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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]

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]

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]

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]