摘要
基于开源飞控Pixhawk开发了一套集成稳定云台、位置与姿态系统(Position and orientation system,POS)数据采集模块的无人机多光谱遥感图像采集系统,同步采集520~920 nm范围内的红、绿和近红外波段信息。以冬小麦为例,分别在越冬期、拔节期、挑旗期和抽穗期进行飞行实验,飞行高度55 m,多光谱影像地面分辨率2.2 cm。采用监督分类与植被指数统计直方图相结合的方式,提出了一种田间尺度小麦覆盖度快速提取的方法,给出归一化植被指数(Normalized difference vegetation index,NDVI)、土壤调节植被指数(Soil-adjusted vegetation index,SAVI)及修正土壤调节植被指数(Modified soil-adjusted vegetation index,MSAVI)对应的植被像元与土壤像元的分类阈值,分别为0.475 6、0.705 6和0.635 0。同时利用基于同步采集的地面分辨率可达0.8 cm的高清可见光遥感图像提取了相应时期的冬小麦覆盖度参考值。结果表明,基于无人机多光谱遥感技术及植被指数法可以较好地提取冬小麦越冬期、拔节期、挑旗期和抽穗期的植被覆盖度信息。与SAVI、MSAVI相比,基于NDVI分类阈值的提取效果最好,绝对误差最小。
Fractional vegetation cover （FVC） is an important index of crop growth status, as well as one of the major factors affecting crop photosynthesis, transpiration and water use efficiency. Currently, there are some problems that satellite remote sensing technology widely used is difficult to meet the requirement of fractional vegetation cover extraction in field scale for the low temporal and spatial resolution, the extraction of vegetation coverage based on artificial ground image is time consuming and laborious, the operating cost is high, and the remote sensing image acquired by the unmanned aerial vehicle （UAV） remote sensing system without integrated gimbal is geometrically distorted. To address the issues above, a UAV multi-spectral remote sensing image acquisition system integrated gimbal and position and orientation system （POS） data acquisition modules was developed, which had the ability to acquire the reflection information for red, green and near-infrared bands between 520 nm and 920 nm. Taking winter wheat as an example, UAV flying experiments were conducted in different growing stages, covering over- wintering period, jointing stage, flag leaf stage and heading date, with 55 m flying height and 2.2 cm muhispectral image resolution. A rapid FVC extraction method was proposed, combining supervised classification with vegetation index histogram, by which the classification thresholds of normalized difference vegetation index （NDVI） , soil-adjusted vegetation index （SAV1） and modified soil-adjusted vegetation index （MSAVI） for field wheat were obtained with the value of 0. 475 6, 0. 705 6 and 0. 635 0,respectively. The FVC reference was extracted based on the visible light remote sensing image with a high spatial resolution of 0.8 cm captured synchronously with multi-spectral image. The results showed that the fractional vegetation cover of winter wheat could be extracted by multi-spectrum remote sensing technology and vegetation index method with good accuracy. Compared with SAVI a
作者
牛亚晓
张立元
韩文霆
邵国敏
NIU Yaxiao;ZHANG Liyuan;HAN Wenting;SHAO Guomin(College of Mechanical and Electronic Engineering, Northwest A＆F University, Yangling, Shaanxi 712100, China;Institute of Water-saving Agriculture in Arid Areas of China, Northwest A＆F University, Yangling, Shaanxi 712100, China;Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling, Shaanxi 712100, China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2018年第4期212-221,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2017YFC0403203)
新疆维吾尔自治区科技支疆项目(2016E02105)
旱区作物需水无人机遥感与精准灌溉技术及装备研发平台项目(2017-C03)
陕西省水利科技项目(2017SLKJ-7)
关键词
冬小麦
植被覆盖度
无人机
多光谱遥感影像
植被指数
监督分类
winter wheat
fractional vegetation cover
unmanned aerial vehicle
multispectral remote sensing image
vegetation index
supervision classification
