基于多源数据的省级树种(组)成数空间分布信息估测方法

摘要

[目的]利用能够反映植被季相变化和物候差异的中空间分辨率高重访周期遥感数据以及其他多源数据,提取区域树种(组)成数空间分布信息,间接表达主要树种(组)的空间分布,为大区域树种(组)空间分布制图提供新的方法和思路.[方法]以吉林省为试验区,以 250 m 空间分辨率的 MODIS NDVI 8 天合成时间序列数据和国家森林资源连续清查固定样地数据为主要数据源,综合利用气象观测数据和地形数据,基于梯度最近邻( GNN)方法对省级树种(组)成数进行估测.首先利用典型对应分析( CCA)对特征变量进行特征变换;然后采用 k-NN 方法对树种(组)成数进行分层估测,并对 k-NN 方法中的 k值进行优选,分析 k-NN估测精度随 k值的变化规律;最后基于 9 个县的森林资源二类调查样地和省级一类清查固定样地数据,对树种(组)成数分布图进行精度检验.[结果]对在吉林省分布较广的蒙古栎、白桦、紫椴、春榆、杨树、胡桃楸和长白落叶松 7 个树种(组)成数进行估测,并制作相应的树种(组)成数空间分布图.估测结果表明,树种成数分布与固定样地成数分布呈现出一致的空间分布特征.其中,县级尺度下的 k-NN 预测精度检验结果为:R2 为0. 83,RMSE 为0. 35; 在20 km × 20 km,30 km × 30 km,40 km × 40 km 和50 km × 50 km 4 个尺度下的 k-NN 估测结果显示,各类树种(组)在 40 km × 40 km 和 50 km × 50 km 尺度下的估测结果较优,春榆在各个尺度下的估测精度均较高,其平均 RMSE 为 0. 35,蒙古栎的估测精度相对较低,其平均 RMSE 为 0. 65.在不同尺度下的估测结果表明,随着 k值的增加,RMSE 均呈现先快速减小、后趋于相对平衡的趋势,根据该规律可确定最佳 k值.另外,k-NN 分层估测的估测精度高于 k-NN 直接估测的估测精度,其在不同尺度下的RMSE 相对直接估测的结果均低 0. 1 左右.[结论]本文提出的基于多源数据的森林树种(组)成数空间分布估测方法是一种有效的森林参数估测方法,基于该方法能够获取较高精度的树种(组)成数空间分布图.为了得到最佳的估测效果,需要对 k-NN 方法中的 k值进行优选,该值将随试验区和数据有所不同.另外,采用分层估测的策略可以有效提高最终估测精度.%[Objective]Remote sensing technique provides a highly effective means for extracting tree species ( group) spatial distribution information. The objective of this paper is to develop a method for estimating the provincial spatial distribution information of forest tree species ( group) composition using multi-sources data. Thus it could indicate the spatial distribution information of the main tree species ( group ) and provide a new method for extracting vegetation information in large area. [Method]The experiments were carried out over the test site of the whole Jilin Province. The time series MODIS NDVI product of 250 m pixel size and 8 days cloudy free composite and the permanent forest plot data collected by the national forest inventory ( NFI ) were used as the key data sources. The weather observation data and topography data were also integrated into the data sources. We developed a gradient nearest neighbor ( GNN ) based approach for estimating provincial forest tree species ( group) composition distribution information. Firstly,the method of canonical correspondence analysis ( CCA ) was implemented to extract effective composited features from the original dataset. Secondly,the k-nearest neighbors ( k-NN) method was applied on the extracted feature space to estimate forest tree species ( group ) composition number using one two-layer stratification scheme. As the value of k needs to be determined,the changing trend of k-NN estimation accuracy with the k values was analyzed. Finally,the estimation accuracy for each tree species ( group ) of the developed method was validated using the forest plot data of 9 counties collected by the forest resources inventory in second level and the forest plot data collected by the NFI as reference.[Result]7 tree species ( group) composition numbers including Quercus mongolica,Betula platyphylla,Tilia amurensis, Ulmus davidiana,Populus,Juglans mandshurica and Larix olgensis were extracted and the corresponding distribution maps were produced. The results showed a good consistency with the fixed plots in field. Taking county as statistic unit,the following quantitative technical targets have been achieved: the coefficient of determination ( R2 ) was 0. 83,and the RMSE was 0. 34. Specifically,the accuracy has been further validated by dividing the whole coverage of Jilin Province into grids of 20 km × 20 km,30 km × 30 km,40 km × 40 km and 50 km × 50 km,taking the forest plot data collected by the NFI as reference and the grid as statistic unit. Better results could be achieved at the scale of 40 km × 40 km and 50 km × 50 km. The RMSE of Ulmus davidiana composition number was 0. 35 and the RMSE of Quercus mongolica composition number was 0. 65. The optimal k-value could be determined for the phenomenon that the RMSE firstly reduced and then tended steady with the rising k-value. In addition,the estimation accuracy of the two-layer stratification estimation method was higher than that of the direct estimation method. The results showed that: the average RMSE of estimating tree species ( group) composition using two-layer stratification estimation method was 0. 1 less than that using direct estimation method.[Conclusion]The proposed method for estimating the provincial spatial distribution information of forest tree species (group) composition using multi-sources data has proved to be an effective method to estimate forest parameters. Based on this method,the distribution map of forest tree species ( group) composition numbers was successfully produced with high accuracy. The results indicated that the value of k needs to be optimized in order to obtain a better result,which varies depending on the experimental area and the selected data. In addition,the estimation accuracy could be improved effectively using two-layer stratification estimation method.