ALBERT

Description: Monitoring of land use, land-use changes, and forestry (LULUCF) plays a crucial role in biodiversity and global environmental challenges. In 2015, the Food and Agriculture Organization of the United Nations (FAO) launched the Global Forest Survey (GFS) integrating medium- (MR) and very-high-resolution (VHR) images through the FAO’s Collect Earth platform. More than 11,150 plots were inventoried in the Temperate FAO ecozone in Europe to monitor LULUCF from 2000 to 2015. As a result, 2.19% (VHR) to 2.77% (MR/VHR) of the study area underwent LULUCF, including a 0.37% (VHR) to 0.43% (MR/VHR) net increase in forest lands. Collect Earth and VHR images have also (i) allowed for shaping a preliminary structure of the land-use network, showing that cropland was the land type that changed most and that cropland and grassland were the more frequent land uses that generated new forest land, (ii) shown that, in 2015, mixed and monospecific forests represented 44.3% and 46.5% of the forest land, respectively, unlike other forest sources, and (iii) shown that 14.9% of the area had been affected by disturbances, particularly wood harvesting (67.47% of the disturbed forests). According to other authors, the area showed a strong correlation between canopy mortality and reported wood removals due to the transition from past clear-cut systems to “close-to-nature” silviculture.

Description: In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94≤R2≤0.98, 5.37≤RMSE≤7.9 and 20≤MAE≤33.

Description: The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km2 spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km2 BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO2 emission peak and carbon neutralization.

Description: Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

Description: Urban compactivity models are increasing around the globe, and sustainability has become the new buzzword. In recent decades, the focus of ecological responsibility has been shifted to the world’s cities, as they are the source of excessive consumption, major waste production, social inequalities, and global imbalances of economic wealth. This literature review is a contribution to the exploration of compactivity models that urgently aim at more sustainable forms of urban land-use, habitation, and transportation and considers: (i) compact cities; (ii) the 15-minute city; (iii) eco-villages/urban villages; (iv) transit oriented development; and (v) transit-corridor-livability. In the second section, we will address the debate on the need for governing authorities and the interdependence between micro-, meso- and macro dynamics for the implementation of transformational plans on a longue-durée. The work will be concluded with the presentation of a set of questions for exploring the need for a priority shift in political decision-making, the role of leadership articulation, and socio-economic inequity under the umbrella of environmental public anthropology.

Description: Yilan Bay is in the northeast corner of Taiwan at the junction of the East China Sea (ECS) and the Pacific Ocean. This study clarified the composition of water masses adjacent to Yilan Bay. The upper seawater in the bay is characterized by Kuroshio surface water, Taiwan warm current water, and shelf mixed water masses. The flow field in this area is mainly determined by the inter-actions among the northeastern Taiwan countercurrent, Kuroshio Current (KC), and tidal currents. The fall season is the main rainfall period in Yilan Bay, which causes a large amount of river runoff and a further increase in chlorophyll concentration, and the salinity of the upper water layer is observed much lower than other seasons. Water with a high chlorophyll concentration can flow into the ECS with ebb currents and the KC with ebb and flood currents. Combining hourly geosynchronous ocean color imager data and numerical simulation flow field helps us understand short-term changes of chlorophyll concentration. The trajectories of the drifters and virtual particle simulations help us understand the sources and movement of ocean currents in Yilan Bay. The seasonal swing of the KC path outside the bay is an important factor affecting the flow field and hydrological characteristics.

Description: An ionospheric anomaly is the irregular change of the ionosphere. It may result in potential threats for the ground-based augmentation system (GBAS) supporting the high-level precision approach. To counter the hazardous anomalies caused by the steep gradient in ionospheric delays, customized monitors are equipped in GBAS architectures. A major challenge is to rapidly detect the ionospheric gradient anomaly from environmental noise to meet the safety-critical requirements. A one-class support vector machine (OCSVM)-based monitor is developed to clearly detect ionospheric anomalies and to improve the robust detection speed. An offline-online framework based on the OCSVM is proposed to extract useful information related to anomalous characteristics in the presence of noise. To validate the effectiveness of the proposed framework, the influence of noise is fully considered and analyzed based on synthetic, semi-simulated, and real data from a typical ionospheric anomaly event. Synthetic results show that the OCSVM-based monitor can identify the anomaly that cannot be detected by other commonly-used monitors, such as the CCD-1OF, CCD-2OF and KLD-1OF. Semi-simulation results show that compared with other monitors, the newly proposed monitor can improve the average detection speed by more than 40% and decrease the minimum detectable gradient change rate to 0.002 m/s. Furthermore, in the real ionospheric anomaly event experiment, compared with other monitors, the OCSVM-based monitor can improve the detection speed by 16%. The result indicates that the proposed monitor has encouraging potential to ensure integrity of the GBAS.

