县域尺度森林地上生物量遥感估测方法研究

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56t/hm2，其中低（<40t/hm2）、中（40~60t/hm2）、高（>60t/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。

Remote Sensing Based Estimation of Forest Aboveground Biomass at County Level

In this paper, Xixia County in Henan Province was chosen as the case study area, and Landsat 8 image 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 coefficient (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 elevation and slope, soil conditions (e.g. brightness, wetness), the vegetation index (vertical vegetation index, effective leaf area index) were the six enforcing variables impacting regional forest carbon; In 2013, the unit forest biomass 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.