摘要
植被识别和特征研究是智慧校园的有机组成部分。该文应用高光谱遥感设备对中国矿业大学(北京)学院路校区20种典型植被的反射光谱数据进行采集并对反射光谱基本特征、光谱时序变化、相关植被指数以及低通滤波变换进行分析,研究结论:(1)植被反射光谱基本特征相似,紫叶李在可见光波段的波峰位置存在偏移,八宝和黄杨的反射率波形特征明显;(2)植被反射率时序变化与物候规律较为一致,地毯草和果树类存在偏差;(3)银杏、紫叶李、刚竹、木槿、忍冬、石榴和八宝的NDVI特征明显,悬铃树、槭树、刚竹、柏树、叉子圆柏、山桃、八宝以及爬山虎的EVI特征明显;(4)柏树、紫藤和悬铃树的低通滤波反射特征明显。研究成果有助于植被的辨识,为智慧校园在植被管理建设方面奠定了基础。
Vegetation recognition and characteristic research is an integral part of smart campus.In this paper,hy‐perspectral remote sensing equipment is used to collect the reflectance spectral data of 20 typical vegetation in the College Road Campus of China University of Mining and Technology-Beijing,and the basic characteristics of re‐flection spectrum,spectral time series change,related vegetation index and low-pass filter transformation are ana‐lyzed.The research conclusions are as follows:(1)The basic characteristics of reflectance spectra of vegetation are similar,the peak position of purple plum in visible band is offset,and the reflectance waveform characteristics of Baopai and Boxwood are obvious;(2)The temporal variation of vegetation reflectance was consistent with phenol‐ogy,but there were deviations between carpet grass and fruit trees.(3)The NDVI characteristics of Gin kgo biloba,Prunus purpura,Phyllostachys japonicae,Hibiscus japonicae,Pomegranate and Eight species were obvious,and the EVI characteristics of Plane tree,maple,Phyllostachys,Cypress,Sabina,Shantao,Babao and Parthenocissus are ob‐vious;(4)Cypress,Wisteria and Plane tree have obvious low-pass filter reflection characteristics.The research results contribute to the identification of vegetation and lay a foundation for the construction of vegetation management in smart campus.
作者
霍江润
李晶
曹泽远
夏颖聪
李珂
王子涵
HUO Jiangrun;LI Jing;CAO Zeyuan;XIA Yingcong;LI Ke;WANG Zihan(College of Geosciences and Surveying Engineering,China University of Mining and Technology-Beijing,Beijing,100083 China;State Key Laboratory Coal Resources and Safe Mining,Beijing,100083 China)
出处
《科技资讯》
2021年第29期5-11,共7页
Science & Technology Information
基金
北京市大学生科学研究与创业行动计划项目资助(项目编号:C202002158)。
关键词
高光谱
校园植被
树种识别
光谱特征变换
Hyperspectral
Campus vegetation
Tree species identification
Spectral feature transformation