Description: Remote sensing methodologies could contribute to a more sustainable agriculture, such as monitoring soil preparation for cultivation, which should be done properly, according to the topographic characteristics and the crop’s nature. The objectives of this work are to (1) demonstrate the potential of unmanned aerial vehicle (UAV) technology in the acquisition of 3D data before and after soil tillage, for the quantification of mobilised soil volume; (2) propose a methodology that enables the co-registration of multi-temporal DTMs that were obtained from UAV surveys; and (3) show the relevance of quality control and positional accuracy assessment in processing and results. An unchanged-area-matching method based on multiple linear regression analysis was implemented to reduce the deviation between the Digital Terrain Models (DTMs) to calculate a more reliable mobilised soil volume. The production of DTMs followed the usual photogrammetric-based Structure from Motion (SfM) workflow; the extraction of fill and cut areas was made through raster spatial modelling and statistical tools to support the analysis. Results highlight that the quality of the differential DTM should be ensured for a reliable estimation of areas and mobilised soil volume. This study is a contribution to the use of multi-temporal DTMs produced from different UAV surveys. Furthermore, it demonstrates the potential of UAV data in the understanding of soil variability within precision agriculture.

Description: In hyperspectral image (HSI) classification, convolutional neural networks (CNN) have been attracting increasing attention because of their ability to represent spectral-spatial features. Nevertheless, the conventional CNN models perform convolution operation on regular-grid image regions with a fixed kernel size and as a result, they neglect the inherent relation between HSI data. In recent years, graph convolutional networks (GCN) used for data representation in a non-Euclidean space, have been successfully applied to HSI classification. However, conventional GCN methods suffer from a huge computational cost since they construct the adjacency matrix between all HSI pixels, and they ignore the local spatial context information of hyperspectral images. To alleviate these shortcomings, we propose a novel method termed spectral-spatial offset graph convolutional networks (SSOGCN). Different from the usually used GCN models that compute the adjacency matrix between all pixels, we construct an adjacency matrix only using pixels within a patch, which contains rich local spatial context information, while reducing the computation cost and memory consumption of the adjacency matrix. Moreover, to emphasize important local spatial information, an offset graph convolution module is proposed to extract more robust features and improve the classification performance. Comprehensive experiments are carried out on three representative benchmark data sets, and the experimental results effectively certify that the proposed SSOGCN method has more advantages than the recent state-of-the-art (SOTA) methods.

Description: In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to realize autonomous navigation in semi-known environments. Specifically, the proposed framework utilizes the dueling double deep recurrent Q-learning, which can implement global path planning with the obstacle map as input. Moreover, the proposed framework combined with contrastive learning-based feature extraction can conduct real-time autonomous obstacle avoidance with monocular vision effectively. The experimental results demonstrate that our framework exhibits remarkable performance for both global path planning and autonomous obstacle avoidance.

Description: Australia’s Great Barrier Reef (GBR) is a globally unique and precious national resource; however, the geomorphic and benthic composition and the extent of coral habitat per reef are greatly understudied. However, this is critical to understand the spatial extent of disturbance impacts and recovery potential. This study characterizes and quantifies coral habitat based on depth, geomorphic and benthic composition maps of more than 2164 shallow offshore GBR reefs. The mapping approach combined a Sentinel-2 satellite surface reflectance image mosaic and derived depth, wave climate, reef slope and field data in a random-forest machine learning and object-based protocol. Area calculations, for the first time, incorporated the 3D characteristic of the reef surface above 20 m. Geomorphic zonation maps (0–20 m) provided a reef extent estimate of 28,261 km2 (a 31% increase to current estimates), while benthic composition maps (0–10 m) estimated that ~10,600 km2 of reef area (~57% of shallow offshore reef area) was covered by hard substrate suitable for coral growth, the first estimate of potential coral habitat based on substrate availability. Our high-resolution maps provide valuable information for future monitoring and ecological modeling studies and constitute key tools for supporting the management, conservation and restoration efforts of the GBR.

