The global availability of high spatial resolution images makes mapping tree species distribution possible for better management of forest resources. Previous research mainly focused on mapping single tree species, but information about the spatial distribution of all kinds of trees, especially plantations, is often required. This research aims to identify suitable variables and algorithms for classifying land cover, forest, and tree species. Bi-temporal ZiYuan-3 multispectral and stereo images were used. Spectral responses and textures from multispectral imagery, canopy height features from bi-temporal stereo imagery, and slope and elevation from the stereo-derived digital surface model data were examined through comparative analysis of six classification algorithms including maximum likelihood classifier (MLC), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The results showed that use of multiple source data—spectral bands, vegetation indices, textures, and topographic factors—considerably improved land-cover and forest classification accuracies compared to spectral bands alone, which the highest overall accuracy of 84.5% for land cover classes was from the SVM, and, of 89.2% for forest classes, was from the MLC. The combination of leaf-on and leaf-off seasonal images further improved classification accuracies by 7.8% to 15.0% for land cover classes and by 6.0% to 11.8% for forest classes compared to single season spectral image. The combination of multiple source data also improved land cover classification by 3.7% to 15.5% and forest classification by 1.0% to 12.7% compared to the spectral image alone. MLC provided better land-cover and forest classification accuracies than machine learning algorithms when spectral data alone were used. However, some machine learning approaches such as RF and SVM provided better performance than MLC when multiple data sources were used. Further addition of canopy height features into multiple source data had no or limited effects in improving land-cover or forest classification, but improved classification accuracies of some tree species such as birch and Mongolia scotch pine. Considering tree species classification, Chinese pine, Mongolia scotch pine, red pine, aspen and elm, and other broadleaf trees as having classification accuracies of over 92%, and larch and birch have relatively low accuracies of 87.3% and 84.5%. However, these high classification accuracies are from different data sources and classification algorithms, and no one classification algorithm provided the best accuracy for all tree species classes. This research implies the same data source and the classification algorithm cannot provide the best classification results for different land cover classes. It is necessary to develop a comprehensive classification procedure using an expert-based approach or hierarchical-based classification approach that can employ specific data variables and algorithm for each tree species class.

Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem.

No abstract

No abstract

In this paper, we consider the automatic text summarization as a challenging task of machine learning. We proposed a novel summarization system architecture which employs Gene Expression Programming technique as its learning mechanism. The preliminary experimental results have shown that our prototype system outperforms the baseline systems.