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

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

Flood Hazard Assessment Mapping in Burned and Urban Areas

This study proposes a simple method to produce a flood hazard assessment map in burned and urban areas, where primary data are scarce. The study area is a municipal unit of Nea Makri, a coastal part of the eastern Attica peninsula (central Greece), which has been strongly urbanized and suffered damage from urban fires in 2018. Six factors were considered as the parameters most controlling runoff when it overdraws the drainage system’s capacity. The analytical hierarchy process (AHP) method and a geographical information system (GIS) were utilized to create the flood hazard assessment map. The outcome revealed that the areas with highest flood hazard are distributed in the eastern and southern parts of the study area, as a result of the combination of lowlands with gentle slopes, torrential behavior of the streams, streams covered by construction, increasing urbanization and burned areas. The uncertainty and the verification analyses demonstrate a robust behavior for the model predictions, as well as reliability and accuracy of the map. Comparing the existing urban fabric and road network to the potential flood hazard areas showed that 80% of the urban areas and 50% of the road network were situated within areas prone to flood. The method may be applied to land use planning projects, flood hazard mitigation and post-fire management