基于KNN方法的大兴安岭地区森林地上碳储量遥感估算  

Remote Sensing Estimation of Aboveground Forest Carbon Storage in Daxing'an Mountains Based on KNN Method

作　　者：戚玉娇[1] 李凤日[1] 

机构地区：[1]东北林业大学林学院,哈尔滨150040

出　　处：《林业科学》2015年第5期46-55,共10页Scientia Silvae Sinicae

基　　金：国家"十二五"科技支撑计划课题(2012BAD22B02);林业公益性行业科研专项(201004026);长江学者和创新团队发展计划(IRT1054)

摘　　要：【目的】采用KNN方法进行碳储量估测,并对估测后的数据进行各种校正处理,绘制森林地上碳储量的空间分布图,为我国森林碳储量和固碳潜力的研究提供基础数据和科学依据。【方法】以黑龙江省大兴安岭为研究区(50°05'—53°33'N,121°11'—127°01'E),基于2010年森林资源连续清查固定样地和同年Landsat5 TM影像数据,利用k-邻近法(KNN)在像素级水平上对森林地上碳储量进行估算。采用多准则方法分东、南、北和中4个区域对样地坐标和其对应的影像光谱值进行坐标重配准,并根据实测样地数据对坐标重配置前后不同林分类型地上碳储量估测精度进行评价;针对KNN方法像素级估测结果存在明显的高值区域低估和低值区域高估现象,应用直方图匹配方法对估测结果进行变动范围调整;并根据样地实测碳储量和KNN估测值间的回归关系对调整后的结果分区域进行进一步匹配校正后处理,绘制森林碳储量的空间分布图。【结果】总体来说,本研究区域像元尺度KNN估测的欧式距离优于马氏距离,均方根误差随着最邻近值k的增大而降低,当k大于6时变化缓慢,并逐渐趋于稳定;坐标误差校正后,各林分类型森林地上碳储量的估测精度均显著提高,平均均方根误差由17.23降低到14.3 t·hm-2;直方图匹配后,各区域样地点高值区域低估和低值区域高估现象均有很大程度改善,实测值和估测值间的相关关系明显增强,然而高值地区(碳储量大于20 t·hm-2)出现过高估计现象;经匹配校正后处理的均值、标准差、直方图和累积频率分布图更接近样地实测值,均方根误差也明显降低,高值地区过高估计现象得到很好校正。【结论】森林资源清查数据、遥感数据及KNN方法相结合逐渐成为区域尺度森林参数空间连续估测的重要手段。同利用光谱值和森林参数建立的回归模型相比,KNN方法能够更多地考虑到森�[Objective]Forest is the major terrestrial carbon pool. Accurate assessment of forest carbon storage and its spatial distribution is the key to investigating the terrestrial carbon cycle. [Method]Based on the PSPs data from continuous forest resource inventory and Landsat5 TM in 2010,the k-nearest neighbor （ KNN ） method was used to estimate,at the pixel level,the aboveground carbon storage in Daxing’an Mountains of Heilongjiang Province. The field PSP data and its corresponding satellite image information were reassigned using a multi-criteria approach in east,south, northand middle regions. The accuracy estimation of different forests before and after the reassignment was also evaluated according to the data of PSPs. In view of the phenomenon that the pixel level KNN estimation having the large values underestimated and small values overestimated,the histogram matching method was used to adjust the variation range of the estimation results. Then,further correction treatment was applied to each region according to the regression equations of field data and the estimation data from the histogram matching until the spatial distribution map of forest carbon storage was drawn.[Result]Overall,Euclidean distance was better than Mahalanobis in our study area at the pixel level of KNN estimation. The root mean square error decreased with the increase of the nearest neighbor k,whereas,the tendency was slow down and gradually stabilized when k is greater than 6 . The estimate accuracy was improved significantly at the pixel level in each forest type when the coordinate errors was corrected,and the average root mean square error was reduced from 17. 23 to 14. 3 t · hm^-2. After histogram matching,the phenomenon of underestimation for high value and overestimation for low value was greatly improved in each region,and the correlation between filed data and estimation data was enhanced obviously. However,high value area （ carbon storage value was larger than 20 t·hm^-2 ） was overestimated evidently. The mean value

关 键 词：KNN 森林地上碳储量 遥感 坐标配准 直方图匹配 

分 类 号：S758.5[农业科学—森林经理学]