文献类型：期刊文献

中文题名：小兴安岭主要树种冠层光谱季相变化研究

英文题名：The Changes of Forest Canopy Spectral Reflectance with Seasons in Xiaoxing' anling

作者：徐光彩[1] 庞勇[1] 李增元[1] 赵凯瑞[1] 刘鲁霞[1]

第一作者：徐光彩

通信作者：Pang, Y.

机构：[1]中国林业科学研究院资源信息研究所

年份：2013

卷号：33

期号：12

起止页码：3303-3307

中文期刊名：光谱学与光谱分析

外文期刊名：Spectroscopy and Spectral Analysis

收录：MEDLINE(收录号：24611391);CSTPCD;;EI(收录号：20140117158144);Scopus(收录号：2-s2.0-84891047265);北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；PubMed;

基金：国家高技术研究发展计划项目(2012AA12A306);国家自然科学基金项目(41071272)资助

语种：中文

中文关键词：森林冠层;光谱分析;多季相

外文关键词：Forest canopy; Spectral analysis~ Multi-seasons

分类号：TP79

摘要：森林每年随季节变化而出现形态和生理机能的规律性变化,该变化在一定程度通过其光谱特征有规律地展现。准确地掌握森林冠层光谱特征随季节变化的规律不仅是遥感解译的关键,也为树种识别、动态监测和生化参数反演提供理论基础。利用地物光谱仪对研究区9个主要树种的冠层光谱数据进行长期观测,获取了春夏秋冬四个季节的反射光谱曲线并生成一阶导数曲线,同时还计算了常用的植被特征参数,进而分析不同树种在相同季相和不同季相的光谱特征,对比不同树种在可见光和近红外波段的季相变化特征和差异,探索遥感手段用于树种分类识别的最佳波段、最佳时相。结果表明:不同树种在各生长季光谱具有独特的特征,落叶树种光谱特征因季节的改变而呈现有规律的变化,而常绿树种光谱特征年变化不明显。光谱特征的变化有效地反应了物候的变化,应用多季相的数据进行分类可以取得最好的效果,常绿树种和落叶树种光谱特征在冬季差异明显,而夏季是采用单季相遥感数据进行树种识别的最佳时节。
The ASD FieldSpec portable spectrometer was adopted to collect canopy reflectance spectrum data of the 9 main tree species in study area by a long-term observation to get the data of the four seasons Then the smoothed reflectance curve and the first derivation curve from 350 to 1 400 nm and several commonly used vegetation spectral characteristic parameters were genera- ted to analyse seasonal change characteristics and variation of the 9 tree species in visible and near-infrared band and to explore the best band characteristics and period for species identification. The results showed that different trees had different and rather unique spectral features during the four seasons. The spectral characteristics of the deciduous trees have regular changes with the cycle of the seasons, whereas those of the evergreen tree species have no significant changes in one year. As well changes in the spectral characteristics could effectively reflect forest phenology changes, and it is proposed that the optimal strategy for tree species classification may be the integration and analysis of multi-seasonal spectral data. Evergreen trees and deciduous trees in the winter have obvious differences in the canopy spectral characteristics and the best single-season remote sensing data for tree species recognition is in summer.