• 论文与研究报告 •

### 基于KNN方法的大兴安岭地区森林地上碳储量遥感估算

1. 东北林业大学林学院 哈尔滨 150040
• 收稿日期:2014-06-05 修回日期:2015-03-01 出版日期:2015-05-25 发布日期:2015-06-11
• 通讯作者: 李凤日
• 基金资助:

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

Qi Yujiao, Li Fengri

1. School of Forestry, Northeast Forestry University Harbin 150040
• Received:2014-06-05 Revised:2015-03-01 Online:2015-05-25 Published:2015-06-11

【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林参数同光谱值之间的非线性依赖关系; 但KNN估测方法除了受距离度量标准、最邻近值k的大小以及影像波段的选取等因素影响外,还存在如样地坐标和对应的影像光谱值匹配误差、像素级估测结果多呈明显集中分布趋势等问题,使得该方法的应用受到一定限制。本文的研究表明,对这些因素进行合理的校正,将更有利于区域尺度森林参数的精确估计和反演。

Abstract:

【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, standard deviation, histogram and cumulative frequency distribution graph of the final corrected values through the further correction treatment were more close to those of the field values, and the overestimation in high value area was also well corrected. 【Conclusion】 The integration of forest inventory plot data, satellite image data with the KNN method has gradually become a popular approach for spatial continuous estimation of forest vegetation parameters over large regions. Compared with the regression model established by the spectral value and forest parameters, KNN method is more focuses on the nonlinear dependence between forest parameters and spectral values. However, the KNN estimation method is not only influenced by the distance metric standard, the nearest neighbor k and the image band selection, but it also has the problems such as the location errors of field plots with respect to the satellite image, the tendency to having a suppressed variation range at the pixel level, which make this method subjected to a certain application restrictions. This study indicated that if these impact factors were reasonably corrected, it would be more conducive to the accurate estimation and inversion of forest parameters at regional scale.