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Estimating bathymetric information is vital for aquaculture and navigation applications. Free, high-resolution satellite imagery provides a cost-effective solution for routine bathymetric measurements. We tested six algorithms to retrieve water depth in the Mwanza Gulf of Lake Victoria using Sentinel-2 satellite imagery: the conventional Stumpf method, Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Neural Network (NNET), and Support Vector Machine (SVM). In-situ depth points collected via echo sounders were used to train and validate the algorithms. Performance evaluation metrics included coefficient of determination (R2), mean absolute error (MAE), root-mean-square error (RMSE), and spatial autocorrelation of residuals. Among the algorithms tested, the Stumpf model exhibited moderate performance with an R2 of 0.441, higher MAE (2.078 m), and RMSE (2.964 m) values. The RF algorithm improved performance with an R2 of 0.957, lower MAE (0.476 m), and RMSE (0.823 m). The GBM and XGB algorithms achieved R2 values of 0.960 and 0.956, respectively, with low MAE (0.484 m for GBM, 0.482 m for XGB) and RMSE (0.795 m for GBM, 0.830 m for XGB) values. The NNET algorithm outperformed the GBM and XGB models, obtaining an R2 of 0.963, the lowest MAE (0.438 m), and RMSE (0.761 m). The SVM algorithm demonstrated the best performance with an R2 of 0.965, the lowest MAE (0.403 m), and RMSE (0.745 m), implying the highest accuracy in depth estimation. SVM also showed stable generalization across different locations with insignificant spatial autocorrelation of residuals. Therefore, SVM is recommended for repetitive bathymetry calculations. [ABSTRACT FROM AUTHOR]

Knowledge of the number of building stories (NoS) is critical for understanding and regulating the urban development process. Existing approaches often transform building heights into numbers of stories using a specific empirical story-height coefficient, e.g., 3 meters for 1 story. However, the story heights of different buildings might differ for various reasons, such as different functional types within a city, differences in urban planning regulations among cities, or the regulations in different construction years. Based on a theoretical analysis and empirical statistics regarding the changes in vertical building information in seasonal composite images, we present a novel method for directly estimating the NoS in individual buildings from optical images. Specifically, four seasonal composite Sentinel-2 images taken within a year are utilized to estimate the NoS of each building with a modified object-detection network to make full use of vertical building information. The proposed method is called the Stories number EstimAtion from Seasonal composite images with an Object detection Network (SEASONet) method. Both theoretical analysis and empirical statistics are used to determine why seasonal composite optical images can effectively provide vertical building information. To validate the performance of the proposed method, we collect data from 61 Chinese cities with various building types, train the model with data from 47 cities (1365 998 buildings) and quantitatively test the model using data from the remaining 14 cities (246 584 buildings). In addition, M 3 Net using ZY-3 multiview images for the pixel-level estimation of building heights is adapted for comparison. The experimental results show that SEASONet achieves lower mean absolute error (MAE) and root mean square error (RMSE) values than M 3 Net over all 14 cities used for testing. Ablation experiments show that the four seasonal composite images are the keys for improving the estimation of the number of stories in high-rise buildings. A comparison with the results of two state-of-the-art methods that use empirical coefficients to convert building height to story number further confirms the superiority of the proposed method, especially its effectiveness in estimating the number of stories in high-rise buildings. • Geometric relationship among the Sun, building height and date of an image. • Empirical statistics on seasonal changes of building shadows in China. • Seasonal composite Sentinel-2 images for building story number estimation. [ABSTRACT FROM AUTHOR]

