摘要
光学遥感是获取宏观地表植被覆盖信息的重要手段,但常绿树种之间物候差异小,关于亚热带地区常绿林型的遥感识别研究相对较少。遥感林型识别存在尺度效应,从实际应用视角出发,常绿林型遥感识别的最优空间分辨率仍然不清楚。本研究以湖南省会同县为例,利用Pléiades(2 m)、RapidEye(5 m)、Landsat-8(15、30 m)4种光学遥感影像,结合光谱、纹理、植被覆盖度等特征变量与随机森林模型,探讨了3种典型亚热带常绿林型:杉木林(Chinese fir forest,CFF)、马尾松林(Masson pine forest,MPF)、常绿阔叶林(evergreen broadleaved forest,EBF)的最优遥感识别分辨率以及尺度效应问题。结果表明:研究区地表覆盖分类精度随影像空间分辨率的降低呈现先降低后上升的变化趋势,在2 m时具有最佳分类精度(Kappa=0.70,总精度=0.77)。3种林型的识别精度随空间分辨率的上升均表现出先降低后上升的变化规律,识别率(rate of identification,RI)范围分别为:RICFF=68%~87%、RIMPF=55%~84%、RIEBF=29%~74%。杉木林与马尾松林的漏分误差(omission error,OE)与错分误差(commission error,CE)低于常绿阔叶林(OECFF=0.26~0.46,CECFF=0.32~0.53;OEMPF=0.31~0.50,CEMPF=0.31~0.46;OEEBF=0.47~0.71,CEEBF=0.39~0.66)。本研究证实了亚热带常绿林型的遥感识别存在明显的尺度效应,30 m分辨率的Landsat-8影像相比高分辨率遥感影像因具备更丰富的光谱信息而具有更高的识别精度。本研究表明,常绿林型的遥感识别不宜盲目追求高空间分辨率,需要综合考虑遥感传感器光谱配置与空间分辨率之间的内在权衡。
Optical remote sensing(ORS)is the primary tool to obtain information on regionalscale vegetation cover.Few efforts have been made to identify forest types within subtropical evergreen forests using ORS.Scale effects have been reported in literature,yet the optimal spatial resolution to identify evergreen forest types is still unclear in practical application.In this study,we used four types of ORS imagery[Pléiades(2 m),RapidEye(5 m),and Landsat-8(15 m and30 m)]to investigate whether the classification of three typical evergreen forest types[Chinese fir forest(CFF),Masson pine forest(MPF),and evergreen broadleaved forest(EBF)]in subtropical landscapes would be influenced by scale effects.Moreover,we tested the optimal spatial resolution for forest classification.The Random Forest Model was combined with predictive fea-tures derived from spectral reflectance,image texture,and vegetation coverage to map land-cover types at four spatial resolutions.The results showed that the overall accuracy(OA)of four landcover maps had a U-curve tendency with increasing spatial resolution.The 2 m Pléiades image generated the highest classification accuracy(Kappa=0.70,OA=0.77)among four types of images.The accuracy of three evergreen forest types also had a similar U-curve tendency.The ranges of the rate of identification(RI)were RICFF=68%-87%,RIMPF=55%-84%,and RIEBF=29%-74%.The CFF and MPF generated lower classification errors in terms of omission error(OECFF=0.26-0.46;OEMPF=0.31-0.50)and commission error(CECFF=0.32-0.53;CEMPF=0.31-0.46)compared with the EBF(CEEBF=0.39-0.66;OEEBF=0.47-0.71).Our results showed that the identification of forest types in subtropical regions is clearly subject to scale effects.Despite this,Landsat-8 imagery at 30 m resolution can produce the highest mapping precision due to its broader spectrum sensors.We proposed that the practical mapping of forest types in subtropical areas should consider the inherent trade-off between spectral features and spatial resolution of remote sensors rather than blindl
作者
张悦楠
房磊
乔泽宇
陈龙池
张伟东
郑晓
江涛
ZHANG Yue-nan;FANG Lei;QIAO Ze-yu;CHEN Long-chi;ZHANG Wei-dong;ZHENG Xiao;JIANG Tao(Shandong University of Science and Technology,Qingdao 266590,Shandong,China;CAS Key Laboratory of Forest Ecology and Management,Institute of Applied Ecology,Chinese Academy of Sciences,Shenyang 110016,China;Huitong Experimental Station of Forest Ecology of Chinese Academy of Sciences,Shenyang 110016,Chi-na;Qingyuan Forest CERN,Chinese Academy of Sciences,Shenyang 113300,China)
出处
《生态学杂志》
CAS
CSCD
北大核心
2020年第5期1636-1650,共15页
Chinese Journal of Ecology
基金
国家重点研发计划项目(2017YFA0604403)资助。
关键词
林型识别
尺度效应
高分辨率遥感
地表覆盖制图
随机森林
亚热带森林
forest type identification
scale effect
high resolution remote sensing
land cover mapping
random forest
subtropical forest
