CAJ | 학술논문

以河南西峡县2013年 Landsat 8影像及同期217块森林资源连续清查固定样地数据为信息源，以9个植被指数、3个地形指数为自变量，建立多元线性回归、决策与回归树、装袋算法、随机森林4种遥感估测模型；采用十折交叉验证，及相关系数、绝对误差、均方根误差、相对误差、相对均方根误差5个指标，对遥感估测模型进行精度评价，在此基础上，对研究区域2013年的森林地上部分生物量进行遥感估测和空间分析。结果表明：在4种遥感估测模型中，随机森林综合性能最高，装袋法次之，多元线性回归最低；在12个自变量中，地形（海拔、坡度）、土壤（亮度指数、湿度指数）、植被生长状况（垂直植被指数、有效叶面积指数）6个因子是影响研究区域森林地上部分生物量的重要环境变量；2013年，研究区域单位面积森林生物量为38.56 t／hm2，其中低（＜40 t／hm2）、中（40～60 t／hm2）、高（＞60 t／hm2）的面积分别占59.92％、24.30％、15.78％；研究区域森林地上部分生物量较高的区域，主要分布在交通不便、森林茂密、人类干扰活动较少的北部石质山区，而较低的区域，主要分布在交通发达，人口密度大，坡度较为平缓的南部鹳河谷地。
이하남서협현2013년 Landsat 8영상급동기217괴삼림자원련속청사고정양지수거위신식원，이9개식피지수、3개지형지수위자변량，건립다원선성회귀、결책여회귀수、장대산법、수궤삼림4충요감고측모형；채용십절교차험증，급상관계수、절대오차、균방근오차、상대오차、상대균방근오차5개지표，대요감고측모형진행정도평개，재차기출상，대연구구역2013년적삼임지상부분생물량진행요감고측화공간분석。결과표명：재4충요감고측모형중，수궤삼림종합성능최고，장대법차지，다원선성회귀최저；재12개자변량중，지형（해발、파도）、토양（량도지수、습도지수）、식피생장상황（수직식피지수、유효협면적지수）6개인자시영향연구구역삼임지상부분생물량적중요배경변량；2013년，연구구역단위면적삼림생물량위38.56 t／hm2，기중저（＜40 t／hm2）、중（40～60 t／hm2）、고（＞60 t／hm2）적면적분별점59.92％、24.30％、15.78％；연구구역삼임지상부분생물량교고적구역，주요분포재교통불편、삼림무밀、인류간우활동교소적북부석질산구，이교저적구역，주요분포재교통발체，인구밀도대，파도교위평완적남부관하곡지。
In this paper,Xixia County in Henan Province was chosen as the case study area,and Landsat 8 im-age in 2013 and 217 fixed plot data of forest resources continuous survey in the same period were collected as the main information to estimate forest above ground carbon in the study area.Four remote sensing based models namely multivariate linear regression (MLR),classification and regression tree (CART),bagging (Bagging)and random forest (RF)were established by using 9 vegetation index and three terrain variables.Five indicators of correlation co-efficient (COR),mean absolute error (MAE),root mean squared error (RMSE),relative absolute error (RAE), root relative squared error (RRSE)were figured out to evaluate the performance of the four models by using 10 fold cross validation method.Then the model with the best performance was applied to predict forest aboveground biomass in 2013.Results showed that:Among the four models,the performance of random forest was the highest,followed by bagging method,while the performance of multiple linear regression was the lowest;The terrain factors including ele-vation and slope,soil conditions (e.g.brightness,wetness),the vegetation index (vertical vegetation index,effec-tive leaf area index)were the six enforcing variables impacting regional forest carbon;In 2013,the unit forest bio-mass in study area was 38.56 t/hm2 ,in which the percentage of low (<40),medium (40 -60)and high (>60) was 59.92%,24.30% and 15.78%,respectively;The places with higher forest above ground biomass in study area was mainly distributed in the northern rocky mountains with inconvenient traffic conditions,high forest cover and less human disturbance,while places with lower forest biomass was located in the southern Guan River valley with good traffic conditions,high population density and gentle slope.