Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion

© 2019 by the authors. Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region

Outcomes of elective liver surgery worldwide: a global, prospective, multicenter, cross-sectional study

Background: The outcomes of liver surgery worldwide remain unknown. The true population-based outcomes are likely different to those vastly reported that reflect the activity of highly specialized academic centers. The aim of this study was to measure the true worldwide practice of liver surgery and associated outcomes by recruiting from centers across the globe. The geographic distribution of liver surgery activity and complexity was also evaluated to further understand variations in outcomes. Methods: LiverGroup.org was an international, prospective, multicenter, cross-sectional study following the Global Surgery Collaborative Snapshot Research approach with a 3-month prospective, consecutive patient enrollment within January–December 2019. Each patient was followed up for 90 days postoperatively. All patients undergoing liver surgery at their respective centers were eligible for study inclusion. Basic demographics, patient and operation characteristics were collected. Morbidity was recorded according to the Clavien–Dindo Classification of Surgical Complications. Country-based and hospital-based data were collected, including the Human Development Index (HDI). (NCT03768141). Results: A total of 2159 patients were included from six continents. Surgery was performed for cancer in 1785 (83%) patients. Of all patients, 912 (42%) experienced a postoperative complication of any severity, while the major complication rate was 16% (341/2159). The overall 90-day mortality rate after liver surgery was 3.8% (82/2,159). The overall failure to rescue rate was 11% (82/ 722) ranging from 5 to 35% among the higher and lower HDI groups, respectively. Conclusions: This is the first to our knowledge global surgery study specifically designed and conducted for specialized liver surgery. The authors identified failure to rescue as a significant potentially modifiable factor for mortality after liver surgery, mostly related to lower Human Development Index countries. Members of the LiverGroup.org network could now work together to develop quality improvement collaboratives

Monitoring Short-Term Morphobathymetric Change of Nearshore Seafloor Using Drone-Based Multispectral Imagery

Short-term changes in shallow bathymetry affect the coastal zone, and therefore their monitoring is an essential task in coastal planning projects. This study provides a novel approach for monitoring shallow bathymetry changes based on drone multispectral imagery. Particularly, we apply a shallow water inversion algorithm on two composite multispectral datasets, being acquired five months apart in a small Mediterranean sandy embayment (Chania, Greece). Initially, we perform radiometric corrections using proprietary software, and following that we combine the bands from standard and multispectral cameras, resulting in a six-band composite image suitable for applying the shallow water inversion algorithm. Bathymetry inversion results showed good correlation and low errors (<0.3 m) with sonar measurements collected with an uncrewed surface vehicle (USV). Bathymetry maps and true-color orthomosaics assist in identifying morphobathymetric features representing crescentic bars with rip channel systems. The temporal bathymetry and true-color data reveal important erosional and depositional patterns, which were developed under the impact of winter storms. Furthermore, bathymetric profiles show that the crescentic bar appears to migrate across and along-shore over the 5-months period. Drone-based multispectral imagery proves to be an important and cost-effective tool for shallow seafloor mapping and monitoring when it is combined with shallow water analytical models

Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum be available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets with centimeter resolution. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery as an alternative to costly hyperspectral sensors for drones. Combining drone-based RGB and multispectral imagery into a single cube dataset provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require the input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types with a maximum depth of six meters. The use of suitable end-member spectra, which are representative of the seafloor types of the study area, are important parameters in model tuning. The results of this study are promising, showing good correlation (R2 &gt; 0.75 and Lin&rsquo;s coefficient &gt; 0.80) and less than half a meter average error when they are compared with sonar depth measurements. Consequently, the integration of imagery from various drone-based sensors (visible range) assists in producing detailed bathymetry maps for small-scale shallow areas based on optical modelling

Geomorphometric analysis of nearshore sedimentary bedforms from high-resolution multi-temporal satellite-derived bathymetry

Monitoring sedimentary bedforms is crucial for coastal planning projects. Detailed shallow bathymetry is a fundamental tool for analyzing sedimentary bedforms, however it is often unavailable due to fieldwork limitations. This study utilizes high resolution satellite-derived bathymetry (SDB) for monitoring nearshore bedforms at the northern coast of Chania (Crete, Greece). A random forest technique is employed for training and predicting SDB using two types of multispectral satellite imagery along with ground-truth sonar data. SDB maps have an error of 0.5 meter and allowed for identifying nearshore crescentic bar systems which are the result of local hydrodynamic activity. Bedform metrics were extracted by applying geomorphological indices. Crescentic bars found to be changing shape on annual scale. Nearshore seafloor with soft substrate, changes rapidly and temporal SDB mapping is a fundamental approach for effective coastal monitoring. Multi-temporal SDB maps provide observational evidence about local hydrodynamics which is beneficial in coastal engineering applications