Study On Basal Area And Composition Of Forest Tree Species(Group) Estimation Based On Multi-sources Data

Access to forest tree species(group) of spatial distribution information is one of the important contents of Nation Forest Inventory, not only can provide information support for the national forest resources management decision-making, but also can be used as one of the important input data to the research of forest ecosystem carbon cycle models. Remote sensing technique provides a highly effective means for extracting tree species(group) spatial distribution information. NDVI time-series data with medium spatial resolution and high revisit frequence incorporate intra-annual vegetation phenology and seasonal change and have been widely used in extracting vegetation information in large area. Furthermore, the spatial distribution of tree species(group) is influenced by environmental factors. We can extracte tree species(group) information more accurate by adding temperature, precipitation, topography and other types of data. The ground survey data is a support for establishing and testing species(Group) spatial distribution information extraction model. Nation Forest Inventory plot data are widely used by scholars at home and abroad. But there is still no study on extracting tree species(group) spatial distribution information based on multi-sources data in China.Therefore, the paper carries on a method research of tree species(group) spatial distribution information retrival using mutli-sources data with the latest research developments abroad as basis.We developed a Gradient Nearest Neighbor(GNN) based approach for estimating provincial forest tree species(group) composition and forest tree species(group) basal area per unit area(m2/hm2) distribution information with 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) as the key data sources, and with integrated utilization of weather observation data and topography data. The GNN method firstly applies Canonical Correspondence Analysis(CCA) to extract effective composited features from the original dependent and independent dataset, then it applies the k Nearest Neighbors(k-NN) method inthe extracted feature space to estimate forest tree species(group) composition number and tree species(group) basal area using one two-layers stratification scheme, the result from which can be used to indicate the spatial distribution of forest tree species(group). The method described above has been studied with the whole Hei Longjiang Province and Jilin Province as test sites,and the basal area spatial density distribution map of 9 tree species(group) of Hei Longjiang Province and tree species composition spatial distribution map of Jilin Province of 7 tree species were produced. The research results are as follows:(1) We studied the effect of k-NN parameter optimization on the estimation accuracy.When we estimate basal area spatial density distribution map of 9 tree species(group) of Hei Longjiang Province and 7 ree species composition spatial distribution of Jilin Province, the value of k needs to be optimized respectively. We summed up the method to determine the optimal k- valueby analyzing the changing trend of k-NN estimation accuracy with the k values.(2) This paper developed a two-layers stratification estimation method. We used a two-layers stratification method to estimate forest tree species(group) composition and forest tree species(group) basal area. The accuracy of direct estimation method and two-layers stratification estimation method was comapared. The results show that: the average R2 of estimating forest tree species(group) basal area of Hei Longjiang Province using two-layers stratification estimation method is 0.07 higher than that using direct estimation method; the average RMSE of estimating tree species(group) composition of Jilin Province using two-layers stratification estimation method is 0.1 less than that using direct estimation method.So, It can improve the estimation accuracy using two-layers stratification estimation method.(3) We adopted two methods for validating the forest tree species(group) spatial distribution mapping results. In the case of the first validation method, the accuracy is computed by dividing the whole coverage of the province into grids of several different size,taking the forest plot data collected by the NFI as reference and the grid as statistic unit. The average RMSE of estimating 9 tree species(group) basal area of Hei Longjiang Province is0.44-1.68 and the 7 tree species(group) tree species(group) composition of Jilin Province is0.35-0.65. In the case of the second accuracy validation method, the accuracy for each tree species(group) is computed with the forest plot data of the 9 counties collected by the forest resources inventory in second level as reference data and taking county as statistic unit. The coefficient of determination(R2) of 0.83 and RMSE of 0.34 were achieved for the tree species(group) tree species(group) composition map of Jilin Province. The validation results show the effectiveness of the developed method of this paper, and the main tree species spatial distrition maps thuse produeed have great potential application value on supporting macro-scale decision-making for forest resources management and for regional forest ecological system carbon cycle researchs in China.