CAJ | 학술논문

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。
삼림매년수계절변화이출현형태화생리궤능적규률성변화，해변화재일정정도통과기광보특정유규률지전현。준학지장악삼림관층광보특정수계절변화적규률불부시요감해역적관건，야위수충식별、동태감측화생화삼수반연제공이론기출。이용지물광보의대연구구9개주요수충적관층광보수거진행장기관측，획취료춘하추동사개계절적반사광보곡선병생성일계도수곡선，동시환계산료상용적식피특정삼수，진이분석불동수충재상동계상화불동계상적광보특정，대비불동수충재가견광화근홍외파단적계상변화특정화차이，탐색요감수단용우수충분류식별적최가파단、최가시상。결과표명：불동수충재각생장계광보구유독특적특정，락협수충광보특정인계절적개변이정현유규률적변화，이상록수충광보특정년변화불명현。광보특정적변화유효지반응료물후적변화，응용다계상적수거진행분류가이취득최호적효과，상록수충화락협수충광보특정재동계차이명현，이하계시채용단계상요감수거진행수충식별적최가시절。
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 .