Description: The Fabry–Pérot interferometer (FPI) and meteor radar are two important techniques for measuring the horizontal wind field in mesopause region, the observations of which still lack comprehensive comparison. Kunming Observatory (25.6°N,103.8°E) has deployed both instruments in recent years and provides collocated meteor radar and FPI observations. The meteor radar measures the horizontal wind fields over 24 hours every day continuously, whereas the FPI can only work during the night with clear air condition. FPI horizontal wind data from the 892.0-nm airglow emission (with a peak height at ~87 km) from 26 January to 8 February 2019 were comparatively analyzed with simultaneous meteor radar observations, which cover the range between 80 and 90 km with a vertical resolution of 1.8 km. It was found that the temporal variations in the horizontal wind fields observed by the FPI and meteor radar were generally consistent with one another, with the highest 2-D correlation coefficients of 0.91 (0.88) at 88 (87) km for the meridional (zonal) wind, which agreed with the peak height of OH airglow emission observed by the TIMED/SABER instrument. In addition, the correlation coefficient for the weighted meteor radar horizontal wind by OH concentration between 86 and 88 km and 85 and 89 km increased slightly from 0.91 (0.89) to 0.92 (0.89) for the meridional (zonal) wind, which indicated the contribution of OH concentration beyond the peak height to the FPI wind observations. We also found that the absolute horizontal wind values detected by two instruments were linearly correlated with a slope of ~1.3 for both wind components, and meteor radar wind observations were usually larger than the FPI observations.

Description: This article describes the outcomes of the development of the project MATES to STEAM. The project is aimed at integrating Construction 4.0 content to a recently started new degree on Technologies on Civil Engineering. This integration is underpinned by the creation of STEAM-rich activities that can complement such degree. The philosophical design of these activities followed three requirements: (i) the activities should infuse Construction 4.0-related technologies, (ii) the activities should foster motivation among students with a STEAM vision by-design and (iii) the activities should be designed with a hardware-software independent perspective (open-source, accessible, affordable). Cornerstone and capstone projects as well as a set of workshops represent the demonstrators of these activities. All these demonstrators are knitted together in a single path in which an educational attempt to fill the identified Construction 4.0 gaps is proposed. The STEAM perspective provides completeness to the whole development. During the last two years, the project was developed and the design, the development and implementation of several demonstrators were completed. In the years to come, a systematic deployment and analysis of such demonstrators is expected when a full implementation of the new degree of Technologies in Civil Engineering will be addressed.

Description: Building typification is of theoretical interest and practical significance in map generalization. It aims to transform an initial set of buildings to a subset, while maintaining the essential distribution characteristics and important individual buildings. This study focuses on buildings located in residential suburban or rural areas and generalizes them to medium or small scale, for which the typification process can be viewed as point-similar object selection that generates exemplars in local building clusters. From this view, we propose a novel building typification approach using affinity propagation exemplar-based clustering. Based on a sparse graph constructed on the input building set, the proposed approach considers all buildings as potential cluster exemplars and keeps passing messages between those objects; thus, high-quality representative objects (i.e., exemplars) of the initial building set can be obtained and further outputted as the typified result. Experiments with real-life building data show that the proposed method is superior to the two existing representative methods in maintaining the overall distribution characteristics. Meanwhile, the importance of each individual building and the constraints of the road network can be embedded flexibly in this method, which gives some advantages in terms of preserving important buildings and the local structural distribution along the road, etc.

Description: This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

Description: Secondary cities are rapidly growing areas in low- and middle-income countries that lack data, planning, and essential services for sustainable development. Their rapid, informal growth patterns mean secondary cities are often data-poor and under-resourced, impacting the ability of governments to target development efforts, respond to emergencies, and design sustainable futures. The United Nations’ Sustainable Development Goal (SDG) 11 focuses on inclusive, safe, resilient, and sustainable cities and human settlements. SDG Indicator (SDGI) 11.3.1 calculates the ratio of land consumption rate to population growth rate to enhance inclusive and sustainable urbanization. Our paper compares three cities—Denpasar, Indonesia; Kharkiv, Ukraine; and Mekelle, Ethiopia—that were part of the Secondary Cities (2C) Initiative of the U.S. Department of State, Office of the Geographer and Global Issues to assess SDGI 11.3.1. The 2C Initiative focused on field-based participatory mapping for data generation to assist city planning. Urban form and population data are critical for calculating and visually representing this ratio. We examine the spatial extent of each city to assess land use efficiency (LUE) and track changes in urban form over time. With limited demographic and spatial data for secondary cities, we speculate whether SDGI 11.3.1 is useful for small- and medium-sized cities.

Description: Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.

Description: With the rapid development in the global economy and technology, urbanization has accelerated. It is important to characterize the urban expansion and determine its driving force. In this study, we used the Xiaonan District in Hubei Province, China, as an example to map and quantify the spatiotemporal dynamics of urban expansion from the two perspectives of built-up area and urban land in 1990–2020 by using remote sensing images. The location of rivers was found to be a primary limiting factor for spatial patterns and expansion of the built-up area. The transfer of the city center and the main direction of expansion generally corresponded well to the topography, policies, and development strategies. The built-up area expanded faster than the urban population in 1995–2020, which caused a waste in land resources. The results showed that the urban expansion first decreased and then increased during the research period. The increase in the proportion of the secondary industry was the main driving force of the urban expansion. Based on the characteristics of urban expansion in the past three decades, we conclude that the urban land of Xiaonan District will expand quickly in the future and will occupy vast agricultural land. The government must deploy control measures to balance the benefits and costs of urban expansion.

