林业科学2016，Vol.52Issue(1)：18-29,12.DOI:10.11707/j.1001-7488.20160103
基于多源数据的省级树种(组)成数空间分布信息估测方法
Estimation of Provincial Spatial Distribution Information of Forest Tree Species ( Group) Composition Using Multi-Sources Data
摘要
Abstract
[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.关键词
多源数据/GNN/CCA/k-NN/MODISNDVI/树种成数/制图Key words
multi-data sources/GNN/CCA/k-NN/MODIS NDVI/tree species composition/mapping分类
农业科技引用本文复制引用
曹宇佳,陈尔学,李世明..基于多源数据的省级树种(组)成数空间分布信息估测方法[J].林业科学,2016,52(1):18-29,12.基金项目
高分辨率对地观测系统重大专项(民用部分)"高分林业遥感应用示范系统"(21 -Y30B05 -9001 -13/15 -1). （民用部分）
