Search

Tropical forests are acknowledged for providing important ecosystem services and are renowned as "the lungs of the planet Earth" due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for 'forest' classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity. [ABSTRACT FROM AUTHOR]

With large-scale urbanization globally, the world is facing environmental problems brought about by high-density cities. To achieve sustainable development in Hong Kong, especially for the heavily loaded Cross Harbour Tunnel district, The Hong Kong Polytechnic University proposes a Green Deck over the Cross Harbour Tunnel to enhance the neighboring environments. Green belts, buildings, and a new exhibition center are planned for this deck. This will require an increase in the guest capacity of nearby hotels in the Tsim Sha Tsui East (TSTE) area. This study, therefore, investigated the development potential of the 23 existing buildings in TSTE and the environmental impact brought about by the increase of plot ration (PR)/building height (BH) in that area. Spatial analyzes of solar exposure, wind ventilation, and air temperature were conducted based on four different scenarios with various PR/BH; human thermal comfort brought about by PR/BH changes were also measured. A comparison of different scenarios revealed no significant changes in the surrounding environment. The study concludes that there is potential for PR/BH relaxations in TSTE to increase spaces to meet increased population flow. [ABSTRACT FROM AUTHOR]

The complete surface temperature (T c) in urban areas, defined as the mean temperature of the total active surface area, is an important variable in urban micro-climate research, specifically for assessment of the urban surface energy balance. Since most vertically-oriented building facets are not observed by a nadir-viewing remote imaging radiometer, the radiometric surface temperature (T r) measured at a specific view angle cannot be used with existing heat transfer equations to estimate radiative and convective fluxes in the urban environment. Thus, it is necessary to derive T c for city neighborhoods. This study develops a simple method to estimate T c from T r with the aid of the Temperatures of Urban Facets in 3D (TUF-3D) numerical model, which calculates 3-D sub-facet scale urban surface temperatures for a variety of surface geometries and properties, weather conditions and solar angles. The effects of geometric and meteorological characteristics – e.g., building planar area index (λ p), wall facet area index (F), solar irradiance – on the difference between T c and T r were evaluated using the TUF-3D model. Results showed the effects of geometric and meteorological characteristics on the difference between T c and T r differ between daytime and nighttime. The study then sought to predict the relationship between T r and T c , using λ p , F , and solar irradiance for daytime and only using λ p and F for nighttime. Based on the simulated data from TUF-3D, the resulting relationships achieve a coefficient of determination (r 2 ) of 0.97 and a RMSE of 1.5 K during daytime, with corresponding nighttime values of r 2 = 0.98 and RMSE = 0.69 K. The relationships between T r and T c are evaluated using high resolution airborne thermal images of daytime urban scenes: r 2 = 0.75 and RMSE = 1.09 K on August 6, 2013 at 12:40 pm; and r 2 = 0.86 and RMSE = 1.86K on October 24, 2017 at 11:30 am. The new relationships were also applied to estimate T c from T r in Hong Kong retrieved from Landsat 5 Thematic Mapper (TM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). In the present climatic context, the difference between T c and T r can reach 10 K during daytime in summer, and 6 K during daytime in winter, with seasonal variation attributable to the variations in shortwave irradiance. The nighttime difference between T c and T r can also reach 2 K in both summer and spring seasons. • Parameterization of complete surface temperature over complex urban areas • Geometric effects on the difference between T r and T c were first evaluated. • Relationships between T r and T c characterized by urban geometry were developed. • Validation with high-spatial-resolution airborne thermal images was undertaken. [ABSTRACT FROM AUTHOR]

Traffic-related emissions are well-known factors in urban environment which may have adverse implication on human health. Estimating vehicular emissions in urban areas provides an understanding of the air pollution caused by traffic. However, existing microscopic approaches cannot simulate the traffic flows and emissions for an entire city and most of the macroscopic approaches are usually highly complex and require priori knowledge about vehicles' route options. This study, therefore, proposes a straightforward and robust approach to simulate vehicular flows and estimated transport emissions at a city scale via a deterministic approach and by applying the Cell Transmission Model (CTM) to simplify the modeling of vehicles' route selections. Under a space-time integrated framework, we firstly simulate a time-dependent distribution of urban vehicular flows and then estimate pollutant emissions of Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Violate Organic Compounds (VOC) for traffic flows on weekday and weekend. Finally, the spatiotemporal patterns of traffic flows as well as traffic emissions were visualized and illustrated under a space-time integrated framework. With accuracies of around 67.4% to 70%, the results demonstrated the feasibility of the proposed approach for estimating city-scale traffic flows and emissions from road transport. • This work presents a straightforward and robust approach to simulate the time-dependent distribution of urban vehicular flows. • It adopts a deterministic approach and the Cell Transmission Model to simplify the modeling of vehicles route selections. • Various pollutant emissions are estimated at the city level, with accuracies of around 78.6% to 70%. • Spatio-temporal patterns of traffic flows and emissions are visualized and analyzed under a space-time integrated framework. [ABSTRACT FROM AUTHOR]

Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring. [ABSTRACT FROM AUTHOR]

• Distinguish urban tree species with terrestrial hyperspectral camera (SPECIM IQ). • 450 bi-monthly images were acquired for 75 urban trees belonging to 19 tree species. • Hyperspectral phenology models were developed to explore seasonalities of trees. • A Deep Neural Network is used to identify tree species from hyperspectral imagery. Urban trees exhibit a wide range of ecosystem services that have long been unveiled and increasingly reported. The ability to map tree species and analyze tree health conditions would become vividly essential. Remote sensing techniques, especially hyperspectral imaging, are being evolved for species identification and vegetation monitoring from spectral reponse patterns. In this study, a hyperspectral library for urban tree species in Hong Kong was established comprising 75 urban trees belonging to 19 species. 450 bi-monthly images were acquired by a terrestrial hyperspectral camera (SPECIM-IQ) from November 2018 to October 2019. A Deep Neural Network classification model was developed to identify tree species from the hyperspectral imagery with an overall accuracy ranging from 85% to 96% among different seasons. Representative spectral reflectance curves of healthy and unhealthy conditions for each species were extracted and analyzed. The hyperspectral phenology models were developed to achieve high accuracy and optimization of data acquisition. The bi-monthly canopy signatures and vegetation indices revealed different seasonality patterns of evergreen and deciduous species in Hong Kong. We explored the utility of terrestrial hyperspectral remote sensing and Deep Neural Network for urban tree species identification and characterizing. This provides a unique baseline to understand hyperspectral characteristics and seasonality of urban tree species in Hong Kong that can also contribute to hyperspectral imaging and database development elsewhere in the world. [ABSTRACT FROM AUTHOR]

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by "standard Case-2 Regional/Coast Colour" (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters. [ABSTRACT FROM AUTHOR]