Description: The results of long-term satellite monitoring of oil pollution of the sea surface in the southeastern Baltic Sea (SEB) are discussed in this paper. From June 2004 to December 2020, in total, 2780 Synthetic Aperture Radar (SAR) images from different satellites were received and analyzed. There were 788 oil spills detected in the study area. The oil spills were concentrated along the main shipping routes in the SEB. The volume of the detected oil spills was estimated. The average size of the spill was about 2 km2 or 0.8 m3. Seasonal variability of oil pollution shows a decrease in the number of oil detections in the autumn–winter period, which is associated with the prevalence of unfavorable wind conditions that limit the use of SAR technology for oil spill detection and navigation for small ships. In situ measurements show that seasonal variation in the concentration of oil products in seawater is characterized by a maximum in April and a minimum in July. Since 2007, a decrease in oil detections has been observed for the entire Baltic Sea, including the study area. The interannual variability also shows a decrease in the concentration of oil products in the water column. In the southeastern Baltic Sea, the volume of oil products released yearly to the sea surface from ships does not exceed 0.1% of the average instantaneous presence of oil products in the water column.

Description: New technologies have allowed traditional map production criteria to be modified or even subverted. Starting from the communication sciences—journalism in particular—and digital humanities via the history of communication, we show how to use interactive digital maps for the production and publication of knowledge through and/or for participation. Firstly, we establish the theoretical-conceptual framework necessary to base the practices, dividing the elements into three areas: interactive maps and knowledge production (decentralization, pluralization, reticularization, and humanization), maps as instruments to promote political and social participation (egalitarianism, horizontality, and criticism), and maps as instruments for the visualization of data that favors the user experience (interactivity, multimediality, reticularity of reading, and participation). Next, we present two cases that we developed to put into practice the theoretical concepts that we established: the Mapa Infoparticipa (Infoparticipa Map), which shows the results of the evaluation of the transparency of public administrations, and the Ciutadania Plural (Plural Citizenship) web platform for the production of social knowledge about the past and the present. This theoretical and practical model shows the possibilities of interactive maps as tools to promote political participation and as instruments for the construction of social knowledge in a collaborative, participatory, networked way.

Description: Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.

Description: Dramatic urban land expansion and its internal sub-fraction change during 2000–2020 have taken place in Africa; however, the investigation of their spatial heterogeneity and dynamic change monitoring at the continental scale are rarely reported. Taking the whole of Africa as a study area, the synergic approach of normalized settlement density index and random forest was applied to assess urban land and its sub-land fractions (i.e., impervious surface area and vegetation space) in Africa, through time series of remotely sensed images on a cloud computing platform. The generated 30-m resolution urban land/sub-land products displayed good accuracy, with comprehensive accuracy of over 90%. During 2000–2020, the evaluated urban land throughout Africa increased from 1.93 × 104 km2 to 4.18 × 104 km2, with a total expansion rate of 116.49%, and the expanded urban area of the top six countries accounted for more than half of the total increments, meaning that the urban expansion was concentrated in several major countries. A turning green Africa was observed, with a continuously increasing ratio of vegetation space to built-up area and a faster increment of vegetation space than impervious surface area (i.e., 134.43% vs., 108.88%) within urban regions. A better living environment was also found in different urbanized regions, as the newly expanded urban area was characterized by lower impervious surface area fraction and higher vegetation fraction compared with the original urban area. Similarly, the humid/semi-humid regions also displayed a better living environment than arid/semi-arid regions. The relationship between socioeconomic development factors (i.e., gross domestic product and urban population) and impervious surface area was investigated and both passed the significance test (p 〈 0.05), with a higher fit value in the former than the latter. Overall, urban land and its fractional land cover change in Africa during 2000–2020 promoted the well-being of human settlements, indicating the positive effect on environments.

Description: Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that “blue carbon” plays in global carbon cycle.

Description: The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin.

Description: Although remote sensors have been increasingly providing dense data and deriving reanalysis data for inversion of particulate matters, the use of these data is considerably limited by the ground monitoring samples and conventional machine learning models. As regional criteria air pollutants, particulate matters present a strong spatial correlation of long range. Conventional machine learning cannot or can only model such spatial pattern in a limited way. Here, we propose a method of a geographic graph hybrid network to encode a spatial neighborhood feature to make robust estimation of coarse and fine particulate matters (PM10 and PM2.5). Based on Tobler’s First Law of Geography and graph convolutions, we constructed the architecture of a geographic graph hybrid network, in which full residual deep layers were connected with graph convolutions to reduce over-smoothing, subject to the PM10–PM2.5 relationship constraint. In the site-based independent test in mainland China (2015–2018), our method achieved much better generalization than typical state-of-the-art methods (improvement in R2: 8–78%, decrease in RMSE: 14–48%). This study shows that the proposed method can encode the neighborhood information and can make an important contribution to improvement in generalization and extrapolation of geo-features with strong spatial correlation, such as PM2.5 and PM10.

