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Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R2adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R2adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation. [ABSTRACT FROM AUTHOR]

He attended the signing ceremony of 'Deepening Cooperation between Guangdong, Hong Kong and Macao and Promoting the GBA Construction Framework Agreement' in Hong Kong on 1 July 2017. From a "World Factory" to China's Bay Area: A Review of the Outline of the Development Plan for the Guangdong-Hong Kong-Macao Greater Bay Area The Outline of the Development Plan (ODP) for the Guangdong-Hong Kong-Macao Greater Bay Area, (a region formerly know n as the Pearl River Delta - PRD - or the "world's factory") marks China's determination to transform traditional manufacturing and accomplish the Fourth Industrial Revolution. [Extracted from the article]

A new method, which is called decomposition-composition (D-C) method, is proposed for the nonlinear dimensionality reduction (NLDR) of data lying on the multicluster manifold. The main idea is first to decompose a given data set into clusters and independently calculate the low-dimensional embeddings of each cluster by the decomposition procedure. Based on the intercluster connections, the embeddings of all clusters are then composed into their proper positions and orientations by the composition procedure. Different from other NLDR methods for multicluster data, which consider associatively the intracluster and intercluster information, the D-C method capitalizes on the separate employment of the intracluster neighborhood structures and the intercluster topologies for effective dimensionality reduction. This, on one hand, isometrically preserves the rigid-body shapes of the clusters in the embedding process and, on the other hand, guarantees the proper locations and orientations of all clusters. The theoretical arguments are supported by a series of experiments performed on the synthetic and real-life data sets. In addition, the computational complexity of the proposed method is analyzed, and its efficiency is theoretically analyzed and experimentally demonstrated. Related strategies for automatic parameter selection are also examined. Index Terms--Data visualization, decomposition-composition method (D-C method), isometric feature mapping, multicluster manifold, nonlinear dimensionality reduction (NLDR).

Geographically weighted regression model with a spatially autoregressive term of the response variable (GWR‐Lag model for short) is a useful tool to simultaneously model spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. The profile maximum likelihood estimation has been developed to calibrate the model, where a single bandwidth is used for all of the spatially varying coefficients. However, due to different spatial scales at which the explanatory variables might operate, the coefficients may have different levels of spatial heterogeneity and cannot be effectively estimated by a single bandwidth. To deal with such multiscale problem, a backfitting method is proposed to calibrate the GWR‐Lag model, where the optimal bandwidth size is separately selected for each coefficient and the autoregressive parameter is estimated by maximizing the log‐likelihood function. A simulation study with a comparison of the GWR‐based profile maximum likelihood (GWR‐ML) estimation is conducted to assess the performance of the backfitting approach. The empirical results show that the backfitting method not only provides useful scale information for each explanatory variable, but also yields more accurate estimators of the coefficients. Furthermore, a real‐life data set is analyzed to demonstrate the effectiveness of the backfitting approach. [ABSTRACT FROM AUTHOR]

Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered. [ABSTRACT FROM AUTHOR]

Accurately identified trees can serve as a basis of estimating forest variables through the individual tree-based approach. Increasing richness of remote sensing data also provides opportunities to explore the potential uses of various types of data sources. This study adopted three widely used remote sensing data, including airborne light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) photography and traditional digital aerial photos (DAP), and aimed to investigate their potentials on estimating tree heights and extracting individual tree information in four forested sites in Hong Kong with different tree compositions. Image-based point clouds were generated through photogrammetry. Local maxima and region growing methods were adopted to identify treetops and delineate tree crowns, respectively, with different fixed and variable window size settings. Tree heights obtained from remote sensing datasets resulted in correlation coefficients (r) = 0.58–0.94 and root-mean-square errors (RMSE) = 1.33–3.78 m compared to field-measured values and similar levels of correspondences among the datasets. Point cloud characteristics were highlighted through point-based and profile-based analysis. The highest F-scores for treetop detections in each site ranged from 0.53 to 0.69 with variations caused by different window sizes and data sources. Matched rates of reference trees were positively correlated (r = 0.19–0.49) with geometric properties including diameter at breast height (DBH), tree height, crown area, and distance to the nearest neighbour. No single remote sensing dataset was clearly superior in all methodologies in this study, but unique properties were demonstrated in terms of both data acquisitions and analysis. Knowledge and testing on both characteristics of study areas and data sources were important when deciding the best window size parameters. Heterogeneity of forest environment could be a major factor hindering the delineation performance with further influences on plot-level difference and tree-level detectability. [ABSTRACT FROM AUTHOR]