Climate change will be a powerful stressor on ecosystems and biodiversity in the second half of the 21st century. In this study, we used the satellite-derived Normalized Difference Vegetation Index (NDVI) to examine a 34-year trend along with the response of vegetation to climate indicators surrounding the world's largest megacity: the Pearl River Delta (PRD) of China. An overall increasing trend is observed in vegetation productivity metrics over the study period 1982 to 2015. Increase in winter productivity in both natural ecosystems and croplands is more related to increasing temperatures (r = 0.5–0.78), than to changes in rainfall. For growing season productivity, negative correlations with temperature were observed in cropland regions, and some forests in the northern part of PRD region, suggesting high-temperature stress on crop production and forest vegetation. However, increased winter and spring temperatures provide higher opportunities for cropping in winter. During the decade 1995–2004, vegetation productivity metrics showed a reversal in the upward trend. The geographical and biological complexity of the region under significant climatic and development impacts suggests causative factors would be synergistic. These include our observed decrease in sunshine hours, increasing cloud cover associated with atmospheric aerosols from industrial and urban development, direct pollution effects on plant growth, and exceedance of high temperature growth thresholds. [ABSTRACT FROM AUTHOR]

The impact of socio-economic restriction measures in the United States showed a significant reduction in nitrogen dioxide (NO 2) emissions due to the changes in commuting patterns, travel restrictions, and reduced social and economic activities. The data from 60 observation stations across different states representing of different urban settings were used to assess the impacts of dynamic changes in human mobility and static emission sources on NO 2 concentrations. Two predictive models, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) and Long Short-Term Memory (LSTM), were used to forecast NO 2 concentrations under a counterfactual scenario in the assumption of no lockdown was implemented. The predicted concentrations were then compared to the observed concentrations to evaluate the difference between the two scenarios, during lockdown and after lockdown by using the last five-years (2015–2019) average as baseline. Results showed that reduction in NO 2 varied across stations due to a combination of factors such as human mobility, population density, income, local climate, and stationary sources of NO 2 emissions in the neighbourhood. The stations located in high-income regions with higher population density experienced a greater reduction in mobility, resulting in a greater variation in NO 2 levels during both periods. In contrast, stations in densely populated low-income regions experienced lower variation in mobility and NO 2 levels during the lockdown period but showed recovery during the post-lockdown period. Stations located outside the city with very low population density experienced no significant reduction in NO 2 emissions. The study highlights the disparities in NO 2 reduction and income inequality exposed by the pandemic, which may have scientific impacts on air quality and health disparities. • Dynamic human mobility and static emissions sources found major NO 2 contributors. • NO 2 has disproportionate variation depending on the income and population density. • Populous low-income areas showed less mobility reduction & faster NO 2 recovery. • Populous high-income areas showed greater human mobility and NO 2 reductions. • Marine and mixed-humid climate zones showed a greater reduction in NO 2. [ABSTRACT FROM AUTHOR]

People's exposure to green space is a critical link between urban green space and urban residents' health. Since green space may affect human health through multiple pathways regarding diverse human health outcomes, the measurement of people's exposure to green space must be tailored to concrete study contexts and research questions. In this scoping review, we systematically categorized the available green space representations and metrics in the last two decades that can be used to derive people's exposure to green space regarding different research topics. A three-phase systematic review was conducted after a generalized search of relevant research articles from the three most-used publication databases, namely Scopus, the Web of Science, and PubMed. We identified 260 research articles that particularly discuss green space representations and metrics. We further developed a multi-pathway framework to articulate the complicated context issues in green space studies. We categorized the most relevant green space representations and metrics into five groups, including green space indices, the delineation, inventory, and usage of green space, the spatiotemporal evolution of green space, the attributes and components of green space, and the green space landscape and fragmentation. Finally, we discussed the inter-conversion between different green space representations and metrics, the "mobility-turn" in green space studies and how it may affect the derivation of people's exposure to green space, and other potential methodological issues in measuring people's exposure to green space. Our scoping review provides the most comprehensive framework and categories for deriving people's exposure to green space to date, which may strongly support a broad range of studies that concern green space's health effects. • We systematically categorized green space representations and metrics in the last two decades. • A three-phase systematic review was conducted after a generalized search of relevant articles. • We developed a multi-pathway framework to articulate the complicated context issues in green space studies. • We categorized the most relevant green space representations and metrics into 5 groups. • We discussed how the mobility turn may affect the derivation of people's exposure to green space. [ABSTRACT FROM AUTHOR]

Harvesting solar energy on rooftops can be a promising solution for providing affordable energy. This requires accurately estimating spatio-temporal solar photovoltaic (PV) potential on urban surfaces. However, it is still a challenge to obtain a fast and accurate estimation of rooftop solar PV potential over large urban built-up areas. Thus, this study proposes a parametric-based method to estimate annual rooftop solar irradiation at a fine spatial resolution. Specifically, seven parameters (Digital Surface Model, Sky View Factor, shadow from buildings, shadow from terrain, building volume to façade ratio, slope, and aspect) are determined that having great importance in modeling rooftop solar irradiation. Three machine learning methods (Random Forest (RF), Gradient Boost Regression Tree (GBRT), and AdaBoost) trained by the selected parameters are cross-compared based on R 2 , Mean Absolute Error (MAE), and computation time. As a case study in Hong Kong, China, the RF outperformed GBRT and AdaBoost, with R 2 = 0. 77 and M A E = 22. 83 kWh/m 2 / year. The time for training and prediction of rooftop solar irradiation is within 13 h, achieving a 99.32% reduction in time compared to the physical-based hemispherical viewshed algorithm. These results suggest that the proposed method can provide an accurate and fast estimation of rooftop solar irradiation for large datasets. • The parametric assessment of rooftop solar irradiation was conducted. • Random Forest, Gradient Boost Regression Tree, and AdaBoost models were compared. • The RF model showed superior accuracy (R 2 = 0.77) and fast computation. • A data and model dual-driven approach was used. [ABSTRACT FROM AUTHOR]

The urban heat island effect poses a growing threat to human society, especially in densely populated and developed megacities. With the introduction of the Local Climate Zones (LCZ) framework, new perspectives and findings have been brought to urban heat island studies. This study investigated the cooling effect of vegetation and albedo on the surface urban heat island (SUHI) in the classification system of LCZ during different seasons, using three Chinese megacities as case study areas. Single-factor linear regression and Pearson's correlation coefficient were applied to analyze the seasonal cooling effect of both albedo and the NDVI on the SUHI within different LCZs. The results show that (1) the variability of the SUHI is reflected in its dominance and intensity within certain LCZs in different cities and in the efficiency of cooling factors; (2) the cooling effect of vegetation is dominant in each season, and the cooling effect produced by albedo within specific seasons can be differentiated by LCZs. This study provides valuable information for the mitigation of the SUHI magnitude in specific regions and at specific times of the year. [ABSTRACT FROM AUTHOR]

Modeling thematic and spatial dynamic behaviors of urban heat islands (UHIs) over time is important for understanding the evolution of this phenomenon to mitigate the warming effect in urban areas. Although previous studies conceptualized that a UHI only has a single life cycle with spatial behaviors, a UHI can be detected to appear and disappear several times periodically in terms of thematic and spatial integrated behaviors. Such multiple behaviors have not yet been illustrated with proof or evidence. This study conceptualizes a UHI as an object which has thematic and spatial behaviors simultaneously and proposes several graphs to depict periodic life‐cycle transitions triggered by behaviors. The conceptualized behaviors have been modeled and implemented in an object‐relational database management system and temperature readings collected from numerous weather stations were interpolated as temperature images per hour. The results of this study indicate that the model could track the spatial and thematic evolution of UHIs continuously and reveal their periodical patterns and abnormal cases. [ABSTRACT FROM AUTHOR]

