Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM), have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP) estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems.

Hong Kong as an international metropolis is well known for its vibrant urban life, which attracts millions of mass conventional tourists for shopping and business. Recently, ecotourism has flourished and attracts ecotourists who visit the ecological and cultural resources of the rural landscape, country parks and marine parks. Yan Chau Tong, also known as ‘Double Haven’, is remote from the urban centre, which helps preserve its natural flavour and cultural heritages such as Hakka walled villages. It is noted as an important site for developing ecotourist activities. A symbiosis of humans and nature cannot be achieved without a sound planning and zoning scheme. This paper investigates the use of IKONOS imagery and a geographic information system for rational planning of ecotourist activities and conservation measures. Through image classification of IKONOS and integrated with ecological data, essential habitats can be mapped and a spatial database is established. Suitable sites for recreational activities (including camping, heritage visiting, hiking and snorkelling) and important ecological habitats can both be identified using multiple criteria evaluation techniques. The use of a multiple objective land allocation model helps resolve conflict between recreation and conservation. Sites are generally identified as (a) those where conservation or recreation should receive the highest priority; (b) those that can be treated as additional recreation or conservation sites; and (c) conflict areas that can serve both purposes. The result can assist in planning land resources for ecotourism to satisfy both objectives and attain sustainability in the area.

Band selection was performed based on hyperspectral data taken at 400 ‐ 900 nm spectral range for 25 subtropical tree species in Hong Kong. Stepwise discriminant analysis and hierarchical clustering were used to select bands with high discriminatory power. Bands selected from stepwise discriminant analysis mainly lied along the green, red and near‐infrared bands. Using hierarchical clustering, in addition to the blue, green, red and near‐infrared bands, spectral bands along the blue‐green edge, green‐red edge and red curves were found to have important information to discriminate the 25 tree species. Both techniques could generate accurate identification of tree species with 89% overall accuracy and the results were significantly better than using all the bands.

Mangroves have significant social, economic, environmental, and ecological values but they are under threat due to human activities. An accurate map of mangrove species distribution is required to effectively conserve mangrove ecosystem. This study evaluates the synergy of WorldView-3 (WV-3) spectral bands and high return density LiDAR-derived elevation metrics for classifying seven species in mangrove habitat in Mai Po Nature Reserve in Hong Kong, China. A recursive feature elimination algorithm was carried out to identify important spectral bands and LiDAR (Airborne Light Detection and Ranging) metrics whilst appropriate spatial resolution for pixel-based classification was investigated for discriminating different mangrove species. Two classifiers, support vector machine (SVM) and random forest (RF) were compared. The results indicated that the combination of 2 m resolution WV-3 and LiDAR data yielded the best overall accuracy of 0.88 by SVM classifier comparing with WV-3 (0.72) and LiDAR (0.79). Important features were identified as green (510–581 nm), red edge (705–745 nm), red (630–690 nm), yellow (585–625 nm), NIR (770–895 nm) bands of WV-3, and LiDAR metrics relevant to canopy height (e.g., canopy height model), canopy shape (e.g., canopy relief ratio), and the variation of height (e.g., variation and standard deviation of height). LiDAR features contributed more information than spectral features. The significance of this study is that a mangrove species distribution map with satisfactory accuracy can be acquired by the proposed classification scheme. Meanwhile, with LiDAR data, vertical stratification of mangrove forests in Mai Po was firstly mapped, which is significant to bio-parameter estimation and ecosystem service evaluation in future studies.

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.

Involvement in leisure can be hampered by health concerns. The onset of the severe acute respiratory syndrome (a.k.a atypical pneumonia) epidemic in Hong Kong interfered with the daily life of the people, a large part of which involved leisure and recreation. Ordinarily, common leisure activities include shopping, dining, hanging out in the shopping malls, as well as numerous other pursuits. During the SARS epidemic people avoided crowded areas, while the outdoors provided alternative destinations to the usual indoor activities. This paper presents the results of research that investigated the ways in which people engage in leisure and recreation at the time of health scare. Authorities and the media reported an increased rate of visits to the countryside, and this investigation was centred on why people were attracted to the countryside environment. Results indicated that participation in leisure and recreation in the outdoors at the time of SARS was in the company of family members (42%) and friends (50%). Fourteen percent of respondents visited the countryside in response to the fear of the SARS epidemic. While the use of demographic variables is useful in analysis, psychographic variables that include attitudes, interest in leisure in the outdoors as well as lifestyles also adds to the understanding of the respondents concern for leisure and recreation in the event of a psychological trauma brought on by SARS.