Description: Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and lack of assessment for different lodging types. To address this issue, based on the mechanism of different lodging types, the Archimedean copula function was used to describe the joint probability distribution of wind speed and precipitation, and the lodging risk assessment model of maize was established. By comparing the goodness of fit, when the rank correlation coefficient of these two is positive and negative, the corresponding optimal joint probability distribution functions are the Gumbel copula and Frank copula. According to the spatial distribution of lodging risk, the area from Liaodong Bay northward to Tongyu, Jilin province in the Northeast and the North China Plain has a high frequency of lodging, in which the probability of stalk lodging is two to four times that of root lodging. Finally, we discussed how to apply the lodging risk distribution results to optimize the maize variety test sites to improve the efficiency and reliability of the existing test system. The method proposed in this paper comprehensively considers the synergistic effect of multiple factors and can provide technical support for other risk assessment.

Description: Extended reality (XR) technology is increasingly often considered in practical applications related to urban planning and smart city management. It offers many advantages as a new visualization technique that gives its users access to places that are not available in material space and a unique perspective on existing objects. It can provide immersive multi-sensory experience that can induce emotional response in participatory planning. However, standard mode of implementation that relies on mobile phone applications and VR headsets has a disadvantage when it comes to availability and accessibility. Here we test the WebXR solution that can mitigate those problems. We have created six AR and VR environments that resembled common urban planning scenarios and conducted usability tests with people having planning and GIS background. Results indicate that WebXR can provide useful solution in urban planning when the interface and environment resemble common practices and situations encountered in real life. Environments that have introduced new digital affordances like AR measurements or semi-transparent walkable scale models were rated lower. Users evaluated presented environment as having high usability and expressed their positive attitude toward using XR in their professional practice mainly as a participatory and visualization tool.

Description: Rising sea levels pose one of the greatest threats to coastal zones. However, sea-level changes near the coast, particularly absolute sea-level changes, have been less well monitored than those in the open ocean. In this study, we aim to investigate the potential of Global Navigation Satellite Systems Interferometric Reflectometry (GNSS-IR) to measure coastal absolute sea-level changes and tie on-land (coastal GNSS) and offshore (satellite altimetry) observations into the same framework. We choose three coastal GNSS stations, one each in regions of subsidence, uplift and stable vertical land motions, to derive both relative sea levels and sea surface heights (SSH) above the satellite altimetry reference ellipsoid from 2008 to 2020. Our results show that the accuracy of daily mean sea levels from GNSS-IR is

Description: In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Description: Carbon neutrality has positive impacts on people, nature and the economy, and buildings represent the “last mile” sector in the transition to carbon neutrality. Carbon neutrality is characterized by the decarbonization of operations and maintenance, in addition to zero emissions in electricity and other industry sectors. Taking China’s commercial buildings as an example, this study is the first to perform an extensive data analysis for a step-wise carbon neutral roadmap of building operations via the analysis of a dynamic emission scenario. The results reveal that the carbon emissions abatement of commercial building operations from 2001 to 2018 was 1460.85 (±574.61) mega-tons of carbon dioxide (Mt CO2). The carbon emissions of commercial building operations will peak in the year 2039 (±5) at 1364.31 (±258.70) Mt, with emission factors and energy intensity being the main factors influencing the carbon peak. To move toward carbon neutral status, an additional 169.73 Mt CO2 needs to be cut by 2060, and the low emission path toward carbon neutrality will lead to the realization of the carbon peak of commercial buildings in 2024, with total emissions of 921.71 Mt. It is believed that cutting emissions from the operation of buildings in China will require a multi-sectoral synergistic strategy. It is suggested that government, residents, enterprises, and other stakeholders must better appreciate the challenges to achieve a substantial carbon reduction and the need for urgent action in the building sector in order to achieve carbon neutrality.

Description: Publicly available optical remote sensing images from platforms such as Sentinel-2 satellites contribute much to the Earth observation and research tasks. However, information loss caused by clouds largely decreases the availability of usable optical images so reconstructing the missing information is important. Existing reconstruction methods can hardly reflect the real-time information because they mainly make use of multitemporal optical images as reference. To capture the real-time information in the cloud removal process, Synthetic Aperture Radar (SAR) images can serve as the reference images due to the cloud penetrability of SAR imaging. Nevertheless, large datasets are necessary because existing SAR-based cloud removal methods depend on network training. In this paper, we integrate the merits of multitemporal optical images and SAR images to the cloud removal process, the results of which can reflect the ground information change, in a simple convolution neural network. Although the proposed method is based on deep neural network, it can directly operate on the target image without training datasets. We conduct several simulation and real data experiments of cloud removal in Sentinel-2 images with multitemporal Sentinel-1 SAR images and Sentinel-2 optical images. Experiment results show that the proposed method outperforms those state-of-the-art multitemporal-based methods and overcomes the constraint of datasets of those SAR-based methods.