This study evaluates the association between antivirals (Molnupiravir and Nirmatrelvir-Ritonavir) and all-cause and respiratory mortality and organ dysfunction among high-risk COVID-19 patients during an Omicron outbreak. Two cohorts, Nirmatrelvir-Ritonavir versus control and Molnupiravir versus control, were constructed with inverse probability treatment weighting to balance baseline characteristics. Cox proportional hazards models evaluated the association of their use with all-cause mortality, respiratory mortality, and all-cause sepsis (a composite of circulatory shock, respiratory failure, acute liver injury, coagulopathy, and acute liver impairment). Patients recruited were hospitalized and diagnosed with the COVID-19 Omicron variant between February 22, 2022 and April 15, 2022, and followed up until May 15, 2022. The study included 17,704 patients. There were 4.67 and 22.7 total mortalities per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio, − 18.1 [95% CI − 23.0 to − 13.2]; hazard ratio, 0.18 [95% CI, 0.11–0.29]). There were 6.64 and 25.9 total mortalities per 1000 person-days in the Molnupiravir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, − 19.3 [95% CI − 22.6 to − 15.9]; hazard ratio, 0.23 [95% CI 0.18–0.30]). In all-cause sepsis, there were 13.7 and 35.4 organ dysfunction events per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, − 21.7 [95% CI − 26.3 to − 17.1]; hazard ratio, 0.44 [95% CI 0.38–0.52]). There were 23.7 and 40.8 organ dysfunction events in the Molnupiravir and control groups respectively before adjustment (weighted incidence ratio per 1000 person-days, − 17.1 [95% CI, − 20.6 to − 13.6]; hazard ratio, 0.63 [95% CI 0.58–0.69]). Among COVID-19 hospitalized patients, use of either Nirmatrelvir-Ritonavir or Molnupiravir compared with no antiviral use was associated with a significantly lower incidence of 28-days all-cause and respiratory mortality and sepsis. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) phenomenon occurring within urban areas or city-clusters is increasingly becoming a severe problem in the urbanization process. Previous research mainly focusing on static UHI modeled at fixed time instants is not capable to track the evolutionary process of the UHIs in both time and space domains. This research designs an object-oriented dynamic model to reconstruct the evolutionary process of UHIs. Each UHI is modeled as a spatiotemporal field-object with its own life-cycle. The dynamic behavior of an UHI is defined by a series of filiations (e.g. expansion and contraction). The model was implemented in an object-relational database and applied to air temperature data collected from weather stations in hourly basis over 7 days. The behaviors of UHI were extracted from the data. Results suggest that the model can effectively identify different behaviors and status of UHIs, and reveal the spatiotemporal behavior of each of them. [ABSTRACT FROM AUTHOR]

By employing PM 2.5 observation data, ground-based lidar measurements, MODIS and CALIPSO satellite images, meteorological data, and back trajectories analysis, we investigate a trans-boundary transport of aerosols during a large-area haze episode in China during 3–5 January 2015. The ground-based lidar observations indicated similar episodes of external aerosols passing through and mixing into three East China cities. A considerable amount of total AOD below 3 km (46% in average) was contributed by the external aerosol layers during passing over and importing. CALIPSO satellite observations of central and eastern China revealed a high altitude pollutant belt on January 3. Although the severest ground pollution was found in central and south-eastern Hebei, the high altitude pollution transport was greater in south-western Shandong, north-western Jiangsu, and north-western Anhui. These observations along with the analysis of air mass trajectories and wind fields indicates pollutants moving from Hebei, Henan and Hubei probably contributed to the haze pollution in Shandong and Jiangsu. This study reveals haze transports from North China Plain to East China could be a common phenomenon influenced by the winter monsoon in northern China. Hence, effective control of air pollution requires collaboration among different cities and provinces throughout China. The long-term measurements of aerosol vertical properties by ground-based lidar and CALIPSO are extremely valuable in quantifying the contributions of external factors and will be helpful in validating and improving various air quality models. [ABSTRACT FROM AUTHOR]

The need to protect forests and enhance the capacity of mountain ecosystems is highlighted in the U.N.'s Sustainable Development Goal (SDG) 15. The worst-hit areas of the 2008 Wenchuan Earthquake in southwest China were mountainous regions with high biodiversity and the impacted area is typical of other montane regions, with the need for detecting vegetation changes following the impacts of catastrophes. While the widely used remotely sensed vegetation indicator NDVI is available from various satellite data sources, these satellites are available for different monitoring periods and durations. Combining these datasets proved challenging to make a continuous characterization of vegetation change over an extended time period. In this study, compared with linear regression, multiple linear regression, and random forest, Convolutional Neural Networks (CNNs) performed best with an average R2 of 0.819 (leave-one-out cross-validation). Thus, the CNNs model was selected to establish the map of the overlapping periods of two remote-sensing products: SPOT-VGT NDVI and PROBA-V NDVI, to reconstruct a SPOT-VGT NDVI for the period from June 2014 to December 2018 in the worst-hit areas of the Wenchuan earthquake. We analyzed the original and reconstructed SPOT-VGT NDVI in the hard-hit areas of the Wenchuan earthquake from 1999 to 2018, and we concluded that NDVI showed an overall upward trend throughout the study period, but experienced a sharp decline in 2008 and reached its lowest value a year later (2009). Vegetation recovery was rapid from 2009 until 2011 after which, it returned to a pattern of slower natural growth (2012–2018). The Longmenshan fault zone experienced the greatest vegetation damage and initiation of recovery there has caused the overall regional average recovery to lag by 1–2 years. In areas where the land was denuded of vegetation (i.e., effectively all vegetation was stripped from the surface) after the earthquake, the damage exceeded what was experienced anywhere else in the entire study area, and by 2018 it remained unrestored. In the 15 years since the earthquake, the areas that were denuded were expected to recover to the level of restoration equivalent with the NDVI of 2007, as was the case in other earthquake-damaged regions. In addition to the earthquake and the immediate loss of vegetation, the Chinese government's Grain for Green Policy, the elevation ranges within the region, the forest's phenological conditions, and human activities all had an impact on vegetation recovery and restoration. The reconstructed NDVI provides a long-term continuous record, which contributes to the identifying changes that are improving predictive forest recovery models and to better vegetation management following catastrophic disturbances, such as earthquakes. [ABSTRACT FROM AUTHOR]

