作物胁迫无人机遥感监测研究评述

doi:10.12082/dqxxkx.2019.180397

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (4): 512-523.doi: 10.12082/dqxxkx.2019.180397

作物胁迫无人机遥感监测研究评述

黄耀欢^1,²(), 李中华^1,^2,^*, 朱海涛³

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

收稿日期:2018-08-24 修回日期:2018-12-24 出版日期:2019-04-24 发布日期:2019-04-24
作者简介:
作者简介：黄耀欢(1982-)，男，安徽黄山人，副研究员，主要从事遥感与GIS应用研究。E-mail: huangyh@lreis.ac.cn
基金资助:
国家重点研发计划项目(2016YFC0208202、2017YFB0503005)

The Use of UAV Remote Sensing Technology to Identify Crop Stress: A Review

Yaohuan HUANG^1,²(), Zhonghua LI^1,^2,^*, Haitao ZHU³

1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094,China

Received:2018-08-24 Revised:2018-12-24 Online:2019-04-24 Published:2019-04-24
Contact: Zhonghua LI
Supported by:
National Key Research and Development Program of China, No.2016YFC0208202, 2017YFB0503005.

摘要/Abstract

摘要：

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。

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

Abstract:

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.

Key words: UAV, remote sensing monitoring, crop stress, spectral imaging, thermal infrared sensor, agricultural development, review

黄耀欢, 李中华, 朱海涛. 作物胁迫无人机遥感监测研究评述[J]. 地球信息科学学报, 2019, 21(4): 512-523.DOI:10.12082/dqxxkx.2019.180397

Yaohuan HUANG, Zhonghua LI, Haitao ZHU. The Use of UAV Remote Sensing Technology to Identify Crop Stress: A Review[J]. Journal of Geo-information Science, 2019, 21(4): 512-523.DOI:10.12082/dqxxkx.2019.180397

图/表 5

表1

表2

图1

表3

作物胁迫探测常用植被指数

指数名称	指数公式	胁迫类型	参考文献编号
归一化植被指数	$NDVI = (NIR - R) (NIR + R)$	病虫害、水分、杂草、氮素	[13]、[41]、[52]-[55]
比值植被指数	$SR = NIR R$	病虫害	[53]
过绿指数	$ExG = 2 g - r - b$ $r = R (R + G + B)$ $g = G (R + G + B)$ $b = B (R + G + B)$	杂草胁迫	[13] [50]
归一化红绿差异指数	$NGRDI = (G - R) (G + R)$	杂草胁迫	[50]
叶绿素吸收反射转化指数和优化土壤调节指数的比值	$TCARI OSAVI = 3 [R 700 - R 670 - 0.2 (R 700 - R 550) (R 700 R 670)] (1 + 0.16) (R 800 - R 670) / (R 800 + R 670 + 0.16)$	水分胁迫	[54]
红绿指数	$RGI = R G$	病虫害胁迫	[51]
红绿植被指数	$GRVI = (G - R) (G + R)$	病虫害胁迫	[51]
过红指数	$ExR = 1.4 R - G$	病虫害胁迫	[17]
可见光大气阻抗植被指数	$VARI = (G - R) (G + R - B)$	氮素胁迫	[18]
蓝光标准化值	$BLSV = B (R + G + B)$	氮素胁迫	[18]
比值光谱指数	$RSI = R 738 R 522$	氮素胁迫	[25]
转化植被指数	$TVI = NDVI + 0.5$	病虫害胁迫	[53]
光化学反射指数	$PRI = (R 570 - R 530) (R 570 + R 530)$	水分胁迫	[51]
标准化光化学指数	$PRInorm = PR I 570 [RDVI × (R 700 R 670)]$	水分胁迫	[51]
重归一化植被指数	$RDVI = (R 800 - R 670) R 800 + R 670$	水分胁迫	[51]
修正叶绿素吸收指数	$MCARI = [R 701 - R 671 - 0.2 (R 701 - R 549)] (R 701 R 671)$	重金属胁迫	[23]

表3

表4

常用的水分胁迫监测指数

指数名称	公式	参考文献编号
植被水分胁迫指数	$CWSI = (T canopy - T wet) / (T dry - T wet)$	[28]、[30]、[40]、[51]、[61]
气孔导度指数	$Ig = (T dry - T canopy) / (T canopy - T wet)$	[54]
第二气孔导度指数	$I 3 = (T canopy - T wet) / (T dry - T canopy)$	[54]
非胁迫上限指数	$DANS = T canopy - T cNS$	[61]
上层冠层阈值	$DACT = max [0, T canopy - T critical]$	[61]
时间温度阈值	$TTT = ∑ h = 0 24 h, when T canopy > T critical$	[61]
整合非胁迫上限指数	$IDANS = ∫ h = 024 (T canopy - T cNS) d h$	[61]
整合上层冠层阈值	$IDACT = ∫ h = 024 max [0, (T canopy - T critical)] d h$	[61]

表4

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类型	载荷/kg	飞行时间/min	典型作业高度/m	优点	缺点
降落伞	≈ 1.5	10~30	>100	操作简单、成本低	抗风能力弱、载荷受限、不适合快速移动
飞艇	>3.0	≈ 600	>100	载荷大,垂直起降、空中悬停、姿态平稳、安全系数高	抗风能力弱、使用成本高、效率低、飞行速度慢
多旋翼无人机	0.8~8.0	8~120	50~5000	自动导航、定点悬停、定点起飞、降落、多载荷、转场方便、对起降场地要求低	飞行时间短、遥控电子信号易受外界干扰
固定翼无人机	1.0~10	30~240	50~5000	自动导航、航时长、多载荷、转场方便、速度快、效率高、抗风能力强	不能定点悬停、过快的飞行速度可能会影响拍摄的图像质量、起降场地要求较高
垂直起降固定翼无人机	1.0~15	30~240	50~5000	垂直起降、定点悬停、多载荷、速度快、转场方便、对起降场地要求低	耗油大,气动布局复杂

传感器类型	作物胁迫应用	优点	缺点	参考文献
数码相机	可见外部伤害、生长状况	成本低、直观便捷	仅限于可见光波段能够监测的特征	[12]-[18]
多光谱相机	氮素胁迫、水分胁迫、病虫害胁迫	获取便捷、成本低、周期短	仅限于有限的几个波段	[12]-[14]、[19]-[21]
高光谱相机	各种作物胁迫	可以监测的作物胁迫类型比较多	图像处理程序繁杂、价格高昂	[22]-[25]
热红外相机	气孔导度、水分胁迫	非接触测量作物温度,方便快捷	受环境影响较大、较小的温度差异难以被监测、难以消除土壤影响	[26]-[30]
LIDAR	作物高度、生物量估测	丰富的点云信息	成本高、数据处理量大	[31]
SAR	数字控制喷雾器或肥料撒播机的使用率、生物量估测、作物倒伏	可以探测静止目标、可以测距	灵敏度受噪声吸收、背景噪音等的限制,采样率低于基于激光的传感器	[32]

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

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