摘要
基于遥感光谱特征准确识别优势树种类型对于区域林业资源的监测和经营具有重要意义,也是当前亟待解决的重要科学问题。伴随遥感技术的发展,利用时间序列高分影像能够有效获取林分树种不同物候期生长特性及其冠层光谱动态信息,有利于克服区域森林类型精细识别中普遍存在的异物同谱难题。以中国东北地区赤峰市旺业甸国有林场为试验区,采用覆盖完整自然年的共36景高分一号(GF-1)WFV时间序列数据(16 m),提取包含不同优势树种生长阶段特征的林分冠层光谱归一化植被指数(NDVI),结合支持向量机(SVM)模型对研究区内5种典型优势树种:油松、落叶松、山杨、白桦和蒙古栎,进行不同时间尺度下(单季相、全季相、逐月和逐旬)的光谱识别研究。同时,分别基于原始时序光谱及其一阶、二阶和三阶微分变换结果,探讨了不同分辨率时序NDVI光谱及其3种微分变换结果对区域森林优势树种的识别效果。结果显示,基于不同尺度的时间序列数据能够获得比不同季节单时相数据更好的树种识别结果(p<0.05),其中采用全季相数据的树种总分类精度相比于春、夏和秋不同季节的单季相数据结果,分别提高了7.67%,6.64%和3.6%,表明时间序列影像中所包含的植被物候信息对于区分不同森林树种类型十分重要,同时秋季是采用单时相数据的最佳识别季节(p<0.05);在不同时间序列数据中,基于逐旬的NDVI数据显著优于基于逐月和全季相数据的光谱识别结果(p<0.05),而基于全季相数据的光谱识别结果最低(p<0.05),表明更密集的时序光谱信息有利于区域树种类型识别精度的提升。此外,结合光谱微分变换后的树种识别结果比仅采用原始NDVI时间序列的识别结果精度更高(p<0.05),其中基于逐旬和逐月时间分辨率数据的最高识别精度能够达到82.1%和78.74%,分别提升了3.38%和2.95%。研究表明采用基于�
Ensuring the accuracy of forest trees species recognition based on remote sensing spectral detail information has strong practical significance and value in forestry resources monitoring and management,which is also an important scientific issue to be settled.The time-series remotely sensed data with high resolution can distinguish small canopy spectrum variation caused by different phenological growth characteristics of different forest tree species effectively,which is expected to solve the common problem of the isomorphism in multispectral recognition of tree species.To clarify this situation,we study the Wangyedian forest farm in Chifeng of Inner Mongolia,northeast China,by using a total of 36 scenes covering the whole year medium-high resolution satellite observations(at 16 m spatial resolution)which were supported with GF-1 WFV(wide field view)to extract various time series of NDVI reflectance data.The data contain all the seasonal phases and phenological growth stages of different tree species and are propitious for the fine recognition of forest types.Five dominant forest types of Pinus tabulaeformis,Larix gmelinii,Populus davidiana,Betula platyphylla,and Quercus mongolica forest were classified and recognized using Support Vector Machine(SVM)classifier at different time scales(single season,every quarter,month-to-month and every ten-days).We also explore the effects of different time scales of NDVI reflectance data and differential transformation methods on the recognition of regional forest dominant tree species,based on the original sequence spectrum and the first,second and third order differential transformation,respectively.The results showed that Autumn is the best single season to identify the dominant tree species in the study area(p<0.05),and the largely improved recognition accuracy of forest tree species can be obtained from different time series data than single season data across all different seasons(p<0.05).Compared with the single data of spring,summer and autumn,the overall accuracy(OA)b
作者
徐凯健
田庆久
徐念旭
岳继博
唐少飞
XU Kai-jian;TIAN Qing-jiu;XU Nian-xu;YUE Ji-bo;TANG Shao-fei(International Institute for Earth System Science,Nanjing University,Nanjing 210023,China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology,Nanjing University,Nanjing 210023,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2019年第12期3794-3800,共7页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划重点专项(2017YFD0600903)
国家自然科学基金项目(41771370)
国家科技重大专项(03-Y20A04-9001-17/18&30-Y20A07-9003-17/18)
民用航天技术预先研究项目(Y7K00100KJ)资助
关键词
树种识别
时序NDVI
微分变换
高分一号
支持向量机
Tree species classification
NDVI time-series
Differential transformation
GF-1
Support vector machine