Crescentic nearshore sandbars (CNSBs) that are observed in the shallow waters (< 10 m) of sandy beaches are important for understanding coastal dynamics because of their strong interaction with nearshore circulation. However, their formation, originating from shore-parallel straight nearshore sandbars (SNSBs), has rarely been observed in the field because their occurrence is typically short (less than a week). In this study, a process in which a nearly SNSB changed into a fully developed CNSB was observed using satellite images, field surveys, and video monitoring data at a sandy beach in South Korea. Freely available Sentinel-2 satellite images and the bathymetry data measured by echosounders were used to find out formation of an SNSB system after attack of Typhoon Tapah in September 2019 and that the SNSB changed to a CNSB in February 2020. The process was narrowed down using video monitoring data and hydrodynamic measurements, observing that two storm waves with maximum wave heights of >3 m developed in the site over a one-month period in January 2020 when the CNSB formed. The first storm wave had a sharp peak wave height that reached ~5 m and lasted ~2 days. The second storm wave had lower wave energy but several peak waves of ~4 m height and a total storm period of ~6 days. During both storm periods, the infragravity wave energy increased and strong (>0.5 m/s) offshore and longshore (northwest) currents developed for ~1 day and ~ 3 days for the first and second storm respectively. The results from video monitoring data show that a nearly developed SNSB system transformed into a weakly developed CNSB system after the first storm when alongshore variability on the nearly SNSB was intensified to become horns and bays of the weak CNSB. During the second storm, the CNSB system fully developed as the horns moved further onshore and the bays further offshore shaping the clear horn and bay pattern. This indicates that positive feedback between the flows and sediments played a key role for the formation of the CNSB and, therefore, the self-organization mechanism might be appropriate to describe the process during the two storm periods. Specifically, the high infragravity wave energy and strong quasi-steady currents were important because they could trigger the development of rip channels during the first storm whereas the channels were further strengthened by the long consistency of currents during the second storm. • Formation of CSNB under storm wave conditions was observed using satellite images, video monitoring and field experiment data. • Crescentic pattern was triggered by the first storm and the CNSB was fully formed during the second storm. • Development of quasi-steady flows during the two storms was a key factor in the CNSB formation through positive feedback between the flows and sediments, supporting the self-organization as a primary mechanism. • Long-term consistency of high waves during the second storm played a role in shaping the crescentic pattern by moving the sediments in the horns/bays further onshore/offshore. [ABSTRACT FROM AUTHOR]

Modern cities are constantly growing; this fact provokes strong environmental pressure as pollution, health problems, stress, and other troubles which as a whole reduces the citizens' life quality. Some decades ago, the concept sustainable urban planning was created; the concept intends to generate friendly cities with a planned development. This research contributes to this task evaluating the potential that urban forests could have in the design of green corridors for Latin American intermediate cities (case study, Temuco, Chile). For the analysis and the generation of data, the Geographic Information System was applied. Also, the multispectral images were used with data derived from the Normalized Difference Vegetation Index (NDVI). The satellite used was the Sentinel-2, which gives red and infrared spectral information with a resolution of 10 × 10 m pixels providing vital information to analyze the quality of the vegetation. Methodologies applied were based on forestry ecosystem samples as well as on satellite technology. This helped to define the quality of the urban green areas allowing the connection to future green corridors. From these tools it was found that the green areas possess good quality vegetation, establishing the sanity, the form, the plant vigor, the stress, the chlorophyll activity and the vegetal cover. The results demonstrate that these combined methodologies of forestry ecology and geospatial tools, such as NDVI, are a good possibility to generate a continuous monitoring and follow up system of public areas, which in turn will allow a planning of those cities that contribute to a sustainable urban planning and with a better life quality for their population. [ABSTRACT FROM AUTHOR]

Twenty-two years of suspended particulate matter (SPM) concentrations in the Yellow River estuary and adjacent sea, China were derived from 532 Landsat and Sentinel 2A/B satellite images. Optimal SPM retrieval model was selected by comparing five state-of-art models using 79 in-situ datasets and recalibrated to ensure consistency among multiple-sensor-derived SPM concentrations. SPM in the estuary, in South Bohai Bay, and Laizhou Bay exhibited distinct temporal variations. 73% and 52% of the interannual and monthly SPM variations near the river mouth were explained by riverine water and sediment discharge, showing impact of the operation of the Xiaolangdi Reservoir and Water-Sediment Regulation Scheme. Land area accretion and erosion in river delta are associated with SPM variation. Riverine impacts on SPM rapidly declined off-shore because of the rapid deposition of the coarse-grain sediment. Ocean current and wind-wave forces explained high concentrations and intra-annual variations of SPM in the South Bohai Bay and Laizhou Bay. • 22 years SPM from multiple satellite sensors at Yellow River estuary (YRE), China • SPM retrieval model was recalibrated to ensure consistency among multiple sensors. • SPM in YRE and Laizhou/South Bohai Bay, China show distinct seasonal patterns. • Riverine human activities cause great temporal variations of SPM in the estuary. • Relationship between SPM variation and land area change in YRD was revealed. [ABSTRACT FROM AUTHOR]