关键词: 多源数据, GF-1宽幅多光谱数据, MODIS NDVI遥感数据, 随机森林, 面向对象, 土地覆盖分类

Abstract: [Objective] In order to meet the urgent needs of national forest inventory (NFI) for monitoring national forest resources on a macro-scale, an object-oriented regional land cover classification method was developed in this paper by using permanent forest plot data of NFI and integrating the corresponding spatial and temporal resolution advantages of GF-1 WFV multi-spectral data and MODIS remote sensing data.[Method] The test site is located in the central of the Xiaoxing'an mountain region in Heilongjiang Province. The GF-1 WFV multispectral data, time series MODIS NDVI product of 8 days synthetic (250 m spatial resolution), the permanent forest plot data collected by the NFI and some field survey data are employed as the key data sources. After image segmentation, spectral, texture and shape features from GF-1 WFV multi-spectral data and NDVI features from times series MODIS NDVI data are extracted for each object. Based on these features, the random forests algorithm is adopt to select the best features automatically and then the classification and regression tree is used to administer the supervised classification. The permanent forest plot data are used to build and validate the decision tree classifier. This method has been investigated with the data collected for the study site.[Result] Experimental results show that the overall accuracy and Kappa coefficient of the developed method combing multi-sources data can reach 89.46% and 0.874 respectively, with significant improvement compared with that using either GF-1 WFV multi-spectral data or MODIS NDVI time series data alone.[Conclusion] The land cover classification method developed in this study is appropriate for integrating GF-1 data and GF-4 data for land cover classification mapping and can improve accuracy of land cover classification effectively.Compared with existed research work,this paper has developed one land cover classification method of much more practical application value with some special features such as focusing on the operational application needs and the characteristics of NFI,optimizing the key procedures such as classification objects generation,feature extraction,feature selection,classifier training,accuracy validation,and so on.The land cover classification method developed is useful for improving the automation and standardization of the technique flow to produce forest land cover maps implemented by NFI.

Key words: multi-sources data, GF-1 WFV multi-spectral data, MODIS NDVI remote sensing data, random forest, objected-oriented, land cover classification