This study analyzes seasonal characteristics and long-term variations in aerosol optical parameters in Hong Kong from 2006 to 2021 using AERONET data and satellite-based observations based on the extreme-point symmetric mode decomposition (ESMD) model. The dominant aerosol types in Hong Kong are mixed aerosols and urban/industrial aerosols with fine-mode sizes, and slightly absorbing or non-absorbing properties. Aerosol optical depth (AOD), Angstrom exponent (AE) and single scattering albedo (SSA) varied seasonally with a lower AOD but higher AE and SSA in summer, and elevated AOD but lower AE and SSA in spring and winter. The long-term variations show the year 2012 to be a turning point, with an upward trend in AOD and AE before 2012 and then downwards after 2012. However, for SSA, a rising trend was exhibited in both pre- and post-2012 periods, but with a larger gradient in the first period. The ESMD analysis shows shorter-term, non-linear fluctuations in aerosol optical parameters, with alternating increasing and declining trends. The examination of the relationships between AOD and meteorological factors based on the extreme gradient boosting (XGBoost) method shows that the effects of weather conditions on AOD are complex and non-monotonic. A lower relative humidity, higher wind speed in southwest directions and lower temperature are beneficial to the abatement of aerosol loads in Hong Kong. In conclusion, the findings of this study enhance the understanding of aerosol properties and the interactions between aerosol loading and meteorological factors. [ABSTRACT FROM AUTHOR]

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day. [ABSTRACT FROM AUTHOR]

High-frequency aerosol observation from a new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land, especially over heterogeneous land surfaces. In this study, a geometry-discrete minimum reflectance aerosol retrieval algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 Advanced Himawari Imager (AHI) sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in East Asia from 2015 to 2020, with a correlation coefficient (${R}$) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05% +15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement and demonstrate the robustness of the proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution. [ABSTRACT FROM AUTHOR]

Accurately quantifying carbon mitigation of operational photovoltaics (PVs) influenced by dynamic geo-environment is crucial for developing suitable initiatives on renewable energy transition. However, previous studies made strong assumptions to avoid modelling spatial heterogeneity of carbon footprints or ignore weather and shadowing effects on PV electricity generation, making the estimated results unreliable and even causing false policymaking. To tackle this challenge, we developed a novel model coupling multi-sourced data modelling and life-cycle assessment to estimate spatially heterogenous carbon mitigation of all the operational rooftop PVs in an entire city. It is built by three hierarchal modules: (i) segmenting PV areas from high-resolution satellite imagery, by using Deep Solar PV Refiner, an advanced semantic segmentation network; (ii) estimating electricity generation in the segmented PV areas, by using a well-developed 3D solar irradiation model that considers the effects of land surface solar irradiation influenced by weather and shadowing effects produced by 3D buildings; (iii) quantifying carbon mitigation potential of PVs, by developing a spatial-aware life-cycle model to track the life-cycle carbon footprints of PVs from production, transportation, operation, to decommission. Investigating Singapore by 2020, we reveal that industrial, airport, and residential areas have the largest rooftop PV installation. We also suggest a carbon emission rate of 13.20 g-CO 2 /kWh, a carbon payback time of 0.81 years, and an energy payback time of 0.94 years, showing an improved carbon mitigation capability compared to the past years. This study contributes to GIS data modelling and helps understand the geospatial characteristics of urban-scale PV carbon mitigation. • Developed life-cycle-assessment integrated multi-sourced geospatial data model. • Segmented rooftop PV areas using an advanced semantic segmentation model. • Estimated installed PV electricity output using 3D solar estimation model. • Suggested significant carbon mitigation potential from rooftop PVs in Singapore. • Contributes to renewable energy transition and Information Geography development. [ABSTRACT FROM AUTHOR]

Abstract: Despite continuous efforts to retrieve aerosol optical depth (AOD) using a conventional 5-channel meteorological imager in geostationary orbit, the accuracy in urban areas has been poorer than other areas primarily due to complex urban surface properties and mixed aerosol types from different emission sources. The two largest error sources in aerosol retrieval have been aerosol type selection and surface reflectance. In selecting the aerosol type from a single visible channel, the season-dependent aerosol optical properties were adopted from long-term measurements of Aerosol Robotic Network (AERONET) sun-photometers. With the aerosol optical properties obtained from the AERONET inversion data, look-up tables were calculated by using a radiative transfer code: the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Surface reflectance was estimated using the clear sky composite method, a widely used technique for geostationary retrievals. Over East Asia, the AOD retrieved from the Meteorological Imager showed good agreement, although the values were affected by cloud contamination errors. However, the conventional retrieval of the AOD over Hong Kong was largely underestimated due to the lack of information on the aerosol type and surface properties. To detect spatial and temporal variation of aerosol type over the area, the critical reflectance method, a technique to retrieve single scattering albedo (SSA), was applied. Additionally, the background aerosol effect was corrected to improve the accuracy of the surface reflectance over Hong Kong. The AOD retrieved from a modified algorithm was compared to the collocated data measured by AERONET in Hong Kong. The comparison showed that the new aerosol type selection using the critical reflectance and the corrected surface reflectance significantly improved the accuracy of AODs in Hong Kong areas, with a correlation coefficient increase from 0.65 to 0.76 and a regression line change from τ MI [basic algorithm] =0.41τ AERONET +0.16 to τ MI [new algorithm] =0.70τ AERONET +0.01. [Copyright &y& Elsevier]

This study compares the space‐time patterns and characteristics of high‐risk areas of COVID‐19 transmission in Hong Kong between January 23 and April 14 (the first and second waves) and between July 6 and August 29 (the third wave). Using space‐time scan statistics and the contact tracing data of individual confirmed cases, we detect the clusters of residences of, and places visited by, both imported and local cases. We also identify the built‐environment and demographic characteristics of the high‐risk areas during different waves of COVID‐19. We find considerable differences in the space‐time patterns and characteristics of high‐risk residential areas between waves. However, venues and buildings visited by the confirmed cases in different waves have similar characteristics. The results can inform policymakers to target mitigation measures in high‐risk areas and at vulnerable groups, and provide guidance to the public to avoid visiting and conducting activities at high‐risk places. [ABSTRACT FROM AUTHOR]

Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates and demonstrates the state of the art in remote sensing techniques for detailed landslide hazard assessment applicable to large areas. Since the most common methods of landslide hazard assessment using simple inventories and weighted overlays are heavily dependent on three-dimensional terrain visualization and analysis, stereo satellite images from the IKONOS Very High Resolution (VHR) sensor are used for this study. The DEMs created from IKONOS stereo images appear to be much more accurate and sensitive to micro-scale terrain features than a DEM created from digital contour data with a 2 m contour interval. Pan-sharpened stereo IKONOS images permit interpretation of recent landslides as small as 2–3 m in width as well as relict landslides older than 50 years. A cost–benefit analysis comparing stereo air photo interpretation with stereo satellite image interpretation suggests that stereo satellite imagery is usually more cost-effective for detailed landslide hazard assessment over large areas. [Copyright &y& Elsevier]