Description: The present study investigates the issue of computer-aided daylight evaluation in an office room with a light shelf and dropped translucent ceiling. In this type of room, daylight is admitted from two sources: (i) a standard window in the wall and (ii) via a light shelf and clerestory window, which illuminate the plenum located above the working space. The light from the plenum is transmitted through the translucent ceiling into the office room. The present study is based on data obtained through a computer-aided daylight simulation by DeLuminæ (DL-Light, ver. 11.0.9, and DL-Instant, ver. 6.1.4) software using the Radiance engine and real weather data for Wroclaw, Poland, at 51st lat. N. An office room of 12 × 6 m with different shading and daylight distribution scenarios was simulated (Variants 1–5). Next, the useful daylight illuminance (UDI (%)) for the range of 300–3000 lx and daylight glare probability (DGP) were calculated. To further optimize the daylighting scenarios, an adaptive shading system was simulated, which was activated when the illuminance value dropped below 300 lx. In the final variant, Variant 6, mean UDI300–3000 values were recorded to be above 80% for 95% of the area of the work plane. This allows the conclusion that a light shelf and translucent ceiling guide daylight deep into the room, improving uniformity and reducing glare when the standard window is covered by an adaptive shading system.

Description: With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain.

Description: Water depth estimation in seaports is essential for effective port management. This paper presents an empirical approach for water depth determination from satellite imagery through the integration of multiple datasets and machine learning algorithms. The implementation details of the proposed approach are provided and compared against different existing machine learning algorithms with a single training set. For a single training set and a single machine learning method, our analysis shows that the proposed depth estimation method provides a better root-mean-square error (RMSE) and a higher coefficient of determination (R2) under turbid water conditions, with overall RMSE and R2 improvements of 1 cm and 0.7, respectively. The developed method may be employed in monitoring dredging activities, especially in areas with polluted water, mud and/or a high sediment content.

Description: Moored upward-looking Acoustic Doppler Current Profilers (ADCPs) can be used to observe sea ice draft. While previous studies relied on the availability of auxiliary pressure sensors to measure the instrument depth of the ADCP, we present an adaptive approach that infers instrument depth from ADCP bottom track (BT) mode measurements of error velocity and range. The ADCP-derived ice draft time series are validated with data from adjacent Upward-Looking Sonar (ULS) moorings. We demonstrate that this method can be used to obtain daily mean sea ice draft time series that, on average, are within 20% of ULS-derived draft time series. ULS and ADCP ice draft time series were observed by four moorings in the Laptev Sea and show correlations between 0.7 and 0.9. This new approach is not a substitute for high-frequency, high-precision ULS measurements of ice draft but it provides a low-cost opportunity to derive daily mean ice draft time series accessing existing ADCP data that have not been not used for that purpose to date. This method has the potential to close data gaps and extend existing ice draft time series in all ice-covered regions and supports the validation of sea ice thickness products from satellite missions such as CryoSat-2, SMOS or ENVISAT.

Description: The scarcity of surface water resources in the dry season in the Kilinochchi district increases the demand for freshwater. Therefore, the main objective of this study is to delineate potential groundwater zones in Kilinochchi, Sri Lanka, using integrated remote sensing (RS), geographic information systems (GIS) and the analytical hierarchy process (AHP). Groundwater potential zones are demarcated for the Kilinochchi district by overlaying thematic layers: geology, geomorphology, land use/land cover, soil types, drainage density, slope, lineament, and rainfall. The thematic layers were integrated into a geographic information system, and a weighted overlay analysis was carried out to delineate groundwater zones. Thus the resultant map is categorized into five different potential zones: very low (59.12 km2), low (207.78 km2), moderate (309.89 km2), high (507.74 km2), and very high (111.26 km2). The groundwater potential map was validated with the existing seventy-nine wells, which indicated a good prediction accuracy of 81.8%. This suggests that the results obtained by integrating RS-GIS and AHP are well-matched with the existing well water depth. The AHP approach based on RS-GIS was a handy and efficient technique for assessing potential groundwater zones. This research will help policymakers better manage the Kilinochchi district’s groundwater resources and give scope for further research into groundwater exploration in the area.