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information. [ABSTRACT FROM AUTHOR]

Hong Kong has great potential to develop ecotourism as an alternate tourism experience. In order to provide visitors with satisfaction and conserve and sustain natural resources, a comprehensive zoning plan is indispensable to identify areas for various purposes and activities. Geographic information system combined with multi-criteria decision analysis procedure can be used to formulate these plans in an objective and efficient way. This study used this approach to objectively identify potential sites for various ecotourism activities and tourism development potential in Lantau Island, Hong Kong SAR. The results were then used to evaluate the Lantau development plan proposed by the government. By integrating with quality conservation information, the island was further classified into four zones namely sanctuary, nature conservation, outdoor recreation, and tourism development through multi-objective analysis. The results showed that proposed sites for various recreational activities are of medium to high suitability, while other potential sites with high suitability were revealed. In addition, sites with conflicting objectives and those required further protection were also identified. The results can be used as an unbiased guide for Lantau development. This study also demonstrated ecotourism planning and development can be practically supported by geo-information tools combined with proper multi-criteria decision analysis procedures based on which better decision can be made. [ABSTRACT FROM AUTHOR]

Mangrove habitat is one of the most highly productive ecosystems. The distribution of mangrove species acts as an inventory to formulate conservation management plans. This study explored the potential of combining hyperspectral (Earth-observing (EO)-1 Hyperion) and multi-temporal synthetic aperture radar (SAR) (Environmental Satellite (Envisat) ASAR) data, supported byin situfield surveys, to map mangrove species. Hyperspectral imaging captures a number of narrow contiguous spectral bands providing richer spectral details than those obtained from traditional broadband sensors. All-weather radar sensing allows continuous data acquisition and its signal penetrability can reveal canopy structural characteristics, which offer an additional data dimension that is not available in optical sensing. Through combining the two data types, this study achieved three objectives. First, facing the issue of dimensionality and limited field samples, feature selection techniques from computer science were adopted to select spectral and radar features that are crucial for mangrove species discrimination. Second, classification accuracy using various combinations of spectral and radar features was evaluated. Third, classification algorithms including maximum likelihood (ML), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM) were used to estimate species distribution, and classification accuracy was compared. Results suggested that feature selection techniques are capable of identifying salient features in spectral and radar space that can effectively discriminate between mangrove species. Combining optical and radar data can improve classification accuracy. Among the classifiers, ANN produces more accurate and robust estimation. [ABSTRACT FROM PUBLISHER]

The article discusses the views of Spanish philosopher Francisco de Vitoria concerning the concepts of the supernatural and natural sovereignty. It examines Vitoria's views on sovereignty regarding Amerindians, the concept of the law of nations, also known as ius gentium, and the concept of sovereignty that pertains to the communal body as derived from the marriage of the Biblical characters and Adam and Eve. It also discusses Vitoria's idea of a Christian nation.

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. [ABSTRACT FROM AUTHOR]

This paper proposes a novel rough set approach to discover classification rules in real-valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real-valued or integer-valued decision system into an interval-valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real-life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data. [ABSTRACT FROM AUTHOR]

The green space of Hong Kong was studied using a normalized difference vegetation index (NDVI) derived from SPOT images. Spatial patterns of the magnitude and variability of 'greenness' were analyzed based on the mean and entropy of NDVI at the tertiary planning unit (TPU) level. Continuous loss of green space due to urban encroachment and hill fires was identified in many parts of the territory between 1987 and 1995. However, establishment of green belts and improved landscaping, as found in many new development projects, such as the Tsang Kwan O new town, resulted in the continuous gain in green space or percentage of woodland in a few locations. [ABSTRACT FROM AUTHOR]