Background Nosocomial outbreaks with superspreading of coronavirus disease 2019 due to a possible airborne transmission have not been reported. Methods Epidemiological analysis, environmental samplings, and whole-genome sequencing (WGS) were performed for a hospital outbreak. Results A superspreading event that involved 12 patients and 9 healthcare workers (HCWs) occurred within 9 days in 3 of 6 cubicles at an old-fashioned general ward with no air exhaust built within the cubicles. The environmental contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was significantly higher in air grilles (>2 m from patients' heads and not within reach) than on high-touch clinical surfaces (36.4%, 8 of 22 vs 3.4%, 1 of 29, P  = .003). Six (66.7%) of 9 contaminated air exhaust grilles were located outside patient cubicles. The clinical attack rate of patients was significantly higher than of HCWs (15.4%, 12 of 78 exposed patients vs 4.6%, 9 of 195 exposed HCWs, P  = .005). Moreover, the clinical attack rate of ward-based HCWs was significantly higher than of nonward-based HCWs (8.1%, 7 of 68 vs 1.8%, 2 of 109, P  = .045). The episodes (mean ± standard deviation) of patient-care duty assignment in the cubicles was significantly higher among infected ward-based HCWs than among noninfected ward-based HCWs (6.0 ± 2.4 vs 3.0 ± 2.9, P  = .012) during the outbreak period. The outbreak strains belong to SARS-CoV-2 lineage B.1.36.27 (GISAID clade GH) with the unique S -T470N mutation on WGS. Conclusions This nosocomial point source superspreading event due to possible airborne transmission demonstrates the need for stringent SARS-CoV-2 screening at admission to healthcare facilities and better architectural design of ventilation systems to prevent such outbreaks. Portable high-efficiency particulate filters were installed in each cubicle to improve ventilation before resumption of clinical service. [ABSTRACT FROM AUTHOR]

Over the last decade, 90% of Big Data has been generated by people living in urban areas. With the advent of Internet of Things (IoT) and the increased use of the internet, Social Media has become an integral part of people's daily lives. Millions of unstructured data are being sent to the cloud every second, providing opinions practically on any discourse. This makes microblogs such as Twitter, Instagram, WeChat, and Facebook smart instruments for urban planners to harvest 'big data' on socioeconomics, urban dynamics, transportation, land uses, resilience, etc. This study proposed a framework for social media big data mining and data analytics using Twitter. It demonstrated the functionalities of the framework on a case study using Natural Language Processing and Machine Learning techniques like Latent Dirichlet Allocation and VADER Sentiment Analysis to mine, clean, process, and validate the data. The validated results from the case study showed high accuracy that Social Media Big Data can be used to study the spatiotemporal dynamism of community challenges. • A framework was developed to pre-process location-based social media big data for sustainable urban planning and management • The methodologies used for the proposed framework were demonstrated using a case study • Data from the case study was validated and found have high accuracy, which justified the usability of the framework • All other steps involved are fully justified for research Contributions • All codes and procedures are provided in the appendix and links to the GitHub Libraries are also provided [ABSTRACT FROM AUTHOR]

Building-integrated photovoltaics (BIPV) can produce power while occupying little urban space. Photovoltaic-integrated shading devices (PVSDs) are a key component of BIPV that can generate electricity while blocking excess daylight. However, previous studies have lacked a systematic design of PVSDs that accurately estimates the trade-offs between indoor sunshade duration and electricity generation. This study proposes a multi-objective optimization framework for maximizing PV potential, minimizing PV area, and enabling proper sunshade duration in complex urban surfaces. A GIS-based spatiotemporal analysis and optimization approach was applied to three PVSD planning scenarios: (i) parallel to the horizontal land surface, (ii) inclined at an angle equal to the local latitude, and (iii) rotated in real-time to keep the PV surface perpendicular to the solar radiation. Different PV widths are determined under different scenarios considering power generation and solar shading duration. In the real-time rotating scenario, the optimized 0.7-m-wide PVSDs can generate 0.861 GWh of electricity annually, with a competitive average power generation efficiency of 0.811 kWh/m2/day and a solar shading duration of 6.61 h/day. Flexible installation scenarios are suggested to account for shading from other upper PVSDs and surrounding buildings. This study can facilitate solar farming in global cities and contribute to renewable energy penetration. [ABSTRACT FROM AUTHOR]

The surface urban heat island intensity (SUHII) when using nadir‐viewing radiometric and complete surface temperature (Tr and Tc) was evaluated. The urban areas of the Kowloon peninsula and Hong Kong Island were selected and four daytime Landsat TM images and two nighttime ASTER images were collected to retrieve Tr and then model Tc based on a semi‐empirical model. SUHIIs were estimated using both the retrieved Tr and modelled Tc. High spatial resolution (HR) airborne thermal images (0.2 m) observed at 12:10 noon on Oct 24, 2017 were used to retrieve Tc directly. Results based on HR data and satellite data were consistent and indicated that the geometry of the built‐up space had a larger impact on SUHII when using Tc (SUHIIc) than Tr (SUHIIr). During daytime SUHIIc decreased while SUHIIr showed a very slight increase with building density. Both SUHIIc and SUHIIr decreased with higher building height but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr decreased with increasing building height variance and increased with increasing sky view factor (SVF). The rate of decrease with building height variance for SUHIIc was larger than SUHIIr. The rate of increase of SUHIIc with SVF was higher than SUHIIr. During nighttime, geometry effects on SUHIIc and SUHIIr were different from daytime. Both SUHIIc and SUHIIr increased with building density, while the rate of increase of SUHIIc with building density, as well as with building height, was much higher than SUHIIr. Both SUHIIc and SUHIIr decreased with SVF, but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr initially increased with building height variance and then remained approximately constant. We also evaluated the UHI intensity: SUHIIc was much closer to UHII than SUHIIr. Overall, building geometry had larger impact on SUHIIc than on SUHIIr, that is, SUHIIc is more representative of urban climate than SUHIIr. [ABSTRACT FROM AUTHOR]

Accessibility of emergency medical care is one of the crucial factors in evaluating national primary medical care systems. While many studies have focused on this issue, there was a fundamental limit to the measurement of accessibility of emergency rooms, because the commonly used census-based population data are difficult to provide realistic information in terms of time and space. In this study, we evaluated the geographical accessibility of emergency rooms in South Korea by using dynamic population counts from mobile phone data. Such population counts were more accurate and up-to-date because they are obtained by aggregating the number of mobile phone users in a 50-by-50 m grid of a locational field, weighted by stay time. Considering both supply and demand of emergency rooms, the 2-step floating catchment analysis was implemented. As a result, urban areas, including the capital city Seoul, showed lower accessibility to emergency rooms, whereas rural areas recorded higher accessibility. This result was contrary to the results analyzed by us based on census-based population data: higher accessibility in urban areas and lower in rural. This implies that using solely census data for accessibility analysis could lead to certain errors, and adopting mobile-based population data would represent the real-world situations for solving problems of social inequity in primary medical care. [ABSTRACT FROM AUTHOR]