Description: Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over a large area, and LST is highly correlated with Ta. To measure the Ta in a broad area, we studied a Ta retrieval method through Deep Neural Network (DNN) using in-situ data and satellite data of South Korea from 2014 to 2017. To retrieve accurate Ta, we selected proper input variables and conditions of a DNN model. As a result, Normalized Difference Vegetation Index, Normalized Difference Water Index, and 11- and 12-µm band data were applied to the DNN model as input variables. And we also selected proper condition of the DNN model with test various conditions of the model. In validation result in the DNN model, the best accuracy of the retrieved Ta showed an correlation coefficient value of 0.98 and a root mean square error (RMSE) of 2.19 K. And then we additional 3 analysis to validate accuracy which are spatial representativeness, seasonal analysis and time series analysis. We tested the spatial representativeness of the retrieved Ta. Results for window sizes less than 132 × 132 showed high accuracy, with a correlation coefficient of over 0.97 and a RMSE of 1.96 K and a bias of −0.00856 K. And in seasonal analysis, the spring season showed the lowest accuracy, 2.82 K RMSE value, other seasons showed high accuracy under 2K RMSE value. We also analyzed a time series of six the Automated Synoptic Observing System (ASOS) points (i.e., locations) using data obtained from 2018 to 2019; all of the individual correlation coefficient values were over 0.97 and the RMSE values were under 2.41 K. With these analysis, we confirm accuracy of the DNN model was higher than previous studies. And we thought the retrieved Ta can be used in other studies or climate model to conduct urban problems like urban heat islands and to analyze effects of arctic oscillation.

Description: Many researchers have unraveled innovative ways of examining geographic information to better understand the determinants of crime, thus contributing to an improved understanding of the phenomenon. Property crimes represent more than half of the crimes reported in Portugal. This study investigates the spatial distribution of crimes against property in mainland Portugal with the primary goal of determining which demographic and socioeconomic factors may be associated with crime incidence in each municipality. For this purpose, Geographic Information System (GIS) tools were used to analyze spatial patterns, and different Poisson-based regression models were investigated, namely global models, local Geographically Weighted Poisson Regression (GWPR) models, and semi-parametric GWPR models. The GWPR model with eight independent variables outperformed the others. Its independent variables were the young resident population, retention and dropout rates in basic education, gross enrollment rate, conventional dwellings, Guaranteed Minimum Income and Social Integration Benefit, purchasing power per capita, unemployment rate, and foreign population. The model presents a better fit in the metropolitan areas of Lisbon and Porto and their neighboring municipalities. The association of each independent variable with crime varies significantly across municipalities. Consequently, these particularities should be considered in the design of policies to reduce the rate of property crimes.

Description: Semi-natural grasslands contribute highly to biodiversity and other ecosystem services, but they are at risk by the spread of invasive plant species, which alter their habitat structure. Large area grassland monitoring can be a powerful tool to manage invaded ecosystems. Therefore, WorldView-3 multispectral sensor data was utilized to train multiple machine learning algorithms in an automatic machine learning workflow called ‘H2O AutoML’ to detect L. polyphyllus in a nature protection grassland ecosystem. Different degree of L. polyphyllus cover was collected on 3 × 3 m2 reference plots, and multispectral bands, indices, and texture features were used in a feature selection process to identify the most promising classification model and machine learning algorithm based on mean per class error, log loss, and AUC metrics. The best performance was achieved with a binary classification of lupin-free vs. fully invaded 3 × 3 m2 plot classification with a set of 7 features out of 763. The findings reveal that L. polyphyllus detection from WorldView-3 sensor data is limited to large dominant spots and not recommendable for lower plant coverage, especially single plant detection. Further research is needed to clarify if different phenological stages of L. polyphyllus as well as time series increase classification performance.

Description: Soil erosion is one of the most challenging environmental issues in the world, causing unsustainable soil loss every year. In South Africa, several episodes of gully erosion have been documented and clearly linked to the presence of Quaternary colluvial deposits on the Drakensberg Mountain footslopes. The aim of this study was to identify and assess the triggering factors of gully erosion in the upper Mkhomazi River basin in KwaZulu-Natal, South Africa. We compiled a gully inventory map and applied remote sensing techniques as well as field surveys to validate the gully inventory. The gullies were subdivided into slope gullies and fluvial gullies. We derived susceptibility maps based on the spatial distribution of gully types to assess the most important driving factors. A stochastic modeling approach (MaxEnt) was applied, and the results showed two susceptibility maps within the spatial distribution of the gully erosion probability. To validate the MaxEnt model results, a subset of the existing inventory map was used. Additionally, by using areas with high susceptibilities, we were able to delineate previously unmapped colluvial deposits in the region. This predictive mapping tool can be applied to provide a theoretical basis for highlighting erosion-sensitive substrates to reduce the risk of expanding gully erosion.

