remote sensing for crop stress monitoring are discussed.The advancement of UAV remote sensing technology,could contribute to improved identification and monitoring of crop stress in the near future.
来源 地球信息科学学报 ,2019,21(4):512-523 【核心库】
DOI 10.12082/dqxxkx.2019.180397
关键词 无人机 ; 遥感监测 ; 作物胁迫 ; 光谱成像 ; 热红外传感器 ; 农业发展 ; 评述
地址

1. 中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京, 100101  

2. 中国科学院大学, 北京, 100049  

3. 环境保护部卫星环境应用中心, 北京, 100094

语种 中文
文献类型 研究性论文
ISSN 1560-8999
学科 测绘学
基金 国家重点研发计划项目
文献收藏号 CSCD:6477391

参考文献 共 63 共4页

1.  杜广平. 植物与植物生理,2007 被引 3    
2.  Martinelli F. Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development,2015,35(1):1-25 被引 14    
3.  廖小罕. 无人机遥感众创时代. 地球信息科学学报,2016,18(11):1439-1447 被引 30    
4.  杨海军. 化工污染气体无人机遥感监测. 地球信息科学学报,2015,17(10):1269-1274 被引 7    
5.  金伟. 无人机遥感发展与应用概况. 遥感信息,2009(1):88-92 被引 70    
6.  孙中宇. 轻小型无人机低空遥感及其在生态学中的应用进展. 应用生态学报,2017,28(2):528-536 被引 34    
7.  宋晓阳. 融合数字表面模型的无人机遥感影像城市土地利用分类. 地球信息科学学报,2018,20(5):703-711 被引 11    
8.  郭琪. 无人机在气象监测中的应用. 广东气象,2016,38(6):64-66 被引 2    
9.  Alheit K V. Multiple-line cross QTL mapping for biomass yield and plant height in triticale. Theoretical and Applied Genetics,2013,127(1):251-260 被引 1    
10.  Zhang C. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture,2012,13(6):693-712 被引 38    
11.  Cui J. Landmark extraction and state estimation for UAV operation in forest. Control conference,2013:5210-5215 被引 1    
12.  Lopez-Granados F. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agriculture,2016,17(2):183-199 被引 8    
13.  Lopez-Granados F. Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agronomy for Sustainable Development,2016,36(4):67 被引 3    
14.  Roope Nasi. Estimating biomass and nitrogen amount of barley and grass using UAV and aircraft based spectral and photogrammetric 3D features. Remote Aens,2018,10(7):1082 被引 1    
15.  李宗南. 基于小型无人机遥感的玉米倒伏面积提取. 农业工程学报,2014,30(19):207-213 被引 47    
16.  祝锦霞. 基于无人机和地面数字影像的水稻氮素营养诊断研究. 浙江大学学报(农业与生命科学版),2010,36(1):78-83 被引 17    
17.  Castro A I D. Detection of laurel wilt disease in avocado using low altitude aerial imaging. Plos One,2015,10(4):e0124642 被引 2    
18.  李红军. 无人机搭载数码相机航拍进行小麦、玉米氮素营养诊断研究. 中国生态农业学报,2017(12):1832-1841 被引 18    
19.  Zamanallah M. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods,2015,11(1):35 被引 24    
20.  Severtson D. Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture,2016,17(6):659-677 被引 6    
引证文献 4

1 赵静 多旋翼无人机近地遥感光谱成像装置研制 农业工程学报,2020,36(3):78-85
被引 2

2 韩文霆 基于无人机遥感的玉米水分利用效率与生物量监测 农业机械学报,2021,52(5):129-141
被引 6

显示所有4篇文献

论文科学数据集

1. 2017年5-8月河北怀来无人机高光谱农田植被反射率站点数据

2. 2020年东营市垦利区部分区域0.2m无人机热红外遥感温度差异灰度影像数据集

3. 2020年东营市垦利区部分区域0.2m无人机热红外DOM遥感影像成果数据集

数据来源:
国家对地观测科学数据中心
PlumX Metrics
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