Urban fragmentation is generally regarded as a strong urban structural polarisation that is closely related to sustainability, yet a comprehensive understanding of the management of different fragmentation scenarios and their causes is still lacking. The Wuhan metropolitan area, as a rapidly urbanising region, shows clear evidence of different forms of urban fragmentation due to rapid urbanisation and spatial differentiation in pedestrian network structure, neighbourhood residential pattern and land use structure. In this study, an integrated framework for examining the fragmentation of dynamic and complex metropolitan areas, is proposed. The results indicate that the 49 highly fragmented sub-districts are distributed with large gated communities, industrial parks, green spaces and rivers/lakes. The fragmentation forms of the Wuhan metropolitan area include morphological, connectional and functional (variety and conflict) fragmentation, which constitute six scenarios. The geographical detector model manifests that urban fragmentation is the result of natural, socio–economic and construction factors. The increase in spatial debris driven by housing prices (q = 0.334), gross domestic product (q = 0.282), population density (q = 0.359), constructive index (q = 0.292) and road density (q = 0.314) has dramatically affected spatial organisation co–evolution. Additionally, there are synergistic enhancement effects between each pair of driving factors, i.e., bivariate or nonlinear interaction strengthens the impact of each other factor on the index of urban fragmentation. The strongest bivariate interaction is between housing prices and constructive index, with a value of 0.503. The largest nonlinear interaction is between constructive index and industrial index, with a value of 0.487. These interactions all further exacerbate urban fragmentation. These findings not only contribute to an integrated methodological framework, but also provide scientific implications for the purpose of sustainable urban development and cleaner production planning. • Using fine–scale land use information to examine fragmentation in a metropolitan. • Fragmentation is quantified and characterised by morphology, connection and function. • Population density is found to have the greatest impact on urban fragmentation. • Bivariate/nonlinear interactions among drivers enhance the impact on fragmentation. • This empirical research highlights the sustainable urban form and anti–fragmentation. [ABSTRACT FROM AUTHOR]

Hong Kong is an international metropolis with a highly dense population. As a result, it faces enormous challenges in terms of land supply. As part of the Hong Kong Government's initiative to increase land supply, the Civil Engineering and Development Department (CEDD) proposed minor relaxation of the maximum plot ratio/building height restrictions for 21 target sites in Kai Tak Development Area (KTDA). Although CEDD has explored the feasibility of increasing development intensity by assessing environmental impacts, infrastructure capacity and public consultation, these reviews and assessments were conducted based on the 2D GIS. Since the spatial distribution of land unit in the real world is three-dimensional, 3D GIS can help us look into the world in true perspective and make informed decisions. This study aims to investigate the viability of minor relaxation of maximum plot ratio/building height restrictions of 21 sites in KTDA through 3D modeling and 3D spatial analyses, including skyline, visual impact, shadow and solar exposure. Regarding to the 21 target sites, four scenarios with different plot ratios and building heights were built and compared. The results indicate that minor relaxation of maximum plot ratio and building height leads to (i) minor effect on skyline (ii) minor effect on visual impact and (iii) slight changes in shadow and solar exposure both in winter and summer. Therefore, in light of the findings from this study, scenario 4 is the recommended reasonable scale to relax the maximum plot ratio/building height restriction for the target sites in KTDA. Besides, this study can also be applied in the urban renewal studies and other new development areas in Hong Kong, or even in other densely populated cities. [ABSTRACT FROM AUTHOR]

The characteristics of springtime aerosols, including their optical and microphysical properties, were analyzed for the months of March to May of 2009 in Gwangju (35.23°N, 126.84°E), Korea. A high Light Detection and Ranging (LIDAR)-derived aerosol depolarization ratio (δ) of 0.25 ± 0.04 was determined on dust particles during the observation period. The Ångström exponent values of the 440–870 nm wavelength pair (Å 440–870 ) and single-scattering albedo at 675 nm (Ω 675 ) measured by a CIMEL sun/sky radiometer were 0.77 ± 0.19 and 0.95 ± 0.01, respectively. The elevated dust layers reached a maximum elevation of 4 km above sea level. Anthropogenic/smoke particles that originated from highly populated/industrialized regions could be distinguished by their relatively smaller particle size (Å 440–870 ranged between 1.33 and 1.36) and higher light-absorbing (Ω 675 of 0.92 ± 0.01) characteristics. These aerosols are mostly distributed at altitudes < 1.2 km. The root-mean-square deviation (RMSD) between the aerosol optical depth (AOD, τ) derived from LIDAR (τ LIDAR ) and from the CIMEL sun/sky radiometer (τ CIMEL ) varied with respect to the surface PM 10 concentration. The RMSD between τ LIDAR and τ CIMEL was as low as 13% under lower PM 10 concentration levels (< 100 μg/m 3 ). In contrast, the RMSD between τ LIDAR and τ CIMEL increased three times (~ 31%) under high surface PM 10 concentration levels (> 100 μg/m 3 ). These results suggest that the accuracy of τ LIDAR is influenced by specific atmospheric conditions, regardless of its uncertainty. [ABSTRACT FROM AUTHOR]

Promoting the use of solar photovoltaic (PV) systems in global cities can be an effective way to cope with severe environmental problems caused by the consuming of fossil fuels. However, a complex urban environment challenges the effective use of PV systems for practical applications. Essentially, this is a spatial optimization problem, where the goal is maximizing the harvesting of solar energy while minimizing occupied urban surfaces. To address this problem, this paper proposes three hierarchical optimizations. First, computational optimization provides a parallel architecture for an established 3D solar estimation model to achieve spatially scalable computation with high spatio-temporal resolution. Second, priority optimization determines the use of different urban partitions considering various constraints. Third, capacity optimization analyzes the spatial and quantitative distribution of solar potential, constrained by the smallest solar irradiation and the minimum surface area to be used. The overall optimization framework is then set to obtain the minimum PV installation capacity required to meet the real demand with the identification of urban surfaces to be equipped with PV modules. By using smart meter data with high temporal resolution in the city of Bologna, Italy, our analysis not only provides executable plans to meet the real demand but also reveals that rebalance and storage capacity are needed to achieve a real-time self-supportive architecture. The proposed analytic and optimization framework can promote distributed PV systems in urban areas and facilitate energy transition adapted to a variety of applications. • High-speed computation of solar PV potential on 3D urban envelopes. • Determine use of different urban partitions considering various constraints. • Determine the smallest solar irradiation and the minimum surface area to be used. • Identify space to equip with PV modules using real-time smart meter data. • Reveals rebalance and storage capacity for real-time self-supportive architecture. [ABSTRACT FROM AUTHOR]

Scooter-sharing has been introduced as a new transportation mode. However, e-scooters have a limited battery capacity and require frequent charging, which causes the operational cost significantly high and hinders the viability of the service. To tackle this problem, this study proposes a solar charging solution with the creation of a real-time shareability network that maximizes the scooter-sharing capability and minimizes the total trip distance, constrained by e-scooters with real-time battery levels. Specifically, hourly solar potential is simulated based on a three-dimensional solar irradiation model so that photovoltaic (PV) electricity generation can be estimated when PV modules are installed at the parking stations, which enables solar charging when the origin–destination matrix of scooter-sharing trips is clustered and associated to the charging stations. As a case study in Singapore, the proposed solar charging system only needs 1–3 m 2 PV modules at each station and 24%–67% of the total number of e-scooters to support almost all the real trips with a 98% reduction of trips used for charging. The system also supports 90% of on-demand mobility for at least three consecutive days without solar charging, which suggests the resilience of the system and inspires us to promote the proposed solar charging in the other global cities. • Developed a solar charging system to charge e-scooters at the parking stations. • Developed a battery-level-aware real-time shareability network for scooter-sharing. • Estimated spatio-temporal distribution of solar irradiation on 3D urban surfaces. • Only needs 24%–27% of the fleet size to obtain 98% reduction of charging trips. • Only needs 1–3 m2 PV module at each station to save the charging cost dramatically. [ABSTRACT FROM AUTHOR]