Description: The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics (P-DLA) for bathymetry without in-situ depth data. This is done using WorldView-2 images of blue and green bands. Further, the pixel sampling principles for solving the four key parameters of the model are summarized. Firstly, this paper elaborates on the physical mechanism of the P-DLA model. All unknown parameters of the P-DLA model are solved by different types of sampling pixels extracted from multispectral images for bathymetric measurements. Ganquan Island and Zhaoshu Island, where accuracy evaluation is performed for the bathymetric results of the P-DLA model with in-situ depth data, were selected to be processed using the method to evaluate its performance. The root mean square errors (RMSEs) of the Ganquan Island and Zhaoshu Island results are 1.69 m and 1.74 m with the mean relative error (MREs) of 14.8% and 18.3%, respectively. Meanwhile, the bathymetric inversion is performed with in-situ depth data using the traditional dual-band log-linear regression model (DLR). The results show that the accuracy of the P-DLA model bathymetry without in-situ depth data is roughly equal to that of the DLR model water depth inversion based on in-situ depth data. The results indicate that the P-DLA model can still obtain relatively ideal bathymetric results despite not having actual bathymetric data in the model training. It also demonstrates underwater microscopic features and changes in the islands and reefs.

Description: Accurate quantification of vertical structure (or 3D structure) and its change of a city is essential for understanding the evolution of urban form, and its social and ecological consequences. Previous studies have largely focused on the horizontal structure (or 2D structure), but few on 3D structure, especially for long time changes, due to the absence of such historical data. Here, we present a new approach for 3D reconstruction of urban history, which was applied to characterize the urban 3D structure and its change from 1986 to 2017 in Shenzhen, a megacity in southern China. This approach integrates the contemporary building height obtained from the increasingly available data of building footprint with building age estimated based on the long-term observations from time-series Landsat imagery. We found: (1) the overall accuracy for building change detection was 87.80%, and for the year of change was 77.40%, suggesting that the integrated approach provided an effective method to cooperate horizontal (i.e., building footprint), vertical (i.e., building height), and temporal information (i.e., building age) to generate the historical data for urban 3D reconstruction. (2) The number of buildings increased dramatically from 1986 to 2017, by eight times, with an increased proportion of high-rise buildings. (3) The old urban areas continued to have the highest density of buildings, with increased average height of buildings, but there were two emerging new centers clustered with high-rise buildings. The long-term urban 3D maps allowed characterizing the spatiotemporal patterns of the vertical dimension at the city level, which can enhance our understanding on urban morphology.

Description: A new phase-based filter (called improved analytical signal (IAS)) is introduced to detect buried unexploded ordnance (UXO) precisely from magnetic fields using the arcsine function of the ratio of the first-order vertical derivative of the analytical signal to the first-order derivatives of the x-, y-, and z-components of the analytical signal. The calculations are computed in the frequency domain and then transformed back into the space domain using the inverse Fourier transform. The filter has been tested on magnetic data collected at a test site with UXO bodies of variable orientation. It was also validated on magnetic data measured at a former army artillery range in Slovakia. The results show that the IAS filter not only revealed better imaging of the UXO bodies compared to the other commonly used filters but also produced a high-resolution image with much less influence of noise.

Description: Haiyang-2C (HY-2C) is a dynamic, marine-monitoring satellite that was launched by China and is equipped with an onboard dual-frequency GPS receiver named HY2_Receiver, which was independently developed in China. HY-2C was successfully launched on 21 September 2020. Its precise orbit is an important factor for scientific research applications, especially for marine altimetry missions. The performance of the HY2_Receiver is assessed based on indicators such as the multipath effect, ionospheric delay, cycle slip and data utilization, and assessments have suggested that the receiver can be used in precise orbit determination (POD) missions involving low-Earth-orbit (LEO) satellites. In this study, satellite-borne GPS data are used for POD with a reduced-dynamic (RD) method. Phase centre offset (PCO) and phase centre variation (PCV) models of the GPS antenna are established during POD, and their influence on the accuracy of orbit determination is analysed. After using the PCO and PCV models in POD, the root mean square (RMS) of the carrier-phase residuals is around 0.008 m and the orbit overlap validation accuracy in each direction reaches approximately 0.01 m. Compared with the precise science orbit (PSO) provided by the Centre National d’Etudes Spatiales (CNES), the RD orbit accuracy of HY-2C in the radial (R) direction reaches 0.01 m. The accuracy of satellite laser ranging (SLR) range validation is better than 0.03 m. Additionally, a new method is proposed to verify the accuracy of the RD orbit of HY-2C by using space-borne Doppler orbitography and radiopositioning integrated by satellite (DORIS) data directly. DORIS data are directly compared to the result calculated using the accurate coordinates of beacons and the RD orbit, and the results indicate that the external validation of HY-2C RD orbit has a range rate accuracy of within 0.0063 m/s.