Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi’ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

Spectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. It amounts to identifying a set of pure spectral signatures, which are called endmembers, and their corresponding fractional, draftrulesabun-dances in each pixel of the hyperspectral image. Over the last years, different algorithms have been developed for each of the three main steps of the spectral unmixing chain: 1) estimation of the number of endmembers in a scene; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene. However, few algorithms can perform all the stages involved in the hyperspectral unmixing process. Such algorithms are highly desirable to avoid the propagation of errors within the chain. In this paper, we develop a new algorithm, which is termed robust collaborative nonnegative matrix factorization (R-CoNMF), that can perform the three steps of the hyperspectral unmixing chain. In comparison with other conventional methods, R-CoNMF starts with an overestimated number of endmembers and removes the redundant endmembers by means of collaborative regularization. Our experimental results indicate that the proposed method provides better or competitive performance when compared with other widely used methods.

This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom‐up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation‐based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This ‘bottom‐up’ ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO–CA model has been used to simulate rural–urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO–CA method can have a better performance than the decision‐tree CA method.

Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m², have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.