Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis

Multiscale methods have become progressively valuable in geomorphometric analysis as data have become increasingly detailed. This paper evaluates the theoretical and empirical properties of several common scaling approaches in geomorphometry. Direct interpolation (DI), cubic convolution resampling (RES), mean aggregation (MA), local quadratic regression (LQR), and an efficiency optimized Gaussian scale-space implementation (fGSS) method were tested. The results showed that when manipulating resolution, the choice of interpolator had a negligible impact relative to the effects of manipulating scale. The LQR method was not ideal for rigorous multiscale analyses due to the inherently non-linear processing time of the algorithm and an increasingly poor fit with the surface. The fGSS method combined several desirable properties and was identified as an optimal scaling method for geomorphometric analysis. The results support the efficacy of Gaussian scale-space as a general scaling framework for geomorphometric analyses.

Vibration Analyses of a Gantry Structure by Mobile Phone Digital Image Correlation and Interferometric Radar

The study presents results from applying the Real Aperture Radar interferometry technique and Digital Image Correlation through a mobile phone camera to identify static and dynamic deformations of a gantry during surveying operations on the Michelangelo’s David at the Galleria dell’Accademia di Firenze Museum in Florence. The statue has considerable size and reaches an elevation of more than seven meters on its pedestal. An ad-hoc gantry was designed and deployed, given the cramped operating area around the statue. The scanner had a stability control system that forbid surveying in instrument movements. However, considering the unicity of the survey and its rare occurrence, the previous survey had been carried out in the year 2000; verifying stability and recording deformations is a crucial task, and necessary for validation. As the gantry does not have an on-board stability sensor, and considering the hi-survey accuracy requested, a redundant, contactless, remote monitoring system of the gantry and the statue stability was chosen to guarantee the maximum freedom of movement around the David to avoid any interference during scanning operations. Thanks to the TInRAR technique, the gantry and the statue were monitored with an accuracy of 0.01 mm. At the same time, a Digital Image Correlation analysis was performed on the gantry, which can be considered a Multi-Degree-Of-Freedom (MDOF) system, to accurately calculate the vibration frequency and amplitude. A comparison between TInRAR and DIC results reported substantial accordance in detecting gantry’s oscillating frequencies; a predominant oscillation frequency of 1.33 Hz was identified on the gantry structure by TinSAR and DIC analysis.

Image Assisted Total Stations for Structural Health Monitoring—A Review

Measuring structures and its documentation is one of the tasks of engineering geodesy. Structural health monitoring (SHM) is defined as a periodic or continuous method to provide information about the condition of the construction through the determination of measurement data and their analysis. In SHM, wide varieties of sensors are used for data acquisition. In the following, the focus is on the application of image assisted total stations (IATS). The combination of tacheometry and photogrammetric measurement offers high flexibility and precision. Different approaches of automated detecting and matching whose applications have been tested in practice are briefly explained. A distinction is made between built-in cameras (commercial) and external camera systems (prototypes). Various successful applications of IATS in the field of SHM are presented and explained.

Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.

Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances According to Local Topography

The use of Light Detection and Ranging (LiDAR) is becoming more and more common in different landscape exploration domains such as archaeology or geomorphology. In order to allow the detection of features of interest, visualization filters have to be applied to the raw Digital Elevation Model (DEM), to enhance small relief variations. Several filters have been proposed for this purpose, such as Sky View Factor, Slope, negative and positive Openness, or Local Relief Model (LRM). The efficiency of each of these methods is strongly dependent on the input parameters chosen in regard of the topography of the investigated area. The LRM has proved to be one of the most efficient, but it has to be parameterized in order to be adapted to the natural slopes characterizing the investigated area. Generally, this setting has a single value, chosen as the best compromise between optimal values for each relief configuration. As LiDAR is mainly used in wide areas, a large distribution of natural slopes is often encountered. The aim of this paper is to propose a Self AdaptIve LOcal Relief Enhancer (SAILORE) based on the Local Relief Model approach. The filtering effect is adapted to the local slope, allowing the detection at the same time of low-frequency relief variation on flat areas, as well as the identification of high-frequency relief variation in the presence of steep slopes. First, the interest of this self-adaptive approach is presented, and the principle of the method, compared to the classical LRM method, is described. This new tool is then applied to a LiDAR dataset characterized by various terrain configurations in order to test its performance and compare it with the classical LRM. The results of this test show that SAILORE significantly increases the detection capability while simplifying it.

Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

This study validates the hourly satellite based and reanalysis based global horizontal irradiance (GHI) for sites in South Africa. Hourly GHI satellite based namely: SOLCAST, Copernicus Atmosphere Monitoring Service (CAMS), and Satellite Application Facility on Climate Monitoring (CMSAF SARAH) and two reanalysis based, namely, Fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and Modern-Era Retrospective Analysis for Research and Applications (MERRA2) were assessed by comparing in situ measured data from 13 South African Weather Service radiometric stations, located in the country’s six macro climatological regions, for the period 2013–2019. The in situ data were first quality controlled using the Baseline Surface Radiation Network methodology. Data visualization and statistical metrics relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) were used to evaluate the performance of the datasets. There was very good correlation against in situ GHI for the satellite based GHI, all with R2 above 0.95. The R2 correlations for the reanalysis based GHI were less than 0.95 (0.931 for ERA5 and 0.888 for MERRA2). The satellite and reanalysis based GHI showed a positive rMBE (SOLCAST 0.81%, CAMS 2.14%, CMSAF 2.13%, ERA5 1.7%, and MERRA2 11%), suggesting consistent overestimation over the country. SOLCAST satellite based GHI showed the best rRMSE (14%) and rMAE (9%) combinations. MERRA2 reanalysis based GHI showed the weakest rRMSE (37%) and rMAE (22%) combinations. SOLCAST satellite based GHI showed the best overall performance. When considering only the freely available datasets, CAMS and CMSAF performed better with the same overall rMBE (2%), however, CAMS showed slightly better rRMSE (16%), rMAE(10%), and R2 (0.98) combinations than CMSAF rRMSE (17%), rMAE (11%), and R2 (0.97). CAMS and CMSAF are viable freely available data sources for South African locations.

Modelling Physical Accessibility to Public Green Spaces in Switzerland to Support the SDG11

Urban sprawl has a strong impact on the provision and use of green spaces and, consequently, on the benefits that society can derive from these natural ecosystems, especially in terms of public health. In looking at the Sustainable Development Goals and other regional policy frameworks, there is a strong need for quantifying access to green spaces. This study presents and applies a methodology to model the physical accessibility at national and sub-national scales to public green spaces (i.e., urban green spaces and forests) in Switzerland, using AccessMod and ArcGIS travel time functions. We found that approximately 75% and 36% of the Swiss population can access the nearest urban green space within 5 min and 15 min, respectively, using motorized transport. For motorized access to the nearest forest patch, 72% and 52% of the population are within 5 min and 15 min, respectively. When considering only the main urban areas, approximately 55% of the population can walk to the nearest urban green space within 5 min. However, a high heterogeneity in access exists at cantonal and municipal levels, depending on road density, green space density, and population distribution. Despite some possible challenges in correctly delineating public green spaces, our methodology offers a replicable approach offering not only insights into sustainable urban development, but also the facilitation of comparison with other European countries.

Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series

The Environmental Data Portal EnviDat aims to fuse data publication repository functionalities with next-generation web-based environmental geospatial information systems (web-EGIS) and Earth Observation (EO) data cube functionalities. User requirements related to mapping and visualization represent a major challenge for current environmental data portals. The new Cloud Optimized Raster Encoding (CORE) format enables an efficient storage and management of gridded data by applying video encoding algorithms. Inspired by the cloud optimized GeoTIFF (COG) format, the design of CORE is based on the same principles that enable efficient workflows on the cloud, addressing web-EGIS visualization challenges for large environmental time series in geosciences. CORE is a web-native streamable format that can compactly contain raster imagery as a data hypercube. It enables simultaneous exchange, preservation, and fast visualization of time series raster data in environmental repositories. The CORE format specifications are open source and can be used by other platforms to manage and visualize large environmental time series.

The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar

The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.

Effectiveness of Protected Areas in the Pan-Tropics and International Aid for Conservation

Evaluation of the effectiveness of protected areas is critical for forest conservation policies and priorities. We used 30 m resolution forest cover change data from 1990 to 2010 for ~4000 protected areas to evaluate their effectiveness. Our results show that protected areas in the tropics avoided 83,500 ± 21,200 km2 of deforestation during the 2000s. Brazil’s protected areas have the largest amount of avoided deforestation at 50,000 km2. We also show the amount of international aid received by tropical countries compared to the effectiveness of protected areas. Thirty-four tropical countries received USD 42 billion during the 1990s and USD 62 billion during the 2000s in international aid for biodiversity conservation. The effectiveness of international aid was highest in Latin America, with 4.3 m2/USD, led by Brazil, while tropical Asian countries showed the lowest average effect of international aid, reaching only 0.17 m2/USD.