CAJ | 학술논문

采用角规实地调查黄丰桥林场90个杉木人工纯林样地胸高断面积，利用样地SPOT5遥感信息与地理信息，建立了杉木胸高断面积多元线性回归估测模型。首先对样地采用GIS软件进行缓冲处理，缓冲后每个样地的面积为1hm2^；然后提取样地遥感光谱信息与纹理信息等21个因子和4个GIS因子，采用逐步回归分析法筛选出6个因子作为模型自变量；最后分别采用普通最小二乘法（OLS）和偏最小二乘法（PLS）建立了杉木胸高断面积多元回归模型。研究结果表明：OLS回归模型的预测精度为82．2％，均方根误差（RMSE）为5．12m^3／hm^2；PLS回规模型的预测精度为83．9％，均方根误差（RMSE）为4．2m^2／hm^2，PLS和0LS回归模型在杉木胸高断面积估测中均取得了较好的效果，用中高分辨率遥感影像在估测森林结构参数上是可行的。
채용각규실지조사황봉교림장90개삼목인공순림양지흉고단면적，이용양지SPOT5요감신식여지리신식，건립료삼목흉고단면적다원선성회귀고측모형。수선대양지채용GIS연건진행완충처리，완충후매개양지적면적위1hm2^；연후제취양지요감광보신식여문리신식등21개인자화4개GIS인자，채용축보회귀분석법사선출6개인자작위모형자변량；최후분별채용보통최소이승법（OLS）화편최소이승법（PLS）건립료삼목흉고단면적다원회귀모형。연구결과표명：OLS회귀모형적예측정도위82．2％，균방근오차（RMSE）위5．12m^3／hm^2；PLS회규모형적예측정도위83．9％，균방근오차（RMSE）위4．2m^2／hm^2，PLS화0LS회귀모형재삼목흉고단면적고측중균취득료교호적효과，용중고분변솔요감영상재고측삼림결구삼수상시가행적。
The surveying of Chinese fir basal area by fielding 90 sample plots witn angle gauges liau tied out in Huangfengqiao forest farm,the multiple linear regression estimation model of basal area was set up based on remote sensing and geographic information. First, each sample plot was buffered by GIS software to 1 hectare;Then from which 2l RS index factors such as spectral and texture information and 4 GIS index factors were extracted ,in which 6 index factors were screened out as independent model variables through stepwise re- gression analysis;Last the multiple regression model was built by using OLS and PLS respectively. The results showed that： the model predicted accuracy was 82.2% and RMSE was 5.12 m^2/hm^2 by using OLS; the model predicted accuracy was 83.9% and RMSE was 4.21 m^2/hm^2 by using PLS ;The adoption of OLS and PLS serv- ices well in basal area estimation, to estimate forest structural parameters can achieve good effects by using high resolution remote sensing images.