The identification of species within an ecosystem plays a key role in formulating an inventory for use in the development of conservation management plans. The classification of mangrove species typically involves intensive field surveys, whereas remote sensing techniques represent a cost-efficient means of mapping and monitoring mangrove forests at large scales. However, the coarse spectral resolution of remote sensing technology has up until recently restricted the ability to identify individual species. The more recent development of very high-resolution spatial optical remote sensing sensors and techniques has thus provided new opportunities for the accurate mapping of species within mangrove forests over large areas. When dealing with the complex problems associated with discriminating among species, classifier performance could be enhanced through the adoption of more intrinsic features; such as textural and differential spectral features. This study explored the effectiveness of textural and differential spectral features in mapping mangrove inter-species obtained from WorldView-3 high-spatial-resolution imagery for mangrove species in Hong Kong. Due to the different arrangement of leaves, the branch density, and the average height and size of plants, we found that the differential spectral features could aid in reducing inner-species variability and increasing intra-species separation. Using a combination of textural and differential spectral features thus represents a promising tool for discriminating among mangrove species. Experimental results suggest that combining these features can greatly improve mapping accuracy, thereby providing more reliable mapping results.

Classification of mangrove species using satellite images is important for investigating the spatial distribution of mangroves at community and species levels on local, regional and global scales. Hence, studies of mangrove deforestation and reforestation are imperative to support the conservation of mangrove forests. However, accurate discrimination of mangrove species remains challenging due to many factors such as data resolution, species number and spectral confusion between species. In this study, three different combinations of datasets were designed from Worldview-3 and Radarsat-2 data to classify four mangrove species, Kandelia obovate (KO), Avicennia marina (AM), Acanthus ilicifolius (AI) and Aegiceras corniculatum (AC). Then, the Rotation Forest (RoF) method was employed to classify the four mangrove species. Results indicated the benefits of dual polarimetric SAR data with an improvement of accuracy by 2–3%, which can be useful for more accurate large-scale mapping of mangrove species. Moreover, the difficulty of classifying different mangrove species, in order of increasing difficulty, was identified as KO < AM < AI < AC. Dual polarimetric SAR data are recognized to improve the classification of AI and AC species. Although this improvement is not remarkable, it is consistent for all three methods. The improvement can be particularly important for large-scale mapping of mangrove forest at the species level. These findings also provide useful guidance for future studies using multi-source satellite data for mangrove monitoring and conservation.

Incorporating optical and polarimetric synthetic-aperture radar (SAR) data to estimate impervious surface is useful but challenging due to their different geometric imaging mechanism and the high diversity of urban land covers. The recent development of deep convolutional networks (DCN) opens a promising opportunity by automatically extracting the deep features from both data sets. In this study, a small-patched DCN (SDCN) was designed to estimate the impervious surface from optical and SAR data. Benchmark methods, e.g., GoogLeNet, VGG16, ResNet50, and the support vector machine were employed for comparison. Two study sites in the most complex metropolitan of China, the Guangdong-Hong Kong-Macau Greater Bay Area, were selected to assess the proposed method. Experimental results indicated the effectiveness of proposed SDCN with a better accuracy outperforming other benchmark methods. Furthermore, we found that 60%–80% of training samples performed comparably with the whole training set, indicating that a large number of training samples may not be necessary in all cases, depending on the settings of some factors (e.g., number of epochs). Generally, SDCN appears more suitable than other methods in terms of combining the optical and SAR data and improved the accuracy of estimating impervious surface.

Numerous studies on environmental modeling and ecological process lay emphasis on the fundamental information of the impervious surface area (ISA). However, accurate ISA extraction from high-resolution satellite images remains challenging due to both the high diversity of land covers and shadow effects from tall buildings and trees. To address the problem, a discriminative Optical-SAR-LiDAR dictionary sparse representation classification (OSLD-SRC) method was proposed using high-resolution WorldView-2, GeoEye-1, TerraSAR-X, and airborne LiDAR data. First, it used multisource data and fuzzy samples by low-pass filtering (LPF) to solve the problem of roads and buildings misclassification; second, it learned the Optical-SAR-LiDAR dictionary for nonshadow and shadow classes, and then used discriminative sparse coding method for classification to reduce the shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method.

Many target detectors commonly utilize a single a priori target spectral signature as an input. However, the detection results are greatly affected by the quality of the a priori target spectral signature because the spectral variability phenomenon is universal and anisotropic in hyperspectral image data. This paper proposes a sparse representation-based method to generate an optimized target spectrum from limited target training samples, which is able to alleviate the impact of spectral variability on hyperspectral target detection. When lacking comprehensive knowledge about the target object of interest, an optimized representative target spectrum should be expected to be reconstructed by the hyperspectral data themselves in a sparse representation manner following the characteristics of the data structure and then be generated by a set of selected candidate pixels that contain the target signal with a varying status. With the optimized a priori target signature, the experimental results of the detection of different characteristics of objects with three different types of hyperspectral images confirm the effectiveness, robustness, and generality performance of the proposed method.