• Leaf traits mediate large leaf-to-air temperature difference (Δ T) variability. • Leaf width, V cmax , and stomatal slope are the dominant traits. • The trait's relative importance in regulating Δ T is largely dynamic within a day. • Model-derived trait-Δ T relationships agree well with field observations. Leaf thermoregulation and consequent leaf-to-air temperature difference (Δ T) are tightly linked to plant metabolic rates and health. Current knowledge mainly focus on the regulation of environmental conditions on Δ T , while an accurate assessment of biotic regulations with field data remains lacking. Here, we used a trait-based model that integrates a coupled photosynthesis-stomatal conductance model with a leaf energy balance model to explore how six leaf traits (i.e. leaf width, emissivity, visible and near-infrared light absorptance, photosynthetic capacity— V c,max25 , and stomatal slope— g 1) regulate Δ T variability across the diel timescale. We evaluated the model with field observations collected from temperate to tropical forests. Our results show that: (1) leaf traits mediate large Δ T variability, with the noon-time trait-mediated Δ T variability reaching c. 15.0 °C; (2) leaf width, V c,max25 , and g 1 are the three most important traits and their relative importance in Δ T regulation varies strongly across the diel timescale; and (3) model-derived trait-Δ T relationships match field observations that were collected close to either midday or midnight. These findings advance our understanding of biotic controls of leaf-level Δ T variability, highlighting a trait-based representation of leaf energy balance that can improve simulations of diverse leaf thermoregulation strategies across species and physiological responses to climate change. [ABSTRACT FROM AUTHOR]

Abstract: This paper uses three different types of water vapor observation instruments, radiosonde, AERONET sunphotometer and GPS, to infer the regression coefficients of one WVR (model: WVR-1100) in Hong Kong – a coastal city with high humidity. The regression using the three types of reference water vapor data is performed on a monthly basis for 6 months from January to June 2012. In order to evaluate the WVR regression accuracies, a water vapor-assisted (WV-assisted) GPS Precise Point Positioning (PPP) method is proposed. The inferred water vapor data are directly injected into PPP computation to correct the water vapor wet tropospheric delay in GPS signals. In principle, water vapor of better accuracy will produce GPS PPP solutions of higher accuracy. Our analysis results show that the radiosonde, AERONET and GPS data all can be used to regress WVR and produce accurate WVR water vapor if the regressed instruments have good data quality. We find that the WVR water vapor inferred from GPS water vapor regression has the most reliable regression results. The vertical component of PPP solutions is very stable, with consistent biases (bias varying by 0.38cm) and standard deviations (bias variation by 0.59cm) over a 6-month period in 2012. When sufficient AEROENT water vapor data are available for WVR regression, the WVR water vapor accuracy will become compatible with that inferred from GPS water vapor regression. However AERONET water vapor measurements are seriously affected by weather condition and can be obtained only in sunny and clear conditions. Compared with the bias variation of 0.38cm using GPS water vapor to regress WVR, the WVR water vapor data regressed by radiosonde result in a bias variation of 3.95cm in the PPP vertical component during the 6-month period. All of the regressed WVR contain a bias, which possibly results from the fact that the WVR, GPS, AERONET and radiosonde stations are all horizontally and vertically separated. Overall, the WVR water vapor data inferred from GPS water vapor regression are regarded to be most reliable and it is the most suitable data source for WVR regression. [Copyright &y& Elsevier]

Abstract: This study demonstrates that thermal satellite images combined with ‘in situ’ ground data can be used to examine models of heat island genesis and thus identify the main causes of urban heat islands (UHIs). The models, although proposed over 30years ago, have not been thoroughly evaluated due to a combination of inadequate ground data and the low resolution of thermal satellite data. Also there has been limited understanding of the relevance of satellite-derived surface temperatures to local and regional scale air temperatures. A cloud-free ASTER thermal image of urban and rural areas of Hong Kong was obtained on a winter night with a well-developed heat island, accompanied by a 148km vehicle traverse of air temperatures. Over the whole traverse a high R 2 of 0.80 was observed between surface and air temperatures, with the two datasets showing a similar amplitude and general trend, but with the surface exhibiting much higher local variability than air temperature. Gradients in both surface and air temperature could be related to differences in land cover, with little evidence of large scale advection, thus supporting the population/physical structure model of UHI causation, rather than the advection model. However, the much higher surface and air temperatures observed over the largest urban area, Kowloon, than over any smaller urban centre with similar physical structure in the New Territories, would seem more indicative of the advection model. The image and ground data suggest that Kowloon''s urban canopy layer climate is mainly influenced by local city structure, but it is also modified by a strongly developed, regional scale urban boundary layer which has developed over the largest urban centre of Kowloon, and reinforces heating from both above and below. [Copyright &y& Elsevier]

The aim of this study is to characterize the urban heat island (UHI) intensity in Hong Kong. The first objective is to explore the UHI intensity in Hong Kong by using the mobile transverse and remote sensing techniques. The second objective is to produce a satellite-derived air temperature image by integrating satellite remote sensing with a mobile survey, the methodology involved in making simultaneous ground measurements when the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite made an overpass. The average UHI intensity of Hong Kong was about 2°–3.5°C, although a very high value of 12°C UHI was observed on a calm winter night by ASTER. The satellite-derived surface temperature was compared with the in situ measurements. The bias was found to be only about 1.1°C. A good correlation was also found between the in situ surface and air temperature pair of readings at nighttime on 31 January 2007. The linear regression lines of temperatures in urban and suburban areas were then used to convert the satellite-derived surface temperatures into air temperatures. The satellite-derived air temperatures showed a good correlation with temperatures observed by 12 fixed stations. It is possible to derive the nighttime air temperature from the satellite surface temperature on calm and clear winter nights. [ABSTRACT FROM AUTHOR]

Climate change influences the vulnerability of urban populations worldwide. To improve their adaptive capacity, the implementation of nature-based solutions (NBS) in urban areas has been identified as an appropriate action, giving urban planning and development an important role towards climate change adaptation/mitigation and risk management and resilience. However, the importance of extensively applying NBS is still underestimated, especially regarding its potential to induce significantly positive environmental and socioeconomic impacts across cities. Concerning environmental impacts, monitoring and evaluation is an important step of NBS management, where earth observation (EO) can contribute. EO is known for providing valuable disaggregated data to assess the modifications caused by NBS implementation in terms of land cover, whereas the potential of EO to uncover the role of NBS in urban metabolism modifications (e.g., energy, water, and carbon fluxes and balances) still remains underexplored. This study reviews the EO potential in the monitoring and evaluation of NBS implementation in cities, indicating that satellite observations combined with data from complementary sources may provide an evidence-based approach in terms of NBS adaptive management. EO-based tools can be applied to assess NBS' impacts on urban energy, water, and carbon balances, further improving our understanding of urban systems dynamics and supporting sustainable urbanization. [ABSTRACT FROM AUTHOR]

Restrictions on human and industrial activities due to the coronavirus (COVID-19) pandemic have resulted in an unprecedented reduction in energy consumption and air pollution around the world. Quantifying these changes in environmental conditions due to government-enforced containment measures provides a unique opportunity to understand the patterns, origins and impacts of air pollutants. During the lockdown in Pakistan, a significant reduction in energy demands and a decline of ∼1786 GWh (gigawatt hours) in electricity generation is reported. We used satellite observational data for nitrogen dioxide (NO 2), carbon monoxide (CO), sulphur dioxide (SO 2), aerosol optical depth (AOD) and land surface temperature (LST) to explore the associated environmental impacts of shifts in energy demands and emissions across Pakistan. During the strict lockdown period (March 23 to April 15, 2020), we observed a reduction in NO 2 emissions by 40% from coal-based power plants followed by 30% in major urban areas compared to the same period in 2019. Also, around 25% decrease in AOD (at 550 nm) thickness in industrial and energy sectors was observed although no major decrease was evident in urban areas. Most of the industrial regions resumed emissions during the 3rd quarter of April 2020 while the urban regions maintained reduced emissions for a longer period. Nonetheless, a gradual increase has been observed since April 16 due to relaxations in lockdown implementations. Restrictions on transportation in the cities resulted in an evident drop in the surface urban heat island (SUHI) effect, particularly in megacities. The changes reported as well as the analytical framework provides a baseline benchmark to assess the sectoral pollution contributions to air quality, especially in the scarcity of ground-based monitoring systems across the country. Image 1 [ABSTRACT FROM AUTHOR]

Tropical forests are the main reservoirs for global biodiversity and climate control. As secondary forests are now more widespread than primary forests, understanding their functioning and role in the biosphere is increasingly important. This includes understanding how they achieve stability, how they accumulate species and build biodiversity and how they cycle nutrients and carbon. This study investigates how we can restore tropical secondary forests to resemble high biomass, highly biodiverse and stable ecosystems seen today only in primary, undisturbed forests. The study used historic aerial photographs and recent high-resolution satellite images from 1945 to 2014 to map forest patches with five age categories, from 14-years to over 70-years, in Hong Kong's degraded tropical landscape. A forest inventory comprising 28 quadrats provided a rare opportunity to relate patterns of species composition at different stages during the succession with topographic and soil characteristics. The topographic variables accounted for 15% of the variance in species abundance, and age of forest stands explained 29%. Species richness rapidly increased after the first 15 years, but was lower in old-growth, than in medium age forest. This is attributed to the inability of late-successional species to disperse into the young forests as the natural dispersal agents (birds, mammals) have been lost. Light-loving pioneers which are unable to tolerate the shade of older forests, cannot regenerate in their own shade, therefore species diversity declines after a few decades. For ecosystem restoration in tropical secondary forests, introduction of late-successional species is necessary to assist natural succession, given the absence of native fauna, seed dispersal agents, and the surrounding altered environment. We also show that remote sensing can play a pivotal role in understanding the recovery and functioning of secondary forest regeneration as its contribution to the biosphere is increasingly important. Unlabelled Image • Forest age classes determined through multi-temporal remote sensing. • Higher elevation forest is more diverse but less abundant. • Topography explained 15% of the variance in the species composition. • Species diversity is not increasing in the older forest patches. • Later successional species in the landscape are restricted to Feng Shui woods. [ABSTRACT FROM AUTHOR]

Greenery and open spaces play significant roles in environmentally sustainable societies, providing urban ecosystem services and economic benefits that reduce urban poverty. Current urban poverty research has solely focused on top-down observations or direct human exposure to greenery and open spaces and has failed to sense landscape characteristics, including occupation and inequality, representing the social attributes of urban poverty. This paper demonstrates the potential to better understand certain social characteristics, including occupation and inequality between urban greenery and open spaces, and to further investigate their relationship with urban poverty. Percentage and aggregation indicators are proposed based on street view images to estimate the occupation and inequality between human perception-based greenery and open spaces. The relationship between human perception and urban poverty is accordingly analysed using geographically weighted regression (GWR). The GWR model results attain an R-squared value of 0.583 and further reveal that the relationships between human perception-based landscapes and urban poverty are spatially non-stationary, indicating varying relationships across space. This implication leads to an improved understanding of the relationship between greenery and open-space landscapes and living conditions and to further allowing effective policies to help identify deprived areas. • Human perception-based greenery and open-space landscapes are understood from the perception of occupation and inequality. • A geographically weighted regression model is applied to investigate human perception-based landscapes and urban poverty. • The relationships between human perception-based landscapes and urban poverty are spatially non-stationary. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics. [ABSTRACT FROM AUTHOR]

With the development of global urbanization, high-density cities faces enormous challenges on land supply. Hong Kong is a megacity with a long-term lack of land and housing supply. To increase the development intensity of built-up areas in Hong Kong, a minor relaxation of plot ratio/building height restrictions for twenty-one sites in the three target study areas of Kai Tak Development Area has been approved by the Town Planning Board in 2015. This study aims at providing scientific evidence on the impact of this relaxation and possible further relaxations using 3D spatial analyses and simulation technologies. After a 3D model of Kai Tak and surrounding areas being generated, effects of increased PR/BH on skyline, visibility, shadow and insolation, air temperature, and wind ventilation were successfully analysed based on five scenarios. Research findings indicated that, except the extreme case of Scenario 5, no significant changes were found in Scenario 2, Scenario 3 and Scenario 4 when compared with the original plan of Scenario 1. As a pilot study focusing in Hong Kong, this research may also serve to provide new methodological and theoretical insights to facilitate rational discussion and debate on changing land development density in other similar hyper-dense cities. • We measure impacts of minor relaxation of maximum plot ratio/building height restrictions. • 3D models of Kai Tak and surrounding areas are established in GIS virtual environment. • We simulate changes of visual and microenvironment using 3D spatial analyses under five scenarios. • The most recommended scenario and implications are discussed based on the analysis results. • The proposed approach can be applied to other cities in the similar context. [ABSTRACT FROM AUTHOR]

• A UV image detection system was designed for rapid visual detection of UV absorption gas. • It consisted of a high quantum efficiency UV camera, multiple different wavelength filters and gas regulators. • The UV images of SO 2 were obtained at different concentrations and temperatures. • The detection limit for SO 2 was as low as 4 ppb. We demonstrate a multi-wavelength ultraviolet (UV) imaging detection system for gaseous pollutants with absorption characteristics in the UV band. As a proof of concept, the detection of sulfur dioxide (SO 2) was used as an example to develop a method based on UV imaging. The critical component of the system is a highly sensitive CCD array (1024 × 1024 pixels) UV camera with high quantum efficiency (QE) in the UV range (>30% transmission at 200–240 nm, >50% transmission at 240–400 nm), multiple different wavelength bandpass filters (270 nm, 280 nm, 300 nm, and 310 nm for SO 2), environmental condition controller (temperature range 278–318 K, relative humidity range 35–85%), and a compact and robust design (362 mL of the gas absorption cell with a 4.2 m optical path length). The advantages of this system are convenient, fast, and can realize visualization. The system was applied for detecting SO 2 in various conditions (different concentrations, temperatures, and humidity). After optimization, the detection limit for SO 2 detection is 4 ppb, and the uncertainty of concentration measurement is only about 2.7%. [ABSTRACT FROM AUTHOR]